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A Biomathematical Model of Particle Clearance and Retention in the Lungs of Coal Miners

A Biomathematical Model of Particle Clearance and Retention in the Lungs of Coal Miners
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  Regulatory Toxicology and Pharmacology  34, 69–87 (2001)doi:10.1006/rtph.2001.1479, available online at on A Biomathematical Model of Particle Clearance and Retentionin the Lungs of Coal Miners I. Model Development Eileen D. Kuempel, ∗ ,1 Ellen J. O’Flaherty, 1 Leslie T. Stayner, ∗ Randall J. Smith, ∗ Francis H. Y. Green, †  and Val Vallyathan † ∗ Education and Information Division, Risk Evaluation Branch, National Institute for Occupational Safety and Health, Cincinnati,Ohio 45226-1998;   † Department of Pathology, University of Calgary, Calgary, Alberta, Canada; and   ‡ Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown,West Virginia  Received September 15, 2000 Tounderstandbetterthefactorsinfluencingtherela-tionshipsamongairborneparticleexposure,lungbur-den, and fibrotic lung disease, we developed a bio-logicallybasedkineticmodel topredictthelong-termretentionofparticlesinthelungsofcoal miners.Thismodel includes alveolar, interstitial, and hilar lymphnodecompartments.The131minersin thisstudyhadworked in the Beckley, West Virginia, area and diedduringthe1960s.Thedatausedtodevelopthismodelinclude exposure to respirable coal mine dust by in-tensityandduration within each job,lungandlymphnodedust burdensat autopsy,pathological classifica-tion offibrotic lungdisease,andsmokinghistory.Ini-tial parameter estimates for this model were basedon both human and animal data of particle deposi-tion and clearance and on the biological and physi-cal factorsinfluencingtheseprocesses.Parameter es-timation and model fit to the data were determinedusing least squares. Results show that the end-of-lifelungdust burdensin thesecoal minersweresub-stantiallyhigher thanexpectedfromfirst-order clear-ance kinetics, yet lower than expected from theoverloading of alveolar clearance predicted from ro-dent studies. The best-fitting and most parsimoniousmodel includes processes for first-order alveolar-macrophage-mediated clearance and transfer of par-ticles to the lung interstitium. These results are con-sistent with the particle retention patterns observedpreviouslyinthelungsofprimates.Thefindingsindi-catethatrodentmodelsextrapolatedtohumans,with-out adjustment for thekinetic differencesin particleclearanceandretention,wouldbeinadequatefor pre-dicting lung dust burdens in humans. Also, this hu-manlungkineticmodelpredictsgreaterretainedlungdust burdens from occupational exposure than pre- 1 Affiliation at time of this study: College of Medicine, Departmentof Environmental Health, University of Cincinnati, Cincinnati, OH. dictedfromcurrenthumanmodelsbasedonlower ex-posuredata.Thismodelisusefulforriskassessmentof particle-induced lungdiseases,by estimatingequiva-lentinternaldosesinrodentsandhumansandpredict-ing lung burdens in humans with occupational dustexposures.  C  2001AcademicPress INTRODUCTION Abiomathematical dosimetry model describes the re-lationship between the external exposure and the inter-nal dose. In this study, the model describes the relation-ship between the respirable particles in the air a workerbreathes and the retained mass of particles in the lungsand lung-associated (hilar) lymph nodes. Internal doseestimates may better predict disease development thanexternal exposure estimates because of the interac-tion between the contaminant and the target tissue.If the relationship between external exposure and in-ternal dose is not proportional (as in capacity-limitedmetabolism or clearance), then exposure would be apoor surrogate for internal dose. Rodent studies sug-gest that particle clearance from the lungs can becomeimpaired due to overloading of alveolar-macrophage-mediated clearance. If overloading occurs in humans,then workers’ external exposures to particles may pro-vide a poor estimate of their internal dose (i.e., lung dustburden). Alternatively, if the kinetic processes influenc-ing particle disposition in the lungs of humans and ro-dents are not related by a standard scaling factor (e.g.,allometric), then the extrapolation of a rodent dosime-try model to humans may provide a poor estimate ofthe internal dose in humans at a given exposure. A hu-man lung dosimetry model can be used to improve doseestimation in either of these situations. It can be useddirectly (without interspecies extrapolation) to predict 69 0273-2300/01 $35.00Copyright  C   2001 by Academic PressAll rights of reproduction in any form reserved.  