权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Big data screening for associations between medication use and ALS

大数据筛选药物使用与 ALS 之间的关联

基本信息

批准号：
9233275
负责人：
Marc G Weisskopf
金额：
$ 23.29万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-15 至 2019-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9233275
关键词：
Adult Affect Age of Onset American Amyotrophic Lateral Sclerosis Attention Big Data Biological Clinical Trials Combination Medication Complex Data Data Set Databases Denmark Disease Drug Compounding Elderly Epidemiology Etiology Face Genetic Goals Health Health system In Vitro Incidence Individual Investigation Israel Laboratory Research Logic Neurodegenerative Disorders Outcome Patients Pharmaceutical Preparations Pharmacologic Substance Population Prevalence Probability Process Registries Research Riluzole Risk Role Survival Analysis System Testing Therapeutic Therapeutic Effect Therapeutic Intervention Therapeutic Uses Time Toxicology Variant Work base biological systems case control clinical phenotype design epidemiologic data epidemiology study forest health data high dimensionality high throughput screening insight non-genetic novel novel therapeutics prospective screening transmission process

项目摘要

PROJECT SUMMARY/ABSTRACT Our long-term goal is to identify medications, or combinations of medications, that may affect incidence of amyotrophic lateral sclerosis (ALS) or alter the progression of ALS. In vitro efforts to exploring single compounds for efficacy in ALS therapeutics based on proposed biological mechanisms for the disease is a valuable and warranted approach, but it is inherently slow because of the need to test compounds one at a time. It also does not explore effects of combinations and it cannot test the role of these compounds in ALS incidence. Given the wide array of medications that older adults take, and the possibility that different combinations of medications may be relevant, we propose that a valuable parallel approach would be an epidemiological screening process to test whether any currently used medications are related to ALS incidence or survival. This would be akin to in vitro high throughput screening, but using novel statistical approaches to explore high dimensional “big” epidemiological data (many people, many medications) for associations with ALS and ALS survival: specifically, boolean logic regression and random forests in a nested case-control and survival analysis framework. These approaches allow for efficiently exploring high-dimensional, likely correlated, data for associations between individual medications and different combinations of medications and an outcome, here ALS. In order to accomplish this, we propose to use two parallel very large data sets with prospectively and objectively collected pharmaceutical and health data: The Danish Registry System and the Clalit Health System in Israel, with a total of approximately 4,300 ALS cases and over 300,000 controls. By using the two data sets in different populations we will increase the probability of identifying causally related compounds by identifying those that screen positive in both populations. The results of this work have the possibility to identify currently used medications or combinations of these medications that can affect ALS and survival with ALS. Any positive results could open up new research avenues that include targeted clinical trials as well as potentially new directions for epidemiological studies and laboratory research into underlying mechanisms.

项目总结/摘要我们的长期目标是确定可能影响发生率的药物或药物组合，肌萎缩侧索硬化症（ALS）或改变ALS的进展。在体外努力探索单一基于所提出的疾病的生物学机制的用于ALS治疗的功效的化合物是一种这是一种有价值和有保证的方法，但由于需要一次测试一种化合物，时间它也没有探索组合的效果，它不能测试这些化合物在ALS中的作用。发病率。考虑到老年人服用的药物种类繁多，联合用药可能是相关的，我们建议，一个有价值的平行方法将是一个流行病学筛查过程，以测试目前使用的任何药物是否与ALS发病率相关或者生存这将类似于体外高通量筛选，但使用新的统计方法，探索高维“大”流行病学数据（许多人，许多药物）与 ALS和ALS生存：具体来说，布尔逻辑回归和随机森林在一个嵌套的病例对照和生存分析框架。这些方法允许有效地探索高维的，可能的相关的，单个药物和不同药物组合之间的关联数据，一个结果，这里是ALS。为了实现这一点，我们建议使用两个并行的非常大的数据集，前瞻性和客观地收集药物和健康数据：丹麦注册系统和以色列Clalit卫生系统，共有约4，300例ALS病例和超过30万例对照。通过在不同人群中使用这两个数据集，我们将增加识别因果关系的概率。通过鉴定在两个群体中筛选阳性的化合物。这项工作的结果有确定目前使用的药物或这些药物的组合可能会影响ALS的可能性，生存与ALS任何积极的结果都可能开辟新的研究途径，包括有针对性的临床试验以及流行病学研究和实验室研究潜在的新方向，机制等