Browsing by Author "Zhang, P."

Now showing 1 - 3 of 3
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    Graphic Mining of High-Order Drug Interactions and Their Directional Effects on Myopathy Using Electronic Medical Records
    (Wiley, 2015-08) Du, L.; Chakraborty, A.; Chiang, C.-W.; Cheng, L.; Quinney, S.K.; Wu, H.; Zhang, P.; Li, L.; Shen, L.; Department of Medicine, IU School of Medicine
    We propose to study a novel pharmacovigilance problem for mining directional effects of high-order drug interactions on an adverse drug event (ADE). Our goal is to estimate each individual risk of adding a new drug to an existing drug combination. In this proof-of-concept study, we analyzed a large electronic medical records database and extracted myopathy-relevant case control drug co-occurrence data. We applied frequent itemset mining to discover frequent drug combinations within the extracted data, evaluated directional drug interactions related to these combinations, and identified directional drug interactions with large effect sizes. Furthermore, we developed a novel visualization method to organize multiple directional drug interaction effects depicted as a tree, to generate an intuitive graphical and visual representation of our data-mining results. This translational bioinformatics approach yields promising results, adds valuable and complementary information to the existing pharmacovigilance literature, and has the potential to impact clinical practice.
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    Identification and Mechanistic Investigation of Drug-Drug Interactions Associated With Myopathy: A Translational Approach
    (Wiley Blackwell (John Wiley & Sons), 2015-09) Han, X.; Quinney, S. K.; Wang, Z.; Zhang, P.; Duke, J.; Desta, Z.; Elmendorf, J. S.; Flockhart, D. A.; Li, L.; Department of Medical & Molecular Genetics, IU School of Medicine
    Myopathy is a group of muscle diseases that can be induced or exacerbated by drug-drug interactions (DDIs). We sought to identify clinically important myopathic DDIs and elucidate their underlying mechanisms. Five DDIs were found to increase the risk of myopathy based on analysis of observational data from the Indiana Network of Patient Care. Loratadine interacted with simvastatin (relative risk 95% confidence interval [CI] = [1.39, 2.06]), alprazolam (1.50, 2.31), ropinirole (2.06, 5.00), and omeprazole (1.15, 1.38). Promethazine interacted with tegaserod (1.94, 4.64). In vitro investigation showed that these DDIs were unlikely to result from inhibition of drug metabolism by CYP450 enzymes or from inhibition of hepatic uptake via the membrane transporter OATP1B1/1B3. However, we did observe in vitro synergistic myotoxicity of simvastatin and desloratadine, suggesting a role in loratadine-simvastatin interaction. This interaction was epidemiologically confirmed (odds ratio 95% CI = [2.02, 3.65]) using the data from the US Food and Drug Administration Adverse Event Reporting System.
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    Impact of diagnosis of diabetes on health-related quality of life among high risk individuals: the Diabetes Prevention Program outcomes study
    (Springer International Publishing, 2014-02) Marrero, D.; Pan, Q.; Barrett-Connor, E.; de Groot, Mary; Zhang, P.; Percy, C.; Florez, H.; Ackermann, R.; Montez, M.; Rubin, R. R.; DPPOS Research Group; Department of Medicine, IU School of Medicine
    Purpose The purpose of this study is to assess if diagnosis of type 2 diabetes affected health-related quality of life (HRQoL) among participants in the Diabetes Prevention Program/Diabetes Prevention Program Outcome Study and changes with treatment or diabetes duration. Methods 3,210 participants with pre-diabetes were randomized to metformin (MET), intensive lifestyle intervention (ILS), or placebo (PLB). HRQoL was assessed using the SF-36 including: (1) 8 SF-36 subscales; (2) the physical component (PCS) and mental component summary (MCS) scores; and (3) the SF-6D. The sample was categorized by diabetes free versus diagnosed. For diagnosed subgroup, mean scores in the diabetes-free period, at 6 months, 2, 4 and 6 years post-diagnosis, were compared. Results PCS and SF-6D scores declined in all participants in all treatment arms (P <.001). MCS scores did not change significantly in any treatment arm regardless of diagnosis. ILS participants reported a greater decrease in PCS scores at 6 months post-diagnosis (P <.001) and a more rapid decline immediately post-diagnosis in SF-6D scores (P = .003) than the MET or PLB arms. ILS participants reported a significant decrease in the social functioning subscale at 6 months (P <.001) and two years (P <.001) post-diagnosis. Conclusions Participants reported a decline in measures of overall health state (SF-6D) and overall physical HRQoL, whether or not they were diagnosed with diabetes during the study. There was no change in overall mental HRQoL. Participants in the ILS arm with diabetes reported a more significant decline in some HRQoL measures than those in the MET and PLB arms that developed diabetes.