Network Analysis of Adverse Drug Interactions

Masataka Takarabe[1] (takarabe@kuicr.kyoto-u.ac.jp)
Shujiro Okuda[1] (okuda@kuicr.kyoto-u.ac.jp)
Masumi Itoh[1] (itoh@kuicr.kyoto-u.ac.jp)
Toshiaki Tokimatsu[1] (tokimatu@kuicr.kyoto-u.ac.jp)
Susumu Goto[1] (goto@kuicr.kyoto-u.ac.jp)
Minoru Kanehisa[1,2] (kanehisa@kuicr.kyoto-u.ac.jp)

[1] Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
[2] Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan


Abstract

Harmful effects associated with use of drugs are caused as a result of their side effects and combined use of different drugs. These drug interactions result in increased or decreased drug effects, or produce other new unwanted effects and are serious problems for medical institutions and pharmaceutical companies. In this study, we created a drug-drug interaction network from drug package inserts and characterized drug interactions. The known information about the potential risk of drug interactions is described in drug package inserts. Japanese drug package inserts are stored in the JAPIC (Japan Pharmaceutical Information Center) database and GenomeNet provides the GenomeNet pharmaceutical products database, which integrate the JAPIC and KEGG databases. We extracted drug interaction data from GenomeNet, where interactions are classified according to risks, contraindications or cautions for coadministration, and some entries include information about enzymes metabolizing the drugs. We defined drug target and drug-metabolizing enzymes as interaction factors using information on them in KEGG DRUG, and classified drugs into pharmacological/chemical subgroups. In the resulting drug-drug interaction network, the drugs that are associated with the same interaction factors are closely interconnected. Mechanisms of these interactions were then identified by each interaction factor. To characterize other interactions without interaction factors, we used the ATC classification system and found an association between interaction mechanisms and pharmacological/chemical subgroups.

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Japanese Society for Bioinformatics