LEAD IDENTIFICATION OF HYDROXAMATE DERIVATIVE AS SELECTIVE HDAC2 INHIBITOR USING COMPUTATIONAL APPROACHES

Abstract

Histone deacetylase (HDAC) inhibitors have been established as a novel class of anticancer agents. The HDAC enzyme plays a vital role in gene transcription for regulation of cell proliferation, migration and apoptosis, immune pathways and angiogenesis. In this work, a series of 49 hydroxamate derivatives with available IC50 data were analyzed by computational method for the identification of leads. 3D-QSAR and pharmacophore modeling investigation were accomplished to identify the crucial pharmacophoric features and correlate 3D-chemical structure with HDAC inhibitory activity. The e-pharmacophore script and phase module were used for development of pharmacophore hypotheses, which characterized the 3D arrangement of molecular features necessary for the presence of biological activity. The 3D-QSAR analyses were carried out for five partial least square (PLS) factor model with excellent information and predictive ability, acquired R2 =0.9824, Q2 =0.8473 and with low standard deviation SD=0.2161. Molecular docking studies showed intermolecular interactions between small molecules and some amino acids, such as GLY140, Zn501, HIS132 and PHE 141 with good GlideScore as compared with that of vorinostat (SAHA).