Bayesian Refinement of the Permeability-Limited Physiologically Based Pharmacokinetic Model for Perfluorooctanoic Acid in Male Rats

Physiologically
based pharmacokinetic (PBPK) modeling is a powerful
technique to inform risk assessment of xenobiotic substances such
as perfluorooctanoic acid (PFOA). In our previous study, a permeability-limited
PBPK model was developed to simulate the toxicokinetics and tissue
distribution of PFOA in male rats. However, due to limited information
on some key model parameters (e.g., protein binding
and active transport rates), the uncertainty of the permeability-limited
PBPK model was quite high. To address this issue, a hierarchical Bayesian
analysis with Markov chain Monte Carlo (MCMC) was applied to reduce
the uncertainty of parameters and improve the performance of the PBPK
model. With the optimized posterior parameters, the PBPK model was
evaluated by comparing its prediction with experimental data from
three different studies. The results show that the uncertainties of
the posterior model parameters were reduced substantially. In addition,
most of the PBPK model predictions were improved: with the posterior
parameters, most of the predicted plasma toxicokinetics (e.g., half-life) and tissue distribution fell well within a factor of
2.0 of the experimental data. Finally, the Bayesian framework could
provide insights into the molecular mechanisms driving PFOA toxicokinetics:
PFOA-protein binding, membrane permeability, and active transport.