title: How to Choose In Vitro Systems to Predict In Vivo Drug Clearance: A System Pharmacology Perspective.
abstract: The 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.
authors.affiliation: Bioinformatics Research Center, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China ; Biomedical Engineering Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA ; School of Informatics and Computing, Indiana University, Indianapolis, IN 46202, USA.
authors.forename: ChienWei
authors.initials: C
authors.lastname: Chiang
authors.affiliation: Bioinformatics Research Center, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China ; Biomedical Engineering Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Liang
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA ; School of Informatics and Computing, Indiana University, Indianapolis, IN 46202, USA.
authors.forename: Hengyi
authors.initials: H
authors.lastname: Wu
authors.affiliation: Bioinformatics Research Center, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China ; Pattern Recognition and Intelligent System Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China.
authors.forename: Weixing
authors.initials: W
authors.lastname: Feng
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA ; Department of Obstetrics and Gynecology, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
authors.forename: Sara K
authors.initials: SK
authors.lastname: Quinney
authors.affiliation: Bioinformatics Research Center, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China ; Biomedical Engineering Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Li
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA ; Department of Medical and Molecular Genomics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
authors.forename: Lang
authors.initials: L
authors.lastname: Li
mesh_terms: D006801:Humans; D066298:In Vitro Techniques; D008657:Metabolic Clearance Rate; D008954:Models, Biological