2008 Poster Sessions : Systematic Exploration of Drug-Disease Relationships for Drug Repositioning Candidates

Student Name : Annie Chiang
Advisor : Atul J. Butte
Research Areas: Artificial Intelligence
Abstract
The development and discovery of a single (approved) drug is a slow (10+ years) and expensive ($500+ million) process. Even among those drugs already approved, the exact mechanism of actions of most drugs is not entirely clear. Drug repurposing or repositioning refers to alternative drug use discoveries which differ from the original intent of the drug. These drug repositioning efforts have many advantages: the drug pharmacokinetics and pharmacodynamics are known. Moreover, drug repositioning 'discoveries' are less costly and quicker than traditional discovery efforts. One bottleneck in these efforts lies in choosing which (therapeutic) indication to test a drug of interest. To address this bottleneck, we systematically evaluated a drug indication-based view of diseases in order to suggest novel repositioning drug uses. We compiled a Drug-Disease Knowledge Base (DrDKB) comprised of 2,022 drugs and 726 diseases. The repositioning candidates were generated using a guilt-by-association approach based on the shared drug indication profiles between any two diseases. Compared with non-repositioning drug candidates, the repositioning candidates were significantly enriched in clinical trials. Among the many interesting suggestions include the use of rituximab for autoimmune diseases and statins for cancer therapies. Based on the enrichment of these candidates in clinical trials, we believe that the suggestions/candidates can help expedite future drug repositioning efforts.

Bio
Annie received Bachelor degrees in both Computer Science and Biology. She graduated with a PhD in Genetics in 2006 from The University of Iowa where she applied computational approaches toward the identification of two novel disease genes causing Bardet-Biedl Syndrome. She is now a postdoctoral fellow in Atul Butte's lab.