70  KUEMPEL ET AL. human doses associated with given exposures. It alsoprovides a biological and empirical basis for determin-ing equivalent doses across species, which should im-prove the accuracy of estimating dose and disease riskin humans.Many biomathematical exposure–dose models forparticles have been developed using data from exper-imental studies in rodents (e.g., Thomas, 1972; Vincent et al. , 1987; Strom  et al. , 1988; Yu  et al. , 1991; St ¨ober et al. , 1989; Bellman  et al. , 1994; Katsnelson  et al. , 1994;Tran  et al. , 1997). Some have also been extrapolated tohumans (Smith, 1985; Yu  et al. , 1991; Yu, 1996). Humanmodels have been developed by the ICRP (1994) andNCRP (1997) to describe particle deposition, clearance,and retention in the entire human respiratory tract.Those models are based largely on experimental studiesin human using radiolabeled tracer particles. A portionof these models describe particle deposition and clear-ance in the gas-exchange region of the lungs, which isthe site of interest in this study. The ICRP model in-cludes three first-order clearance compartments in thealveolar/interstitial (AI) region; a fixed proportion ofrespirable particles depositing in each compartment isassigned (30, 60, and 10%for AI 1 , AI 2 , and AI 3 ), and thecorresponding first-order clearance rate coefficients are0.02, 0.001, and 0.0001 day − 1 , respectively. The three AIcompartments do not directly reflect lung anatomy, butare based on measurements of the activity remaining inthe lungs up to 1 year after inhalation of low-solubility,radioactive particles. The NCRP model describes thepulmonary region of the lungs as a single compartment(first-order clearance rate coefficient 0.006 day − 1 in thefirst 200 days and 0.001 day − 1 thereafter). Both mod-els include terms for particle transport to the tracheo-bronchial region and translocation to the lymph nodecompartments. In the ICRP model, lymph node trans-fer occurs only from AI 3  (first-order clearance rate co-efficient 0.00002 day − 1 ). Faster lymph node transfer isassumed in the NCRP model (first-order clearance ratecoefficient 0.0001 day − 1 ).The human lung model developed in this study fo-cuses on the gas-exchange region of the lungs, whererespirable particles (aerodynamic diameter  < 10  µ m)can deposit. This model includes three compartments,including alveolar, interstitial, and lung-associated(hilar) lymph nodes (Fig. 1). It describes the kinetics ofmass transfer of particles among these compartments.A principal difference in this model compared to theICRP and NCRP models is that it treats the alveolarand interstitial regions of the lung as separate compart-ments, which reflects both the biological structure of thelungs and the disposition of particles in these regions.The biological events that occur in these compartmentsare as follows.When particles deposit in the alveolar region, the pri-mary mechanism for particle clearance is phagocyto- FIG. 1.  Three-compartment human lung dosimetry model. D,dose rate of deposited particles; first-order rate coefficients includealveolar-macrophage-mediated clearance of particles to the tracheo-bronchial region ( K  T ), transfer of particles into the pulmonary inter-stitium ( K  I ), and translocation of particles to the hilar lymph nodes( K  LN );  F   is an exponential decay function that describes overload-ing as a dose-dependent decline in  K  T ; and  M   is the particle massin a given lung region, including that cleared to the tracheobronchialregion ( M  T ), or retained in the alveolar ( M  A ), interstitial ( M  I ), gas-exchange ( M  LU ), or hilar lymph node ( M  LN ) regions. sis by alveolar macrophages. The macrophages trans-port the particles to the tracheobronchi, where theyare removed from the lungs by mucociliary clearance(Bohning and Lippmann, 1992). Particles may becleared from the tracheobronchi by cough, or theymay be swallowed and enter the gastrointestinal