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Browsing by Author "Wu, Hengyi"
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Item How to Choose In Vitro Systems to Predict In Vivo Drug Clearance: A System Pharmacology Perspective(Hindawi Publishing Corporation, 2015) Wang, Lei; Chiang, ChienWei; Liang, Hong; Wu, Hengyi; Feng, Weixing; Quinney, Sara K.; Li, Jin; Li, Lang; Department of Obstetrics and Gynecology, IU School of MedicineThe use of in vitro metabolism data to predict human clearance has become more significant in the current prediction of large scale drug clearance for all the drugs. The relevant information (in vitro metabolism data and in vivo human clearance values) of thirty-five drugs that satisfied the entry criteria of probe drugs was collated from the literature. Then the performance of different in vitro systems including Escherichia coli system, yeast system, lymphoblastoid system and baculovirus system is compared after in vitro-in vivo extrapolation. Baculovirus system, which can provide most of the data, has almost equal accuracy as the other systems in predicting clearance. And in most cases, baculovirus system has the smaller CV in scaling factors. Therefore, the baculovirus system can be recognized as the suitable system for the large scale drug clearance prediction.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.