Ligand-based computational modelling of platelet-derived growth factor beta receptor leading to new angiogenesis inhibitory leads. (December 2017) - Al-Aqtash, Rua'a A.; Zihlif, Malek A.; Hammad, Hana; Nassar, Zeyad D.; Meliti, Jehad Al; Taha, Mutasem O.
Graphical abstract: Highlights: 107 diverse PDGFR-β inhibitors were modeled by a combination of pharmacophore and QSAR analyses. The resulting models were validated by ROC curves and against an external list of compounds. Optimal models were used as 3D search queries against the NCI list of compounds. The most potent hits exhibited low micromolar IC50 values in two angiogenesis bioassays. Abstract: Platelet derived growth factor beta receptor (PDGFR- β) plays an important role in angiogenesis. PDGFR-β 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-β recognition using 107 known PDGFR-β 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.