Identification of structural requirements of estrogen receptor modulators using pharmacoinformatics techniques for application to estrogen therapy

Abstract

An attempt was made in the present study to explore the structural requirements of known estrogen receptor (ER) modulators for biological activity using pharmacoinformatics approaches to elucidate critical functionalities for new, potent and less toxic chemical agents for successful application in estrogen therapy. For this purpose a group of non-steroidal ligands, 7-thiabicyclo[2.2.1]hept-2-ene-7-oxide derivatives were collected from the literature to perform quantitative structure-activity relationship (QSAR), pharmacophore and molecular docking studies. The 2D QSAR models (R for α-and β-subtypes respectively. The functionalities developed in the QSAR and pharmacophore studies were substantiated by molecular docking which provided the preferred orientation of ligands for effective interaction at the active site cavity.

Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme

“…Recently, Islam et al 76 have published a pharmacophore hypothesis, which is qualitatively identical to that proposed by Fang et al 42 (Table 5). Nevertheless, quantitative differences between both models can be observed (Figures 15A vs 16A).…”

“…The differences in chemical feature type and number seemingly indicate the inconsistency among these five theoretical models that, nevertheless, can be eliminated once the chemical features RA and HPA are substituted by HP as proposed Wolber et al, 93 yielding totally three HP features. As such, these three other models can reproduce the one proposed by Fang et al 42 and Islam et al, 76 and, moreover, these five pharmacophore hypotheses are in qualitative agreement with one another in terms of hydrophobic interactions.…”

“…Furthermore, it can be discovered from Figure 13, which exhibits the interactions between ERα-E 2 by LigPlot + (available at http://www.ebi.ac.uk/thornton-srv/software/ LigPlus/) based on the ERα-E 2 co-complex structure (PDB code: 1YIN), that both hydroxyl groups of E 2 can bind with ERα by hydrogen bonds, indicating the necessity of both interactions to exert the ERα-ligand interactions. Consequently, the predictive models proposed by Brogi et al, 41 Fang et al, 42 and Islam et al 76 unanimously adopted one HBA and one HBD chemical features. However, 5, 87, and 88, which only have one hydroxyl group, are strong ERα binders, suggesting that only one hydrogen-bond interaction is sufficient enough to establish strong binding with ERα that, actually, is totally consistent with the postulation made by Katzenellenbogen.…”

“…The predictive hypothesis built by Brogi et al 41 selected 2 HPAs and 1 HP, whereas the ones proposed by Fang et al 42 and Islam et al 76 adopted 3HPs. Furthermore, Hypo A and Hypo B included 2 HPs and 1 RA as well as 2 HPs and 1 HPA, respectively.…”

“…For instance, the interfeature distances between two pairs of HP groups are 4.78 and 3.17 Å in their model (Figure 16A), which only differ from the interfeatures between 1 HP and 1 RA in Hypo A (4.11 ± 3.20 Å) as well as 1 HP and 1 HPA in Hypo B (3.04 ± 3.20 Å), respectively, by small margins (Figure 16B). Thus, the predictive model of Islam et al 76 can be replicated by reassembling certain chemical features constructed in Hypo A or Hypo B.…”