QSAR Analysis of Quinazolinyl-arylurea Derivatives as Potential Anti- Cancer Agents: GA-MLR Chemometric Approach

Abstract

Background: Cancer is the most common malignancy in men and women globally. The tyrosine kinases and serine/threonine kinases are essential to cell mediators for extra & intra-cellular signal transduction processes and play a key role in cell proliferation, differentiation, migration, metabolism, and programmed cell deaths. In this context, kinases are considered as a potential drug target for cancer therapy. Methods: In the present study, a two-dimensional (2D) quantitative structure-activity relationship (2D-QSAR) was performed to analyze anticancer activities of 28 quinazolinyl-arylurea (QZA) derivatives based on the liver (BEL-7402), stomach (MGC-803), and colon (HCC-827) cancer cell lines using multiple linear regression (MLR) analysis. It was accomplished by using 2D-QSAR analysis on the available IC50 data of 28 molecules based on theoretical molecular descriptors to develop predictive models that correlate structural features of QZA derivatives to their anticancer activities. A suitable set of molecular descriptors such as constitutional, topological, geometrical, electrostatic, and quantum-chemical descriptors were calculated to represent the structural features of compounds. The genetic algorithm (GA) method was used to identify the important molecular descriptors to build the QSAR models and used to predict the anti-cancer activities. Results and Discussion: The obtained 2D-QSAR models were vigorously validated using various statistical metrics using leave-one-out (LOO) and external test set prediction approaches. The best predictive models by MLR gave highly significant square of correlation coefficient (R2train) values of 0.799, 0.815, and 0.779 for the training set and the correlation coefficients (R2test) were obtained 0.885, 0.929, and 0.774 for the test set for the liver, stomach, and colon cancer cell lines. The models also demonstrated good predictive power confirmed by the high value of cross-validated correlation coefficient Q2 value of 0.663, 0.717, and 0.671 for three different cancer cell lines. Importantly, the model's quality was judged as well based on mean absolute error (MAE) criteria and the results were consistent with proposed limits by Golbraikh and Tropsha. Conclusion: The QSAR results of the study indicated that the proposed models were robust and free from chance correlation. This study indicated that maxHBint7, SpMax8_Bhm, and ETA_Beta_ns_d have positively contributed descriptors for anti-cancer activity in the liver, stomach, and colon cancer cell lines and a detailed mechanistic interpretation of each model revealed important structural features that were responsible for favorable or unfavorable for anti-cancer activity. The predictive ability of the proposed models was good and may be useful for developing more potent quinazolinyl-arylurea compounds as anti-cancer agents.

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“…The 2D-QSAR model was constructed by employing multiple linear regression (MLR) method using selected variables. The MLR is the traditional and standard approach for multivariate data analysis, it was used to study the relation between one dependent variable (biological activity) and several independent variables (molecular descriptor) [49,50]. In this study, best model was selected based on statistical quality and with minimum number of reliable descriptors which able to explain variance in the activity.…”