Discovering indirect disease-drug relationships from biomedical literature toward drug repurposing

Abstract

Drug development is a time-consuming expensive and high-risk task The uncertainty of drug development has led to the emergence of drug repurposing which is to find the new indications of approved drugs Approved drugs have completed more clinical trial data than newly developed drugs Therefore drug repurposing is safer and faster than conventional approaches of drug development This study aims to infer disease-drug indirect relations via disease-gene and gene-drug relations from large-scale biomedical literature We propose a pattern-based relation extraction method using dependency grammar to identify disease gene and drug relations to construct disease-gene and gene-drug bipartite networks In these bipartite networks we can understand that a disease is caused by the involvement of gene product from disease-gene network We can also understand the interaction between protein and drug from gene-drug bipartite network However these networks produce a large number of indirect relations between disease and drug We propose a novel ranking method to prioritize the indirect relations The concept of the ranking method is based on drug similarity which is defined by repurposed drugs and approved drugs If a repurposed drug and an approved drug have highly similar interactions with the common genes the repurposed drug might have a new indication that it has similar effects as that of the approved drug Our pattern-based relation extraction method performs a higher precision of 0 86 than baseline methods Because our drug similarity method obtains an R-square score of 0 80 with ATC code similarity our drug vector space is suitable to calculate drug similarity Therefore the ranking method achieves a MAP score of 0 37 in top 100 popular diseases Finally we select the repurposed drugs of ovarian cancer prostate cancer lung cancer colorectal cancer leukemia and breast cancer for validation by literature study and clinical trials

Date of Award	2014 Jul 31
Original language	English
Supervisor	Jung-Hsien Chiang (Supervisor)