Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records

Novel Myopathy Associated Drug Interactions

Jon D. Duke, Xu Han, Zhiping Wang, Abhinita Subhadarshini, Shreyas D. Karnik, Xiaochun Li, Stephen D. Hall, Yan Jin, John Callaghan, Marcus J. Overhage, David A. Flockhart, R. Matthew Strother, Sara Quinney, Lang Li

Research output: Contribution to journalArticle

59 Citations (Scopus)

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.

Original languageEnglish
Article numbere1002614
JournalPLoS Computational Biology
Volume8
Issue number8
DOIs
StatePublished - Aug 2012

Fingerprint

Drug interactions
Electronic medical equipment
drug interactions
Electronic Health Records
muscular diseases
Muscular Diseases
Drug Interactions
electronics
Drugs
drug
Loratadine
Electronics
drugs
prediction
Prediction
Interaction
Pharmaceutical Preparations
Metabolism
Promethazine
Enzymes

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Ecology
  • Molecular Biology
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Computational Theory and Mathematics

Cite this

Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records : Novel Myopathy Associated Drug Interactions. / Duke, Jon D.; Han, Xu; Wang, Zhiping; Subhadarshini, Abhinita; Karnik, Shreyas D.; Li, Xiaochun; Hall, Stephen D.; Jin, Yan; Callaghan, John; Overhage, Marcus J.; Flockhart, David A.; Strother, R. Matthew; Quinney, Sara; Li, Lang.

In: PLoS Computational Biology, Vol. 8, No. 8, e1002614, 08.2012.

Research output: Contribution to journalArticle

Duke, Jon D. ; Han, Xu ; Wang, Zhiping ; Subhadarshini, Abhinita ; Karnik, Shreyas D. ; Li, Xiaochun ; Hall, Stephen D. ; Jin, Yan ; Callaghan, John ; Overhage, Marcus J. ; Flockhart, David A. ; Strother, R. Matthew ; Quinney, Sara ; Li, Lang. / Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records : Novel Myopathy Associated Drug Interactions. In: PLoS Computational Biology. 2012 ; Vol. 8, No. 8.
@article{566506b9e81c412f8ddd32ff21d4b4ce,
title = "Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records: Novel Myopathy Associated Drug Interactions",
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.",
author = "Duke, {Jon D.} and Xu Han and Zhiping Wang and Abhinita Subhadarshini and Karnik, {Shreyas D.} and Xiaochun Li and Hall, {Stephen D.} and Yan Jin and John Callaghan and Overhage, {Marcus J.} and Flockhart, {David A.} and Strother, {R. Matthew} and Sara Quinney and Lang Li",
year = "2012",
month = "8",
doi = "10.1371/journal.pcbi.1002614",
language = "English",
volume = "8",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "8",

}

TY - JOUR

T1 - Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records

T2 - Novel Myopathy Associated Drug Interactions

AU - Duke, Jon D.

AU - Han, Xu

AU - Wang, Zhiping

AU - Subhadarshini, Abhinita

AU - Karnik, Shreyas D.

AU - Li, Xiaochun

AU - Hall, Stephen D.

AU - Jin, Yan

AU - Callaghan, John

AU - Overhage, Marcus J.

AU - Flockhart, David A.

AU - Strother, R. Matthew

AU - Quinney, Sara

AU - Li, Lang

PY - 2012/8

Y1 - 2012/8

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84866067701&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866067701&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1002614

DO - 10.1371/journal.pcbi.1002614

M3 - Article

VL - 8

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 8

M1 - e1002614

ER -