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Comput Biol Chem. 2014 Jun;50:50-9. doi: 10.1016/j.compbiolchem.2014.01.006. Epub 2014 Jan 24.

Pharmacoepidemiological characterization of drug-induced adverse reaction clusters towards understanding of their mechanisms.

Author information

1
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho Uji, Kyoto 611-0011, Japan. Electronic address: smizutan@kuicr.kyoto-u.ac.jp.
2
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho Uji, Kyoto 611-0011, Japan. Electronic address: noro@kuicr.kyoto-u.ac.jp.
3
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho Uji, Kyoto 611-0011, Japan. Electronic address: kot@kuicr.kyoto-u.ac.jp.
4
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho Uji, Kyoto 611-0011, Japan. Electronic address: goto@kuicr.kyoto-u.ac.jp.

Abstract

A big challenge in pharmacology is the understanding of the underlying mechanisms that cause drug-induced adverse reactions (ADRs), which are in some cases similar to each other regardless of different drug indications, and are in other cases different regardless of same drug indications. The FDA Adverse Event Reporting System (FAERS) provides a valuable resource for pharmacoepidemiology, the study of the uses and the effects of drugs in large human population. However, FAERS is a spontaneous reporting system that inevitably contains noise that deviates the application of conventional clustering approaches. By performing a biclustering analysis on the FAERS data we identified 163 biclusters of drug-induced adverse reactions, counting for 691 ADRs and 240 drugs in total, where the number of ADR occurrences are consistently high across the associated drugs. Medically similar ADRs are derived from several distinct indications for use in the majority (145/163=88%) of the biclusters, which enabled us to interpret the underlying mechanisms that lead to similar ADRs. Furthermore, we compared the biclusters that contain same drugs but different ADRs, finding the cases where the populations of the patients were different in terms of age, sex, and body weight. We applied a biclustering approach to catalogue the relationship between drugs and adverse reactions from a large FAERS data set, and demonstrated a systematic way to uncover the cases different drug administrations resulted in similar adverse reactions, and the same drug can cause different reactions dependent on the patients' conditions.

KEYWORDS:

Adverse drug reaction; Biclustering; Drug indication

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