The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation

Autor: Ivanka Tsakovska, Arianna Bassan, Elena Fioravanzo, Mark T. D. Cronin, Merilin Al Sharif, Petko Alov, Aleksandra Mostrag-Szlichtyng, Andrea-N. Richarz, Vessela Vitcheva, Andrew Worth, Ilza Pajeva, Simona Kovarich, Chihae Yang
Rok vydání: 2015
Předmět:
0301 basic medicine
RA1190
Models
Molecular
Quantitative structure–activity relationship
Computer science
In silico
Peroxisome proliferator-activated receptor
Quantitative Structure-Activity Relationship
Toxicology
Bioinformatics
Ligands
01 natural sciences
Molecular Docking Simulation
Risk Assessment
Sensitivity and Specificity
03 medical and health sciences
Cell Line
Tumor
Cricetinae
Adverse Outcome Pathway
Chlorocebus aethiops
Toxicity Tests
Animals
Humans
QD
Databases
Protein
chemistry.chemical_classification
Virtual screening
Binding Sites
Molecular Structure
Reproducibility of Results
Haplorhini
Hep G2 Cells
0104 chemical sciences
Fatty Liver
PPAR gamma
010404 medicinal & biomolecular chemistry
030104 developmental biology
HEK293 Cells
chemistry
Docking (molecular)
COS Cells
Feasibility Studies
Pharmacophore
Protein Binding
Zdroj: Toxicology. 392
ISSN: 1879-3185
Popis: 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: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=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.
Databáze: OpenAIRE
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