QSAR analysis of coumarin-based benzamides as histone deacetylase inhibitors using CoMFA, CoMSIA and HQSAR methods

Abstract

Histone deacetylases (HDACs) as the promising therapeutic targets for the treatment of cancer and other diseases, modify chromatin structure and contribute to aberrant gene expression in cancer. Inhibition of HDACs is emerging as an important strategy in human cancer therapy and HDAC inhibitors (HDACIs) enable histone to maintain a high degree of acetylation. In this work, molecular modeling studies, including CoMFA, CoMFA-RF, CoMSIA and HQSAR and molecular docking were performed on a series of coumarin-based benzamides as HDAC inhibitors. The statistical qualities of generated models were justified by internal and external validation, i.e., cross-validated correlation coefficient (q2), non-cross-validated correlation coefficient (rncv2) and predicted correlation coefficient (rpred2), respectively. The CoMFA (q2, 0.728; rncv2, 0.982; rpred2, 0.685), CoMFA-RF (q2, 0.764; rncv2, 0.960; rpred2, 0.552), CoMSIA (q2, 0.671; rncv2, 0.977; rpred2, 0.721) and HQSAR models (q2, 0.811; rncv2, 0.986; rpred2, 0.613) for training and test set of HDAC inhibition of HCT116 cell line yielded significant statistical results. Therefore, these QSAR models were excellent, robust and had better predictive capability. Contour maps of the QSAR models were generated and validated by molecular docking study. The final QSAR models could be useful for the design and development of novel potent HDAC inhibitors in cancer treatment. The amido and amine groups of benzamide part as scaffold and the bulk groups as a hydrophobic part were key factors to improve inhibitory activity of HDACIs.