Title: The Application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation
Authors: AL SHARIF MERILINTSAKOVSKA IVANKAPAJEVA ILZAALOV PETKOFIORAVANZO ELENABASSAN ARIANNAKOVARICH SIMONAYANG CHIHAEMOSTRAG-SZLICHTYNG A.VITCHEVA V.WORTH ANDREWRICHARZ ANDREACRONIN MARK
Citation: TOXICOLOGY vol. 392 p. 140-154
Publisher: ELSEVIER IRELAND LTD
Publication Year: 2017
JRC N°: JRC100308
ISSN: 0300-483X
URI: http://www.sciencedirect.com/science/article/pii/S0300483X16300099
http://publications.jrc.ec.europa.eu/repository/handle/JRC100308
DOI: 10.1016/j.tox.2016.01.009
Type: Articles in periodicals and books
Abstract: The 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.
JRC Directorate:Health, Consumers and Reference Materials

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