Comparison of human and rodent lung dosimetry models for particle clearance and retention

Understanding the pathogenesis of occupational coal and silica dust-associated lung disease

Workers in the mining and construction industries are at increased risk of respiratory and other diseases as a result of being exposed to harmful levels of airborne particulate matter (PM) for extended periods of time. While clear links have been established between PM exposure and the development of occupational lung disease, the mechanisms are still poorly understood. A greater understanding of how exposures to different levels and types of PM encountered in mining and construction workplaces affect pathophysiological processes in the airways and lungs and result in different forms of occupational lung disease is urgently required. Such information is needed to inform safe exposure limits and monitoring guidelines for different types of PM and development of biomarkers for earlier disease diagnosis. Suspended particles with a 50% cut-off aerodynamic diameter of 10 µm and 2.5 µm are considered biologically active owing to their ability to bypass the upper respiratory tract's defences and penetrate deep into the lung parenchyma, where they induce potentially irreversible damage, impair lung function and reduce the quality of life. Here we review the current understanding of occupational respiratory diseases, including coal worker pneumoconiosis and silicosis, and how PM exposure may affect pathophysiological responses in the airways and lungs. We also highlight the use of experimental models for better understanding these mechanisms of pathogenesis. We outline the urgency for revised dust control strategies, and the need for evidence-based identification of safe level exposures using clinical and experimental studies to better protect workers' health.

Silicosis exposure-response in a cohort of tin miners comparing alternate exposure metrics

BACKGROUND.: The detailed lung radiographic response to silica exposure has not been described. In estimating the exposure-response relationship in silicosis with statistical models, the absence of baseline (unattributable) risk can disable relative-rate estimation or produce widely varying estimates. This obstructs identification of optimum exposure metrics and invalidates comparisons and meta-analyses, which assume a common background rate. METHODS.: A cohort of 3,000 Chinese tin miners with more than 1,000 cases of silicosis was analyzed for the period 1961-1994. Regular surveillance documented three stages of silicosis. To examine the exposure-response relationship, the intercept in relative-rate models was fixed to correspond to 1% of the observed silicosis rate. Exposure metrics for contributions in different time-windows were simultaneously evaluated, as were burden and cumulative burden metrics. RESULTS.: Silica exposures that most contributed to silicosis onset occurred in the period 5-10 years prior (excess annual rate per 10 mg-year/m(3) , ER = 0.158, 95% CI = 0.125-0.192, or 16% per year). During 10-20 year prior, the excess rate contribution was much smaller (ER = 0.048, 95% CI = 0.037-0.060) but larger again during 20-30 year prior to onset (ER = 0.112, 95% CI = 0.098-0.126). For advanced silicosis, all time periods contributed about equally to the rate of onset. CONCLUSIONS.: Reliable estimates of parameters were observed, demonstrating exposure contributions over time. Burden metrics with different half-lives suggested some reversibility for silicosis onset with a half-life of 20 years. Advanced silicosis was best predicted with a cumulative burden metric which was consistent with prior observations that previously deposited silica continues to cause pulmonary damage.

Application of Markov chain Monte Carlo analysis to biomathematical modeling of respirable dust in US and UK coal miners

A biomathematical model was previously developed to describe the long-term clearance and retention of particles in the lungs of coal miners. The model structure was evaluated and parameters were estimated in two data sets, one from the United States and one from the United Kingdom. The three-compartment model structure consists of deposition of inhaled particles in the alveolar region, competing processes of either clearance from the alveolar region or translocation to the lung interstitial region, and very slow, irreversible sequestration of interstitialized material in the lung-associated lymph nodes. Point estimates of model parameter values were estimated separately for the two data sets. In the current effort, Bayesian population analysis using Markov chain Monte Carlo simulation was used to recalibrate the model while improving assessments of parameter variability and uncertainty. When model parameters were calibrated simultaneously to the two data sets, agreement between the derived parameters for the two groups was very good, and the central tendency values were similar to those derived from the deterministic approach. These findings are relevant to the proposed update of the ICRP human respiratory tract model with revisions to the alveolar-interstitial region based on this long-term particle clearance and retention model.

Estimating the lung burden from exposure to aerosols of depleted uranium

Following exposure to aerosols of depleted uranium (DU), biological samples show the presence of a synthetic mixture of natural uranium and DU. By partitioning the uranium in the 24-h urine sample along the isotopic fractions of natural uranium and DU, one can study the kinetics of these subpopulations independently. A linear model is developed to estimate the lung burden of DU from measurements of DU in 24-h urine samples, years after inhalational exposure to aerosols of DU. This model takes into account the intracellular dissolution of the retained particles and the precipitation of a significant fraction of the dissolved DU as insoluble uranyl phosphates.

