Characterizing risk assessments for the development of occupational exposure limits for engineered nanomaterials.

The commercialization of engineered nanomaterials (ENMs) began in the early 2000's. Since then the number of commercial products and the number of workers potentially exposed to ENMs is growing, as is the need to evaluate and manage the potential health risks. Occupational exposure limits (OELs) have been developed for some of the first generation of ENMs. These OELs have been based on risk assessments that progressed from qualitative to quantitative as nanotoxicology data became available. In this paper, that progression is characterized. It traces OEL development through the qualitative approach of general groups of ENMs based primarily on read-across with other materials to quantitative risk assessments for nanoscale particles including titanium dioxide, carbon nanotubes and nanofibers, silver nanoparticles, and cellulose nanocrystals. These represent prototypic approaches to risk assessment and OEL development for ENMs. Such substance-by-substance efforts are not practical given the insufficient data for many ENMs that are currently being used or potentially entering commerce. Consequently, categorical approaches are emerging to group and rank ENMs by hazard and potential health risk. The strengths and limitations of these approaches are described, and future derivations and research needs are discussed. Critical needs in moving forward with understanding the health effects of the numerous EMNs include more standardized and accessible quantitative data on the toxicity and physicochemical properties of ENMs.

Taking stock of the occupational safety and health challenges of nanotechnology: 2000-2015.

Engineered nanomaterials significantly entered commerce at the beginning of the 21st century. Concerns about serious potential health effects of nanomaterials were widespread. Now, approximately 15 years later, it is worthwhile to take stock of research and efforts to protect nanomaterial workers from potential risks of adverse health effects. This article provides and examines timelines for major functional areas (toxicology, metrology, exposure assessment, engineering controls and personal protective equipment, risk assessment, risk management, medical surveillance, and epidemiology) to identify significant contributions to worker safety and health. The occupational safety and health field has responded effectively to identify gaps in knowledge and practice, but further research is warranted and is described. There is now a greater, if imperfect, understanding of the mechanisms underlying nanoparticle toxicology, hazards to workers, and appropriate controls for nanomaterials, but unified analytical standards and exposure characterization methods are still lacking. The development of control-banding and similar strategies has compensated for incomplete data on exposure and risk, but it is unknown how widely such approaches are being adopted. Although the importance of epidemiologic studies and medical surveillance is recognized, implementation has been slowed by logistical issues. Responsible development of nanotechnology requires protection of workers at all stages of the technological life cycle. In each of the functional areas assessed, progress has been made, but more is required.

Occupational safety and health criteria for responsible development of nanotechnology.

Organizations around the world have called for the responsible development of nanotechnology. The goals of this approach are to emphasize the importance of considering and controlling the potential adverse impacts of nanotechnology in order to develop its capabilities and benefits. A primary area of concern is the potential adverse impact on workers, since they are the first people in society who are exposed to the potential hazards of nanotechnology. Occupational safety and health criteria for defining what constitutes responsible development of nanotechnology are needed. This article presents five criterion actions that should be practiced by decision-makers at the business and societal levels-if nanotechnology is to be developed responsibly. These include (1) anticipate, identify, and track potentially hazardous nanomaterials in the workplace; (2) assess workers' exposures to nanomaterials; (3) assess and communicate hazards and risks to workers; (4) manage occupational safety and health risks; and (5) foster the safe development of nanotechnology and realization of its societal and commercial benefits. All these criteria are necessary for responsible development to occur. Since it is early in the commercialization of nanotechnology, there are still many unknowns and concerns about nanomaterials. Therefore, it is prudent to treat them as potentially hazardous until sufficient toxicology, and exposure data are gathered for nanomaterial-specific hazard and risk assessments. In this emergent period, it is necessary to be clear about the extent of uncertainty and the need for prudent actions.

Overview of Risk Management for Engineered Nanomaterials.

