In Silico Identification and Pharmacological Evaluation of Novel Endocrine Disrupting Chemicals That Act via the Ligand-Binding Domain of the Estrogen Receptor α

Endocrine disrupting chemicals (EDCs) pose a significant threat to human health, society, and the environment. Many EDCs elicit their toxic effects through nuclear hormone receptors, like the estrogen receptor alpha (ER alpha). In silico models can be used to prioritize chemicals for toxicological evaluation to reduce the amount of costly pharmacological testing and enable early alerts for newly designed compounds. However, many of the current computational models are overly dependent on the chemistry of known modulators and perform poorly for novel chemical scaffolds. Herein we describe the development of computational, three-dimensional multi-conformational pocket-field docking, and chemical-field docking models for the identification of novel EDCs that act via the ligand-binding domain of ER alpha. These models were highly accurate in the retrospective task of distinguishing known high-affinity ER alpha modulators from inactive or decoy molecules, with minimal training. To illustrate the utility of the models in prospective in silico compound screening, we screened a database of over 6000 environmental chemicals and evaluated the 24 top-ranked hits in an ER alpha transcriptional activation assay and a differential scanning fluorimetry-based ER alpha binding assay. Promisingly, six chemicals displayed ER alpha agonist activity (32nM-3.98 mu M) and two chemicals had moderately stabilizing effects on ER alpha. Two newly identified active compounds were chemically related beta-adrenergic receptor (beta AR) agonists, dobutamine, and ractopamine (a feed additive that promotes leanness in cattle and poultry), which are the first beta AR agonists identified as activators of ER alpha-mediated gene transcription. This approach can be applied to other receptors implicated in endocrine disruption.