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

项目总结/摘要 2019冠状病毒病（COVID-19）是一种国家和全球公共卫生紧急情况。因为 致病病毒是新的，目前的治疗选择是非常有限的，有效的疫苗可以 1 - 2年后。因此，迫切需要针对该疾病的有效治疗剂。而 虽然新药的开发正在进行中，但这一进程是缓慢和资源密集型的。在短期至中期 从长远来看，一个上级策略是重新利用现有的药物来治疗这种疾病。超过100种药物 已在体外、计算机模拟或理论上显示出对 SARS-CoV-2，引起COVID-19的病毒，或它引起的炎症免疫反应。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，因为只有一小部分 这些药物正在进行临床试验。这些药物中的大多数是慢性药物，因此有数百万种 美国人正在使用它们。这项研究的第一个目的是评估任何保护的程度， 这些药物可预防COVID-19的严重并发症，同时调整已知的风险因素， 混杂因素。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有害性的特定人口统计学和/或临床亚组。改变医疗保健 数据库是COVID-19研究数据库的一部分，包含最新的健康保险索赔数据， 大约三分之一的美国人。使用该数据库，本研究将评估这些药物对 COVID-19阳性患者的四个重要结局风险：需要住院，使用 机械通气休克死亡结果将根据风险因素进行风险调整， 用于预测COVID-19的不良结果。这项研究将进一步挖掘第二和第三次的数据- 药物或药物组合与不同患者亚群之间的相互作用， 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 揭示回归模型中的高阶统计相互作用，从而识别 子组和复杂性，并不总是明显的传统回归模型。如果结果显示 具有高度保护作用的候选药物，这些药物可以优先用于前瞻性临床研究。药物 显示有害作用的药物可以考虑在感染或高危患者中停药。

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.