论文标题
Saverunner：一种基于网络的算法用于药物重新利润及其应用于Covid-19
SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19
论文作者
论文摘要
新的人类冠状病毒COVID-19/SARS-COV-2的新颖性以及缺乏有效的药物和疫苗的新颖性引起了与这种全球大流行作斗争的各种策略。这些策略中的许多策略都依赖于现有药物的重新定位，这些药物可能会缩短时间并减少与从头毒品发现相比的成本。在这项研究中，我们提出了一种用于药物重新定位的新的基于网络的算法，称为Saverunner（搜索标签的药物和网络），该算法通过量化药物靶标与疾病特异性蛋白之间的相互作用，通过在人类相互作用中通过新型网络相似性衡量药物相同的邻居之间的相同关联，通过量化药物靶标与疾病特异性蛋白之间的相互作用来预测药物 - 疾病的关联。具体而言，我们将Saverunner应用于14个选定疾病的面板上，并具有有关疾病引起疾病基因的合并知识，并且发现与Covid-19有关遗传相似性，合并症或与之关联的临时药物有关治疗Covid-19。我们专门针对SARS子网，确定了282种可再效应的药物，其中包括一些用于CoVID-19治疗的标签外药物最多的药物，以及实际上用于临床实践中的5种药物的新组合疗法。此外，为了最大程度地提高推定的下游验证实验的效率，我们根据基于网络的相似性值对24个潜在的抗SARS-COV重新验证药物进行了优先级。这些排名最高的药物包括ACE抑制剂，单克隆抗体和凝血酶抑制剂。最后，我们的发现是通过进行基因集富集分析来验证的，该分析证实，大多数网络预测的可重复的药物可能对人类冠状病毒感染具有潜在的治疗效果。
The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity, comorbidity, or for their association to drugs tentatively repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs, actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies, and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.
