Evaluating impacts of physiological variability on human equivalent doses using PBPK models
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Physiologically based pharmacokinetic (PBPK) models are regularly used to estimate human exposure levels that result in internal doses equal to those predicted for laboratory animals exposed to substances according to specific experimental dosing regimens, perhaps at doses associated with an adverse health outcome. Using scalar parameter values representing an “average” adult human, one can use a PBPK model to estimate a scalar “human equivalent dose” (HED), which refers to the human concentration (for inhalation exposure) or dose (for oral exposure) of a substance that is expected to induce the same magnitude of toxic effect for a human as that observed for laboratory animals exposed to a specified concentration or dose. However, such scalar values do not address variability among humans or uncertainty in parameter values. The World Health Organization International Programme on Chemical Safety (IPCS) has proposed a chemical hazard characterization approach, APROBA, that seeks to incorporate these and other elements of uncertainty to generate probabilistic reference values for chemicals. A key assumption in the APROBA approach is that various underlying distributions, including distributions of HEDs, are lognormal. We sought to evaluate this assumption by performing simulations using published PBPK models for dichloromethane and chloroform. We investigated how the shapes of HED distributions were impacted when we made different assumptions about the distributions of PBPK model parameters. To account for pharmacokinetic (PK) variability in humans, we used Monte Carlo methods to randomly draw sets of values for the PBPK model parameters based on distributions that describe uncertainty and human variability. We then used reverse dosimetry to obtain samples of HEDs. Using the Royston normality test, we found that while some HED distributions were lognormal, this depended on the distributions chosen to represent parameter variability as well as the applied doses. For higher doses (which generally coincide with higher internal dose metrics), HED distributions were less likely to be lognormal. Also, while lognormal parameter distributions produced mainly lognormal HED distributions, uniform parameter distributions produced dramatically less lognormal results. In the future, our conclusions about HED distributions and the impact of parameter distributions may be generalized by investigating other PBPK models to better characterize uncertainty in reverse dosimetry calculations.