EAGER: Collaborative Research: Computational Public Drug Surveillance

点击翻译按钮获取中文摘要

Adverse drug reactions (ADR) (undesired or excessive responses drugs) have been linked with significant morbidity and mortality, and account for as much as 5% of all admissions. A drug-drug interaction (DDI) is a type of ADR involving two or more drugs. Reports suggest that 50% percent of the drugs withdrawn in the U.S. by the Food and Drug Administration (FDA) from 1999 to 2003 were linked with significant DDIs. The ADR profile of a given drug is rarely complete at the time the drug is approved by FDA. Hence, after a drug has been in use by the general population (with significant diversity in race, gender, age, lifestyle), often previously unidentified DDIs are discovered. To complicate matters, certain populations of patients, e.g., psychiatric patients, are often concurrently treated with multiple medications. The potential interactions between multiple drugs are neither well understood nor completely characterized. Voluntary reporting, the basic mechanism used by the FDA to monitor new drugs, suffers from underreporting, delayed reporting, uneven quality of reports, and even lack of reports of rare DDIs.Against this background, this collaborative project aims to explore the feasibility of a novel computational approach to the problem of drug-drug interaction surveillance. It seeks to develop new methods for predicting molecular level interactions between drugs from data gleaned from online sources and digital social media. The project aims to test the hypothesis that such online data, in combination with with data from traditional drug related databases can be used to reliably predict potential DDIs much sooner than possible using current methods. The effectiveness of the approach is assessed through verification of predictions against future reports. If successful, the project could lead to effective, proactive computational approaches to drug interaction surveillance, with benefits to federal, local and public health agencies, drug companies, clinical practitioners, the patients, and the public at large. Early detection of adverse DDIs could lead to improved patient care, and significant reduction in healthcare costs and lawsuits involving DDIs. The project offers enhanced opportunities for collaboration among investigators with expertise in computational and health sciences. It also offers research-based training opportunities to students at West Virgina University and the University of Virginia. Results of the research will be freely disseminated to the broader academic and research community.