Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.
BACKGROUND
Identifying drug-drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning.


RESULTS
Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions.
Tari
Luis
L
Disease and Translational Informatics, Hoffmann-La Roche, Nutley, NJ 07110, USA. luis.tari@roche.com
Anwar
Saadat
S
Liang
Shanshan
S
Cai
James
J
Baral
Chitta
C
D057225:Data Mining; D016208:Databases, Factual; D004347:Drug Interactions; D004798:Enzymes / Q000378:metabolism; D005240:Feasibility Studies; D006801:Humans; D008128:Logic; D009323:Natural Language Processing; D004364:Pharmaceutical Preparations / Q000008:administration & dosage / Q000378:metabolism
D004798:Enzymes; D004364:Pharmaceutical Preparations
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