Literature DB >> 30413628

Towards Further Verification of Physiologically-Based Kidney Models: Predictability of the Effects of Urine-Flow and Urine-pH on Renal Clearance.

Takanobu Matsuzaki1, Daniel Scotcher1, Adam S Darwich1, Aleksandra Galetin1, Amin Rostami-Hodjegan2.   

Abstract

In vitro-in vivo extrapolation (IVIVE) of renal excretory clearance (CLR) using the physiologically based kidney models can provide mechanistic insight into the interplay of multiple processes occurring in the renal tubule; however, the ability of these models to capture quantitatively the impact of perturbed conditions (e.g., urine flow, urine pH changes) on CLR has not been fully evaluated. In this work, we aimed to assess the predictability of the effect of urine flow and urine pH on CLR and tubular drug concentrations (selected examples). Passive diffusion clearance across the nephron tubule membrane was scaled from in vitro human epithelial cell line Caco-2 permeability data by nephron tubular surface area to predict the fraction reabsorbed and the CLR of caffeine, chloramphenicol, creatinine, dextroamphetamine, nicotine, sulfamethoxazole, and theophylline. CLR values predicted using mechanistic kidney model at a urinary pH of 6.2 and 7.4 resulted in prediction bias of 2.87- and 3.62-fold, respectively. Model simulations captured urine flow-dependent CLR, albeit with minor underprediction of the observed magnitude of change. The relationship between drug solubility, urine flow, and urine pH, illustrated in simulated intratubular concentrations of acyclovir and sulfamethoxazole, agreed with clinical data on tubular precipitation and crystal-induced acute kidney injury. This study represents the first systematic evaluation of the ability of the mechanistic kidney model to capture the impact of urine flow and urine pH on CLR and drug tubular concentrations with the aim of facilitating refinement of IVIVE-based mechanistic prediction of renal excretion.
Copyright © 2019 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2018        PMID: 30413628     DOI: 10.1124/jpet.118.251413

Source DB:  PubMed          Journal:  J Pharmacol Exp Ther        ISSN: 0022-3565            Impact factor:   4.030


  9 in total

1.  Prediction of Drug Clearance from Enzyme and Transporter Kinetics.

Authors:  Priyanka R Kulkarni; Amir S Youssef; Aneesh A Argikar
Journal:  Methods Mol Biol       Date:  2021

2.  Mechanistic PBPK Modeling of Urine pH Effect on Renal and Systemic Disposition of Methamphetamine and Amphetamine.

Authors:  Weize Huang; Lindsay C Czuba; Nina Isoherranen
Journal:  J Pharmacol Exp Ther       Date:  2020-03-20       Impact factor: 4.030

3.  Prediction of Maternal and Fetal Acyclovir, Emtricitabine, Lamivudine, and Metformin Concentrations during Pregnancy Using a Physiologically Based Pharmacokinetic Modeling Approach.

Authors:  Khaled Abduljalil; Amita Pansari; Jia Ning; Masoud Jamei
Journal:  Clin Pharmacokinet       Date:  2022-01-24       Impact factor: 6.447

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

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:  2020-05-22

Review 5.  Computational Models for Clinical Applications in Personalized Medicine-Guidelines and Recommendations for Data Integration and Model Validation.

Authors:  Catherine Bjerre Collin; Tom Gebhardt; Martin Golebiewski; Tugce Karaderi; Maximilian Hillemanns; Faiz Muhammad Khan; Ali Salehzadeh-Yazdi; Marc Kirschner; Sylvia Krobitsch; Lars Kuepfer
Journal:  J Pers Med       Date:  2022-01-26

6.  PBPK Simulation-Based Evaluation of Ganciclovir Crystalluria Risk Factors: Effect of Renal Impairment, Old Age, and Low Fluid Intake.

Authors:  Daniel Scotcher; Aleksandra Galetin
Journal:  AAPS J       Date:  2021-12-14       Impact factor: 4.009

7.  A pH-independent electrochemical aptamer-based biosensor supports quantitative, real-time measurement in vivo.

Authors:  Shaoguang Li; Andrés Ferrer-Ruiz; Jun Dai; Javier Ramos-Soriano; Xuewei Du; Man Zhu; Wanxue Zhang; Yuanyuan Wang; M Ángeles Herranz; Le Jing; Zishuo Zhang; Hui Li; Fan Xia; Nazario Martín
Journal:  Chem Sci       Date:  2022-06-27       Impact factor: 9.969

8.  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

9.  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 in total

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