Occupational exposure to engineered nanomaterials (ENMs) is considered a new and challenging occurrence. Preliminary information from laboratory studies indicates that workers exposed to some kinds of ENMs could be at risk of adverse health effects. To protect the nanomaterial workforce, a precautionary risk management approach is warranted and given the newness of ENMs and emergence of nanotechnology, a naturalistic view of risk management is useful. Employers have the primary responsibility for providing a safe and healthy workplace. This is achieved by identifying and managing risks which include recognition of hazards, assessing exposures, characterizing actual risk, and implementing measures to control those risks. Following traditional risk management models for nanomaterials is challenging because of uncertainties about the nature of hazards, issues in exposure assessment, questions about appropriate control methods, and lack of occupational exposure limits (OELs) or nano-specific regulations. In the absence of OELs specific for nanomaterials, a precautionary approach has been recommended in many countries. The precautionary approach entails minimizing exposures by using engineering controls and personal protective equipment (PPE). Generally, risk management utilizes the hierarchy of controls. Ideally, risk management for nanomaterials should be part of an enterprise-wide risk management program or system and this should include both risk control and a medical surveillance program that assesses the frequency of adverse effects among groups of workers exposed to nanomaterials. In some cases, the medical surveillance could include medical screening of individual workers to detect early signs of work-related illnesses. All medical surveillance should be used to assess the effectiveness of risk management; however, medical surveillance should be considered as a second line of defense to ensure that implemented risk management practices are effective.

Development of risk-based nanomaterial groups for occupational exposure control.

Given the almost limitless variety of nanomaterials, it will be virtually impossible to assess the possible occupational health hazard of each nanomaterial individually. The development of science-based hazard and risk categories for nanomaterials is needed for decision-making about exposure control practices in the workplace. A possible strategy would be to select representative (benchmark) materials from various mode of action (MOA) classes, evaluate the hazard and develop risk estimates, and then apply a systematic comparison of new nanomaterials with the benchmark materials in the same MOA class. Poorly soluble particles are used here as an example to illustrate quantitative risk assessment methods for possible benchmark particles and occupational exposure control groups, given mode of action and relative toxicity. Linking such benchmark particles to specific exposure control bands would facilitate the translation of health hazard and quantitative risk information to the development of effective exposure control practices in the workplace. A key challenge is obtaining sufficient dose-response data, based on standard testing, to systematically evaluate the nanomaterials' physical-chemical factors influencing their biological activity. Categorization processes involve both science-based analyses and default assumptions in the absence of substance-specific information. Utilizing data and information from related materials may facilitate initial determinations of exposure control systems for nanomaterials.

Mortality among U.S. underground coal miners: a 23-year follow-up.

BACKGROUND: The mortality experience over 22-24 years of 8,899 working coal miners initially medically examined in 1969-1971 at 31 U.S. coal mines was evaluated. METHODS: A cohort life-table analysis was undertaken on underlying causes of death, and proportional hazards models were fitted to both underlying, and underlying and contributing causes of death. RESULTS: Elevated mortality from nonviolent causes, nonmalignant respiratory disease (NMRD), and accidents was observed, but lung cancer and stomach cancer mortality were not elevated. Smoking, pneumoconiosis, coal rank region, and cumulative coal mine dust exposure were all predictors of mortality from nonviolent causes and NMRD. Mortality from nonviolent causes and NMRD was related to dust exposure within the complete cohort and also for the never smoker subgroup. Dust exposure relative risks for mortality were similar for pneumoconiosis, NMRD, and chronic airways obstruction. CONCLUSIONS: The findings confirm and enlarge upon previous results showing that exposure to coal mine dust leads to increased mortality, even in the absence of smoking.

An epidemiological study of the role of chrysotile asbestos fibre dimensions in determining respiratory disease risk in exposed workers.

