Browsing by Subject "drug-drug interaction"

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    CYP2B6 Genotype‐Dependent Inhibition of CYP1A2 and Induction of CYP2A6 by the Antiretroviral Drug Efavirenz in Healthy Volunteers
    (ASCPT, 2019) Metzger, Ingrid F.; Dave, Nimita; Kreutz, Yvonne; Lu, Jessica B. L.; Galinsky, Raymond E.; Desta, Zeruesenay; Pharmacology and Toxicology, School of Medicine
    We investigated the effect of efavirenz on the activities of cytochrome P450 (CYP)1A2, CYP2A6, xanthine oxidase (XO), and N‐acetyltransferase 2 (NAT2), using caffeine as a probe. A single 150 mg oral dose of caffeine was administered to healthy volunteers (n = 58) on two separate occasions; with a single 600 mg oral dose of efavirenz and after treatment with 600 mg/day efavirenz for 17 days. Caffeine and its metabolites in plasma and urine were quantified using liquid chromatography/tandem‐mass spectrometry. DNA was genotyped for CYP2B6*4 (785A>G), CYP2B6*9 (516G>T), and CYP2B6*18 (983T>C) alleles using TaqMan assays. Relative to single‐dose efavirenz treatment, multiple doses of efavirenz decreased CYP1A2 (by 38%) and increased CYP2A6 (by 85%) activities (P < 0.05); XO and NAT2 activities were unaffected. CYP2B6*6*6 genotype was associated with lower CYP1A2 activity following both single and multiple doses of efavirenz. No similar association was noted for CYP2A6 activity. This is the first report showing that efavirenz reduces hepatic CYP1A2 and suggesting chronic efavirenz exposure likely enhances the elimination of CYP2A6 substrates. This is also the first to report the extent of efavirenz–CYP1A2 interaction may be efavirenz exposure‐dependent and CYP2B6 genotype‐dependent.
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    Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System
    (Wiley, 2020-03) Wang, Xueying; Li, Lang; Wang, Lei; Feng, Weixing; Zhang, Pengyue; BioHealth Informatics, School of Informatics and Computing
    With increasing trend of polypharmacy, drug-drug interaction (DDI)-induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score-adjusted three-component mixture model (PS-3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug-drug-ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS-3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS-3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS-3CMM prioritizes DDI signals differently. PS-3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS-3CMM is a new method that is complement to the existing DDI signal detection methods.