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A Liver-Centric Multiscale Modeling Framework for Xenobiotics
We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that spa...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026379/ https://www.ncbi.nlm.nih.gov/pubmed/27636091 http://dx.doi.org/10.1371/journal.pone.0162428 |
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author | Sluka, James P. Fu, Xiao Swat, Maciej Belmonte, Julio M. Cosmanescu, Alin Clendenon, Sherry G. Wambaugh, John F. Glazier, James A. |
author_facet | Sluka, James P. Fu, Xiao Swat, Maciej Belmonte, Julio M. Cosmanescu, Alin Clendenon, Sherry G. Wambaugh, John F. Glazier, James A. |
author_sort | Sluka, James P. |
collection | PubMed |
description | We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics. |
format | Online Article Text |
id | pubmed-5026379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50263792016-09-27 A Liver-Centric Multiscale Modeling Framework for Xenobiotics Sluka, James P. Fu, Xiao Swat, Maciej Belmonte, Julio M. Cosmanescu, Alin Clendenon, Sherry G. Wambaugh, John F. Glazier, James A. PLoS One Research Article We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics. Public Library of Science 2016-09-16 /pmc/articles/PMC5026379/ /pubmed/27636091 http://dx.doi.org/10.1371/journal.pone.0162428 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Sluka, James P. Fu, Xiao Swat, Maciej Belmonte, Julio M. Cosmanescu, Alin Clendenon, Sherry G. Wambaugh, John F. Glazier, James A. A Liver-Centric Multiscale Modeling Framework for Xenobiotics |
title | A Liver-Centric Multiscale Modeling Framework for Xenobiotics |
title_full | A Liver-Centric Multiscale Modeling Framework for Xenobiotics |
title_fullStr | A Liver-Centric Multiscale Modeling Framework for Xenobiotics |
title_full_unstemmed | A Liver-Centric Multiscale Modeling Framework for Xenobiotics |
title_short | A Liver-Centric Multiscale Modeling Framework for Xenobiotics |
title_sort | liver-centric multiscale modeling framework for xenobiotics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026379/ https://www.ncbi.nlm.nih.gov/pubmed/27636091 http://dx.doi.org/10.1371/journal.pone.0162428 |
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