BACKGROUND: Evidence from toxicological studies indicates that the risk of respiratory diseases varies with asbestos fibre length and width. However, there is a total lack of epidemiological evidence concerning this question. METHODS: Data were obtained from a cohort mortality study of 3072 workers from an asbestos textile plant which was recently updated for vital status through 2001. A previously developed job exposure matrix based on phase contrast microscopy (PCM) was modified to provide fibre size-specific exposure estimates using data from a re-analysis of samples by transmission electron microscopy (TEM). Cox proportional hazards models were fit using alternative exposure metrics for single and multiple combinations of fibre length and diameter. RESULTS: TEM-based cumulative exposure estimates were found to provide stronger predictions of asbestosis and lung cancer mortality than PCM-based estimates. Cumulative exposures based on individual fibre size-specific categories were all found to be highly statistically significant predictors of lung cancer and asbestosis. Both lung cancer and asbestosis were most strongly associated with exposure to thin fibres (<0.25 microm). Longer (>10 microm) fibres were found to be the strongest predictors of lung cancer, but an inconsistent pattern with fibre length was observed for asbestosis. Cumulative exposures were highly correlated across all fibre size categories in this cohort (0.28-0.99, p values <0.001), which complicates the interpretation of the study findings. CONCLUSIONS: Asbestos fibre dimension appears to be an important determinant of respiratory disease risk. Current PCM-based methods may underestimate asbestos exposures to the thinnest fibres, which were the strongest predictor of lung cancer or asbestosis mortality in this study. Additional studies are needed of other asbestos cohorts to further elucidate the role of fibre dimension and type.

Development of a fibre size-specific job-exposure matrix for airborne asbestos fibres.

OBJECTIVE: To develop a method for estimating fibre size-specific exposures to airborne asbestos dust for use in epidemiological investigations of exposure-response relations. METHODS: Archived membrane filter samples collected at a Charleston, South Carolina asbestos textile plant during 1964-8 were analysed by transmission electron microscopy (TEM) to determine the bivariate diameter/length distribution of airborne fibres by plant operation. The protocol used for these analyses was based on the direct transfer method published by the International Standards Organization (ISO), modified to enhance fibre size determinations, especially for long fibres. Procedures to adjust standard phase contrast microscopy (PCM) fibre concentration measures using the TEM data in a job-exposure matrix (JEM) were developed in order to estimate fibre size-specific exposures. RESULTS: A total of 84 airborne dust samples were used to measure diameter and length for over 18,000 fibres or fibre bundles. Consistent with previous studies, a small proportion of airborne fibres were longer than >5 microm in length, but the proportion varied considerably by plant operation (range 6.9% to 20.8%). The bivariate diameter/length distribution of airborne fibres was expressed as the proportion of fibres in 20 size-specific cells and this distribution demonstrated a relatively high degree of variability by plant operation. PCM adjustment factors also varied substantially across plant operations. CONCLUSIONS: These data provide new information concerning the airborne fibre characteristics for a previously studied textile facility. The TEM data demonstrate that the vast majority of airborne fibres inhaled by the workers were shorter than 5 mum in length, and thus not included in the PCM-based fibre counts. The TEM data were used to develop a new fibre size-specific JEM for use in an updated cohort mortality study to investigate the role of fibre dimension in the development of asbestos-related lung diseases.

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.

Pulmonary inflammation and crystalline silica in respirable coal mine dust: dose-response.

