Application of 4D-QSAR Studies to a Series of Raloxifene Analogs and Design of Potential Selective Estrogen Receptor Modulators

Abstract

Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis was applied on a series of 54 2-arylbenzothiophene derivatives, synthesized by Grese and coworkers, based on raloxifene (an estrogen receptor-alpha antagonist), and evaluated as ERa ligands and as inhibitors of estrogen-stimulated proliferation of MCF-7 breast cancer cells. The conformations of each analogue, sampled from a molecular dynamics simulation, were placed in a grid cell lattice according to three trial alignments, considering two grid cell sizes (1.0 and 2.0 Å). The QSAR equations, generated by a combined scheme of genetic algorithms (GA) and partial least squares (PLS) regression, were evaluated by “leave-one-out” cross-validation, using a training set of 41 compounds. External validation was performed using a test set of 13 compounds. The obtained 4D-QSAR models are in agreement with the proposed mechanism of action for raloxifene. This study allowed a quantitative prediction of compounds’ potency and supported the design of new raloxifene analogs.

Keywords

• four dimensional quantitative structure-activity relationship (4D-QSAR)
• ligand based drug design (LBDD)
• molecular modeling
• estrogen receptor alpha (ERa)
• estrogen receptor beta (ERb)
• selective estrogen receptor modulator (SERM)
• raloxifene
• ligand binding domain (LBD)