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 致病病毒是一种新的病毒，目前的治疗选择极其有限，而有效的疫苗 可能是1-2年后。因此，迫切需要有效的治疗方法来对抗这种疾病。 虽然新药的开发正在进行中，但这一过程缓慢且资源密集型。简而言之- 从中期来看，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。完毕 美国食品和药物管理局(FDA)已经批准的100种药物已经在体外、硅胶中显示， 或者理论上对导致新冠肺炎的SARS-CoV-2病毒或高炎性疾病的效果 它会引起免疫反应。目前尚不清楚其中有多少具有显著的保护作用。 在实际患者身上，因为这些药物中只有一小部分处于临床试验中。这些特工中的大多数是 慢性药物，因此有数百万美国人已经在使用它们。第一个目标 这项研究的目的是评估这些药物对严重的 在调整已知的危险因素和混杂因素的同时，新冠肺炎的并发症也有所增加。第二个目标是 搜索药物或药物组合与特定人口之间的其他相互作用 和/或可能具有保护性或危害性的临床亚群。改变医疗保健数据库的一部分 的最新健康保险索赔数据，包含约1- 所有美国人中的第三位。使用这个数据库，这项研究将评估这些药物对风险的影响 新冠肺炎阳性患者的四项重要结果：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数据，为第二和 药物或药物组合与不同患者亚群之间的三级相互作用 使用一种名为可行解算法(FSA)的新型机器学习方法。FSA支持 研究人员发现回归模型中的高阶统计相互作用，这导致了 识别传统回归中并不总是明显的子组和复杂性 模特们。如果结果显示候选药物具有高度保护作用，这些药物可以优先用于 前瞻性临床研究。显示有害影响的药物可考虑在以下时间停止使用 感染或高危患者。

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.