Literature DB >> 29361388

Quantitative analysis of elevation of serum creatinine via renal transporter inhibition by trimethoprim in healthy subjects using physiologically-based pharmacokinetic model.

Tomohisa Nakada1, Toshiyuki Kudo2, Toshiyuki Kume3, Hiroyuki Kusuhara4, Kiyomi Ito5.   

Abstract

Serum creatinine (SCr) levels rise during trimethoprim therapy for infectious diseases. This study aimed to investigate whether the elevation of SCr can be quantitatively explained using a physiologically-based pharmacokinetic (PBPK) model incorporating inhibition by trimethoprim on tubular secretion of creatinine via renal transporters such as organic cation transporter 2 (OCT2), OCT3, multidrug and toxin extrusion protein 1 (MATE1), and MATE2-K. Firstly, pharmacokinetic parameters in the PBPK model of trimethoprim were determined to reproduce the blood concentration profile after a single intravenous and oral administration of trimethoprim in healthy subjects. The model was verified with datasets of both cumulative urinary excretions after a single administration and the blood concentration profile after repeated oral administration. The pharmacokinetic model of creatinine consisted of the creatinine synthesis rate, distribution volume, and creatinine clearance (CLcre), including tubular secretion via each transporter. When combining the models for trimethoprim and creatinine, the predicted increments in SCr from baseline were 29.0%, 39.5%, and 25.8% at trimethoprim dosages of 5 mg/kg (b.i.d.), 5 mg/kg (q.i.d.), and 200 mg (b.i.d.), respectively, which were comparable with the observed values. The present model analysis enabled us to quantitatively explain increments in SCr during trimethoprim treatment by its inhibition of renal transporters.
Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Clinical pharmacokinetics; Creatinine; Drug interaction; PBPK model; Quantitative analysis; Renal transporter; Trimethoprim

Mesh:

Substances:

Year:  2017        PMID: 29361388     DOI: 10.1016/j.dmpk.2017.11.314

Source DB:  PubMed          Journal:  Drug Metab Pharmacokinet        ISSN: 1347-4367            Impact factor:   3.614


  9 in total

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3.  A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations.

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Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-05-22

4.  A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug-Drug-Gene Interaction Predictions.

Authors:  Denise Türk; Nina Hanke; Thorsten Lehr
Journal:  Pharmaceutics       Date:  2020-11-10       Impact factor: 6.321

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Review 6.  Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview.

Authors:  Courtney Perry; Grace Davis; Todd M Conner; Tao Zhang
Journal:  Curr Pharmacol Rep       Date:  2020-05-12

7.  Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine-Drug Interactions.

Authors:  Daniel Scotcher; Vikram Arya; Xinning Yang; Ping Zhao; Lei Zhang; Shiew-Mei Huang; Amin Rostami-Hodjegan; Aleksandra Galetin
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-05-14

8.  Physiologically-Based Pharmacokinetic Modelling of Creatinine-Drug Interactions in the Chronic Kidney Disease Population.

Authors:  Hiroyuki Takita; Daniel Scotcher; Rajkumar Chinnadurai; Philip A Kalra; Aleksandra Galetin
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-11-23

9.  Identification of Appropriate Endogenous Biomarker for Risk Assessment of Multidrug and Toxin Extrusion Protein-Mediated Drug-Drug Interactions in Healthy Volunteers.

Authors:  Takeshi Miyake; Emi Kimoto; Lina Luo; Sumathy Mathialagan; Lauren M Horlbogen; Ragu Ramanathan; Linda S Wood; Jillian G Johnson; Vu H Le; Manoli Vourvahis; A David Rodrigues; Chieko Muto; Kenichi Furihata; Yuichi Sugiyama; Hiroyuki Kusuhara
Journal:  Clin Pharmacol Ther       Date:  2020-10-09       Impact factor: 6.875

  9 in total

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