期刊
JOURNAL OF MOLECULAR GRAPHICS & MODELLING
卷 42, 期 -, 页码 104-114出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jmgm.2013.03.003
关键词
Migration inhibitory factor; Pharmacophore modeling; Quantitative structure-activity relationship; In silico screening
类别
资金
- Hamdi-Mango Center for Scientific Research at the University of Jordan
Recent research suggested the involvement of migration inhibitor factor (MIF) in cancer and inflammatory diseases, which prompted several attempts to develop new MIF inhibitors. Accordingly, we investigated the pharmacophoric space of 79 MIF inhibitors using seven diverse subsets of inhibitors to identify plausible binding hypotheses (pharmacophores). Subsequently, we implemented genetic algorithm and multiple linear regression analysis to select optimal combination of pharmacophores and physicochemical descriptors capable of explaining bioactivity variation within the training compounds (QSAR model, r(63) = 0.62, F = 42.8, r(LOO)(2) = 0.721, r(PRESS)(2) against 16 external test inhibitors = 0.58). Two orthogonal pharmacophores appeared in the optimal QSAR model suggestive of at least two binding modes available to ligands inside MIF binding pocket. Subsequent validation using receiver operating characteristic (ROC) curves analysis established the validity of these two pharmacophores. We employed these pharmacophoric models and associated QSAR equation to screen the National Cancer Institute (NCI) list of compounds. Eight compounds gave >50% inhibition at 100 mu M. Two molecules illustrated >75% inhibition at 10 mu M. (C) 2013 Elsevier Inc. All rights reserved.
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