Hazard evaluation pipeline for putative androgen disruptors.

<p>Step 1: database production; step 2: <i>in silico</i> binding assay; step 3 <i>in vitro</i> binding assay for the selected dataset; step 4: <i>in vitro</i> activity assays only for the high affinity molecules (positive hits); and identification of agonist (α = 1), partial agonist (1<α<0) and antagonist (α = 0) activity.</p

A Computational Approach to Evaluate the Androgenic Affinity of Iprodione, Procymidone, Vinclozolin and Their Metabolites

<div><p>Our research is aimed at devising and assessing a computational approach to evaluate the affinity of endocrine active substances (EASs) and their metabolites towards the ligand binding domain (LBD) of the androgen receptor (AR) in three distantly related species: human, rat, and zebrafish. We computed the affinity for all the selected molecules following a computational approach based on molecular modelling and docking. Three different classes of molecules with well-known endocrine activity (iprodione, procymidone, vinclozolin, and a selection of their metabolites) were evaluated. Our approach was demonstrated useful as the first step of chemical safety evaluation since ligand-target interaction is a necessary condition for exerting any biological effect. Moreover, a different sensitivity concerning AR LBD was computed for the tested species (rat being the least sensitive of the three). This evidence suggests that, in order not to over−/under-estimate the risks connected with the use of a chemical entity, further <i>in vitro</i> and/or <i>in vivo</i> tests should be carried out only after an accurate evaluation of the most suitable cellular system or animal species. The introduction of <i>in silico</i> approaches to evaluate hazard can accelerate discovery and innovation with a lower economic effort than with a fully wet strategy.</p></div

Experimental (from [27]) and <i>in silico</i> (computed) dissociation constants for the selected compounds with respect to rat AR binding site.

<p>ΔG-MD: binding free energy computed through molecular docking.</p

Experimental (from literature) and <i>in silico</i> (computed) dissociation constants for the three endogenous tested hormones.

<p>DS: Docking Score; ΔG-LM: binding free energy computed through low-mode molecular dynamics simulations; ΔG-MD: binding free energy computed through molecular docking; K_i: dissociation constant computed from molecular docking data.</p

Molecular surface of the binding site and filling dummy atoms in the zebrafish AR LBD model, side (A) and top (B) view.

<p>Molecular surface of the binding site and filling dummy atoms in the zebrafish AR LBD model, side (A) and top (B) view.</p

AR LBD and binding site RMSD values, computed for α-carbons and for whole residues of the three selected receptors.

<p>AR LBD and binding site RMSD values, computed for α-carbons and for whole residues of the three selected receptors.</p

Molecular docking validation dataset and RMSD values between the co-crystallized and the docked ligands.

<p>Molecular docking validation dataset and RMSD values between the co-crystallized and the docked ligands.</p

Low-mode molecular dynamics simulations.

<p>Superposition of the starting conformation (helix 12) and the most energetically favoured open conformations for the agonist-bound LBD (A), the <i>apo</i> LBD (B), and the antagonist-bound LBD (C).</p

Docking Score (DS) (kcal/mol) for the top scoring poses (protein-ligand complexes) for all the compounds of the tested database.

<p>The last three rows contain the DS values for the endogenous hormones in each species.</p

Binding site features for each of the three selected receptors.

<p><i>Size</i> indicates the number of alpha spheres comprising the site. <i>PLB</i> is the Propensity for Ligand Binding score for the contact residues. <i>Hyd</i> indicates the number of hydrophobic contact atoms in the receptor. <i>Side</i> indicates the number of sidechain contact atoms in the receptor.</p