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

使用大数据方法识别 FDA 批准的现有药物，对 2019 年冠状病毒病具有临床保护作用

• 批准号: 10395043
• 负责人: Josh Lambert
• 金额: $ 24.58万
• 依托单位: UNIVERSITY OF CINCINNATI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-20 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10395043
• 关键词: 2019-nCoV; Algorithms; American; Big Data; COVID-19; COVID-19 diagnosis; COVID-19 treatment; Cessation of life; Chronic; Clinical; Clinical Research; Clinical Trials; Data; Databases; Development; Disease; Drug Combinations; Health Insurance; Healthcare; Hospitalization; Immune response; In Vitro; Mechanical ventilation; Modeling; Outcome; Patients; Pharmaceutical Preparations; Process; Research; Research Personnel; Resources; Risk; Risk Factors; SARS-CoV-2 positive; Shock; Statistical Algorithm; Subgroup; Therapeutic; United States Food and Drug Administration; Vaccines; Virus; coronavirus disease; drug candidate; high risk; in silico; insurance claims; machine learning method; novel; novel therapeutics; patient subsets; prospective; protective effect; public health emergency

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.

项目摘要/摘要 2019年冠状病毒病（COVID-19）是国家和全球公共卫生紧急情况。因为 病毒是新颖的，目前的治疗选择非常有限，有效的疫苗可以 距离1 - 2年。因此，迫切需要对疾病有效治疗。尽管 正在开发新药，该过程缓慢且资源密集。在短到中等 术语，一种卓越的策略是重新使用已经存在的药物来治疗该疾病。已经有100多种药物 获得食品药品监督管理局（FDA）的批准已在体外，有机硅中显示 SARS-COV-2，导致COVID-19的病毒或它引起的高炎性免疫反应。是什么 尚不清楚其中有多少对实际患者具有显着的保护作用，因为只有一小部分 这些药物正在临床试验中。这些药物大多数是慢性药物，因此有数百万 已经在使用它们的美国人。这项研究的第一个目的是评估保护程度 这些药物在调整已知风险因素和 混淆者。第二个目的是寻找药物之间的其他相互作用或药物组合 以及可能具有保护性或有害的特定人口统计和/或临床亚组。变化医疗保健 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。使用此数据库，本研究将评估这些药物对 共同199阳性患者的四个重要结果的风险：住院需求，使用 机械通气，冲击和死亡。对于已经很好的危险因素，结果将受到风险调整 建立以预测COVID-19的不良预后。这项研究将进一步挖掘第二和第三的数据 使用A的药物或药物组合和不同亚群之间的订单相互作用 新型机器学习方法称为可行解决方案算法（FSA）。 FSA使研究人员 在回归模型中发现高阶统计相互作用，这导致了鉴定 传统回归模型并不总是显而易见的亚组和复杂性。如果结果显示 具有高度保护作用的候选药物可以优先考虑前瞻性临床研究。毒品 该显示有害影响可以考虑在感染或高危患者中停用。