Literature DB >> 22270945

Utility of a physiologically-based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug-drug-disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice.

Joseph A Grillo1, Ping Zhao, Julie Bullock, Brian P Booth, Min Lu, Kathy Robie-Suh, Eva Gil Berglund, K Sandy Pang, Atiqur Rahman, Lei Zhang, Lawrence J Lesko, Shiew-Mei Huang.   

Abstract

BACKGROUND: Rivaroxaban is an oral Factor Xa inhibitor. The primary objective of this communication was to quantitatively predict changes in rivaroxaban exposure when individuals with varying degrees of renal impairment are co-administered with another drug that is both a P-gp and a moderate CYP3A4 inhibitor.
METHODS: A physiologically based pharmacokinetic (PBPK) model was developed to simulate rivaroxaban pharmacokinetics in young (20-45 years) or older (55-65 years) subjects with normal renal function, mild, moderate and severe renal impairment, with or without concomitant use of the combined P-gp and moderate CYP3A4 inhibitor, erythromycin.
RESULTS: The simulations indicate that combined factors (i.e., renal impairment and the use of erythromycin) have a greater impact on rivaroxaban exposure than expected when the impact of these factors are considered individually. Compared with normal young subjects taking rivaroxaban, concurrent mild, moderate or severe renal impairment plus erythromycin resulted in 1.9-, 2.4- or 2.6-fold increase in exposure, respectively in young subjects; and 2.5-, 2.9- or 3.0-fold increase in exposure in older subjects.
CONCLUSIONS: These simulations suggest that a drug-drug-disease interaction is possible, which may significantly increase rivaroxaban exposure and increase bleeding risk. These simulations render more mechanistic insights as to the possible outcomes and allow one to reach a decision to add cautionary language to the approved product labeling for rivaroxaban.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 22270945     DOI: 10.1002/bdd.1771

Source DB:  PubMed          Journal:  Biopharm Drug Dispos        ISSN: 0142-2782            Impact factor:   1.627


  24 in total

Review 1.  Dose selection based on physiologically based pharmacokinetic (PBPK) approaches.

Authors:  Hannah M Jones; Kapil Mayawala; Patrick Poulin
Journal:  AAPS J       Date:  2012-12-27       Impact factor: 4.009

2.  Prediction of the Effect of Renal Impairment on the Pharmacokinetics of New Drugs.

Authors:  Elisa Borella; Italo Poggesi; Paolo Magni
Journal:  Clin Pharmacokinet       Date:  2018-04       Impact factor: 6.447

3.  Predicting nonlinear pharmacokinetics of omeprazole enantiomers and racemic drug using physiologically based pharmacokinetic modeling and simulation: application to predict drug/genetic interactions.

Authors:  Fang Wu; Lu Gaohua; Ping Zhao; Masoud Jamei; Shiew-Mei Huang; Edward D Bashaw; Sue-Chih Lee
Journal:  Pharm Res       Date:  2014-03-04       Impact factor: 4.200

Review 4.  Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.

Authors:  Clara Hartmanshenn; Megerle Scherholz; Ioannis P Androulakis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-09-19       Impact factor: 2.745

Review 5.  Comparisons between novel oral anticoagulants and vitamin K antagonists in patients with CKD.

Authors:  Ziv Harel; Michelle Sholzberg; Prakesh S Shah; Katerina Pavenski; Shai Harel; Ron Wald; Chaim M Bell; Jeffrey Perl
Journal:  J Am Soc Nephrol       Date:  2014-01-02       Impact factor: 10.121

6.  Simultaneous Assessment of Hepatic Transport and Metabolism Pathways with a Single Probe Using Individualized PBPK Modeling of 14CO2 Production Rate Data.

Authors:  Yoko Franchetti; Thomas D Nolin
Journal:  J Pharmacol Exp Ther       Date:  2019-08-09       Impact factor: 4.030

7.  Factors Affecting Time-Varying Clearance of Cyclosporine in Adult Renal Transplant Recipients: A Population Pharmacokinetic Perspective.

Authors:  Xiaoyan Qiu; Weiwei Qin; Junjun Mao; Luyang Xu; Ming Zhang; Mingkang Zhong
Journal:  Pharm Res       Date:  2021-11-08       Impact factor: 4.200

Review 8.  Oral anticoagulants in older adults with atrial fibrillation.

Authors:  Gwen M Bernacki; Richard C Becker
Journal:  J Thromb Thrombolysis       Date:  2013-11       Impact factor: 2.300

9.  Application of physiologically based pharmacokinetic modeling to the prediction of drug-drug and drug-disease interactions for rivaroxaban.

Authors:  Ruijuan Xu; Weihong Ge; Qing Jiang
Journal:  Eur J Clin Pharmacol       Date:  2018-02-17       Impact factor: 2.953

10.  Complex disease-, gene-, and drug-drug interactions: impacts of renal function, CYP2D6 phenotype, and OCT2 activity on veliparib pharmacokinetics.

Authors:  Jing Li; Seongho Kim; Xianyi Sha; Richard Wiegand; Jianmei Wu; Patricia LoRusso
Journal:  Clin Cancer Res       Date:  2014-06-19       Impact factor: 12.531

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