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ID: ALA1142418
Journal: Bioorg Med Chem
Title: CoMFA and HQSAR studies on 6,7-dimethoxy-4-pyrrolidylquinazoline derivatives as phosphodiesterase10A inhibitors.
Authors: Kulkarni SS, Patel MR, Talele TT.
Abstract: Phosphodiesterase10A (PDE10A) is an important enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as schizophrenia, Huntington's disease, and diabetes mellitus. The objective of the current 3D QSAR study is to uncover some of the structural parameters which govern PDE10A inhibitory activity over PDE3A/B. Thus, comparative molecular field analysis (CoMFA) and hologram quantitative structure-activity relationship (HQSAR) studies were carried out on recently reported 6,7-dimethoxy-4-pyrrolidylquinazoline derivatives as PDE10A inhibitors. The best CoMFA model using atom-fit alignment approach with the bound conformation of compound 21 as the template yielded the steric parameter as a major contributor (nearly 70%) to the observed variations in biological activity. The best CoMFA model produced statistically significant results, with the cross-validated (r(cv)(2)) and conventional correlation (r(ncv)(2)) coefficients being 0.557 and 0.991, respectively, for the 21 training set compounds. Validation of the model by external set of six compounds yielded a high (0.919) predictive value. The CoMFA models of PDE10A and PDE3A/B activity were compared in order to address the selectivity issue of these inhibitors. The best HQSAR model for PDE10A was obtained with an r(cv)(2) of 0.704 and r(ncv)(2) of 0.902 using atoms, bonds, connections, chirality, donor, and acceptor as fragment distinction and default fragment size of 4-7 with three components for the 21 compounds. The HQSAR model predicted the external test-set of compounds well since a good agreement between the experimental and predicted values was verified. Taken together, the present QSAR models were found to accurately predict the PDE10A inhibitory activity of the test-set compounds and to yield reliable clues for further optimization of the quinazoline derivatives in the dataset.
CiteXplore: 18299198