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/文摘