Pharmacophore and QSAR modeling of endothelial nitric oxide synthase inhibitors and subsequent validation and in silico search for new hits

ABSTRACT

Endothelial Nitric Oxide synthase [eNOS] has an emerging role in chronic inflammation and cancer thus prompting continuous attempts to discover new inhibitors of this enzyme. Towards this end, efforts to discover and optimize new eNOS inhibitors are essential. Therefore, we explored the pharmacophoric space of 151 eNOS inhibitors using ten diverse sets of inhibitors to identify high quality pharmacophores. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing a self-consistent quantitative structure-activity relationship [QSAR] of optimal predictive potential [r[2] [121] = 0.77, F = 63.5, r[2][LOO] = 0.62, and r[2] [PRESS] against 30 external test inhibitors = 0.63]. Interestingly, only one pharmacophore emerged in the optimal QSAR equation. Comparisons with the binding site of eNOS and receiver-operating characteristic [ROC] curves analysis established the validity of this QSAR-selected pharmacophore model. We employed the pharmacophoric model and associated QSAR equation to screen the national cancer institute list of compounds [NCI]