Journal
COMPUTATIONAL BIOLOGY AND CHEMISTRY
Volume 71, Issue -, Pages 170-179Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compbiolchem.2017.10.003
Keywords
Angiogenesis; PDGFR-beta; Pharmacophore; QSAR; Virtual screening; PANC-1
Funding
- Deanship of Scientific Research at The University of Jordan
- Jordanian Scientific Research Fund
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Platelet derived growth factor beta receptor (PDGFR-beta) plays an important role in angiogenesis. PDGFR beta expression is correlated with increased vascularity and maturation of blood vessels in cancer. Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis were combined to explore the structural requirements for ligand-PDGFR-beta recognition using 107 known PDGFR-beta inhibitors. Genetic function algorithm (GFA) coupled to k nearest neighbor (kNN) and multiple linear regression (MLR) analysis were employed to generate predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. The successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. The QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new angiogenesis inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Two hits illustrated low micromolar IC50 values in two distinct anti-angiogenesis bioassays. (C) 2017 Elsevier Ltd. All rights reserved.
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