Novel HDAC8 inhibitors: A multi-computational approach

Abnormal HDAC function triggers irregular gene transcription that hampers the essential cellular activities leading to tumour activation and progression. HDAC inhibition has, therefore, been reported as a potential target for cancer treatment. In the present study, a sequential computational framework was carried out to discover newer lead compounds, namely HDAC8 inhibitors for cancer therapy. Pharmacophoric hypotheses were generated based on hydroxamic acid derivatives reported earlier for HDAC inhibition. The model AAADR.122, demonstrated statistical significance (r² = 0.93, Q² = 0.81) and proved robust on validation with a cross-validated correlation coefficient of 0.89. It was utilized to arrive at novel hits through a virtual screening workflow. The specificity of the process was enhanced further by analysing the crucial interactions of the ligands with key catalytic residues, achieved by induced fit docking (PDB ID: 1T64). On assessment, the filtered leads displayed optimal drug like features. Investigations using density functional theory (DFT) also facilitated the recognition of molecular spots in the leads beneficial for HDAC8 interaction. Overall, two leads were proposed for HDAC8 inhibition with potential anti-cancer activity.