Literature DB >> 22232759

Evaluation of exposure change of nonrenally eliminated drugs in patients with chronic kidney disease using physiologically based pharmacokinetic modeling and simulation.

Ping Zhao1, Manuela de L T Vieira, Joseph A Grillo, Pengfei Song, Ta-Chen Wu, Jenny H Zheng, Vikram Arya, Eva Gil Berglund, Arthur J Atkinson, Yuichi Sugiyama, K Sandy Pang, Kellie S Reynolds, Darrell R Abernethy, Lei Zhang, Lawrence J Lesko, Shiew-Mei Huang.   

Abstract

Chronic kidney disease, or renal impairment (RI) can increase plasma levels for drugs that are primarily renally cleared and for some drugs whose renal elimination is not a major pathway. We constructed physiologically based pharmacokinetic (PBPK) models for 3 nonrenally eliminated drugs (sildenafil, repaglinide, and telithromycin). These models integrate drug-dependent parameters derived from in vitro, in silico, and in vivo data, and system-dependent parameters that are independent of the test drugs. Plasma pharmacokinetic profiles of test drugs were simulated in subjects with severe RI and normal renal function, respectively. The simulated versus observed areas under the concentration versus time curve changes (AUCR, severe RI/normal) were comparable for sildenafil (2.2 vs 2.0) and telithromycin (1.6 vs 1.9). For repaglinide, the initial, simulated AUCR was lower than that observed (1.2 vs 3.0). The underestimation was corrected once the estimated changes in transporter activity were incorporated into the model. The simulated AUCR values were confirmed using a static, clearance concept model. The PBPK models were further used to evaluate the changes in pharmacokinetic profiles of sildenafil metabolite by RI and of telithromycin by RI and co-administration with ketoconazole. The simulations demonstrate the utility and challenges of the PBPK approach in evaluating the pharmacokinetics of nonrenally cleared drugs in subjects with RI.

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Year:  2012        PMID: 22232759     DOI: 10.1177/0091270011415528

Source DB:  PubMed          Journal:  J Clin Pharmacol        ISSN: 0091-2700            Impact factor:   3.126


  34 in total

1.  Physiologically based modeling of pravastatin transporter-mediated hepatobiliary disposition and drug-drug interactions.

Authors:  Manthena V S Varma; Yurong Lai; Bo Feng; John Litchfield; Theunis C Goosen; Arthur Bergman
Journal:  Pharm Res       Date:  2012-05-26       Impact factor: 4.200

Review 2.  Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Authors:  Jennifer E Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2015-08-21       Impact factor: 3.922

3.  Development of a Physiologically Based Pharmacokinetic Model for Sinogliatin, a First-in-Class Glucokinase Activator, by Integrating Allometric Scaling, In Vitro to In Vivo Exploration and Steady-State Concentration-Mean Residence Time Methods: Mechanistic Understanding of its Pharmacokinetics.

Authors:  Ling Song; Yi Zhang; Ji Jiang; Shuang Ren; Li Chen; Dongyang Liu; Xijing Chen; Pei Hu
Journal:  Clin Pharmacokinet       Date:  2018-10       Impact factor: 6.447

Review 4.  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

5.  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 6.  Model-based clinical drug development in the past, present and future: a commentary.

Authors:  Holly Kimko; José Pinheiro
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

7.  Application of a physiologically based pharmacokinetic model informed by a top-down approach for the prediction of pharmacokinetics in chronic kidney disease patients.

Authors:  Hiroyuki Sayama; Hiroaki Takubo; Hiroshi Komura; Motohiro Kogayu; Masahiro Iwaki
Journal:  AAPS J       Date:  2014-06-11       Impact factor: 4.009

8.  Prediction of drug disposition in diabetic patients by means of a physiologically based pharmacokinetic model.

Authors:  Jia Li; Hai-Fang Guo; Can Liu; Zeyu Zhong; Li Liu; Xiao-Dong Liu
Journal:  Clin Pharmacokinet       Date:  2015-02       Impact factor: 6.447

Review 9.  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 10.  Towards quantitation of the effects of renal impairment and probenecid inhibition on kidney uptake and efflux transporters, using physiologically based pharmacokinetic modelling and simulations.

Authors:  Vicky Hsu; Manuela de L T Vieira; Ping Zhao; Lei Zhang; Jenny Huimin Zheng; Anna Nordmark; Eva Gil Berglund; Kathleen M Giacomini; Shiew-Mei Huang
Journal:  Clin Pharmacokinet       Date:  2014-03       Impact factor: 6.447

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