Cargando…

Application of biologically based computer modeling to simple or complex mixtures.

The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising...

Descripción completa

Detalles Bibliográficos
Autores principales: Liao, Kai H, Dobrev, Ivan D, Dennison, James E, Andersen, Melvin E, Reisfeld, Brad, Reardon, Kenneth F, Campain, Julie A, Wei, Wei, Klein, Michael T, Quann, Richard J, Yang, Raymond S H
Formato: Texto
Lenguaje:English
Publicado: 2002
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1241278/
https://www.ncbi.nlm.nih.gov/pubmed/12634125
_version_ 1782125365776351232
author Liao, Kai H
Dobrev, Ivan D
Dennison, James E
Andersen, Melvin E
Reisfeld, Brad
Reardon, Kenneth F
Campain, Julie A
Wei, Wei
Klein, Michael T
Quann, Richard J
Yang, Raymond S H
author_facet Liao, Kai H
Dobrev, Ivan D
Dennison, James E
Andersen, Melvin E
Reisfeld, Brad
Reardon, Kenneth F
Campain, Julie A
Wei, Wei
Klein, Michael T
Quann, Richard J
Yang, Raymond S H
author_sort Liao, Kai H
collection PubMed
description The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising approach to deal with chemical mixtures. In the past 15 years or so, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been applied to the toxicologic interactions of chemical mixtures. This approach is promising for relatively simple chemical mixtures; the most complicated chemical mixtures studied so far using this approach contained five or fewer component chemicals. In this presentation we provide some examples of the utility of PBPK/PD modeling for toxicologic interactions in chemical mixtures. The probability of developing predictive tools for simple mixtures using PBPK/PD modeling is high. Unfortunately, relatively few attempts have been made to develop paradigms to consider the risks posed by very complex chemical mixtures such as gasoline, diesel, tobacco smoke, etc. However, recent collaboration between scientists at Colorado State University and engineers at Rutgers University attempting to use reaction network modeling has created hope for the possible development of a modeling approach with the potential of predicting the outcome of toxicology of complex chemical mixtures. We discuss the applications of reaction network modeling in the context of petroleum refining and its potential for elucidating toxic interactions with mixtures.
format Text
id pubmed-1241278
institution National Center for Biotechnology Information
language English
publishDate 2002
record_format MEDLINE/PubMed
spelling pubmed-12412782005-11-08 Application of biologically based computer modeling to simple or complex mixtures. Liao, Kai H Dobrev, Ivan D Dennison, James E Andersen, Melvin E Reisfeld, Brad Reardon, Kenneth F Campain, Julie A Wei, Wei Klein, Michael T Quann, Richard J Yang, Raymond S H Environ Health Perspect Research Article The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising approach to deal with chemical mixtures. In the past 15 years or so, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been applied to the toxicologic interactions of chemical mixtures. This approach is promising for relatively simple chemical mixtures; the most complicated chemical mixtures studied so far using this approach contained five or fewer component chemicals. In this presentation we provide some examples of the utility of PBPK/PD modeling for toxicologic interactions in chemical mixtures. The probability of developing predictive tools for simple mixtures using PBPK/PD modeling is high. Unfortunately, relatively few attempts have been made to develop paradigms to consider the risks posed by very complex chemical mixtures such as gasoline, diesel, tobacco smoke, etc. However, recent collaboration between scientists at Colorado State University and engineers at Rutgers University attempting to use reaction network modeling has created hope for the possible development of a modeling approach with the potential of predicting the outcome of toxicology of complex chemical mixtures. We discuss the applications of reaction network modeling in the context of petroleum refining and its potential for elucidating toxic interactions with mixtures. 2002-12 /pmc/articles/PMC1241278/ /pubmed/12634125 Text en
spellingShingle Research Article
Liao, Kai H
Dobrev, Ivan D
Dennison, James E
Andersen, Melvin E
Reisfeld, Brad
Reardon, Kenneth F
Campain, Julie A
Wei, Wei
Klein, Michael T
Quann, Richard J
Yang, Raymond S H
Application of biologically based computer modeling to simple or complex mixtures.
title Application of biologically based computer modeling to simple or complex mixtures.
title_full Application of biologically based computer modeling to simple or complex mixtures.
title_fullStr Application of biologically based computer modeling to simple or complex mixtures.
title_full_unstemmed Application of biologically based computer modeling to simple or complex mixtures.
title_short Application of biologically based computer modeling to simple or complex mixtures.
title_sort application of biologically based computer modeling to simple or complex mixtures.
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1241278/
https://www.ncbi.nlm.nih.gov/pubmed/12634125
work_keys_str_mv AT liaokaih applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT dobrevivand applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT dennisonjamese applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT andersenmelvine applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT reisfeldbrad applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT reardonkennethf applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT campainjuliea applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT weiwei applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT kleinmichaelt applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT quannrichardj applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures
AT yangraymondsh applicationofbiologicallybasedcomputermodelingtosimpleorcomplexmixtures