An approach to design potent anti-Alzheimer's agents by 3D-QSAR studies on fused 5,6-bicyclic heterocycles as γ-secretase modulators using kNN-MFA methodology

Alzheimer's disease (AD) is a chronic neurodegenerative disease. Current therapies of AD are only symptomatic, therefore the need for the development of new therapies to treat Alzheimer's disease effectively. To achieve this objective quantitative structure-activity relationship (QSAR) studies were carried out as it provides the rationale for the changes in the structure to have more potent A beta(42) inhibitors or anti-Alzheimer's agents. Quantitative structure-activity relationship (QSAR) studies were carried out on a series of 34 fused 5,6-bicyclic heterocycles to investigate the structural requirements of their inhibitory activity against A beta(42). The statistically significant best 3D-QSAR model having cross-validated squared correlation coefficient q(2) = 0.8457 with external predictive ability of pred_r(2) = 0.7556 was developed by SW-kNN. Developed kNN-MFA model highlighted the importance of shape of the molecules, i.e., hydrophobic and steric descriptors at the grid points H_83 and S_183, S_227 for gamma-secretase binding interaction. This model (3D) was found to yield reliable clues for further optimization of fused 5,6-bicyclic heterocycles in the data set. The information rendered by the 3D-QSAR model may lead to a better understanding of the structural requirements of gamma-secretase modulators and can also help in the design of novel potent gamma-secretase modulators. (C) 2013 Production and hosting by Elsevier B.V. on behalf of King Saud University.