tract.Phagocytosed particles may also remain in the alveolarregion, causing lysis of macrophages and contributingto pulmonary inflammation (Newman, 1992). Particlesthat escape alveolar-macrophage-mediated clearancecan penetrate the epithelial cell barrier (depending onsize) into the interstitium of the lungs, where they maybe very slowly cleared to the lung-associated lymph ves-sels, which drain into the hilar lymph nodes (Leak,1977). Particle transfer from the interstitium to thelymph nodes occurs by engulfment of particles into pul-monary lymphatic endothelial cells, where they enterthe lymphatic vessels as free particles, or by phagocy-tosis and transfer by interstitial macrophages (Leak,1977). Particles may also be transported to the lymphnodes by alveolar macrophages (Newman, 1992). Thedosimetric model developed here describes the masstransfer of particles among the lung and lymph node  PARTICLE LUNG KINETICS IN COAL MINERS, I  71compartments. Material that is not cleared from thelungs is retained and represents the particle burden atany given time, which is a measure of internal dose.This model was used to evaluate the biological pro-cesses that may influence particle clearance and re-tention in the lungs of humans. The direct use ofhuman data, when available, avoids the uncertaintyassociated with interspecies extrapolation. It enablescomparison with animal models, which are more com-monly used in risk assessment. Rodent studies haveshown that the chronic inhalation of various types of in-soluble, respirable particles can lead to the impairment,or overloading, of lung clearance (Le Bouffant, 1971;Bolton  et al. , 1983; Wolff  et al. , 1987; Strom  et al. , 1988;Bellmann  et al. , 1991; Muhle  et al. , 1998). In rats withoverloading doses, there is increased penetration of par-ticles through the epithelium into the interstitium, aswell as increased transfer of particles to the lymphnodes (Muhle  et al. , 1990). Pathological responses asso-ciated with overloaded lung burdens in rats include per-sistent inflammation, fibrosis, and lung tumors (Muhle et al. , 1991; Heinrich  et al. , 1995). It is not knownwhether overloading of lung clearance occurs in hu-mans and thus whether the findings from animal stud-ies are predictive of exposure–dose relationships inhumans. It is also not known whether human diseaseresponses to respirable, insoluble particles are associ-ated with overloading.Another process that can result in higher lung dustburdens than expected from first-order clearance is se-questration, the retention of some portion of dust thatis unavailable for clearance (Soderholm, 1981). Seques-tration may occur as a first-order process at any ex-posure (Vincent  et al. , 1987; J ones  et al. , 1988b) or as aconsequence of the overloading of alveolar-macrophage-mediated clearance (Tran  et al. , 1997; St ¨ober  et al. ,1989). Thus, lung dust burdens that exceed the steady-state burden expected with simple first-order clearancecould be due to sequestration, overloading, or a com-bination of both processes. In this human dosimetriclung model, the importance of these kinetic processeswas explored and compared to the findings from the ratstudies. The hypotheses investigated in this study arein humans with long-term occupational exposures torespirable, insoluble particles, the end-of-life lung dustburdens are best described by a model with (1) rodent-based overloading of alveolar clearance, (2) sequestra-tion of particles in the interstitium, or (3)a combinationof both processes. METHODS Data Description  The srcinal data set is based on approximately 600former coal miners who were autopsied at BeckleyAppalachian Regional Hospital in Beckley, WestVirginia, between 1959 and 1973. These cases werecollected systematically from consecutive autopsies bythe late Werner Laqueur, M.D. A subgroup of 430 min-ers was analyzed previously, and results were reportedon agreement of fibrosis determinations from radio-graphic and pathologic examination (Attfield  et al. ,1994; Vallyathan  et al. , 1996). The miners included inthis study are from the subgroup of 141 miners withlung dust burden data. These miners had died during1962 to 1968, and lung tissue samples were collectedand analyzed sequentially. Whole lung serial sectionswere used for pathologic evaluation of the