Discovery of new renin inhibitory leads via sequential pharmacophore modeling, QSAR analysis, in silico screening and in vitro evaluation

The renin–angiotensin–aldosterone system is a major target for the clinical management of hypertension. Development of renin inhibitors has proven to be problematic due to poor bioavailability and complex synthesis. In this study, we combined pharmacophore modeling and quantitative structure–activity relationship (QSAR) analysis to explore the structural requirements for potent renin inhibitors employing 119 known renin ligands. Genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive QSAR. Two binding pharmacophore models emerged in the optimal QSAR equation (r962=0.746, F-statistic = 43.552, rLOO2=0.697, rPRESS2 against 23 test inhibitors = 0.527). The successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. The QSAR equations and their associated pharmacophore models were validated by the identification and experimental evaluation of new promising renin inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hits illustrated IC50 value of 2.6 μM. Successful pharmacophore models were found to be comparable with crystallographically resolved renin binding pocket.

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► Combined pharmacophore and QSAR modeling of 119 renin inhibitors.
► Two pharmacophore models emerged in the optimal QSAR model.
► Models were validated by ROC analysis and comparison with crystallographic PDB structures.
► Pharmacophore and QSAR-based in silico screening identified seven micromolar renin inhibitors with best IC50 value = 2.6 μM.