title: Development of predictive 3D-QSAR CoMFA and CoMSIA models for beta-aminohydroxamic acid-derived tumor necrosis factor-alpha converting enzyme inhibitors.
abstract: A three-dimensional quantitative structure-activity relationship study was performed on a series of beta-aminohydroxamic acid-derived tumor necrosis factor-alpha converting enzyme inhibitors employing comparative molecular field analysis and comparative molecular similarity indices analysis techniques to investigate the structural requirements for the inhibitors, and derive a predictive model that could be used for the design of novel tumor necrosis factor-alpha converting enzyme inhibitors. log P was used as an additional descriptor in the comparative molecular field analysis analysis to study the effects of lipophilic parameters on activity. Inclusion of log P did not improve the models significantly. The statistically significant model was established with 45 molecules, which were validated by a test set of 11 compounds. Ligand molecular superimposition on the template structure was performed by the atom-/shape-based root mean square fit and database alignment methods. Docked conformer based alignment (V) yielded the best predictive comparative molecular field analysis model = 0.673, = 0.860, F-value = 86.073, predictive r (2) = 0.642, with two components, standard error of prediction = 0.394 and standard error of estimates = 0.243 while the comparative molecular similarity indices analysis model yielded = 0.635, = 0.858, F-value = 84.451, predictive r (2) = 0.441 with three components, standard error of prediction = 0.393 and standard error of estimates = 0.245. The contour maps obtained from three-dimensional quantitative structure-activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular field analysis models exhibited good external predictivity as compared with that of comparative molecular similarity indices analysis models. The model generated through comparative molecular field analysis was validated with the IK-682. The data generated from this study may guide our efforts in designing and predicting the tumor necrosis factor-alpha converting enzyme inhibitory activity of novel molecules.
authors.affiliation: Pharmacy Department, Faculty of Technology and Engineering, Kalabhavan, The M. S. University of Baroda, Vadodara-390001, Gujarat, India.
authors.forename: Prashant R
authors.initials: PR
authors.lastname: Murumkar
authors.forename: Shirshendu Das
authors.initials: SD
authors.lastname: Gupta
authors.forename: Vishal P
authors.initials: VP
authors.lastname: Zambre
authors.forename: Rajani
authors.initials: R
authors.lastname: Giridhar
authors.forename: Mange Ram
authors.initials: MR
authors.lastname: Yadav
mesh_terms: D051722:ADAM Proteins / Q000037:antagonists & inhibitors*; D000072198:ADAM17 Protein; D003198:Computer Simulation; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000138:chemical synthesis / Q000737:chemistry / Q000378:metabolism; D006877:Hydroxamic Acids / Q000737:chemistry / Q000378:metabolism; D007769:Lactams / Q000737:chemistry / Q000378:metabolism; D008958:Models, Molecular; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
chemical_list: D004791:Enzyme Inhibitors; D006877:Hydroxamic Acids; C468787:IK 682; D007769:Lactams; D051722:ADAM Proteins; D000072198:ADAM17 Protein