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MODEL1006230040 - Yang2006_Methadone_PKmodel

 

The following model is part of the non-curated branch of BioModels Database. While the syntax of the model has been verified, its semantics remains unchecked. Any annotation present in the models is not a product of BioModels' annotators. We are doing our best to incorporate this model into the curated branch as soon as possible. In the meantime, we display only limited metadata here. For further information about the model, please download the SBML file.


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Reference Publication
Publication ID: 16758333
Yang F, Tong X, McCarver DG, Hines RN, Beard DA.
Population-based analysis of methadone distribution and metabolism using an age-dependent physiologically based pharmacokinetic model.
J Pharmacokinet Pharmacodyn 2006 Aug; 33(4): 485-518
Department of Physiology, Medical College of Wisconsin, Biotechnology and Bioengineering Center, Milwaukee, WI 53226, USA.  [more]
Model
Original Model: CellML logo
Submitter: Camille Laibe
Submission Date: 23 Jun 2010 10:12:08 UTC
Last Modification Date: 25 Jun 2010 13:44:49 UTC
Creation Date: 25 Jun 2010 13:44:49 UTC
Encoders:  Catherine Lloyd
bqbiol:isVersionOf Gene Ontology response to drug
Human Disease Ontology neonatal abstinence syndrome
bqbiol:hasTaxon Taxonomy Homo sapiens
Notes

This a model from the article:
Population-based analysis of methadone distribution and metabolism using an age-dependent physiologically based pharmacokinetic model.
Yang F, Tong X, McCarver DG, Hines RN, Beard DA. J Pharmacokinet Pharmacodyn 2006 Aug;33(4):485-518 16758333 ,
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.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Yang F, Tong X, McCarver DG, Hines RN, Beard DA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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