Methodological issues of using observational human data in lung dosimetry models for particulates

Introduction: The use of human data to calibrate and validate a physiologic ally based pharmacokinetic (PBPK) model has the clear advantage of pertaini ng to the species of interest, namely humans. A challenge in using these da ta is their often sparse, heterogeneous nature, which may require special m ethods. Approaches for evaluating sources of variability and uncertainty in a human lung dosimetry model are described in this study. Methods: A multi variate optimization procedure was used to fit a dosimetry model to data of 131 U.S. coal miners. These data include workplace exposures and end-of-li fe particle burdens in the lungs and hilar lymph nodes. Uncertainty in mode l structure was investigated by fitting Various model forms for particle cl earance and sequestration of particles in the lung interstitium. A sensitiv ity analysis was performed to determine which model parameters had the most influence on model output. Distributions of clearance parameters were esti mated by fitting the model to each individual's data, and this information was used to predict inter-individual differences in lung particle burdens a t given exposures, The influence of smoking history, race and pulmonary fib rosis on the individual's estimated clearance parameters was also evaluated , Results: The model structure that provided the best fit to these coal min er data includes a first-order interstitialization process and no dose-depe ndent decline in alveolar clearance. The parameter that had the largest inf luence on model output is fractional deposition, Race and fibrosis severity category were statistically significant predictors of individual's estimat ed alveolar clearance rate coefficients (P < 0.03 and P < 0.01-0.06, respec tively), but smoking history (ever, never) was not (P < 0.4). Adjustments f or these group differences provided some improvement in the dosimetry model fit (up to 25% reduction in the mean squared error), although unexplained inter-individual differences made up the largest source of variability. Lun g burdens were inversely associated with the miners' estimated clearance pa rameters, e.g, individuals with slower estimated clearance had higher obser ved lung burdens. Conclusions: The methods described in this study were use d to examine issues of uncertainty in the model structure and variability o f the miners' estimated clearance parameters. Estimated individual clearanc e had a large influence on predicted lung burden, which would also affect d isease risk. These findings are useful for risk assessment, by providing es timates of the distribution of lung burdens expected under given exposure c onditions. (C) 2001 Elsevier Science B.V. All rights reserved.