Risk Assessment Extrapolations and Physiological Modeling

TLDR

Risk assessment of environmental chemicals requires extrapolations from animal studies to humans, high to low doses, different exposure routes and timeframes, and can be approached via strict dose correspondence or statistical correlations. The study proposes using physiologically based pharmacokinetic models to achieve more scientifically sound extrapolations. These models incorporate detailed biological information that enables prediction of pharmacokinetic behavior across diverse exposure scenarios. Physiologically based pharmacokinetic models have been successfully developed for volatile and nonvolatile chemicals, demonstrated to perform necessary extrapolations, and proved useful for dose selection and retrospective analysis of experimental data.

Abstract

The process of assessing the risk associated with human exposure to environmental chemicals inevitably relies on a number of assumptions, estimates and rationalizations. One of the more challenging aspects of risk assessment involves the need to extrapolate beyond the range of conditions used in experimental animal studies to predict anticipated human risks. The most obvious extrapolation required is that from the tested animal species to humans; but others are also generally required, including extrapolating from high dose to low dose, from one route of exposure to another and from one exposure timeframe to another. Several avenues are available for attempting these extrapolations, ranging from the assumption of strict correspondence of dose to the use of statistical correlations. One promising alternative for conducting more scientifically sound extrapolations is that of using physiologically based pharmacokinetic models that contain sufficient biological detail to allow pharmacokinetic behavior to be predicted for widely different exposure scenarios. In recent years, successful physiological models have been developed for a variety of volatile and nonvolatile chemicals, and their ability to perform the extrapolations needed in risk assessment has been demonstrated. Techniques for determining the necessary biochemical parameters are readily available, and the computational requirements are now within the scope of even a personal computer. In addition to providing a sound framework for extrapolation, the predictive power of a physiologically based pharmacokinetic model makes it a useful tool for more reliable dose selection before beginning large-scale studies, as well as for the retrospective analysis of experimental results.