Mining directional drug interaction effects on myopathy using the FAERS database

Danai Chasioti, Xiaohui Yao, Pengyue Zhang, Samuel Lerner, Sara Quinney, Xia Ning, Lang Li, Li Shen

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record (EHR) databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our work provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio (OR) to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization.

Original languageEnglish (US)
JournalIEEE Journal of Biomedical and Health Informatics
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Drug interactions
Muscular Diseases
Drug Interactions
Databases
Pharmaceutical Preparations
pamidronate
fluvastatin
Drug Combinations
Gadolinium
Pharmacovigilance
Hydroxymethylglutaryl-CoA Reductase Inhibitors
Levofloxacin
Potassium
Simvastatin
Potassium Chloride
Electronic Health Records
Sulfamethoxazole Drug Combination Trimethoprim
Visualization
Feasibility Studies
Health

Keywords

  • Apriori
  • Directional effect
  • Drugs
  • FAERS
  • frequent itemsets
  • high-order drug interaction
  • Informatics
  • Itemsets
  • Medical diagnostic imaging
  • Visualization

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

Cite this

Mining directional drug interaction effects on myopathy using the FAERS database. / Chasioti, Danai; Yao, Xiaohui; Zhang, Pengyue; Lerner, Samuel; Quinney, Sara; Ning, Xia; Li, Lang; Shen, Li.

In: IEEE Journal of Biomedical and Health Informatics, 01.01.2018.

Research output: Contribution to journalArticle

Chasioti, Danai ; Yao, Xiaohui ; Zhang, Pengyue ; Lerner, Samuel ; Quinney, Sara ; Ning, Xia ; Li, Lang ; Shen, Li. / Mining directional drug interaction effects on myopathy using the FAERS database. In: IEEE Journal of Biomedical and Health Informatics. 2018.
@article{715ab020c9874df2837e1ae325f377df,
title = "Mining directional drug interaction effects on myopathy using the FAERS database",
abstract = "Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record (EHR) databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our work provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio (OR) to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization.",
keywords = "Apriori, Directional effect, Drugs, FAERS, frequent itemsets, high-order drug interaction, Informatics, Itemsets, Medical diagnostic imaging, Visualization",
author = "Danai Chasioti and Xiaohui Yao and Pengyue Zhang and Samuel Lerner and Sara Quinney and Xia Ning and Lang Li and Li Shen",
year = "2018",
month = "1",
day = "1",
doi = "10.1109/JBHI.2018.2874533",
language = "English (US)",
journal = "IEEE Journal of Biomedical and Health Informatics",
issn = "2168-2194",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Mining directional drug interaction effects on myopathy using the FAERS database

AU - Chasioti, Danai

AU - Yao, Xiaohui

AU - Zhang, Pengyue

AU - Lerner, Samuel

AU - Quinney, Sara

AU - Ning, Xia

AU - Li, Lang

AU - Shen, Li

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record (EHR) databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our work provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio (OR) to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization.

AB - Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record (EHR) databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our work provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio (OR) to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization.

KW - Apriori

KW - Directional effect

KW - Drugs

KW - FAERS

KW - frequent itemsets

KW - high-order drug interaction

KW - Informatics

KW - Itemsets

KW - Medical diagnostic imaging

KW - Visualization

UR - http://www.scopus.com/inward/record.url?scp=85054505746&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054505746&partnerID=8YFLogxK

U2 - 10.1109/JBHI.2018.2874533

DO - 10.1109/JBHI.2018.2874533

M3 - Article

AN - SCOPUS:85054505746

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

ER -