formalized under Bayesian inference and implemented with the Markov chain Monte Carlo approach used by Bois (2000a,b). Description of uncertainties in prior simulation might indicate that the approach is not practical without collecting additional data.
Fisher and others have incorporated developmental exposure in utero and via lactation in their PBPK models for perchlorate (Clewell et al. 2003a,b; Fisher et al. 2000); this approach could be applied to trichloroethylene to investigate dose metrics relevant to developmental effects of trichloroethylene exposure. See Chapter 9 for additional guidance on producing developmental PBPK models.
None of the PBPK models for trichloroethylene describes the effect of exposure to chemical mixtures that include trichloroethylene. For example, ethanol and trichloroethylene share enzymatic pathways of metabolism.
A combined PBPK model for trichloroethylene and ethanol would enable investigation of exposure to this mixture. This approach could be used for other mixtures with shared metabolic pathways or common metabolites. A similar approach could be taken to include the effect of disease states on trichloroethylene disposition (e.g., induction of CYP2E1 in diabetes).
In summary, pharmacokinetic models can be useful tools to identify data gaps and research needs to reduce uncertainty in risk assessment. More data and a better understanding of the mode of action for various end points are needed for a revised trichloroethylene pharmacokinetic model, in conjunction with appropriate pharmacodynamic models, to be useful for further understanding the risks posed by trichloroethylene.