Prediction of the PPARα agonism of fibrates by combined MM-docking approaches

Fibrates are peroxisome proliferator-activated alpha receptor (PPARα) activators derived from fibric acid and are the most clinically used therapeutics in the treatment of hypertriglyceridemia. Long standing studies on these drugs have accumulated a large body of experimental data about their biological activity and, more recently, on the molecular mechanism mediating their PPARα agonism. An immense interest for the discovery of new fibrates with improved potency and PPARα selectivity has stimulated many investigations toward a deeper understanding of structure-activity relationships controlling their activity. The present study aimed at investigating the binding properties of a set of 23 fibrates, characterized by similar carboxylic heads but differing in the size and orientation of the hydrophobic portion, using computational approaches. We combined standard docking and molecular mechanics approaches to better describe the adaptation of the protein target to the bound ligand. The agonist potencies were then regressed against the calculated binding energies to elaborate predictive model equations. The obtained models were characterized by good performances realizing a fair trade-off between accuracy and computational costs. The best model was obtained with a regression procedure allowing automatic generation of a training subset from the whole set of trials and filtering out outliers, thus highlighting the importance of regression strategies.