ABCG2 is the principal ABC transporter involved in the multidrug resistance of breast cancer. Looking at the current demand in the development of ABCG2 inhibitors for the treatment of multidrug-resistant cancer, we have explored structural requirements of phenyltetrazole derivatives for ABCG2 inhibition by combining classical QSAR, Bayesian classification modelling and molecular docking studies. For classical QSAR, structural descriptors were calculated from the free software tool PaDEL-descriptor. Stepwise multiple linear regression (SMLR) was used for model generation. A statistically significant model was generated and validated with different parameters (For training set: r = 0.825; Q2 = 0.570 and for test set: r = 0.894, r2pred = 0.783). The predicted model was found to satisfy the Golbraikh and Trospha criteria for model acceptability. Bayesian classification modelling was also performed (ROC scores were 0.722 and 0.767 for the training and test sets, respectively). Finally, the binding interactions of phenyltetrazole type inhibitor with the ABCG2 receptor were mapped with the help of molecular docking study. The result of the docking analysis is aligned with the classical QSAR and Bayesian classification studies. The combined modelling study will guide the medicinal chemists to act faster in the drug discovery of ABCG2 inhibitors for the management of resistant breast cancer.
ATP Binding Cassette Transporter, Subfamily G/antagonists & inhibitors , Neoplasm Proteins/antagonists & inhibitors , Tetrazoles/chemistry , Animals , Bayes Theorem , Breast Neoplasms/drug therapy , Dogs , Drug Design , Drug Resistance, Multiple/drug effects , Drug Resistance, Neoplasm/drug effects , Linear Models , Madin Darby Canine Kidney Cells , Molecular Docking Simulation , Quantitative Structure-Activity Relationship , Tetrazoles/pharmacology
