- Author:
- Geoff Nunns <gnunns1@jhu.edu>
- Date:
- 2010-03-30 11:08:27+13:00
- Desc:
- Replaced model structure with abstract, fixed RDF typos.
- Permanent Source URI:
- https://models.cellml.org/workspace/yang_tong_mccarver_hines_beard_2006/rawfile/5804b08234ff2ac035e9a94f807b660e336bc74d/yang_tong_mccarver_hines_beard_2006.cellml
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This CellML file was generated on 4/06/2008 at 9:26:28 at a.m. using:
COR (0.9.31.955)
Copyright 2002-2008 Dr Alan Garny
http://COR.physiol.ox.ac.uk/ - COR@physiol.ox.ac.uk
CellML 1.0 was used to generate this model
http://www.CellML.org/
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<title>An Age-Dependent Physiologically Based Pharmokinetic Model of Methadone Distribution and Metabolism</title>
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<firstname>Catherine</firstname>
<surname>Lloyd</surname>
<affiliation>
<shortaffil>Bioengineering Institute, The University of Auckland</shortaffil>
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<title>Model Status</title>
<para>
This CellML version of the model has been checked in COR and PCEnv. It will recreate published results, although it only models a single organ (the liver) as well as the veins and arteries, and is based on a 5 year old boy. The model cannot replicate the population analysis described in the paper, which needs stochastic tools that are unavailable to CellML at present.
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<para>
Abstract: Limited pharmacokinetic (PK) and pharmacodynamic (PD) data are available to use in methadone dosing recommendations in pediatric patients for either opioid abstinence or analgesia. Considering the extreme inter-individual variability of absorption and metabolism of methadone, population-based PK would be useful to provide insight into the relationship between dose, blood concentrations, and clinical effects of methadone. To address this need, an age-dependent physiologically based pharmacokinetic (PBPK) model has been constructed to systematically study methadone metabolism and PK. The model will facilitate the design of cost-effective studies that will evaluate methadone PK and PD relationships, and may be useful to guide methadone dosing in children. The PBPK model, which includes whole-body multi-organ distribution, plasma protein binding, metabolism, and clearance, is parameterized based on a database of pediatric PK parameters and data collected from clinical experiments. The model is further tailored and verified based on PK data from individual adults, then scaled appropriately to apply to children aged 0-24 months. Based on measured variability in CYP3A enzyme expression levels and plasma orosomucoid (ORM2) concentrations, a Monte-Carlo-based simulation of methadone kinetics in a pediatric population was performed. The simulation predicts extreme variability in plasma concentrations and clearance kinetics for methadone in the pediatric population, based on standard dosing protocols. In addition, it is shown that when doses are designed for individuals based on prior protein expression information, inter-individual variability in methadone kinetics may be greatly reduced.
</para>
<para>
The complete original paper reference is cited below:
</para>
<para>
Population-based analysis of methadone distribution and metabolism using an age-dependent physiologically based pharmacokinetic model, Feng Yang, Xianping Tong, D. Gail. McCarver, Ronald N. Hines and Daniel A. Beard, 2006, <emphasis>Journal of Pharmacokinetics and Pharmacodynamics</emphasis>
, volume 33, issue 4. <ulink url="http://www.ncbi.nlm.nih.gov/sites/entrez?Db=PubMed&Cmd=ShowDetailView&TermToSearch=16758333&ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum">PubMed ID: 16758333</ulink>
</para>
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<caption>Schematic diagram of a PBPK model consisting of 17 compartments. The lines represent blood flow while the boxes represent organs or systems. Methadone is primarily metabolised in the liver and gastro-intestinal (GI) system, while its elimination mainly occurs through the kidneys. Organs in which methadone are not distributed include skin, adipose, thyroid, pancreas, and bone marrow are grouped together as <emphasis>others</emphasis>.</caption>
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Yang et al.'s age-dependent, physiologically based pharmokinetic model of methadone distribution and metabolism.
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