Indiana University-Purdue University Indianapolis (IUPUI)Polypharmacy leads to increased risk of drug-drug interactions (DDI’s). In this
dissertation, we create a database for quantifying fraction of metabolism (fm) of CYP450
isozymes for FDA approved drugs. A reproducible data collection protocol was
developed to extract key information from publicly available in vitro selective CYP
enzyme inhibition studies. The fm was then estimated from the curated data. Then,
proposed a random control selection approach for nested case-control design for
electronical health records (HER) and electronical medical records (EMR) databases. By
relaxing the matching by case’s index time restriction, random control dramatically
reduces the computational burden compared with traditional control selection
approaches. Using the Observational Medical Outcomes Partnership gold standard and
an EMR database, random control is demonstrated to have better performances as well.
Finally, combining epidemiological studies and pharmacokinetic modeling with fm
database, we detected and evaluated high-dimensional drug-drug interactions among
thirty high frequency drugs. Multi-drug combinations that increased risk of myopathy
were identified in the FAERS and EMR databases by a mixture drug-count response
model (MDCM) model. Twenty-eight 3-way and 43 4-way DDI’s increased ratio of area
under plasma concentration–time curve (AUCR) >2-fold and had significant myopathy
risk in both databases. The predicted AUCR of omeprazole in the presence of
fluconazole and clonidine was 9.35; and increased risk of myopathy was 6.41 (LFDR = 0.002) in FAERS and 18.46 (LFDR = 0.005) in EMR. We demonstrate that combining
health record informatics and pharmacokinetic modeling is a powerful translational
approach to detect high-dimensional DDI’s.2 year
