Identification of Potential Isoform-Selective Histone Deacetylase Inhibitors for Cancer Therapy: a Combined Approach of Structure-Based Virtual Screening Admet Prediction and Molecular Dynamics Simulation Assay

Abstract

Histone deacetylases (HDACs) have gained increased attention as targets for anticancer drug design and development. HDAC inhibitors have proven to be effective for reversing the malignant phenotype in HDAC-dependent cancer cases. However lack of selectivity of the many HDAC inhibitors in clinical use and trials contributes to toxicities to healthy cells. It is believed that the continued identification of isoform-selective inhibitors will eliminate these undesirable adverse effects - a task that remains a major challenge to HDAC inhibitor designs. Here in an attempt to identify isoform-selective inhibitors a large compound library containing 2703000 compounds retrieved from Otava database was screened against class I HDACs by exhaustive approach of structure-based virtual screening using rDOCK and Autodock Vina. A total of 41 compounds were found to show high-isoform selectivity and were further redocked into their respective targets using Autodock4. Thirty-six compounds showed remarkable isoform selectivity and passed drug-likeness and absorption distribution metabolism elimination and toxicity prediction tests using ADMET Predictor and admetSAR. Furthermore to study the stability of ligand binding modes 10ns-molecular dynamics (MD) simulations of the free HDAC isoforms and their complexes with respective best-ranked ligands were performed using nanoscale MD software. The inhibitors remained bound to their respective targets over time of the simulation and the overall potential energy root-mean-square deviation root-mean-square fluctuation profiles suggested that the detected compounds may be potential isoform-selective HDAC inhibitors or serve as promising scaffolds for further optimization towards the design of selective inhibitors for cancer therapy.