Identification of Novel Human HDAC8 Inhibitors by Pharmacophore‐based Virtual Screening and Density Functional Theory Approaches

Abstract

Histone deacetylase (HDAC) enzyme plays a key role in deacetylation mechanism of N‐terminal acetylated lysine residues in histone proteins. HDAC inhibitors have therapeutic potential as anticancer agents. A chemical feature‐based pharmacophore model has been generated from known HDAC8 inhibitors (22 training set compounds) by a 3D QSAR pharmacophore generation approach. The top ranked hypothesis (Hypo1) contained three features of one hydrogen bond donor and two ring aromatics. Hypo1 was cross‐validated using Fischer's randomization by shuffling the activity data in training set compounds. It was also validated by 248 test set compounds with a correlation coefficient of 0.851 between experimental and estimated activities. Thus, the validated Hypo1 was exploited for retrieving novel HDAC8 inhibitor candidates over 109,652 chemical compounds in both Maybridge and Chembridge chemical databases and then the screened compounds were tested by ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and Lipinski's rules to evaluate their druglikeness. Finally, 11 new lead candidates were obtained and the final three drug candidates from them were selected as potential inhibitors based on the results of molecular docking and density functional theory calculations.