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dc.contributor.authorAL SHARIF MERILINen_GB
dc.contributor.authorTSAKOVSKA IVANKAen_GB
dc.contributor.authorPAJEVA ILZAen_GB
dc.contributor.authorALOV PETKOen_GB
dc.contributor.authorFIORAVANZO ELENAen_GB
dc.contributor.authorBASSAN ARIANNAen_GB
dc.contributor.authorKOVARICH SIMONAen_GB
dc.contributor.authorYANG CHIHAEen_GB
dc.contributor.authorMOSTRAG-SZLICHTYNG A.en_GB
dc.contributor.authorVITCHEVA V.en_GB
dc.contributor.authorWORTH ANDREWen_GB
dc.contributor.authorRICHARZ ANDREAen_GB
dc.contributor.authorCRONIN MARKen_GB
dc.date.accessioned2017-12-07T01:17:14Z-
dc.date.available2017-12-05en_GB
dc.date.available2017-12-07T01:17:14Z-
dc.date.created2017-11-21en_GB
dc.date.issued2017en_GB
dc.date.submitted2016-01-25en_GB
dc.identifier.citationTOXICOLOGY vol. 392 p. 140-154en_GB
dc.identifier.issn0300-483Xen_GB
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0300483X16300099en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC100308-
dc.description.abstractThe aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy = 81%, sensitivity = 85% and specificity = 76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q2cv = 0.610, Nopt = 7, SEPcv = 0.505, r2pr = 0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development.en_GB
dc.description.sponsorshipJRC.F.3-Chemicals Safety and Alternative Methodsen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherELSEVIER IRELAND LTDen_GB
dc.relation.ispartofseriesJRC100308en_GB
dc.titleThe Application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulationen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1016/j.tox.2016.01.009en_GB
JRC Directorate:Health, Consumers and Reference Materials

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