Departamento de Farmacología, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
Dirección de Investigación, Instituto Nacional de Geriatría (INGER), Mexico City 10200, Mexico.
Int J Mol Sci. 2023 Jan 6;24(2):1134. doi: 10.3390/ijms24021134.
Parkinson's disease (PD) is the second most common neurodegenerative disease in older individuals worldwide. Pharmacological treatment for such a disease consists of drugs such as monoamine oxidase B (MAO-B) inhibitors to increase dopamine concentration in the brain. However, such drugs have adverse reactions that limit their use for extended periods; thus, the design of less toxic and more efficient compounds may be explored. In this context, cheminformatics and computational chemistry have recently contributed to developing new drugs and the search for new therapeutic targets. Therefore, through a data-driven approach, we used cheminformatic tools to find and optimize novel compounds with pharmacological activity against MAO-B for treating PD. First, we retrieved from the literature 3316 original articles published between 2015-2021 that experimentally tested 215 natural compounds against PD. From such compounds, we built a pharmacological network that showed rosmarinic acid, chrysin, naringenin, and cordycepin as the most connected nodes of the network. From such compounds, we performed fingerprinting analysis and developed evolutionary libraries to obtain novel derived structures. We filtered these compounds through a docking test against MAO-B and obtained five derived compounds with higher affinity and lead likeness potential. Then we evaluated its antioxidant and pharmacokinetic potential through a docking analysis (NADPH oxidase and CYP450) and physiologically-based pharmacokinetic (PBPK modeling). Interestingly, only one compound showed dual activity (antioxidant and MAO-B inhibitors) and pharmacokinetic potential to be considered a possible candidate for PD treatment and further experimental analysis.
帕金森病（PD）是全球老年人中第二常见的神经退行性疾病。此类疾病的药物治疗包括单胺氧化酶 B（MAO-B）抑制剂等药物，以增加大脑中的多巴胺浓度。然而，这些药物有不良反应，限制了它们的长期使用；因此，可能会探索设计毒性更小、效率更高的化合物。在这种情况下，计算化学和计算化学最近为开发新药和寻找新的治疗靶点做出了贡献。因此，我们通过数据驱动的方法，使用计算化学工具寻找和优化具有 MAO-B 抑制活性的新型化合物，用于治疗 PD。首先，我们从文献中检索到 2015 年至 2021 年间发表的 3316 篇原始文章，这些文章实验测试了 215 种天然化合物对 PD 的作用。从这些化合物中，我们构建了一个药理学网络，显示迷迭香酸、白杨素、柚皮素和虫草素是网络中连接度最高的节点。从这些化合物中，我们进行了指纹分析并开发了进化文库，以获得新的衍生结构。我们通过与 MAO-B 的对接测试筛选这些化合物，得到了五个具有更高亲和力和先导样潜力的衍生化合物。然后，我们通过对接分析（NADPH 氧化酶和 CYP450）和基于生理学的药代动力学（PBPK 建模）评估其抗氧化和药代动力学潜力。有趣的是，只有一种化合物表现出双重活性（抗氧化和 MAO-B 抑制剂）和药代动力学潜力，可被认为是治疗 PD 的潜在候选药物，并进一步进行实验分析。
