Department of Biotechnology, PES University, Bangalore, 560085, India.
J Mol Neurosci. 2024 Feb 16;74(1):21. doi: 10.1007/s12031-024-02199-2.
The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.
传统的一种药物针对一个靶点的方法,并未为路易体痴呆症(LBD)带来治疗方案,LBD 是一种主要的进行性神经退行性疾病,其特征是神经元大量丧失。这种与年龄相关的疾病以记忆丧失、幻觉、睡眠障碍、精神健康恶化、瘫痪和认知障碍为标志,所有这些都没有已知的有效治疗方法。本研究采用网络医学管道,重新利用对目标疾病相关基因和蛋白有显著作用的药物。我们利用新颖的 SAveRUNNER 算法来量化从 DrugBank 获得的所有药物与从 Phenopedia 获得的与疾病相关的基因数据集以及人类相互作用组中的靶标之间的接近程度。我们发现,SAveRUNNER 预测的 154 种经 FDA 批准的药物中,大多数被用于治疗神经系统疾病,但一些非标签药物,如喹那普利和司来吉兰,分别有趣地用于治疗高血压和帕金森病(PD)。此外,我们使用 Connectivity Map(CMap)进行基因集富集分析,并使用 EnrichR 进行通路富集分析,以验证从管道方法获得的候选药物的疗效。该研究使我们能够确定突触囊泡途径在我们疾病中的重要作用,并据此从最初由 SAveRUNNER 预测的 154 种药物中确定了 8 种合适的抗抑郁药。这些潜在的抗 LBD 药物是 5-羟色胺、多巴胺和去甲肾上腺素转运体的选择性或非选择性抑制剂。经过验证的选择性 5-羟色胺和去甲肾上腺素抑制剂,如米那普仑、普罗替林和文拉法辛,预计可与影响症状的问题一起用于治疗 LBD。