Discovery of potent adenosine A2a antagonists as potential anti-Parkinson disease agents. Non-linear QSAR analyses integrated with pharmacophore modeling.
Adenosine A2A receptor antagonists are of great interest in the treatment for Parkinson's disease. In this study, we combined extensive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent Adenosine A2A antagonists. Genetic function algorithm (GFA) joined with k nearest neighbor (kNN) analyses were applied to build predictive QSAR models. Successful pharmacophores were complemented with exclusion spheres to improve their receiver operating characteristic curve (ROC) profiles. Best QSAR models and their associated pharmacophore hypotheses were validated by identification of several novel Adenosine A2A antagonist leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated IC50 value of 545.7 nM.
['Khanfar|Mohammad A|MA|', 'Al-Qtaishat|Saja|S|', 'Habash|Maha|M|', 'Taha|Mutasem O|MO|']
D058917:Adenosine A2 Receptor Antagonists D000465:Algorithms D000978:Antiparkinson Agents D015195:Drug Design D006801:Humans D008958:Models, Molecular D010300:Parkinson Disease D021281:Quantitative Structure-Activity Relationship D012372:ROC Curve D043705:Receptor, Adenosine A2A
Antagonists QSAR Pharmacophore Adenosine A(2A)