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

Abstract

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.

Translational toxicology in setting occupational exposure limits for dusts and hazard classification – a critical evaluation of a recent approach to translate dust overload findings from rats to humans

“…A best fit was achieved when the alveolar clearance rate was set invariant, i.e., the two independent studies present convincing evidence that even under the historically-high dust exposure scenarios of coalminers, no lung overload occurred in humans [ 33 , 76 ]. This result and the related Gregoratto model [ 32 ] were confirmed once more in a more recent study using both data sets in a Bayesian analysis via Markov Chain Monte Carlo simulations [ 77 ].…”

Advances in Inhalation Dosimetry Models and Methods for Occupational Risk Assessment and Exposure Limit Derivation

“…( 67 ) In evaluations of several human lung clearance models, a higher-order clearance model that includes sequestration-interstitialization of particles has been shown to best predict the long-term particle retention in humans. ( 65 , 66 , 68 – 70 ) The rat-based overload model (i.e., first-order clearance at low exposure and dose-dependent decline in clearance at high exposure) underpredicted human lung burden at low exposure and overpredicted the lung burden at high exposures. ( 64 , 68 ) Because of the faster normal clearance in rats, only at doses that overload lung clearance does the rat achieve lung burdens that are comparable to those observed in workers in dusty jobs (e.g., coal miners).…”

Risk Analysis Implications of Dose-Response Thresholds for NLRP3 Inflammasome-Mediated Diseases: Respirable Crystalline Silica and Lung Cancer as an Example

“…Comparing averaged individual values to model predictions can reduce the effects of this unmodeled estimation error in individual exposures. The finding that “On average, the model predictions were within a factor of just less than 2 of the experimentally measured amounts of dust in lungs” 67 then suggests that a factor of about 2 may account for most interindividual variability in the PBPK model converting concentrations in air to accumulation of silica in lungs (or, perhaps, in AMs). If so, then since the base model in Table 1 predicts a nominal steady-state AM RCS load in mg equal to about 1115 times the inhaled concentration of RCS in mg/m 3 , different individuals might have loads from about 1115/2 = 557 to 1115.1 × 2 = 2230 times the air concentration of RCS.…”

“…This widens the range of RCS concentration thresholds that might trigger acute inflammation. Based on a Bayesian uncertainty and variability analysis that compared respirable dust loads found in lungs of autopsied coal miners in the United States and the United Kingdom to amounts predicted using a 3-compartment PBPK model similar to the one in Table 1, Sweeney et al 67 concluded thatOn average, the model predictions were within a factor of just less than 2 of the experimentally measured amounts of dust in lungs and lymph nodes. Almost all of the predictions for [individual] miners were within 10-fold of the measured values.This analysis ignored exposure estimation errors and uncertainties by treating individual exposure concentration estimates as if they were known to be accurate measurements of true exposure concentrations.…”

“…This widens the range of RCS concentration thresholds that might trigger acute inflammation. Based on a Bayesian uncertainty and variability analysis that compared respirable dust loads found in lungs of autopsied coal miners in the United States and the United Kingdom to amounts predicted using a 3-compartment PBPK model similar to the one in Table 1, Sweeney et al 67…”