Identifying existing, FDA-approved drugs with clinically protective effects against coronavirus disease 2019 using a big data approach

项目摘要/摘要 冠状病毒病2019(新冠肺炎)是全国性和全球性的突发公共卫生事件。因为 致病病毒是新的，目前的治疗选择极其有限，有效的疫苗可能 一到两年后。因此，迫切需要有效的治疗方法来对抗这种疾病。而当 新药的开发正在进行中，这一过程缓慢且资源密集型。在中短期内 从长远来看，一个更好的策略是重新调整现有药物的用途来治疗这种疾病。已经有100多种药物 由食品和药物管理局(FDA)批准的药物在体外、硅胶或理论上对 SARS-CoV-2，引起新冠肺炎的病毒，或它引发的高炎性免疫反应。是什么 目前尚不清楚其中有多少对实际患者有显著的保护作用，因为只有极小一部分 这些药物正在进行临床试验。这些药物大多是慢性药物，因此有数百万种 已经在使用它们的美国人。这项研究的第一个目的是评估对任何 这些药物避免了新冠肺炎的严重并发症，同时根据已知的危险因素和 混血儿。第二个目标是寻找药物或药物组合之间的额外相互作用 以及可能具有保护性或危害性的特定人口和/或临床亚群。改变医疗保健 数据库是新冠肺炎研究数据库的一部分，包含以下各项的最新医疗保险索赔数据 大约三分之一的美国人。使用这个数据库，这项研究将评估这些药物对 新冠肺炎阳性患者的四个重要结局的风险：需要住院，使用 机械通风、休克和死亡。结果将根据已经很好的风险因素进行风险调整 成立的目的是预测新冠肺炎的糟糕结果。这项研究将进一步挖掘第二和第三的数据- 药物或药物组合与不同患者亚群之间的顺序相互作用 一种新的机器学习方法，称为可行解算法(FSA)。FSA使研究人员能够 为了发现回归模型中的高阶统计相互作用，这导致识别 传统回归模型中并不总是明显的子组和复杂性。如果结果显示 具有高度保护作用的候选药物，这些可以优先用于未来的临床研究。毒品 显示有害影响的药物可以考虑在感染或高危患者中停用。

Project Summary/Abstract Coronavirus Disease 2019 (COVID-19) is a national and global public health emergency. Because the causative virus is novel, the present options for treatment are extremely limited, and an effective vaccine could be 1-2 years away. Thus, there is an urgent need for efficacious therapeutics against the disease. While development of new drugs is under way, that process is slow and resource-intensive. In the short-to-medium term, a superior strategy is to repurpose already existing drugs to treat the disease. Over 100 drugs already approved by the Food and Drug Administration (FDA) have shown in vitro, in silico, or theoretical effect against SARS-CoV-2, the virus that causes COVID-19, or the hyperinflammatory immune response it provokes. What is unclear is how many of these have a significant, protective effect on actual patients, as only a tiny fraction of these drugs is in clinical trials. Most of these agents are chronic medications, and thus there are millions of Americans who are already using them. The first aim of this study is to assess the degree of protection any of these drugs confers against the serious complications of COVID-19 while adjusting for known risk factors and confounders. The second aim is to search for additional interactions between drugs or combinations of drugs and specific demographic and/or clinical subgroups that could be protective or harmful. The Change Healthcare Database, a part of the COVID-19 Research Database, contains up-to-date health insurance claims data for about one-third of all Americans. Using this database, this study will evaluate the impact of these drugs on the risk of four important outcomes in patients who are COVID-19-positive: need for hospitalization, use of mechanical ventilation, shock, and death. Results will be risk-adjusted for the risk factors already well established to predict poor outcomes in COVID-19. This study will further mine the data for second- and third- order interactions between drugs or combinations of drugs and different subpopulations of patients using a novel machine learning method called the Feasible Solution Algorithm (FSA). The FSA enables the researcher to uncover higher-order statistical interactions in regression models, which leads to the identification of subgroups and complexities that are not always apparent with traditional regression models. If the results show candidate drugs with highly protective effects, these can be prioritized for prospective clinical studies. Drugs that show harmful effects can be considered for discontinuation in infected or high-risk patients.