Exploring adverse drug events at the class level.
BACKGROUND
While the association between a drug and an adverse event (ADE) is generally detected at the level of individual drugs, ADEs are often discussed at the class level, i.e., at the level of pharmacologic classes (e.g., in drug labels). We propose two approaches, one visual and one computational, to exploring the contribution of individual drugs to the class signal.


METHODS
Having established a dataset of ADEs from MEDLINE, we aggregate drugs into ATC classes and ADEs into high-level MeSH terms. We compute statistical associations between drugs and ADEs at the drug level and at the class level. Finally, we visualize the signals at increasing levels of resolution using heat maps. We also automate the exploration of drug-ADE associations at the class level using clustering techniques.


RESULTS
Using our visual approach, we were able to uncover known associations, e.g., between fluoroquinolones and tendon injuries, and between statins and rhabdomyolysis. Using our computational approach, we systematically analyzed 488 associations between a drug class and an ADE.


CONCLUSIONS
The findings gained from our exploratory techniques should be of interest to the curators of ADE repositories and drug safety professionals. Our approach can be applied to different drug-ADE datasets, using different drug classification systems and different signal detection algorithms.
Center for Biomedical Informatics Research, Stanford University, Stanford, CA USA.
Rainer
R
Winnenburg
Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD USA.
Alfred
A
Sorbello
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD USA.
Olivier
O
Bodenreider
Adverse drug events; Anatomical Therapeutic Chemical (ATC) drug classification system; Class effect; Drug classes; Heat maps; Pharmacovigilance