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

Background: Cancer is the most common malignancy in men and women globally. Thetyrosine kinases and serine/threonine kinases are essential to cell mediators for extra & intracellularsignal transduction processes and play a key role in cell proliferation, differentiation,migration, metabolism, and programmed cell deaths. In this context, kinases are considered as apotential 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 celllines using multiple linear regression (MLR) analysis. It was accomplished using 2D-QSARanalysis on the available IC50 data of 28 molecules based on theoretical molecular descriptors todevelop predictive models that correlate structural features of QZA derivatives to their anticanceractivities. A suitable set of molecular descriptors, such as constitutional, topological, geometrical,electrostatic, and quantum-chemical descriptors were calculated to represent the structural featuresof compounds. The genetic algorithm (GA) method was used to identify the important moleculardescriptors 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 variousstatistical metrics using leave-one-out (LOO) and external test set prediction approaches. The bestpredictive models by MLR gave highly significant square of correlation coefficient (R2train) valuesof 0.799, 0.815, and 0.779 for the training set, and the correlation coefficients (R2test) were obtained0.885, 0.929, and 0.774 for the test set for the liver, stomach, and colon cancer cell lines. Themodels also demonstrated good predictive power confirmed by the high value of cross-validatedcorrelation 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 andfree from chance correlation. This study indicated that maxHBint7, SpMax8_Bhm, andETA_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 modelrevealed important structural features that were responsible for favorable or unfavorable for anticanceractivity. The predictive ability of the proposed models was good and may be useful fordeveloping more potent quinazolinyl-arylurea compounds as anti-cancer agents.