This study describes the quantitative relationships between early pulmonary responses and the estimated lung-burden or cumulative exposure of respirable-quartz or coal mine dust. Data from a previous bronchoalveolar lavage (BAL) study in coal miners (n = 20) and nonminers (n = 16) were used including cell counts of alveolar macrophages (AMs) and polymorphonuclear leukocytes (PMNs), and the antioxidant superoxide dismutase (SOD) levels. Miners' individual working lifetime particulate exposures were estimated from work histories and mine air sampling data, and quartz lung-burdens were estimated using a lung dosimetry model. Results show that quartz, as either cumulative exposure or estimated lung-burden, was a highly statistically significant predictor of PMN response (P < 0.0001); however cumulative coal dust exposure did not significantly add to the prediction of PMNs (P = 0.2) above that predicted by cumulative quartz exposure (P < 0.0001). Despite the small study size, radiographic category was also significantly related to increasing levels of both PMNs and quartz lung burden (P-values < 0.04). SOD in BAL fluid rose linearly with quartz lung burden (P < 0.01), but AM count in BAL fluid did not (P > 0.4). This study demonstrates dose-response relationships between respirable crystalline silica in coal mine dust and pulmonary inflammation, antioxidant production, and radiographic small opacities.

Biological and statistical approaches to predicting human lung cancer risk from silica.

Chronic inflammation is a key step in the pathogenesis of particle-elicited fibrosis and lung cancer in rats, and possibly in humans. In this study, we compute the excess risk estimates for lung cancer in humans with occupational exposure to crystalline silica, using both rat and human data, and using both a threshold approach and linear models. From a toxicokinetic/dynamic model fit to lung burden and pulmonary response data from a subchronic inhalation study in rats, we estimated the minimum critical quartz lung burden (Mcrit) associated with reduced pulmonary clearance and increased neutrophilic inflammation. A chronic study in rats was also used to predict the human excess risk of lung cancer at various quartz burdens, including mean Mcrit (0.39 mg/g lung). We used a human kinetic lung model to link the equivalent lung burdens to external exposures in humans. We then computed the excess risk of lung cancer at these external exposures, using data of workers exposed to respirable crystalline silica and using Poisson regression and lifetable analyses. Finally, we compared the lung cancer excess risks estimated from male rat and human data. We found that the rat-based linear model estimates were approximately three times higher than those based on human data (e.g., 2.8% in rats vs. 0.9-1% in humans, at mean Mcrit lung burden or associated mean working lifetime exposure of 0.036 mg/m3). Accounting for variability and uncertainty resulted in 100-1000 times lower estimates of human critical lung burden and airborne exposure. This study illustrates that assumptions about the relevant biological mechanism, animal model, and statistical approach can all influence the magnitude of lung cancer risk estimates in humans exposed to crystalline silica.

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.

A biomathematical model of particle clearance and retention in the lungs of coal miners. II. Evaluation of variability and uncertainty.

The objective of this study is to investigate the sources of variability and uncertainty in a previously developed human lung dosimetry model. That three-compartment model describes the retention and clearance kinetics of respirable particles in the gas-exchange region of the lungs. It was calibrated using exposure histories and lung dust burden data in U.S. coal miners. A multivariate parameter estimation and optimization method was developed for fitting the dosimetry model to these human data. Models with various assumptions about overloading of alveolar clearance and interstitialization (sequestration) of particles were evaluated. Variability in the estimated clearance rate coefficients was assessed empirically by fitting the model to groups' and to each miner's data. Distributions of lung and lymph node particle burdens were computed at working lifetime exposures, using the variability in the estimated individual clearance rate coefficients. These findings confirm those of the earlier analysis; i.e., the best-fitting exposure-dose model to these data has substantial interstitialization/sequestration of particles and no dose-dependent decline in alveolar clearance. Among miners with different characteristics for smoking, disease, and race, the group median estimated alveolar clearance rate coefficients varied by a factor of approximately 4. Adjustment for these group differences provided some improvement in the dosimetry model fit to all miners (up to 25% reduction in MSE), although unexplained interindividual differences made up the largest source of variability. The predicted mean lung and lymph node particle burdens at age 75 after exposure to respirable coal mine dust at 2 mg/m(2) for a 45-year working lifetime were 12 g (5th and 95th percentiles, 3.0-26 g) and 1.9 g (0.26-5.3), respectively. This study provides quantitative information on variability in particle retention and clearance kinetics in humans. It is useful for risk assessment by providing estimated lung dust burdens associated with occupational exposure to respirable particles.

