Browsing by Author "Wang, Xueying"
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Item Downregulation of Organic Anion Transporting Polypeptide (OATP) 1B1 Transport Function by Lysosomotropic Drug Chloroquine: Implication in OATP-Mediated Drug-Drug Interactions(ACS, 2016-03) Alam, Khondoker; Pahwa, Sonia; Wang, Xueying; Zhang, Pengyue; Ding, Kai; Abuznait, Alaa H.; Li, Lang; Yue, Wei; Department of Medical & Molecular Genetics, IU School of MedicineOrganic anion transporting polypeptide (OATP) 1B1 mediates the hepatic uptake of many drugs including lipid-lowering statins. Decreased OATP1B1 transport activity is often associated with increased systemic exposure of statins and statin-induced myopathy. Antimalarial drug chloroquine (CQ) is also used for long-term treatment of rheumatoid arthritis and systemic lupus erythematosus. CQ is lysosomotropic and inhibits protein degradation in lysosomes. The current studies were designed to determine the effects of CQ on OATP1B1 protein degradation, OATP1B1-mediated transport in OATP1B1-overexpressing cell line, and statin uptake in human sandwich-cultured hepatocytes (SCH). Treatment with lysosome inhibitor CQ increased OATP1B1 total protein levels in HEK293-OATP1B1 cells and in human SCH as determined by OATP1B1 immunoblot. In HEK293-FLAG-tagged OATP1B1 stable cell line, co-immunofluorescence staining indicated that intracellular FLAG-OATP1B1 is colocalized with lysosomal associated membrane glycoprotein (LAMP)-2, a marker protein of late endosome/lysosome. Enlarged LAMP-2-positive vacuoles with FLAG-OATP1B1 protein retained inside were readily detected in CQ-treated cells, consistent with blocking lysosomal degradation of OATP1B1 by CQ. In HEK293-OATP1B1 cells, without pre-incubation, CQ concentrations up to 100 μM did not affect OATP1B1-mediated [3H]E217G accumulation. However, pre-incubation with CQ at clinically relevant concentration(s) significantly decreased [3H]E217G and [3H]pitavastatin accumulation in HEK293-OATP1B1 cells and [3H]pitavastatin accumulation in human SCH. CQ pretreatment (25 μM, 2 h) resulted in ∼1.9-fold decrease in Vmax without affecting Km of OATP1B1-mediated [3H]E217G transport in HEK293-OATP1B1 cells. Pretreatment with monensin and bafilomycin A1, which also have lysosome inhibition activity, significantly decreased OATP1B1-mediated transport in HEK293-OATP1B1 cells. Pharmacoepidemiologic studies using data from the U.S. Food and Drug Administration Adverse Event Reporting System indicated that CQ plus pitavastatin, rosuvastatin, and pravastatin, which are minimally metabolized by the cytochrome P450 enzymes, led to higher myopathy risk than these statins alone. In summary, the present studies report novel findings that lysosome is involved in degradation of OATP1B1 protein and that pre-incubation with lysosomotropic drug CQ downregulates OATP1B1 transport activity. Our in vitro data in combination with pharmacoepidemiologic studies support that CQ has potential to cause OATP-mediated drug–drug interactions.Item Improved Adverse Drug Event Prediction Through Information Component Guided Pharmacological Network Model (IC-PNM)(IEEE, 2021) Ji, Xiangmin; Wang, Lei; Hua, Liyan; Wang, Xueying; Zhang, Pengyue; Shendre, Aditi; Feng, Weixing; Li, Jin; Li, Lang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthImproving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve (\textAUROC)= 0.82(AUROC)=0.82. On the other hand, the IC-PNM prediction performance improved to \textAUROC= 0.91AUROC=0.91 if we removed the small sample size drug-ADE pairs from the prediction model during validation.Item Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy(Wiley, 2018-02-20) Wang, Xueying; Zhang, Pengyue; Chiang, Chien-Wei; Wu, Hengyi; Shen, Li; Ning, Xia; Zeng, Donglin; Wang, Lei; Quinney, Sara K.; Feng, Weixing; Li, Lang; Radiology and Imaging Sciences, School of MedicineDrug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies.Item 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 ComputingWith 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.Item Regulation of Organic Anion Transporting Polypeptides (OATP) 1B1- and OATP1B3-Mediated Transport: An Updated Review in the Context of OATP-Mediated Drug-Drug Interactions(MDPI, 2018-03-14) Alam, Khondoker; Crowe, Alexandra; Wang, Xueying; Zhang, Pengyue; Ding, Kai; Li, Lang; Yue, Wei; Medical and Molecular Genetics, School of MedicineOrganic anion transporting polypeptides (OATP) 1B1 and OATP1B3 are important hepatic transporters that mediate the uptake of many clinically important drugs, including statins from the blood into the liver. Reduced transport function of OATP1B1 and OATP1B3 can lead to clinically relevant drug-drug interactions (DDIs). Considering the importance of OATP1B1 and OATP1B3 in hepatic drug disposition, substantial efforts have been given on evaluating OATP1B1/1B3-mediated DDIs in order to avoid unwanted adverse effects of drugs that are OATP substrates due to their altered pharmacokinetics. Growing evidences suggest that the transport function of OATP1B1 and OATP1B3 can be regulated at various levels such as genetic variation, transcriptional and post-translational regulation. The present review summarizes the up to date information on the regulation of OATP1B1 and OATP1B3 transport function at different levels with a focus on potential impact on OATP-mediated DDIs.Item Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research(Wiley, 2018) Zhang, Pengyue; Wu, Heng-Yi; Chiang, Chien-Wei; Binkheder, Samar; Wang, Xueying; Zeng, Donglin; Quinney, Sara K.; Li, Lang; BioHealth Informatics, School of Informatics and ComputingDrug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.Item Translational high-dimensional drug Interaction discovery and validation using health record databases and pharmacokinetics models(Wiley, 2017) Chiang, Chien-Wei; Zhang, Pengyue; Wang, Xueying; Wang, Lei; Zhang, Shijun; Ning, Xia; Shen, Li; Quinney, Sara K.; Li, Lang; Medical and Molecular Genetics, School of MedicinePolypharmacy 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.