Abstract

QSAR models supervised by Multiple linear regressions (MLR) and Gaussian kernel support vector machines were developed to predict β2 potency for Sibenadet (Viozan™) and its derivatives along with established LABAs (Formeterol, Salmetrol) and ultra LABA Indacaterol. MLR aided linear QSAR models identified four molecular descriptors MATS6e, GATS5e, Mor17p, R7m+ related to β2 potency while descriptors like R5p+, Lop, Belp4, RDF075m were deduced in prediction of β2 potency in non-linear SVM models. Although, statistical fitness was observed with Gaussian Kernel function SVM models in potency prediction, MLR models proved to be more consistent in predictions. Further MLR and SVM models were statistically validated by internal validation methods like R2CV, RSS and MSS etc. Mechanistic study on linear QSAR models revealed regulative role of atomic autocorrelated electronegativities and polarizabilities in influencing β2 potency.

Keywords: Ultra long acting β2 agonists (uLABA), multiple linear regressions (MLR), support vector machine (SVM), linear and non-linear QSAR models.