Human Androgen Receptor Inhibitors: Computational 3D QSAR Studies to Design Lead Compounds for Treatment of Prostate Cancer

Abstract

Objectives: The inhibition of Androgen receptors have been vigorously pursued as a promising target for the treatment of prostate cancer. A set of 40 training set compounds and 20 test set compounds reported as Androgen receptors inhibitors were analyzed by employing the molecular field analysis (MFA) and Receptor surface analysis (RSA) techniques to investigate the structural requirements for various analogues to inhibit Androgen receptors and to derive a highly predictive model used for the designing of novel Androgen receptors inhibitors. Material and Methods: Pharmacophore generation and 3D-QSAR studies have been performed for developing novel Androgen Receptors inhibitors using Cerius2 and Catalyst programs. QSAR equations have been generated for 40 Androgen Receptors inhibitors employing Molecular Field Analysis (MFA) as well as Receptor surface Analysis (RSA) using Genetic function approximation (GFA) as regression method. Results: The best equations with training set produced r2 value of 0.856 and r2cv value of 0.739 in 2D-mode, r2 value of 0.839 and r2cv value of 0.793 in MFA-model and r2 value of 0.910 and r2cv of 0.856 in the RSA-model. For the 20 test set molecules predicted activities, had a correlation of 0.840 and 0.856 for MFA and RSA with observed activities. Conclusion: The 3D–QSAR models exhibited good correlation and predictive ability. The model showed that steric (CH3) and electrostatic (H +) interactions play an important role in the inhibition of Androgen receptor by the analogues. The model generated could be exploited for further structural modification in order to improve Androgen receptor inhibition activity.