Department of Pharmacy, Pisa University, Pisa, Italy.
Department of Molecular Sciences and Nanosystems, Ca\' Foscari University, Mestre, Italy | Pathology Unit, Centro di Riferimento Oncologico (CRO) IRCCS, Aviano, Italy.
Med Chem. 2022;18(2):249-259. doi: 10.2174/1573406417666210511001241.
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.
This work was aimed to implement a computational strategy for identifying new HDAC inhibitors for ovarian carcinoma treatment among approved drugs.
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.
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.
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.
卵巢癌的进展似乎与 HDAC1、HDAC3 和 HDAC6 的活性有关。一种可能的策略是重新利用已批准的药物作为这些酶的抑制剂，以改善治疗卵巢癌的疗法，减少临床前筛选。
本研究旨在实施一种计算策略，以确定已批准药物中用于治疗卵巢癌的新型 HDAC 抑制剂。
使用 CHEMBL 数据库构建训练、测试和诱饵集，以使用 FLAP 程序对 HDAC1、HDAC3 和 HDAC6 3D-QSAR 模型进行构建和验证。对接和 MD 模拟与生成的模型结合使用，以鉴定新型潜在的 HDAC 抑制剂。对正常和癌细胞进行细胞活力测定和 Western blot 分析，以直接评估化合物的抗增殖活性，并体外评估通过计算机筛选选择的化合物对 HDAC 的抑制作用。
HDAC6 3D-QSAR 模型获得了最佳的定量预测。对已批准药物的筛选突出了一些化合物作为 HDAC 抑制剂的新潜在用途，特别是硝基呋喃衍生物，这些化合物通常因其抗菌活性而闻名，并经常用于癌症治疗中的抗菌辅助治疗。这些衍生物的实验评估显示出对高表达 HDAC6 的癌细胞系具有显著的增殖抑制活性，并增加了乙酰化的微管蛋白水平。
实验结果支持硝基呋喃衍生物与 HDACs 可能存在直接相互作用的假设。除了可能重新利用已批准的药物外，这项工作还表明硝基基团是一种新的锌结合基团，能够与 HDACs 的催化锌离子相互作用。
