Molecular modeling studies of benzothiophene-containing derivatives as promising selective estrogen receptor downregulators: a combination of 3D-QSAR, molecular docking and molecular dynamics simulations

Abstract

Selective estrogen receptor downregulators (SERDs) for the treatment of positive breast cancer can act both as estrogen alpha receptor (ERα) antagonists and degraders. In this study, the optimal antagonist models (CoMFA-A, q2 = 0.660, r2 = 0.996; CoMSIA-A, q2 = 0.728, r2 = 0.992) and degrader models (CoMFA-D, q2 = 0.850, r2 = 0.996; CoMSIA-D, q2 = 0.719, r2 = 0.995) of a series of potent benzothiophene-containing SERDs were constructed to explore the three-dimensional quantitative structure–activity relationship. Internal and external validation indicated that all models exhibited good applicability, high predictive ability and robustness. Contour maps revealed the relationships between the essential structural features and antagonistic and degradation activities. Additionally, molecular docking, molecular dynamics and free energy calculation studies were further performed to investigate the detailed binding mode. Results indicated that several key residues, ARG394, GLU353, PHE404 and ILE424, were crucial for the stability of the ligand binding domain. The hydrophobic, electrostatic and Van der Waals interactions played significant effect on the binding affinity. Finally, ten novel compounds were designed based on above findings, where the predicted activity of compound D8 was equivalent to that of the compound LSZ102. 3D-QSAR, ADMET and bioavailability predictions indicated that all designed compounds with good predicted activity, good physicochemical and bioavailability could be potential candidates for SERDs. These results and combinations of computational methods provided guidance for the rational drug design of novel potential SERDs. Communicated by Ramaswamy H. Sarma