Identification of Pharmacophore for Wild and T877A Mutant Androgen Receptor Antagonist: Challenges in Designing 3D-QSAR For Mutant Protein

Identification of Pharmacophore for Wild and T877A Mutant Androgen Receptor Antagonist: Challenges in Designing 3D-QSAR For Mutant Protein

Divakar Selvaraj (Department of Pharmacology, PSG College of Pharmacy, Coimbatore, India), Sivaram Hariharan (Department of Chemistry, PSG College of Pharmacy, Coimbatore, India) and Ramanathan Muthiah (Department of Pharmacology, PSG College of Pharmacy, Coimbatore, India)
DOI: 10.4018/IJQSPR.2017070105
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Androgen increases the proliferation of prostate cancer cells by activating the androgen receptor (AR). AR antagonists block the androgen mediated proliferation and are used to treat prostate cancer. Antagonist resistance occurs due to the expression of mutations in AR. One particular mutation, T877A, was frequently expressed in relapsed patients. Presently, there is no antagonist in the market without the problem of resistance developing over the period of treatment. A plausible reason for this shortcoming could be the lack of designing because of the unavailability of AR-crystal structure in antagonist bound conformation. Hence, 3D-QSAR becomes a major tool in identification of novel AR antagonists. Three models, ADDHRR.295, AHHRR.266, and AHRR.63 were designed for wild type, T877A mutant, and full (active at both wild and mutant types) AR antagonists respectively. There is a major difference in the angles and lengths of the pharmacophore site points among the 3D-QSAR models. The generated pharmacophore for full antagonists was then used for screening the SPECS database with filters for identifying hits.
Article Preview

Introduction

Activation of AR (androgen receptor) by androgen leads to the proliferation of prostate cells. AR antagonists inhibit the binding of androgens with AR and prevent the proliferation of prostate cells (Gao et al., 2010). However, the AR develops resistance for its antagonists after a treatment median of 12-24 months. This is because of the expression of point mutations in the ligand binding domain of the AR (Tilley et al., 1996, Feldman et al., 2001). The T877A mutation was most frequently expressed among prostate cancer patients (Feldman et al., 2001, Balk et al., 2002). The mutant AR converts the antagonist into agonist (Fenton et al., 1997). Furthermore, the mutated AR could also be activated by steroidal hormones, estrogens, progesterone, etc. (Fenton et al., 1997). This leads to the failure of androgen deprivation therapy because of the activation of AR by non-androgens.

Previously, we had designed and synthesized novel oxbenzimidazole derivatives targeting AR (Elencheran et al., 2016). However, most of the molecules were non selective and inhibited the proliferation of AR negative and positive prostate cancer cell lines. Therefore, in order to overcome this issue, we decided to study the pharmacophoric features of AR antagonists. In this study, we have developed three 3D-QSAR models. These models represent the pharmacophore site points of wild type AR antagonist, T877A mutant AR antagonist, and the full antagonist. The full antagonist, for the purposes of this study, is defined as a molecule that will not function as an agonist or partial agonist with either wild or mutant ARs. The difference between the full antagonist and partial agonist is that the full antagonist will behave as an antagonist irrespective of the AR mutation. The activity of the partial agonist is facultative and depends on the presence of mutation. The partial agonist functions as an agonist in the presence of T877A mutation and acts an antagonist in the absence of the mutation. A good example in this regard is Flutamide. The 3D-QSAR models were compared for differences among them. This study could potentially provide the insight into the necessary structural properties of AR antagonists to avoid resistance developing during treatment.

Data Sets

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 3: 2 Issues (2018): 1 Released, 1 Forthcoming
Volume 2: 2 Issues (2017)
Volume 1: 2 Issues (2016)
View Complete Journal Contents Listing