QSAR studies of quinoline alkaloids camptothecin derivatives for prediction anticancer activity using linear and nonlinear methods

Abstract

AbstractQuinoline alkaloid camptothecin (CPT) derivatives are compounds with a wide range of inhibitory activities and they are the basis of several groups of drugs. CPT is one of the prominent lead compounds in anticancer drug development. It inhibits DNA topoisomerase I (topo I) enzyme and has shown remarkable anticancer activity against lung, colon, rectum, ovarian, breast, and stomach cancers. Quantitative structure–activity relationship (QSAR) is basically a statistical approach correlating the response activity data with descriptors encoding chemical information. In the current research, a QSAR study was performed for modeling and predicting the anticancer activity (pIC50) of 76 CPT derivatives as an inhibitor of DNA topo I using the genetic algorithm multiple linear regression (GA‐MLR) and back propagation artificial neural network (BP‐ANN) methods. The results of this study indicate that the use of constitutional and geometrical descriptors provide good estimate for pIC50. The obtained results of statistical criteria, internal and external validation show the superiority of BP‐ANN model than GA‐MLR to predict the pIC50 values of the investigated compounds. QSAR model developed in this study can be used for design and development of novel potent CPT derivatives and for predicting their anticancer activity.