Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Sluka, James P., Fu, Xiao, Swat, Maciej, Belmonte, Julio M., Cosmanescu, Alin, Clendenon, Sherry G., Wambaugh, John F., Glazier, James A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
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
_version_ 1782454121067970560
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
work_keys_str_mv AT slukajamesp alivercentricmultiscalemodelingframeworkforxenobiotics
AT fuxiao alivercentricmultiscalemodelingframeworkforxenobiotics
AT swatmaciej alivercentricmultiscalemodelingframeworkforxenobiotics
AT belmontejuliom alivercentricmultiscalemodelingframeworkforxenobiotics
AT cosmanescualin alivercentricmultiscalemodelingframeworkforxenobiotics
AT clendenonsherryg alivercentricmultiscalemodelingframeworkforxenobiotics
AT wambaughjohnf alivercentricmultiscalemodelingframeworkforxenobiotics
AT glazierjamesa alivercentricmultiscalemodelingframeworkforxenobiotics
AT slukajamesp livercentricmultiscalemodelingframeworkforxenobiotics
AT fuxiao livercentricmultiscalemodelingframeworkforxenobiotics
AT swatmaciej livercentricmultiscalemodelingframeworkforxenobiotics
AT belmontejuliom livercentricmultiscalemodelingframeworkforxenobiotics
AT cosmanescualin livercentricmultiscalemodelingframeworkforxenobiotics
AT clendenonsherryg livercentricmultiscalemodelingframeworkforxenobiotics
AT wambaughjohnf livercentricmultiscalemodelingframeworkforxenobiotics
AT glazierjamesa livercentricmultiscalemodelingframeworkforxenobiotics