Computational Study of the Binding Modes of Diverse DPN Analogues on Estrogen Receptors (ER) and the Biological Evaluation of a New Potential Antiestrogenic Ligand

Abstract

Estrogen (17β-estradiol) is essential for normal growth and differentiation in the mammary gland. In the last three decades, previous investigations have revealed that Estrogen Receptor Alpha (ERα) plays a critical role in breast cancer. More recently, observations regarding the widespread expression of ERβ-like proteins in normal and neoplastic mammary tissues have suggested that ERβ is also involved in the mentioned pathology. Design of new drugs both steroidal and nonsteroidal that target any of these receptors represents a promise to treat breast cancer although it remains a challenge due to the sequence similarity between their catalytic domains. In this work, we propose a new set of compounds that could effectively target the estrogen receptors ERα and ERβ. These ligands were designed based on the chemical structure of the ERβ-selective agonist Diarylpropionitrile (DPN). The designed ligands were submitted to in silico ADMET studies, yielding in a filtered list of ligands that showed better drug-like properties. Molecular dynamics simulations of both estrogen receptors and docking analysis were carried-out employing the designed compounds, from which two were chosen due to their promising characteristics retrieved from theoretical results (docking analysis or targeting receptor predictions). They were chemically synthetized and during the process, two precursor ligands were also obtained. These four ligands were subjected to biological studies from which it could be detected that compound mol60b dislplayed inhibito