Discovery of novel urokinase plasminogen activator (uPA) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis

• Published in: Journal of molecular modeling 2014, Vol.20 (1), p.2080, Article 2080
• Main Authors: Al-Sha’er, Mahmoud A., Khanfar, Mohammad A., Taha, Mutasem O.
• Format: Article
• Language: English
• Subjects: Algorithms; Antineoplastic Agents - chemistry; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Computer Appl. in Life Sciences; Computer Applications in Chemistry; Drug Discovery; Humans; Ligands; Models, Molecular; Molecular Conformation; Molecular Medicine; Original Paper; Protein Binding; Quantitative Structure-Activity Relationship; ROC Curve; Serine Proteinase Inhibitors - chemistry; Theoretical and Computational Chemistry; Urokinase-Type Plasminogen Activator - antagonists & inhibitors; Urokinase-Type Plasminogen Activator - chemistry
• ISSN: 1610-2940; 0948-5023
• DOI: 10.1007/s00894-014-2080-4

Urokinase plasminogen activator (uPA)—a serine protease—is thought to play a central role in tumor metastasis and angiogenesis and, therefore, inhibition of this enzyme could be beneficial in treating cancer. Toward this end, we explored the pharmacophoric space of 202 uPA inhibitors using seven diverse sets of inhibitors to identify high-quality pharmacophores. Subsequently, we employed genetic algorithm-based quantitative structure-activity relationship (QSAR) analysis as a competition arena to select the best possible combination of pharmacophoric models and physicochemical descriptors that can explain bioactivity variation within the training inhibitors ( r 2 162  = 0.74, F-statistic = 64.30, r 2 LOO  = 0.71, r 2 PRESS against 40 test inhibitors = 0.79). Three orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least three binding modes accessible to ligands within the uPA binding pocket. This conclusion was supported by receiver operating characteristic (ROC) curve analyses of the QSAR-selected pharmacophores. Moreover, the three pharmacophores were comparable with binding interactions seen in crystallographic structures of bound ligands within the uPA binding pocket. We employed the resulting pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds. The captured hits were tested in vitro. Overall, our modeling workflow identified new low micromolar anti-uPA hits.