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

Chien Wei Chiang, Pengyue Zhang, Xueying Wang, Lei Wang, Shijun Zhang, Xia Ning, Li Shen, Sara Quinney, Lang Li

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)287-295
Number of pages9
JournalClinical Pharmacology and Therapeutics
Volume103
Issue number2
DOIs
StatePublished - Feb 1 2018

Fingerprint

Drug Discovery
Drug Interactions
Muscular Diseases
Pharmacokinetics
Databases
Omeprazole
Electronic Health Records
Health
Area Under Curve
Pharmaceutical Preparations
Polypharmacy
Informatics
Fluconazole
Clonidine
United States Food and Drug Administration
Drug-Related Side Effects and Adverse Reactions
Epidemiologic Studies
Food

ASJC Scopus subject areas

  • Pharmacology
  • Pharmacology (medical)

Cite this

Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models. / Chiang, Chien Wei; Zhang, Pengyue; Wang, Xueying; Wang, Lei; Zhang, Shijun; Ning, Xia; Shen, Li; Quinney, Sara; Li, Lang.

In: Clinical Pharmacology and Therapeutics, Vol. 103, No. 2, 01.02.2018, p. 287-295.

Research output: Contribution to journalArticle

Chiang, Chien Wei ; Zhang, Pengyue ; Wang, Xueying ; Wang, Lei ; Zhang, Shijun ; Ning, Xia ; Shen, Li ; Quinney, Sara ; Li, Lang. / Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models. In: Clinical Pharmacology and Therapeutics. 2018 ; Vol. 103, No. 2. pp. 287-295.
@article{e4ffd04e1a0d4e84b999f453645890d1,
title = "Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models",
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.",
author = "Chiang, {Chien Wei} and Pengyue Zhang and Xueying Wang and Lei Wang and Shijun Zhang and Xia Ning and Li Shen and Sara Quinney and Lang Li",
year = "2018",
month = "2",
day = "1",
doi = "10.1002/cpt.914",
language = "English (US)",
volume = "103",
pages = "287--295",
journal = "Clinical Pharmacology and Therapeutics",
issn = "0009-9236",
publisher = "Nature Publishing Group",
number = "2",

}

TY - JOUR

T1 - Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models

AU - Chiang, Chien Wei

AU - Zhang, Pengyue

AU - Wang, Xueying

AU - Wang, Lei

AU - Zhang, Shijun

AU - Ning, Xia

AU - Shen, Li

AU - Quinney, Sara

AU - Li, Lang

PY - 2018/2/1

Y1 - 2018/2/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85040227147&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040227147&partnerID=8YFLogxK

U2 - 10.1002/cpt.914

DO - 10.1002/cpt.914

M3 - Article

C2 - 29052226

AN - SCOPUS:85040227147

VL - 103

SP - 287

EP - 295

JO - Clinical Pharmacology and Therapeutics

JF - Clinical Pharmacology and Therapeutics

SN - 0009-9236

IS - 2

ER -