disease typeand severity of pneumoconioses, emphysema, and lungcancer (Vallyathan  et al. , 1996). The percentage (g/100g dry lung tissue)and composition (coal, noncoal, silica,and total) of dust in the lungs were determined in lab-oratory analyses of the lung tissue (Crable  et al. , 1967,1968; Carlberg  et al. , 1971; Sweet  et al. , 1974), describedbelow. For 58 of these miners, data on the percentageand composition of dust in the hilar lymph nodes werealso available.A gravimetric method was used to determine the con-centrations of coal, noncoal, and total dust (Crable  et al. ,1967, 1968). Digestion and washing procedures wereused to remove the lung tissue, leaving the particu-late matter as a residue. This residue was then driedat 110 ◦ C to a constant weight, which represented thetotal mass of dust in the tissue sample, and then ashedat 380 ◦ C. The amount of coal dust was determined asthe mass lost during ashing, while the noncoal mass wasthat portion remaining after ashing. The silica contentin the noncoal fraction was determined by X-ray diffrac-tion or spectrophotometric methods (Carlberg  et al. ,1971; Sweet  et al. , 1974) (silica data are available for110 of the 131 miners). The resulting dust burdens wereexpressed as mass concentration (g dust/100 g dry tis-sue). For the dosimetric modeling, the dust burden inthe whole lung was needed. Thus, to compute this fromthe mass concentration data available, estimates of thelung weights were needed. The whole lung wet weightswere available, but this information was not used be-cause those values are greatly influenced by the causeof death and the time between death and autopsy. Ide-ally, one would want the whole lung dry weights, butthese data were not available. Therefore, the standardreference value of 1000 g for the lung wet weight wasassumed, and the dry lung tissue weight was assumedto the standard be 20%of the wet tissue weight (ICRP,1975). Some height and weight data were available (forabout half of the miners), but this information was notused to adjust lung weight because it was incompleteand because these factors were considered to have lessinfluence on lung weight than fibrosis, which fills inlung air spaces and is more dense than normal lung tis-sue. To determine the possible influence of using a stan-dard lung weight, an analysis was done later to com-pare the fit of the dosimetry model among miners with  72  KUEMPEL ET AL. severe fibrosis (progressive massive fibrosis or PMF) tothe model fit among the other miners (see Results). Thedata available on the lymph node dust burdens werealso expressed as mass concentration (mg/g dry tissue).A standard reference value of 15 g was assumed forthe wet weight of the hilar lymph nodes (ICRP, 1975).This standard value was used for miners with normal,unenlarged nodes, but most of the coal miners in thisstudy had recorded enlargement of hilar lymph nodes.For those miners, the lymph nodes were assumed tobe five times the normal size, an estimate based onobservations during the pathological examinations ofthese coal miners by two coauthors (F.H.Y.G. and V.V.).No published data were found on the dry-to-wet tissueweights for hilar lymph nodes; however, a value of 33%dry tissue weight was assumed based on unpublisheddata from pathological examinations of coal miners bycoauthors F.H.Y.G. and V.V. For the dosimetry model-ing, the mass concentration values for lung and lymphnodes were converted to total mass of particles (mg) inthe whole organ, and results are reported as total par-ticle mass in grams (g).Occupational histories had been obtained previouslyfrom a standardized questionnaire sent to the next-of-kin soon after the miner’s death, from clinical records,and from coal mine company records (Vallyathan  et al. ,1996). These data include date of first employment inmining, job titles and dates worked, dates unemployedand/or employed in nonmining jobs, date of retirement,and total number of years in mining. Working lifetimeexposure to respirable coal mine dust was estimated foreach miner using the individual’s work history data andjob-specific estimates of the mean airborne dust con-centration. These measurements of the airborne dustconcentration were taken during a sampling surveyby the former U.S. Bureau of Mines (BOM) during1968 and 1969 in 29 