新型HDAC I亚族选择性抑制剂的合理设计、合成及生物活性研究

Histone deacetylase (HDAC) inhibitor is a molecule-targeted anticancer agents which regulates epigenetic modification. In recent decade, great progress has been made in HDAC inhibitor and five agents have been launched. However,side effects were observed for HDAC inhibitors in clinical studies and the possible reason may due to the low selectivities for subtypes and signal-pathway specificities. It has been found that the HDAC Class I (HDACs 1, 2, 3, and 8) play an important role in the proliferation of a variety of cancer cells. However other calsses of HDAC focus on non-histone substrates which could lead to off-target effects.Therefore, developing selective inhibitors of HDAC Class I not only can improve the therapeutic effect but also reduce the side effects. With the sponsor of previous National Natural Science Foundation project (81373281), we established a pharmacophore model that selectively recognizes HDAC8 inhibitors and then developed a series of novel HDAC inhibitors. After further biological evaluation, some compounds showed good HDAC Class I selectivities.In our on-going studies, pharmacophore model will be built for each subtype of the HDAC Class I and then perform virtual screening with the help of chemoinformatics. After synthesizing active compounds, we will evaluate their subtypes selectivity, signal-pathway specificity and cancer stem cells inhibition. We hope to find efficient, low toxicity, novel selective HDAC Class I inhibitors.

组蛋白去乙酰化酶（HDAC）抑制剂作为表观遗传修饰治疗肿瘤的分子靶向药物，近年来取得巨大进展且有多个药物上市。但早期药物因亚型选择性低、调控信号通路特异性差等原因，在临床应用中出现较多副作用。目前研究认为HDAC I亚族（HDAC1,2,3和8）在多种肿瘤的不同发展阶段起重要作用，而其它亚族针对非组蛋白底物易产生脱靶效应。因此研究选择性识别HDAC I亚族的药物可以提高肿瘤治疗效果、减少副作用。在国家自然科学基金（81373281）支持下，我们首次构建选择性识别HDAC８亚型的药效团模型，在随后发展的多种结构类型抑制剂中已发现部分活性化合物具有良好的HDAC I亚族选择性。本课题拟在此基础上进一步构建针对HDAC I亚族其它亚型的药效团模型，借助化学信息学等技术手段进行高内涵虚拟筛选，经定向合成、亚型选择性评价、信号通路及抑制肿瘤干细胞等深入评价，以期发现高效、低毒的新型抗癌药物。

组蛋白去乙酰化酶（HDAC）抑制剂作为表观遗传修饰治疗肿瘤的分子靶向药物，近年来取得巨大进展且有多个药物上市。但早期药物因亚型选择性低、调控信号通路特异性差等原因，在临床应用中出现较多副作用。目前研究认为HDAC I亚族（HDAC1,2,3和8）在多种肿瘤的不同发展阶段起重要作用，而其它亚族针对非组蛋白底物易产生脱靶效应。因此研究选择性识别HDAC I亚族的药物可以提高肿瘤治疗效果、减少副作用。 在本项目的支持下，申请人基于结构生物学研究成果，运用人工智能算法和Alpha-Space计算分析了不同HDAC亚型的蛋白中的可成药口袋，在此基础上应用化学信息学手段，构建了喹啉、咪唑啉二酮等多种结构骨架的化合物库，结合基于药效团虚拟筛选、成药性预测，得到了潜在的具有与I亚族HDAC较强结合能力的化合物结构。在完成相关目标化合物合成的基础上，开展HDAC靶蛋白亲和力选择性研究，并对于亚型选择性高的活性目标结构开展了抗肿瘤细胞增殖活性评价、信号通路和作用机制研究。对于药效突出的活性化合物，进行了成药性评价，目前有1个候选药物分子正在进行临床前研究，完成既定目标。