Background: The progression of ovarian cancer seems to be related to HDAC1, HDAC3, and HDAC6 activity. A possible strategy for improving therapies for treating ovarian carcinoma, minimizing the preclinical screenings, is the repurposing of already approved pharmaceutical products as inhibitors of these enzymes.
Objective: This work was aimed to implement a computational strategy for identifying new HDAC inhibitors for ovarian carcinoma treatment among approved drugs.
Method: The CHEMBL database was used to construct training, test, and decoys sets for performing and validating HDAC1, HDAC3 and HDAC6 3D-QSAR models obtained by using the FLAP program. Docking and MD simulations were used in combination with the generated models to identify novel potential HDAC inhibitors. Cell viability assays and Western blot analyses were performed on normal and cancer cells for a direct evaluation of the anti-proliferative activity and an in vitro estimation of HDAC inhibition of the compounds selected through in silico screening.
Result: The best quantitative prediction was obtained for the HDAC6 3D-QSAR model. The screening of approved drugs highlighted a new potential use as HDAC inhibitors for some compounds, in particular nitrofuran derivatives, usually known for their antibacterial activity and frequently used as antimicrobial adjuvant therapy in cancer treatment. Experimental evaluation of these derivatives highlighted a significant antiproliferative activity against cancer cell lines overexpressing HDAC6, and an increase in acetylated alpha-tubulin levels.
Conclusion: Experimental results support the hypothesis of potential direct interaction of nitrofuran derivatives with HDACs. In addition to the possible repurposing of already approved drugs, this work suggests the nitro group as a new zinc-binding group, able to interact with the catalytic zinc ion of HDACs.
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