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Clin Pharmacol Ther. 2018 Feb;103(2):287-295. doi: 10.1002/cpt.914. Epub 2017 Dec 11.

Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models.

Author information

1
Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
2
Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio, USA.
3
Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, P.R. China.

Abstract

Polypharmacy increases the risk of drug-drug interactions (DDIs). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high-dimensional DDIs among 30 frequent drugs. Multidrug combinations that increased the risk of myopathy were identified in the US Food and Drug Administration Adverse Event Reporting System (FAERS) and electronic medical record (EMR) databases by a mixture drug-count response model. CYP450 inhibition was estimated among the 30 drugs in the presence of 1 to 4 inhibitors using in vitro / in vivo extrapolation. Twenty-eight three-way and 43 four-way DDIs had significant myopathy risk in both databases and predicted increases in the area under the concentration-time curve ratio (AUCR) >2-fold. The high-dimensional DDI of omeprazole, fluconazole, and clonidine was associated with a 6.41-fold (FAERS) and 18.46-fold (EMR) increased risk of myopathy local false discovery rate (<0.005); the AUCR of omeprazole in this combination was 9.35. The combination of health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDIs.

PMID:
29052226
DOI:
10.1002/cpt.914

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