Open Access
Application of biologically based computer modeling to simple or complex mixtures.
Author(s) -
Kai Liao,
Ivan D. Dobrev,
James E. Dennison,
Melvin E. Andersen,
Brad Reisfeld,
Kenneth F. Reardon,
Julie A. Campain,
Wei Wei,
Michael T. Klein,
Richard J. Quann,
Raymond S. H. Yang
Publication year - 2002
Publication title -
environmental health perspectives
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.257
H-Index - 282
eISSN - 1552-9924
pISSN - 0091-6765
DOI - 10.1289/ehp.02110s6957
Subject(s) - physiologically based pharmacokinetic modelling , context (archaeology) , biochemical engineering , computer science , unifac , chemistry , bioinformatics , organic chemistry , pharmacokinetics , activity coefficient , engineering , paleontology , aqueous solution , biology
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.