Application of Biologically Based Computer Modeling to Simple or Complex Mixtures

• Published in: Environmental health perspectives Vol. 110; no. suppl 6; pp. 957 - 963
• Main Authors: 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., Raymond S. H. Yang
• Format: Journal Article
• Language: English
• Published: United States National Institute of Environmental Health Sciences. National Institutes of Health. Department of Health, Education and Welfare 01-12-2002
• Subjects: Application of Computer and Statistical Modeling to Chemical Mixtures; Chemical hazards; Chemical mixtures; Computer Simulation; Dose-Response Relationship, Drug; Drug design; Drug Interactions; Environmental Pollutants - adverse effects; Forecasting; Kinetics; Metabolism; Modeling; Molecules; Petroleum; Pharmacokinetics; Reactants; Risk Assessment; Toxicology; Xenobiotics - adverse effects
• ISSN: 0091-6765; 1552-9924
• DOI: 10.1289/ehp.02110s6957

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.