Exploring QSAR for substituted 2-sulfonyl-phenyl-indol derivatives as potent and selective COX-2 inhibitors using different chemometrics tools.
Selective inhibition of cyclooxygenase-2 inhibitors is an important strategy in designing of potent anti-inflammatory compounds with significantly reduced side effects. The present quantitative structure-activity relationship study, attempts to explore the structural and physicochemical requirements of 2-sulfonyl-phenyl-indol derivatives (n = 30) for COX-2 inhibitory activity using chemical, topological, geometrical, and quantum descriptors. Some statistical techniques like stepwise regression, multiple linear regression with factor analysis as the data preprocessing (FA-MLR), principal component regression analysis, and genetic algorithms partial least squares analysis were applied to derive the quantitative structure-activity relationship models. The generated equations were statistically validated using cross-validation and external test set. The quality of equations obtained from stepwise multiple linear regression, FA-MLR, principal component regression analysis and PLS were in the acceptable statistical range. The best multiple linear regression equation obtained from factor analysis (FA-MLR) as the preprocessing step could predict 77.5% of the variance of the cyclooxygenase-2 inhibitory activity whereas that derived from genetic algorithms partial least squares could predict 84.2% of variances. The results of quantitative structure-activity relationship models suggested the importance of lipophilicity, electronegativity, molecular area and steric parameters on the cyclooxygenase-2 inhibitory activity.
Khoshneviszadeh
Mehdi
M
Medicinal & Natural Products Chemistry Research Center, Shiraz University of Medical Science, Shiraz, Iran. hemmatb@sums.ac.ir
Edraki
Najmeh
N
Miri
Ramin
R
Hemmateenejad
Bahram
B
D000465:Algorithms; D052246:Cyclooxygenase 2 Inhibitors / Q000737:chemistry*; D015195:Drug Design; D007211:Indoles / Q000737:chemistry*; D016018:Least-Squares Analysis; D008956:Models, Chemical; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D012984:Software
D052246:Cyclooxygenase 2 Inhibitors; D007211:Indoles