目的:药物再利用是寻找药物新适应症的一种非常流行的方法,这大大减少了药物设计和发现的时间和成本。组蛋白脱乙酰酶(HDAC)同种型(包括沉默调节蛋白(SIRT))的非选择性抑制剂对诸如癌症的病症有效。在这项研究中,我们使用分子对接技术筛选了食品和药物管理局(FDA)批准的药物,以鉴定出一些有可能用于泛HDAC和泛SIRT抑制剂活性的药物.
方法:使用MacroModel优化FDA批准的药物库。HDAC1-4,6-8,SIRT1-3,5,6的晶体结构是在使用Glide将文库对接到每个结构之前制备的,弗雷德,和AutoDockVina/PyRx。一致性分数来自于从每个软件获得的对接分数。使用Phase进行药效团建模。
结果:基于共识分数,belinostat,贝沙罗汀,和cianidanol作为顶级虚拟pan-HDAC抑制剂出现,而阿洛司琼,Cinacalcet,和茚达特罗作为虚拟泛SIRT抑制剂出现。还通过与分子对接模型一致的药效团建模提出了这些虚拟抑制剂的药效团假设。
结论:共识方法能够根据不同的软件选择性能最好的药物分子,和良好的得分对同种型(虚拟pan-HDAC和pan-SIRT抑制剂)。该研究不仅提出了用于HDAC和SIRT相关疾病的潜在药物,而且为设计有效的从头衍生物提供了见解。
OBJECTIVE: Drug repurposing is a highly popular approach to find new indications for drugs, which greatly reduces time and costs for drug design and discovery. Non-selective inhibitors of histone deacetylase (HDAC) isoforms including sirtuins (SIRTs) are effective against conditions like cancer. In this study, we used molecular docking to screen Food and Drug Administration (FDA)-approved drugs to identify a number of drugs with a potential to be repurposed for pan-HDAC and pan-SIRT inhibitor activity.
METHODS: The library of FDA-approved drugs was optimized using MacroModel. The crystal structures of HDAC1-4, 6-8, SIRT1-3, 5, 6 were prepared before the library was docked to each structure using Glide, FRED, and AutoDock Vina/PyRx. Consensus scores were derived from the docking scores obtained from each software. Pharmacophore modeling was performed using Phase.
RESULTS: Based on the consensus scores, belinostat, bexarotene, and cianidanol emerged as top virtual pan-HDAC inhibitors whereas alosetron, cinacalcet, and indacaterol emerged as virtual pan-SIRT inhibitors. Pharmacophore hypotheses for these virtual inhibitors were also suggested through pharmacophore modeling in agreement with the molecular docking models.
CONCLUSIONS: The consensus approach enabled selection of the best performing drug molecules according to different software, and good scores against isoforms (virtual pan-HDAC and pan-SIRT inhibitors). The study not only proposes potential drugs to be repurposed for HDAC and SIRT-related diseases but also provides insights for designing potent de novo derivatives.