Browsing by Subject "Drug interaction"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Opioid peptides
    (The National Institute on Alcohol Abuse and Alcoholism, 1997) Froehlilch, Janice C.; Medicine, School of Medicine
    Opioid peptides produced in the body act as neuromodulators that modify the actions of other neurotransmitters in the central nervous system. By altering the electrical properties of their target neurons, thereby making these neurons more difficult to excite, opioid peptides can influence the release of various neurotransmitters. As a result of this modulation, opioid peptides can--among other functions--induce pain relief and euphoria as well as affect certain behaviors, including alcohol consumption. Alcohol can activate the opioid peptide system. This mechanism may contribute to alcohol reinforcement and excessive alcohol consumption, because agents that inhibit the opioid peptide system decrease alcohol self-administration in animals and reduce craving and alcohol consumption in human alcoholics. Moreover, a genetically determined, increased responsiveness of the opioid system to alcohol may contribute to a predisposition for alcoholism in some people.
  • Thumbnail Image
    Item
    Translational high-dimesional drug interaction discovery and validation using health record databases and pharmacokinetics models
    (2017-10-31) Chiang, Chien-Wei; Li, Lang; Wu, Huanmei; Liu, Yunlong; Liu, Xiaowen
    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.