Lung Dosimetry and Risk Assessment of Nanoparticles: Evaluating and Extending Current Models in Rats and Humans

Risk assessment of occupational exposure to nanomaterials is needed. Human data are limited, but quantitative data are available from rodent studies. To use these data in risk assessment, a scientifically reasonable approach for extrapolating the rodent data to humans is required. One approach is allometric adjustment for species differences in the relationship between airborne exposure and internal dose. Another approach is lung dosimetry modeling, which provides a biologically-based, mechanistic method to extrapolate doses from animals to humans. However, current mass-based lung dosimetry models may not fully account for differences in the clearance and translocation of nanoparticles. In this article, key steps in quantitative risk assessment are illustrated, using dose-response data in rats chronically exposed to either fine or ultrafine titanium dioxide (TiO2), carbon black (CB), or diesel exhaust particulate (DEP). The rat-based estimates of the working lifetime airborne concentrations associated with 0.1% excess risk of lung cancer are approximately 0.07 to 0.3 mg/m3 for ultrafine TiO2, CB, or DEP, and 0.7 to 1.3 mg/m3 for fine TiO2. Comparison of observed versus model-predicted lung burdens in rats shows that the dosimetry models predict reasonably well the retained mass lung burdens of fine or ultrafine poorly soluble particles in rats exposed by chronic inhalation. Additional model validation is needed for nanoparticles of varying characteristics, as well as extension of these models to include particle translocation to organs beyond the lungs. Such analyses would provide improved prediction of nanoparticle dose for risk assessment.

The genotoxic risk of underground coal miners from Turkey

A cytogenetic monitoring study was carried out on a group of workers from a bituminous coal mine in Zonguldak province of Turkey, to investigate the genotoxic risk of occupational exposure to coal mine dust. Cytogenetic analysis, namely sister chromatid exchanges (SCEs), chromosomal aberrations (CAs) and micronucleus (MN) tests were performed on a strictly selected group of 39 workers and compared to 34 controls matched for gender, age, and habit. Smoking and age were considered as modulating factors. Both SCE and CA frequencies in coal miners appeared significantly higher than in controls. Similarly, there was a significant increase in the frequency of total micronuclei in exposed group as compared to control group. The effect of smoking on the level of SCE and MN was significant in the control group. A positive correlation between the age and the level of SCE was also found in controls. The frequencies of both SCE and CA were significantly enhanced with the years of exposure. The results of this study demonstrated that occupational exposure to coal mine dust leads to a significant induction of cytogenetic damage in peripheral lymphocytes of workers engaged in underground coal mining.

A biomathematical model of particle clearance and retention in the lungs of coal miners

To understand better the factors influencing the relationships among airborne particle exposure, lung burden, and fibrotic lung disease, we developed a biologically based kinetic model to predict the long-term retention of particles in the lungs of coal miners. This model includes alveolar, interstitial, and hilar lymph node compartments. The 131 miners in this study had worked in the Beckley, West Virginia, area and died during the 1960s. The data used to develop this model include exposure to respirable coal mine dust by intensity and duration within each job, lung and lymph node dust burdens at autopsy, pathological classification of fibrotic lung disease, and smoking history. Initial parameter estimates for this model were based on both human and animal data of particle deposition and clearance and on the biological and physical factors influencing these processes. Parameter estimation and model fit to the data were determined using least squares. Results show that the end-of-life lung dust burdens in these coal miners were substantially higher than expected from first-order clearance kinetics, yet lower than expected from the overloading of alveolar clearance predicted from rodent studies. The best-fitting and most parsimonious model includes processes for first-order alveolar-macrophage-mediated clearance and transfer of particles to the lung interstitium. These results are consistent with the particle retention patterns observed previously in the lungs of primates. The findings indicate that rodent models extrapolated to humans, without adjustment for the kinetic differences in particle clearance and retention, would be inadequate for predicting lung dust burdens in humans. Also, this human lung kinetic model predicts greater retained lung dust burdens from occupational exposure than predicted from current human models based on lower exposure data. This model is useful for risk assessment of particle-induced lung diseases, by estimating equivalent internal doses in rodents and humans and predicting lung burdens in humans with occupational dust exposures.

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

INTRODUCTION

The use of human data to calibrate and validate a physiologically based pharmacokinetic (PBPK) model has the clear advantage of pertaining to the species of interest, namely humans. A challenge in using these data is their often sparse, heterogeneous nature, which may require special methods. Approaches for evaluating sources of variability and uncertainty in a human lung dosimetry model are described in this study.

METHODS

A multivariate 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-life particle burdens in the lungs and hilar lymph nodes. Uncertainty in model structure was investigated by fitting various model forms for particle clearance and sequestration of particles in the lung interstitium. A sensitivity analysis was performed to determine which model parameters had the most influence on model output. Distributions of clearance parameters were estimated by fitting the model to each individual's data, and this information was used to predict inter-individual differences in lung particle burdens at given exposures. The influence of smoking history, race and pulmonary fibrosis on the individual's estimated clearance parameters was also evaluated.

RESULTS

The model structure that provided the best fit to these coal miner data includes a first-order interstitialization process and no dose-dependent decline in alveolar clearance. The parameter that had the largest influence on model output is fractional deposition. Race and fibrosis severity category were statistically significant predictors of individual's estimated alveolar clearance rate coefficients (P < 0.03 and P < 0.01-0.06, respectively), but smoking history (ever, never) was not (P < 0.4). Adjustments for 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. Lung burdens were inversely associated with the miners' estimated clearance parameters, e.g. individuals with slower estimated clearance had higher observed lung burdens.

CONCLUSIONS

The methods described in this study were used to examine issues of uncertainty in the model structure and variability of the miners' estimated clearance parameters. Estimated individual clearance had a large influence on predicted lung burden, which would also affect disease risk. These findings are useful for risk assessment, by providing estimates of the distribution of lung burdens expected under given exposure conditions.