Incorporation of Inter- and Intra-Individual Variability in a Chloroform PBPK Model with Inhalation and Dermal Absorption
Y.C. Yang, M. Ouyang, X. Xu, S.W. Wang, P.G. Georgopoulos (EOHSI, UMDNJ - R.W. Johnson Medical School and Rutgers University)
Physiologically based pharmacokinetic (PBPK) modeling offers a rational basis for the extrapolation of toxicokinetic data from acute, high dose experiments in animals, to chronic, low dose exposures in humans. A general drawback of PBPK modeling is that it requires the estimation of extensive sets of parameters. Physiological, anatomical and physicochemical parameters are often typical values, or mean values available in the literature, and are assumed fixed for model development and application. PBPK models are often also optimized by adjusting certain parameters to experimental data while “fixing” others, many of which are not known with accuracy in vitro. This approach does not incorporate physiological and biochemical uncertainties and the presence of inter- and intra-individual variability.
The present study aims to incorporate estimates of this variability in the formulation of a PBPK model for chloroform. There are many datasets available that describe variability of physiological and anatomical attributes within the general population. Age and gender dependent mathematical regression models are typically used for population exposure modeling. However, there is no simple way to describe variability in biochemical processes, due to the lack of appropriate data. Bayesian methods can be applied to partially overcome limited availability of biochemical parameters.
In this case study, 2 sets of time-series of exhaled breath measurements were used to assess inhaled and dermal exposures from the use of chlorinated drinking water. A chloroform PBPK model with distributed parameter descriptions of dermal transport was used to incorporate inter- and intra-individual variability by developing posterior distributions of metabolism-related parameters, using a Markov Chain Monte Carlo (MCMC) method with these new data sets. Age and gender dependent deterministic equations and subject-specific information were used to calculate physiological parameters.
This work had been funded in part by the US Environmental Protection Agency under Cooperative Agreement # EPAR-827033 to Environmental and Occupational Health Sciences Institute (EOHSI). The viewpoints expressed in this work are solely the responsibility of the authors and do not necessarily reflect the views of the US Environmental Protection Agency or its contractors.