Literature DB >> 31604807

Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling.

Weize Huang1, Nina Isoherranen2.   

Abstract

It has been shown that arterial (central) and venous (peripheral) plasma drug concentrations can be very different. While pharmacokinetic studies typically measure drug concentrations from the peripheral vein such as the arm vein, physiologically based pharmacokinetic (PBPK) models generally output simulated concentrations from the central venous compartment that physiologically represents the right atrium, a merge of the superior and inferior vena cava. In this study, a physiologically based peripheral forearm sampling site model was developed and verified using nicotine, ketamine, lidocaine, and fentanyl as model drugs. This verified model allows output of simulated peripheral venous concentrations that can be meaningfully compared with observed pharmacokinetic data from the arm vein. The generalized effect of PBPK model sampling site on simulation output was investigated. Drugs and metabolites with large volumes of distribution showed considerable concentration discrepancy between the simulated central venous compartment and the peripheral arm vein after intravenous or oral administration, resulting in significant differences in values for C max and time taken to reach C max (t max ) In addition, the simulated central venous metabolite profile showed an unexpected profile that was not observed in the peripheral arm vein. Using fentanyl as a model compound, we show that using the wrong sampling site in PBPK models can lead to biased model evaluation and subsequent erroneous model parameter optimization. Such an error in model parameters along with the discrepant sampling site could dramatically mislead the pharmacokinetic prediction in unstudied clinical scenarios, affecting the assessment of drug safety and efficacy. Overall, this study shows that PBPK model publications should specify the model sampling sites and match them with those employed in clinical studies. SIGNIFICANCE STATEMENT: Our study shows that sampling from the central venous compartment (right atrium) during physiologically based pharmacokinetic model development gives rise to biased model evaluation and erroneous model parameterization when observed data are collected from the peripheral arm vein. This can lead to a clinically significant error in predictions of plasma concentration-time profiles in unstudied scenarios. To address this error, we developed and verified a novel peripheral sampling site model to simulate arm vein drug concentrations that can be applied to different drug dosing scenarios.
Copyright © 2020 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2019        PMID: 31604807      PMCID: PMC6904886          DOI: 10.1124/jpet.119.262154

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


  48 in total

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2.  Arterial and venous pharmacokinetics of morphine-6-glucuronide and impact of sample site on pharmacodynamic parameter estimates.

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Review 3.  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
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Journal:  J Pharm Sci       Date:  2005-06       Impact factor: 3.534

5.  The impact of arteriovenous concentration differences on pharmacodynamic parameter estimates.

Authors:  B Tuk; M Danhof; J W Mandema
Journal:  J Pharmacokinet Biopharm       Date:  1997-02

6.  Arterial and venous pharmacokinetics of intravenous heroin in subjects who are addicted to narcotics.

Authors:  K M Rentsch; G A Kullak-Ublick; C Reichel; P J Meier; K Fattinger
Journal:  Clin Pharmacol Ther       Date:  2001-09       Impact factor: 6.875

7.  Compartment model to describe peripheral arterial-venous drug concentration gradients with drug elimination from the venous sampling compartment.

Authors:  J R Jacobs; P A Nath
Journal:  J Pharm Sci       Date:  1995-03       Impact factor: 3.534

8.  Physiologically-Based Pharmacokinetic Models for CYP1A2 Drug-Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam.

Authors:  Hannah Britz; Nina Hanke; Anke-Katrin Volz; Olav Spigset; Matthias Schwab; Thomas Eissing; Thomas Wendl; Sebastian Frechen; Thorsten Lehr
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9.  Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development.

Authors:  Hm Jones; K Rowland-Yeo
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2013-08-14

Review 10.  Recent Advances in Development and Application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance.

Authors:  Masoud Jamei
Journal:  Curr Pharmacol Rep       Date:  2016-04-14
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Journal:  J Pharmacol Exp Ther       Date:  2020-03-20       Impact factor: 4.030

3.  Physiologically Based Pharmacokinetics of Lysosomotropic Chloroquine in Rat and Human.

Authors:  Xin Liu; William J Jusko
Journal:  J Pharmacol Exp Ther       Date:  2020-12-04       Impact factor: 4.030

4.  Current PBPK Models: Are They Predicting Tissue Drug Concentration Correctly?

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Journal:  Drugs R D       Date:  2020-10-17

5.  A permeability- and perfusion-based PBPK model for improved prediction of concentration-time profiles.

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Journal:  Clin Transl Sci       Date:  2022-05-31       Impact factor: 4.438

6.  Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption.

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Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-09-25

7.  Bridging the gap between in silico and in vivo by modeling opioid disposition in a kidney proximal tubule microphysiological system.

Authors:  Tomoki Imaoka; Weize Huang; Sara Shum; Dale W Hailey; Shih-Yu Chang; Alenka Chapron; Catherine K Yeung; Jonathan Himmelfarb; Nina Isoherranen; Edward J Kelly
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  7 in total

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