Multiple substances are considered endocrine disrupting chemicals (EDCs). However, there is a significant gap in the early prioritization of EDC's effects. In this work, in silico and in vitro methods were used to model estrogenicity. Two Quantitative Structure-Activity Relationship (QSAR) models based on Logistic Regression and REPTree algorithms were built using a large and diverse database of estrogen receptor (ESR) agonism. A 10-fold external validation demonstrated their robustness and predictive capacity. Mechanistic interpretations of the molecular descriptors (C-026, nArOH,PW5, B06[Br-Br]) used for modelling suggested that the heteroatomic fragments, aromatic hydroxyls, and bromines, and the relative bond accessibility areas of molecules, are structural determinants in estrogenicity. As validation of the QSARs, ESR transactivity of thirteen persistent organic pollutants (POPs) and suspected EDCs was tested in vitro using the MMV-Luc cell line. A good correspondence between predictions and experimental bioassays demonstrated the value of the QSARs for prioritization of ESR agonist compounds.
- Endocrine disruptor
- Estrogen receptor
- Persistent organic pollutant
- Predictive toxicology
- Reporter gene assay
FingerprintDive into the research topics of 'Assessing the chemical-induced estrogenicity using in silico and in vitro methods'. Together they form a unique fingerprint.
Effects of endocrine disrupting chemicals and their human-relevant mixtures on cellular health and hormone receptor functionAuthor: Amber, M., Jul 2023
Student thesis: Doctoral Thesis › Doctor of Philosophy