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.

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

Interspecies differences in the kinetics and/or mechanisms of particle retention can influence the amount and location of particle retention in the lungs, which can also influence the tissue response to a given particle burden. Dosimetric models may be used to adjust for differences in the exposure-dose relationships in different species, thus allowing for comparison of lung responses at equivalent doses. Although the rat is one of the most frequently used animal models for assessing the risk of exposures to hazardous substances in humans, few data are available for comparison of human and animal responses to inhaled particles. A biologically-based human dosimetric lung model was developed to describe the fate of respirable particles in the lungs of humans, using data from U.S. coal miners and assumptions about the overloading of alveolar clearance from studies in rats. This model includes alveolar, interstitial, and hilar lymph node compartments. The form of the model that provides the best fit to the lung dust burden data in these coal miners includes a first-order interstitialization process and either no dose-dependent decline in alveolar clearance or much less decline than expected from rodent studies. These findings are consistent with the particle retention patterns observed previously in the lungs of primates. This human lung dosimetry model is useful for investigating the factors that may influence the relationships between the airborne particle exposure, lung dust burden, and fibrotic lung disease.

Evaluation of Particle Clearance and Retention Kinetics in the Lungs of U.S. Coal Miners.

Rodent studies are frequently used to assess risk in humans, yet it is not known whether the overloading of lung clearance, as observed in rodents, occurs in humans, or whether overloading is related to particle-related lung diseases in humans. The objective of this study is to develop a biologically based mathematical model to describe the retention and clearance of respirable coal mine dust in the lungs of humans. A human dosimetric lung model was developed that includes alveolar, interstitial, and hilar lymph-node compartments. The model describes the particle mass transfer kinetics among these compartments and clearance via the tracheobronchi. The model was calibrated using data in U.S. coal miners, including individual working lifetime exposure histories and lung and lymph-node particle burdens. The model fit to the human data was evaluated using a least-squared error criterion. The end-of-life lung dust burdens of all coal miners in this study were substantially greater than expected from a simple, linear first-order model with effective clearance, yet their lung and lymph-node dust burdens were lower than expected from the rodent-based overload model, particularly at higher exposures. The best fitting model included a predominant first-order interstitial compartment, in which the particles are essentially sequestered (with very slow clearance to the lymph nodes), and a first-order alveolar clearance compartment with either no dose-dependent decline (overloading) or much less than expected from the rodent studies. These findings are consistent with the findings from magnetopneumography studies of clearance in retired miners and from studies of particle retention patterns in rodents and primates. This human dosimetric lung model is useful for evaluating the kinetic differences of particle retention in humans and rodents, and for evaluating the lung closes in humans given different exposure scenarios.

Sources of uncertainty in dose-response modeling of epidemiological data for cancer risk assessment.

Epidemiologic data is increasingly being used for dose-response analysis in risk assessment. The Environmental Protection Agency (EPA) and other U.S. agencies have expressed a preference for using epidemiologic data rather than toxicologic data when possible. However, there are a number of important sources of uncertainty in using epidemiologic data for this purpose that need to be clearly recognized and, when possible, quantified. This paper presents a critical review of the major sources of uncertainty in the use of epidemiologic data for cancer risk assessment. These may include: (1) study design issues such as potential confounding and other biases, inadequate sample size, and followup, (2) the choice of the data set, (3) specification of the dose-response model, (4) estimation of exposure and dose, and (5) unrecognized variability in susceptibility. Examples from risk assessments for cadmium, asbestos, and diesel exhaust are used to illustrate the potential magnitude of some of these sources of uncertainty. It is shown that the overall uncertainty from these various sources combined may often result in highly uncertain risk estimates from dose-response modeling of epidemiologic data. For this reason, we believe it is best to present a range of possible risk estimates, which, to the extent possible, reflects the variability and uncertainty inherent in the dose-response evaluation of epidemiologic data.