A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases

P. Zhang, L. Du, L. Wang, M. Liu, L. Cheng, C. W. Chiang, H. Y. Wu, S. K. Quinney, L. Shen, L. Li

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Interactions between multiple drugs may yield excessive risk of adverse effects. This increased risk is not uniform for all combinations, although some combinations may have constant adverse effect risks. We developed a statistical model using medical record data to identify drug combinations that induce myopathy risk. Such combinations are revealed using a novel mixture model, comprised of a constant risk model and a dose-response risk model. The dose represents the number of drug combinations. Using an empirical Bayes estimation method, we successfully identified high-dimensional (two to six) drug combinations that are associated with excessive myopathy risk at significantly low local false-discovery rates. From the curve of a dose-response model and high-dimensional drug interaction data, we observed that myopathy risk increases as the drug interaction dimension increases. This is the first time that such a dose-response relationship for high-dimensional drug interactions was observed and extracted from the medical record database.

Original languageEnglish (US)
Pages (from-to)474-480
Number of pages7
JournalCPT: Pharmacometrics and Systems Pharmacology
Volume4
Issue number8
DOIs
StatePublished - Aug 1 2015

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ASJC Scopus subject areas

  • Modeling and Simulation
  • Pharmacology (medical)

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