Extensive ligand-based modeling and in silico screening reveal nanomolar inducible nitric oxide synthase (iNOS) inhibitors
Inducible nitric oxide synthase (iNOS) has been implicated in a variety of diseases prompting several attempts to discover and optimize new iNOS inhibitors. Accordingly, we explored the pharmacophoric space of 143 iNOS inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors to produce self-consistent quantitative structureactivity relationship (QSAR) of optimal predictive potential (correlation coefficient r115=0.83, F=23.92, r LOO 2 = 0.61 , r PRESS 2 against 28 external test inhibitors=0.51). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within iNOS binding pocket. The pharmacophores were validated by comparison with crystallographic complexes of active iNOS inhibitors and receiver operating characteristic (ROC) curves analysis. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds (NCI). Three low nanomolar inhibitors were identified. The most potent hit exhibited irreversible inhibition of iNOS with IC50 value of 1.4nM.
in silico screening inducible nitric oxide synthase pharmacophore modeling quantitative structureactivity relationship