Literature DB >> 21063755

Application of physiologically based pharmacokinetic modeling and clearance concept to drugs showing transporter-mediated distribution and clearance in humans.

Takao Watanabe1, Hiroyuki Kusuhara, Yuichi Sugiyama.   

Abstract

This review illustrates the concept of a rate-determining process in the overall hepatic elimination of anionic drugs that involves transporters in the uptake process. A kinetic study in rats has demonstrated that uptake is the rate-determining process for most anionic drugs, and this is likely to hold true for the hepatic elimination of statins in humans. To simulate the effects of variations in the transporter activities on systemic and liver exposure, a physiologically based pharmacokinetic model was constructed for pravastatin, the overall elimination of which involves OATP1B1 and MRP2 in the hepatic uptake and canalicular efflux, respectively. The plasma concentrations of pravastatin in humans were successfully reproduced using the kinetic parameters extrapolated from in vitro data obtained using human hepatocytes and canalicular membrane vesicles and the scaling factors determined in rats. Sensitivity analyses showed that a variation in hepatic uptake altered the plasma concentration of pravastatin markedly, but had a small effect on the liver concentration, and vice versa for the canalicular efflux. Therefore, variation in the OATP1B1 activities will have small and large impacts on the therapeutic efficacy and adverse effect (myopathy) of pravastatin, respectively, whereas that affecting the MRP2 activities may have an opposite effect (i.e., large and small impacts on the therapeutic efficacy and side effect). This pharmacokinetic characteristics likely hold true for other anionic statins, i.e., variation of OATP1B1 is associated with the risk of adverse reactions, whereas that of sequestration mechanisms causes the variation of their pharmacological effect.

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Year:  2010        PMID: 21063755     DOI: 10.1007/s10928-010-9176-y

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  61 in total

1.  Human liver-specific organic anion transporter, LST-1, mediates uptake of pravastatin by human hepatocytes.

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2.  Effect of SLCO1B1 genetic polymorphism on the pharmacokinetics of nateglinide.

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3.  Inhibitory and inductive effects of rifampin on the pharmacokinetics of bosentan in healthy subjects.

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Journal:  Clin Pharmacol Ther       Date:  2007-01-24       Impact factor: 6.875

Review 4.  Therapeutic implications of renal anionic drug transporters.

Authors:  Rosalinde Masereeuw; Frans G M Russel
Journal:  Pharmacol Ther       Date:  2010-03-20       Impact factor: 12.310

Review 5.  Membrane transporters in drug development.

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Journal:  Nat Rev Drug Discov       Date:  2010-03       Impact factor: 84.694

6.  Acute effects of pravastatin on cholesterol synthesis are associated with SLCO1B1 (encoding OATP1B1) haplotype *17.

Authors:  Mikko Niemi; Pertti J Neuvonen; Ute Hofmann; Janne T Backman; Matthias Schwab; Dieter Lütjohann; Klaus von Bergmann; Michel Eichelbaum; Kari T Kivistö
Journal:  Pharmacogenet Genomics       Date:  2005-05       Impact factor: 2.089

7.  Drug-drug interaction between pitavastatin and various drugs via OATP1B1.

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9.  Bosentan is a substrate of human OATP1B1 and OATP1B3: inhibition of hepatic uptake as the common mechanism of its interactions with cyclosporin A, rifampicin, and sildenafil.

Authors:  Alexander Treiber; Ralph Schneiter; Stephanie Häusler; Bruno Stieger
Journal:  Drug Metab Dispos       Date:  2007-05-11       Impact factor: 3.922

10.  Physiologically based pharmacokinetic modeling to predict transporter-mediated clearance and distribution of pravastatin in humans.

Authors:  Takao Watanabe; Hiroyuki Kusuhara; Kazuya Maeda; Yoshihisa Shitara; Yuichi Sugiyama
Journal:  J Pharmacol Exp Ther       Date:  2008-11-10       Impact factor: 4.030

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  24 in total

Review 1.  Drug Concentration Asymmetry in Tissues and Plasma for Small Molecule-Related Therapeutic Modalities.

Authors:  Donglu Zhang; Cornelis E C A Hop; Gabriela Patilea-Vrana; Gautham Gampa; Herana Kamal Seneviratne; Jashvant D Unadkat; Jane R Kenny; Karthik Nagapudi; Li Di; Lian Zhou; Mark Zak; Matthew R Wright; Namandjé N Bumpus; Richard Zang; Xingrong Liu; Yurong Lai; S Cyrus Khojasteh
Journal:  Drug Metab Dispos       Date:  2019-07-02       Impact factor: 3.922

2.  Mechanistic models describing active renal reabsorption and secretion: a simulation-based study.

Authors:  Melanie A Felmlee; Rutwij A Dave; Marilyn E Morris
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3.  Impact of low-dose ritonavir on danoprevir pharmacokinetics: results of computer-based simulations and a clinical drug-drug interaction study.

Authors:  Micaela B Reddy; Yuan Chen; Joshua O Haznedar; Jennifer Fretland; Steven Blotner; Patrick Smith; Jonathan Q Tran
Journal:  Clin Pharmacokinet       Date:  2012-07-01       Impact factor: 6.447

4.  Permeability comparison between hepatocyte and low efflux MDCKII cell monolayer.

Authors:  Rui Li; Yi-An Bi; Yurong Lai; Kiyohiko Sugano; Stefanus J Steyn; Patrick E Trapa; Li Di
Journal:  AAPS J       Date:  2014-05-23       Impact factor: 4.009

5.  Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System.

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Journal:  Pharm Res       Date:  2016-09-12       Impact factor: 4.200

6.  Consideration of the Unbound Drug Concentration in Enzyme Kinetics.

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7.  A framework for 2-stage global sensitivity analysis of GastroPlus™ compartmental models.

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Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-02-08       Impact factor: 2.745

8.  A Clinical Cassette Dosing Study for Evaluating the Contribution of Hepatic OATPs and CYP3A to Drug-Drug Interactions.

Authors:  Takashi Yoshikado; Kazuya Maeda; Sawako Furihata; Hanano Terashima; Takeshi Nakayama; Keiko Ishigame; Kazunobu Tsunemoto; Hiroyuki Kusuhara; Ken-Ichi Furihata; Yuichi Sugiyama
Journal:  Pharm Res       Date:  2017-05-08       Impact factor: 4.200

9.  A Prediction Model of Drug Exposure in Cirrhotic Patients According to Child-Pugh Classification.

Authors:  Julie Steelandt; Elodie Jean-Bart; Sylvain Goutelle; Michel Tod
Journal:  Clin Pharmacokinet       Date:  2015-12       Impact factor: 6.447

Review 10.  Prediction of pharmacokinetics and drug-drug interactions when hepatic transporters are involved.

Authors:  Rui Li; Hugh A Barton; Manthena V Varma
Journal:  Clin Pharmacokinet       Date:  2014-08       Impact factor: 6.447

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