Quinazoline Derivatives as Anticancer Agents: QSAR, Molecular Docking and in silico Pharmacokinetic Prediction

  Context: Molecular target specific treatment for the cancer is different from the conventional chemotherapy and radiotherapy in terms of selectivity and specificity towards the cancer cells. Amongst the different molecular targets for cancer, EGFR tyrosine kinase is considered more promising molecular target for discovery and development of the novel anticancer agents. EGFR overexpression and deregulation or mutation are observed in the different kinds of epithelial cancer namely non-small-cell lung cancer, colon cancer, breast cancer etc. Aim: To carry out structure based drug design (SBDD) and ligand based drug design (LBDD) approaches on quinazoline derivatives as EGFR inhibitors. Methods: A set of 25 compounds was used to correlate the structural parameter(s) with its inhibitory effect on EGFR using 3D-QSAR (CoMFA and CoMSIA) technique. Further, the molecular docking simulation was carried out to recognize the interaction of ligands with the active site of EGFR. All the compounds from the dataset were aligned using the distill alignment method. Post alignment of the dataset, CoMFA and CoMSIA analysis were executed. Moreover, pharmacokinetic (ADMET) predictions of all the quinazoline derivatives were carried out using admetSAR tool. Results: CoMFA and CoMSIA both the models were found statistically significant with values of the cross-validation correlation coefficient (q2) as 0.757 and 0.524 respectively and conventional correlation coefficient (r2) as 0.925 and 0.855 respectively. Further, the molecular docking simulation revealed that all the quinazoline derivatives were situated at the same place as that of the standard drug erlotinib with reasonable penetration at the active site of the EGFR. Conclusion: Results of this work provide the information regarding the structure activity relationship and important structure requirements for the interaction of compounds at the active site of the receptor. This information provides a hint for the design of novel analogs as EGFR tyrosine kinase inhibitors.