Physiologically Based Pharmacokinetic Modeling of Benzene in Human: A
Bayesian Approach
(Karen Yokley, NCSU)
Abstract:
Benzene is myelotoxic and causes leukemia in humans after extended
periods of high exposure; however, leukemia risks in humans at low
exposures are uncertain. Benzene occurs in the work environment and in
outdoor air, but mostly at concentrations below 1 ppm. It is therefore
important to assess the risk of benzene exposure to humans at low
concentrations. In this paper, we developed a physiologically based
pharmacokinetic (PBPK) model for the uptake and elimination of benzene
in humans to relate the concentration of inhaled benzene to the tissue
doses of benzene and its key metabolites. To apply the mathematical
model to data in humans, the mathematical model must be integrated
into a statistical framework that acknowledges the sources of
variation in the data due to inherent intra- and inter-individual
variation, measurement error, and other data collection issues. The
main contribution of this work is the estimation of population
distribution of key PBPK model parameters using a fully parametric
method. In particular, we employ a Markov Chain Monte Carlo (MCMC)
technique to fit the mathematical model to three sets of data
producing samples from the posterior distributions of the parameters,
from which inference on parameters may be carried out. We hypothesized
that variability in metabolic parameters observed in earlier studies
would be sufficient to explain observed variability in benzene
pharmacokinetics. The resultant simulations captured some but not all
of the observed variability, indicating that one must also account for
variability in physiological parameters, such as organ weights, to
faithfully predict the full human population variability. (This
research was supported by the American Chemistry Council and CIIT.)
This is an abstract of a poster to be presented at the
2004 SEAMS Workshop in Charleston, SC. For more information, visit
the workshop's homepage at math.cofc.edu/SEAMS.