Identification of Selective Receptor Modulators Using Pharmacoinformatics Approaches for Therapeutic Application in Estrogen Therapy

Identification of Selective Receptor Modulators Using Pharmacoinformatics Approaches for Therapeutic Application in Estrogen Therapy

Md Ataul Islam (Department of Chemical Pathology, University of Pretoria and National Health Laboratory Service Tshwane Academic Division, Pretoria, South Africa & School of Health Sciences, University of Kwazulu-Natal, Westville Campus, Durban, South Africa & Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK), Shovonlal Bhowmick (Department of Chemical Technology, University of Calcutta, Kolkata, India) and Achintya Saha (Department of Chemical Technology, University of Calcutta, Kolkata, India)
DOI: 10.4018/IJQSPR.2019040103

Abstract

Pharmacoinformatics strategies have been applied to explore promising selective estrogen receptor (ER) modulators (SERMs). A set of non-steroidal ligands was considered for both ERα and ERβ subtypes. Best pharmacophore models revealed with importance of hydrogen bond acceptor and hydrophobicity for both subtypes, along with an aromatic ring and hydrogen bond donor for α and β subtypes, respectively. Both models were validated, and further considered for virtual screening of National Cancer Institute database. Initial hits were sorted with a number of criteria, and finally the molecules have been proposed as promising SERMs. A molecular docking study explained that screened ligands formed a number of binding interactions with both ERs. The subtype receptors in complex with active and screened compounds were considered for molecular simulations to compare stability of the complexes. An analysis of binding energy found that screened ligands hold a strong affinity towards the selective receptor cavity. The proposed ligands might be promising leads for estrogen therapy after experimental validation tests.
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Introduction

The steroid hormones, estrogens, comprise essential biochemical components in the female reproductive system and also in the maintenance of diverse range of non-reproductive tissues (Hall, Couse, & Korach, 2001). Due to natural decline of estrogen level in women, several biological complications are observed after menopause including hot flushes, vaginal atrophy, mood changes and osteoporosis (An, 2016). Hormone replacement therapy (HRT) are prescribed to control the post-menopausal symptoms in which synthetic estrogen hormones are administered to the body through several applications such as orally, a patch on the skin, as a cream or gel, or with an IUD (intrauterine device) or vaginal ring. Women’s Health Initiative (WHI) studies showed that irrespective of several benefits, HRT increases the risk of breast cancer, coronary heart disease (CHD), stroke, and venous thromboembolic disease (Rossouw et al., 2002; Rossouw et al., 2007; Wassertheil-Smoller et al., 2003). The biochemical mechanism of estrogens involves binding with the estrogen receptors (ERs) which belong to the nuclear receptor super-family and contain conditional transcription factors (Pincus, 1966). The ERs are activated by the estrogen, and in response, bind to DNA followed by regulation of the expression of the target gene (Carroll, 2016). The ER comprises of three functional domains such as (i) ligand independent N-terminal domain (NTD), (ii) central DNA binding domain (DBD), and (iii) the ligand-binding domain (LBD) extended from NH2- to COOH-terminus. For regulating the transcriptional activity of ER, the activation function (AF) domains, AF-1 and AF-2, are located within NTD and LBD, respectively (Kumar et al., 2011). The DBD allows it to directly regulate gene expression events, whereas LBD renders activation of the ligand (Carroll, 2016). The agonist is entirely enclosed by ER and the hydrophobic core of the protein. The orientation of helix-12 (H12), located at the carboxy-terminus of the LBD, is fundamental in distinguishing the functions between agonist and antagonist. Antagonist blocks access to a groove located between helices 3, 4 and 5, and the binding site for co-activators during transcription (AF-2 site) (van Hoorn, 2002). Mainly two subtypes of ERs have been identified, viz. ERα and ERβ. Both subtypes possess significant sequence similarity with a number of differences in respect to distribution and function. The ERα is mainly found in bone, breast, prostate (stroma), uterus, ovary (thecal cells) and brain, whereas ERβ is usually found in ovary (granulose cells), bladder, colon, immune, cardiovascular, and nervous systems (Babiker et al., 2002; Hess, 2003; Korach et al., 2003; Kuiper et al., 1997). Selective ER modulators (SERMs) are synthetically designed non-steroidal small molecules used for the treatment of postmenopausal symptoms along with hormone-responsive breast cancer and osteoporosis (Peng, Sengupta, & Jordan, 2009). Potential SERMs consist of different important molecular features that can act as an estrogen agonist or an antagonist depending on the target tissues (Martinkovich, Shah, Planey, & Arnott, 2014). The most commonly used SERM, tamoxifen is known to have antagonistic effects in breast tissue and potentially used in the treatment of breast cancer (Patel & Bihani, 2017). However, side effects of this SERM have also been reported in several patients, in relation to menopausal symptoms (Moon, Hunter, Moss-Morris, & Hughes, 2017). In recent years, there are a number of other pharmacologic compounds of natural or synthetic origin being used as SERMs such as 4-hydroxytamoxifen, endoxifen, toremifene, droloxifene, idoxifene, raloxifene, arzoxifene, lasofoxifene, bazedoxifene and pipendoxifene (Patel & Bihani, 2017). Among these classified SERMs, two drug candidates, i.e., bazedoxifene and pipendoxifene also entered into clinical evaluations phase for treatment of postmenopausal osteoporosis. It has been reported that bazedoxifene has better binding affinity towards the ERα compared to ERβ subtypes (Singla, Gupta, Upadhyay, Dhiman, & Jaitak, 2018).

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