Development, evaluation and application of 3D QSAR Pharmacophore model in the discovery of potential human renin inhibitors

Abstract

Background: Renin has become an attractive target in controlling hypertension because of the high specificity towards its only substrate, angiotensinogen. The conversion of angiotensinogen to angiotensin I is the first and rate-limiting step of renin-angiotensin system and thus designing inhibitors to block this step is focused in this study.

Methods: Ligand-based quantitative pharmacophore modeling methodology was used in identifying the important molecular chemical features present in the set of already known active compounds and the missing features from the set of inactive compounds. A training set containing 18 compounds including active and inactive compounds with a substantial degree of diversity was used in developing the pharmacophore models. A test set containing 93 compounds, Fischer randomization, and leave-one-out methods were used in the validation of the pharmacophore model. Database screening was performed using the best pharmacophore model as a 3D structural query. Molecular docking and density functional theory calculations were used to select the hit compounds with strong molecular interactions and favorable electronic features.

Results: The best quantitative pharmacophore model selected was made of one hydrophobic, one hydrogen bond donor, and two hydrogen bond acceptor features with high a correlation value of 0.944. Upon validation using an external test set of 93 compounds, Fischer randomization, and leave-one-out methods, this model was used in database screening to identify chemical compounds containing the identified pharmacophoric features. Molecular docking and density functional theory studies have confirmed that the identified hits possess the essential binding characteristics and electronic properties of potent inhibitors.

Conclusion: A quantitative pharmacophore model of predictive ability was developed with essential molecular features of a potent renin inhibitor. Using this pharmacophore model, two potential inhibitory leads were identified to be used in designing novel and future renin inhibitors as antihypertensive drugs.

Figure 1

Structure of the training set compounds. 2D Chemical structures of the 18 training set compounds together with their experimental IC₅₀ values.

Figure 2

The best HypoGen pharmacophoremodel, Hypo1. (A) Chemical features present in Hypo 1 (B) 3D spatial arrangement and the distance constraints between the chemical features. Green color represents HBA, magenta color represents HBD and cyan color represents HY-AL features.

Figure 3

Pharmacophore Mapping. (A) Mapping of the most active compound 1 in the training set on the best pharmacophore model Hypo1. (B) Mapping of the least active compound 18 in the training set on the best pharmacophore model Hypo1. In the pharmacophore model green represents HBA, magenta represents HBD and cyan represents HY-AL features.

Figure 4

Correlation plots. Correlation graph between the experimental activity and the estimated activity for the training set and test set compounds.

Figure 5

Database screening. The flow of procedure used in 3D QSAR pharmacophore modeling.

Figure 6

Molecular docking experiments of 3D QSAR pharmacophore modeling. Compound 1, the most active compound in the training set is shown in blue color whereas HTS05096 and AW00695 are shown in green and red colors, respectively. Molecular overlay of all the three compounds is shown at lower right figure. Hydrogen bond interactions are shown in black dotted lines.

Figure 7

Pharmacophore mapping of final database hit compounds on the best pharmacophore model Hypo1. (A) HTS05096 represented in blue color (B) AW00695 represented in orange color. In the pharmacophore model green represents HBA, magenta represents HBD and cyan represents HY-AL features.

Figure 8

Chemical structures of hit compounds. 2D representation of final hits HTS05096 and AW00695.

Figure 9

The plot of energy gaps. The ΔE (LUMO-HOMO) values of two hit compounds along with the 2 most active and 1 least active compounds of the training set.