underground coal mines through-out the United States (J acobson, 1971). Approximately4300 gravimetric samples of airborne respirable dustwere collected. These BOM data include samples forat least 10 shifts for certain jobs (e.g., coal face jobs),but fewer or no samples for other underground or sur-face jobs. For those jobs, the mean concentrations es-timates were based on samples collected from 1970 to1972 by mine operators as part of a mandated samplingprogram administered by the Mine Safety and HealthAdministration (MSHA) (Attfield and Morring, 1992).The mass median aerodynamic diameter (MMAD) ofthe respirable coal mine dust is approximately 5  µ m(SD 2 . 1  µ m) (J ones  et al. , 1988a; Burkhart  et al. , 1987).The particle size distributions in U.S. mines were shownto not vary significantly across jobs or mining meth-ods, although mine-to-mine differences were observed(Seixas  et al. , 1995). These findings suggest that forminers with the same estimated airborne exposure, thefractional deposition of particles in their lungs wouldbe similar, even if they worked in different types of jobsor with different mining methods (which have changedover time). Assuming miners worked in various minesthroughout their careers, the mine-to-mine differencescould contribute to variability to the exposure–dose re-lationships.The minimum data required for inclusion of a minerin this study were lung dust burden, duration of em-ployment in mining, at least one mining job title (to as-sign intensity of exposure), and pathological grading offibrosis. Of the 141 miners with lung dust burden data,131 had sufficient data for inclusion in this study. Of the131 miners, 58 also had hilar lymph node dust burdendata. Additional data required for the dosimetricmodel-ing were the dates and/or ages at retirement and death(needed to compute the postexposure duration). Threeof the 131 miners did not have this additional data (1 ofwhom did have hilar lymph node data) and were omit-ted from the model calibration. Thus, 128 and 57 minerswere used in the modeling. When the calibrated modelwas used for prediction of lung and lymph node burdens,those 3 miners were included by assuming they had thepostexposure duration equal to the mean values for thewhole group. Model Development  As part of the early model development, a one-compartment model was constructed because it is thesimplest model for describing particle retention in thelungs (Kuempel, 2000). In that model clearance wasdescribed by a single rate coefficient, with alveolarmacrophage-mediated clearance and translocation tothe lymph nodes combined. Model forms with eitherlinear or dose-dependent clearance were evaluated.After determining that the one-compartment modelwas inadequate to describe the particle clearance andretention in these coal miners, we developed a three-compartment lung model (Fig. 1). This model includestwo lung compartments (alveolar and interstitial) anda lung-associated lymph node compartment. The mainprinciples guiding the development of this model werebiological plausibility and parsimony. Three compart-ments is the minimum number compatible with whatis known about the mechanisms of clearance and re-tention of inhaled respirable particles. A description ofthe model parameters is provided in Table 1. The inputfor this dosimetric model is the individual miners’ workhistory data (including intensity and duration of expo-sure in each job and duration not employed in miningor retired). The model output is predicted particle massburden in the lungs and lymph nodes as a function oftime.The model was developed using the softwareAdvanced Continuous Simulation Language (ACSL,1995). The mass balance (input minus output)was eval-uated and determined to be acceptable (i.e., virtuallyzero)before proceeding with model calibration, in which  PARTICLE LUNG KINETICS IN COAL MINERS, I  73 TABLE 1DescriptionofVariablesandConstantsinThree-CompartmentHumanLungDosimetryModel Abbreviation Units Description F  D  None Fractional deposition of airbornerespirable dust in alveolar region V  I  m 3 /day Volume of air inhaled in an 8-h day,heavy work d   Days/year Days exposed/year C  I  mg/m 3 Mean concentration of respirable coalmine dust inhaled, by job a  D   Years Duration of exposure, by job a  K  T  Day − 1 Rate