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Elaborate ligand-based modeling reveal new submicromolar Rho kinase inhibitors

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Abstract

Rho Kinase (ROCKII) has been recently implicated in several cardiovascular diseases prompting several attempts to discover and optimize new ROCKII inhibitors. Towards this end we explored the pharmacophoric space of 138 ROCKII inhibitors to identify high quality pharmacophores. The pharmacophoric models were subsequently allowed to compete within quantitative structure–activity relationship (QSAR) context. Genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing self-consistent QSAR of optimal predictive potential (r 77 = 0.84, F = 18.18, r 2LOO  = 0.639, r 2PRESS against 19 external test inhibitors = 0.494). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within ROCKII binding pocket. Receiver operating characteristic (ROC) curve analyses established the validity of QSAR-selected pharmacophores. Moreover, the successful pharmacophores models were found to be comparable with crystallographically resolved ROCKII binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds Eight submicromolar ROCKII inhibitors were identified. The most potent gave IC50 values of 0.7 and 1.0 μM.

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Acknowledgments

This project was partially sponsored by the Faculty of Graduate Studies (This work is part of PhD. Thesis of Rand Shahin). The authors thank the Deanship of Scientific Research and Hamdi-Mango Center for Scientific Research at the University of Jordan for their generous funds.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan

    Rand Shahin, Saja AlQtaishat & Mutasem O. Taha

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  1. Rand Shahin
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  2. Saja AlQtaishat
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  3. Mutasem O. Taha
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Correspondence to Mutasem O. Taha.

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Shahin, R., AlQtaishat, S. & Taha, M.O. Elaborate ligand-based modeling reveal new submicromolar Rho kinase inhibitors. J Comput Aided Mol Des 26, 249–266 (2012). https://doi.org/10.1007/s10822-011-9509-y

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  • DOI: https://doi.org/10.1007/s10822-011-9509-y

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