Physiologically based pharmacokinetic modeling of benzene metabolism in mice through extrapolation from in vitro to in vivo

Benzene (C6H6) is a highly flammable, colorless liquid. Ubiquitous exposure s result from its presence in gasoline vapors, cigarette smoke, and industr ial processes. Benzene increases the incidence of leukemia in humans when t hey are exposed to high doses for extended periods; however, leukemia risks in humans at low exposures are uncertain. The exposure-dose-response relat ionship of benzene in humans is expected to be nonlinear because benzene un dergoes a series of metabolic transformations, detoxifying and activating, in the liver, resulting in multiple metabolites that exert toxic effects on the bone marrow. We developed a physiologically based pharmacokinetic mode l for the uptake and elimination of benzene in mice to relate the concentra tion of inhaled and orally administered benzene to the tissue doses of benz ene and its key metabolites, benzene oxide, phenol, and hydroquinone. As ma ny parameter values as possible were taken from the literature; in particul ar, metabolic parameters obtained from in vitro studies with mouse liver we re used since comparable parameters are also available for humans. Paramete rs estimated by fitting the model to published data were first-order rate c onstants for pathways lacking in vitro data and the concentrations of micro somal and cytosolic protein, which effectively alter overall enzyme activit y. The model was constrained by using the in vitro metabolic parameters ( m aximum velocities, first-order rate constants, and saturation parameters), and data from multiple laboratories and experiments were used. Despite thes e constraints and sources of variability, the model simulations matched the data reasonably well in most cases, showing that in vitro metabolic consta nts can be successfully extrapolated to predict in vivo data for benzene me tabolism and dosimetry. Therefore in vitro metabolic constants for humans c an subsequently be extrapolated to predict the dosimetry of benzene and its metabolites in humans. This will allow us to better estimate the risks of adverse effects from low-level benzene exposures.