coefficient, alveolar-macrophage-mediated clearance to tracheobronchi K  I  Day − 1 Rate coefficient, transfer from alveolito interstitium K  LN  Day − 1 Rate coefficient, translocation frominterstitium to hilar lymph nodes F   None Exponential decay function, does-dependent reduction in  K  T B   None Slope modifier of  F C   None Shape modifier of  F M  min  mg Minimum lung dust burden associatedwith beginning of dose-dependentdecline in  K  T M  max  mg Maximum lung dust burden associatedwith leveling off of dose-dependentdecline in  K  T a  Individual work history data for each miner (input data). the optimum parameter values were determined. Themodel was calibrated using the coal miner data de-scribed above. These data were initially divided intotwogroups, one group for developing the model (two-thirdsof the data,  n  = 87) and the other for testing it (one-third of the data,  n  = 44), using stratified random dataallocation (RANUNI function, SAS, 1996). The stratawere based on smoking status and cumulative expo-sure, both of which were expected  a priori   to influenceparticle clearance and retention. Following model cali-bration, the model was tested using the reserved data.Then, all the data were combined ( n  = 131) to generatemodel predictions of lung and lymph node dust burdensand to do residuals analysis of the model fit to the data.A subgroup of miners ( n  = 11) whose post-exposureduration was equal to zero (because they died while em-ployed as coal miners) was evaluated separately. Eval-uating the model fit to these miners’ data provides in-formation on the buildup of dust in the lungs withoutthe additional unknown of clearance during the postex-posure (retirement) period. The model fits and parame-ter values for these miners were compared to those forminers who had both exposure and postexposure expe-rience. Model Equations and Description  The equations for the three-compartment humanlung model are provided below. The model describes thekinetics of particle mass transfer in the lungs. Mathe-matically, it consists of a series of nonlinear differentialequations that are integrated over time to predict in-dividuals’ lung and lymph node particle burdens. Thesources and values of the initial parameter values aredescribed in the section “Model-Fitting and ParameterEstimation.”The rate of change of particle mass in the alveoli ( M  A )at any time ( t  ) is defined as dM  A / dt   =  R  D  −  R  T −  R  I , (1)where  R  D  is the deposition rate (mg/year) of inhaled,respirable particles into the alveoli (described in Eq. 2). R  T  is the clearance rate (mg/year) of particles from thealveoli to the tracheobronchi (Eq. 3).  R  I  is the trans-fer rate (mg/year) of particles from the alveoli into theinterstitium (Eq. 5). R  D  =  F  D  × C  I  × V  I  × d  , (2)where  F  D  is the fractional deposition (fraction of the in-haled particle mass that is deposited in the alveolar re-gion of the lungs,  C  I  is the airborne concentration of dustinhaled (mg/m 3 ),  V  I  is the volume of air inhaled in an 8-h workday (m 3 /day), and  d   is the days worked per year(days/year)(estimated as 5days/week × 50weeks/year). R  T =  K  T × 365 × F   × M  A , (3)where  K  T  is the first-order rate coefficient (day − 1 )for particle clearance from the alveoli to thetracheobronchi.  M  A  is defined in Eq. (1), 365 is the daysper year to convert  R  T  to units of year − 1 , and  F   is de-fined in Eq. (4). F   = 1 when  M  A  ≤  M  min  (4a) F   = exp {− B  [( M  A − M  min ) / ( M  max  − M  min )] C } when  M  A  >  M  min .  (4b) F   is a dose-dependent modifying factor of  K  T .  M  min  and M  max  are constants representing the human-equivalentminimum and maximum critical lung dust burdens atwhich the dose-dependent decline in the alveolar clear-ance rate coefficient begins and reaches a maximum,respectively, as predicted from rodent studies (see nextsection);  M  A  is defined in Eq. (1). When  M  A  ≤  M  min ,  F  is set equal to 1; when  M  A  >  M  min ,  F   equals a value(between 0 and 1) that is determined by  B  .  C   is a shapeparameter (set to 1 in this model). R  I  =  K  I  × 365 × M  A , (5)where  K  I  is the first-order rate coefficient (day − 1 )for transfer of particles from the interstitium to the
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