Translational high-dimensional drug Interaction discovery and validation using health record databases and pharmacokinetics models

Abstract

Polypharmacy increases the risk of drug-drug interactions (DDI's). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high-dimensional DDI's among thirty frequent drugs. Multi-drug combinations that increased risk of myopathy were identified in the FDA 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 3-way and 43 4-way DDI's had significant myopathy risk in both databases and predicted increases in the area under the concentration time curve ratio (AUCR) >2-fold. The HD-DDI of omeprazole, fluconazole and clonidine was associated with a 6.41-fold (FAERS) and 18.46-fold (EMR) increase risk of myopathy (LFDR<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 DDI's.