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: 10380869
• 负责人: Josh Lambert
• 金额: $ 19.7万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10380869
• 关键词: 2019-nCoV; Address; Affect; Age; Algorithmic Analysis; Algorithms; American; Angiotensin-Converting Enzyme Inhibitors; Anticoagulants; Antiplatelet Drugs; Big Data; Biometry; Blood Coagulation Disorders; COVID-19; COVID-19 complications; COVID-19 diagnosis; COVID-19 patient; COVID-19 treatment; Cessation of life; Chronic; Clinical; Clinical Data; Clinical Research; Clinical Trials; Communicable Diseases; Data; Data Set; Databases; Development; Disease; Drug Combinations; Goals; Health; Health Insurance; Healthcare; Hospitalization; Immune response; In Vitro; Inpatients; Insurance Carriers; Judgment; Logistic Regressions; Mechanical ventilation; Medical; Medicare; Methods; Mineralocorticoid Receptor; Modeling; Outcome; Outpatients; Patients; Pharmaceutical Preparations; Pharmacoepidemiology; Process; Prospective Studies; Protective Agents; Race; Research; Research Personnel; Resources; Risk; Risk Factors; SARS-CoV-2 positive; Shock; Statistical Algorithm; Subgroup; Testing; Therapeutic; Therapeutic Effect; Time; United States Food and Drug Administration; Vaccines; Virus; Vision; Vulnerable Populations; Work; clinically significant; comorbidity; coronavirus disease; cyclooxygenase 1; cytokine release syndrome; demographics; drug candidate; drug development; drug repurposing; global health emergency; high risk; human data; immunomodulatory therapies; improved; in silico; in vivo; inhibitor; insurance claims; interest; machine learning algorithm; machine learning method; mortality; multidisciplinary; novel; novel therapeutics; off-label use; pandemic disease; patient subsets; preclinical study; prophylactic; prospective; protective effect; public health emergency; sex

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 因果病毒是新颖的，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，但该过程却缓慢且资源密集。在短暂的 待命期限，一种卓越的策略是重新利用已经存在的药物来治疗该疾病。超过 食品药品监督管理局（FDA）已经批准的100种药物已在体外显示在Silico， 或针对SARS-COV-2的理论效应，导致COVID-19的病毒或过度炎症 免疫反应引起。尚不清楚其中有多少具有重要的保护作用 在实际患者中，由于这些药物只有很小的一小部分是在临床试验中。这些代理大多数是 慢性药物，因此已经有数百万的美国人已经在使用它们。第一个目标 这项研究的是评估这些药物中任何一种违反严重性的保护程度 在调整已知危险因素和混杂因素的同时，COVID-19的并发症。第二个目标是 搜索药物或药物组合与特定人群之间的其他相互作用 和/或可能具有保护性或有害的临床亚组。变更医疗保健数据库，其中一部分 在COVID-19研究数据库中，包含最新的健康保险索赔数据，可大约 所有美国人中的第三。使用此数据库，本研究将评估这些药物对风险的影响 在共同199阳性的患者中的四个重要结果：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二和 药物或药物组合之间的三阶相互作用和患者的不同亚群 使用一种新型的机器学习方法，称为可行解决方案算法（FSA）。 FSA启用 研究人员在回归模型中发现高阶统计相互作用，这导致了 在传统回归中并不总是显而易见的亚组和复杂性的识别 型号。如果结果显示具有高度保护作用的候选药物，则可以优先考虑 前瞻性临床研究。可以考虑终止显示有害作用的药物 感染或高危患者。