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.

A super‐combo‐drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy

“…In order to gain more insight into individual drug effects in a drug combination effect, we think additional molecular pharmacology evidence are very much needed to interpret these pharmacoepidemiology evidence. For example, using molecular pharmacology drug interaction experiments and pharmacokinetics model‐based drug interaction predictions, we have recent successfully investigated and concluded several drug combinations that all have increased myopathy risks, including loratadine and simvastatin; chloriquine and simvastatin; and omerprazol, fluconozol, and clonidine …”

“…For example, using molecular pharmacology drug interaction experiments and pharmacokinetics model-based drug interaction predictions, we have recent successfully investigated and concluded several drug combinations that all have increased myopathy risks, including loratadine and simvastatin; chloriquine and simvastatin; and omerprazol, fluconozol, and clonidine. 11,25 In our recent publications, we characterized a nonlinear drug-count response relationship between the number of medications and an AE. 19,20 In that model, however, we have not considered the weighted average among correlated drugs, nor the dosage or the drug exposure time, when testing their associations with the outcome AE.…”

Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System

“…For all the three situations, the total number of reports was chosen to be 100 000, and PS was generated from beta distribution such that PS ∼ Beta (1,6). In each simulation, 10 000 positive DDI signals and 10 000 negative controls were simulated from the uniformly distributed parameters shown in Table 2.…”

Reverse Translational Pharmacology Research Is Driven by Big Data

“…Noticeably, Tatonetti et al 2 tested and validated a large number of drug interactions in two health record databases. Recently, Chiang et al 3 conducted the high-dimensional drug interaction discovery using health record databases and evaluated the pharmacology mechanisms of these drug interactions using pharmacokinetic models. Unlike the conventional T1 to T4 translational research, these translational studies, driven by the big data, reversely connect the pharmaco-epidemiology evidences with in vitro pharmacology experiments.…”

“…tested and validated a large number of drug interactions in two health record databases. Recently, Chiang et al . conducted the high‐dimensional drug interaction discovery using health record databases and evaluated the pharmacology mechanisms of these drug interactions using pharmacokinetic models.…”