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Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions.

Authors :
Jon D Duke
Xu Han
Zhiping Wang
Abhinita Subhadarshini
Shreyas D Karnik
Xiaochun Li
Stephen D Hall
Yan Jin
J Thomas Callaghan
Marcus J Overhage
David A Flockhart
R Matthew Strother
Sara K Quinney
Lang Li
Source :
PLoS Computational Biology, Vol 8, Iss 8, p e1002614 (2012)
Publication Year :
2012
Publisher :
Public Library of Science (PLoS), 2012.

Abstract

Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP) metabolism enzymes identified from published in vitro pharmacology experiments. Using a clinical repository of over 800,000 patients, we narrowed this theoretical set of DDIs to 3670 drug pairs actually taken by patients. Finally, we sought to identify novel combinations that synergistically increased the risk of myopathy. Five pairs were identified with their p-values less than 1E-06: loratadine and simvastatin (relative risk or RR = 1.69); loratadine and alprazolam (RR = 1.86); loratadine and duloxetine (RR = 1.94); loratadine and ropinirole (RR = 3.21); and promethazine and tegaserod (RR = 3.00). When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Based on additional literature data on in vitro drug metabolism and inhibition potency, loratadine and simvastatin and tegaserod and promethazine were predicted to have a strong DDI through the CYP3A4 and CYP2D6 enzymes, respectively. This new translational biomedical informatics approach supports not only detection of new clinically significant DDI signals, but also evaluation of their potential molecular mechanisms.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
8
Issue :
8
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.30d50f9a936c431ab6d2c59646a5fe17
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1002614