Evaluation of occupational exposure: comparison of biological and environmental variabilities using physiologically based toxicokinetic modeling.

PURPOSE: Few studies compare the variabilities that characterize environmental (EM) and biological monitoring (BM) data. Indeed, comparing their respective variabilities can help to identify the best strategy for evaluating occupational exposure. The objective of this study is to quantify the biological variability associated with 18 bio-indicators currently used in work environments. METHOD: Intra-individual (BV(intra)), inter-individual (BV(inter)), and total biological variability (BV(total)) were quantified using validated physiologically based toxicokinetic (PBTK) models coupled with Monte Carlo simulations. Two environmental exposure profiles with different levels of variability were considered (GSD of 1.5 and 2.0). RESULTS: PBTK models coupled with Monte Carlo simulations were successfully used to predict the biological variability of biological exposure indicators. The predicted values follow a lognormal distribution, characterized by GSD ranging from 1.1 to 2.3. Our results show that there is a link between biological variability and the half-life of bio-indicators, since BV(intra) and BV(total) both decrease as the biological indicator half-lives increase. BV(intra) is always lower than the variability in the air concentrations. On an individual basis, this means that the variability associated with the measurement of biological indicators is always lower than the variability characterizing airborne levels of contaminants. For a group of workers, BM is less variable than EM for bio-indicators with half-lives longer than 10-15 h. CONCLUSION: The variability data obtained in the present study can be useful in the development of BM strategies for exposure assessment and can be used to calculate the number of samples required for guiding industrial hygienists or medical doctors in decision-making.

Impact of biological and environmental variabilities on biological monitoring--an approach using toxicokinetic models.

Biological monitoring of occupational exposure is characterized by important variability, due both to variability in the environment and to biological differences between workers. A quantitative description and understanding of this variability is important for a dependable application of biological monitoring. This work describes this variability, using a toxicokinetic model, for a large range of chemicals for which reference biological reference values exist. A toxicokinetic compartmental model describing both the parent compound and its metabolites was used. For each chemical, compartments were given physiological meaning. Models were elaborated based on physiological, physicochemical, and biochemical data when available, and on half-lives and central compartment concentrations when not available. Fourteen chemicals were studied (arsenic, cadmium, carbon monoxide, chromium, cobalt, ethylbenzene, ethyleneglycol monomethylether, fluorides, lead, mercury, methyl isobutyl ketone, penthachlorophenol, phenol, and toluene), representing 20 biological indicators. Occupational exposures were simulated using Monte Carlo techniques with realistic distributions of both individual physiological parameters and exposure conditions. Resulting biological indicator levels were then analyzed to identify the contribution of environmental and biological variability to total variability. Comparison of predicted biological indicator levels with biological exposure limits showed a high correlation with the model for 19 out of 20 indicators. Variability associated with changes in exposure levels (GSD of 1.5 and 2.0) is shown to be mainly influenced by the kinetics of the biological indicator. Thus, with regard to variability, we can conclude that, for the 14 chemicals modeled, biological monitoring would be preferable to air monitoring. For short half-lives (less than 7 hr), this is very similar to the environmental variability. However, for longer half-lives, estimated variability decreased.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Comparison of indices proposed as criteria for assigning skin notation.

OBJECTIVES: Skin notations are used as a hazard identification tool to flag chemicals associated with a potential risk related to transdermal penetration. The transparency and rigorousness of the skin notation assignment process have recently been questioned. We compared different approaches proposed as criteria for these notations as a starting point for improving and systematizing current practice. METHODS: In this study, skin notations, dermal acute lethal dose 50 in mammals (LD(50)s) and two dermal risk indices derived from previously published work were compared using the lists of Swiss maximum allowable concentrations (MACs) and threshold limit values (TLVs) from the American Conference of Governmental Industrial Hygienists (ACGIH). The indices were both based on quantitative structure-activity relationship (QSAR) estimation of transdermal fluxes. One index compared the cumulative dose received through skin given specific exposure surface and duration to that received through lungs following inhalation 8 h at the MAC or TLV. The other index estimated the blood level increase caused by adding skin exposure to the inhalation route at kinetic steady state. Dermal-to-other route ratios of LD(50) were calculated as secondary indices of dermal penetrability. RESULTS: The working data set included 364 substances. Depending on the subdataset, agreement between the Swiss and ACGIH skin notations varied between 82 and 87%. Chemicals with a skin notation were more likely to have higher dermal risk indices and lower dermal LD(50) than chemicals without a notation (probabilities between 60 and 70%). The risk indices, based on cumulative dose and kinetic steady state, respectively, appeared proportional up to a constant independent of chemical-specific properties. They agreed well with dermal LD(50)s (Spearman correlation coefficients -0.42 to -0.43). Dermal-to-other routes LD(50) ratios were moderately associated with QSAR-based transdermal fluxes (Spearman correlation coefficients -0.2 to -0.3). CONCLUSIONS: The plausible but variable relationship between current skin notations and the different approaches tested confirm the need to improve current skin notations. QSAR-based risk indices and dermal toxicity data might be successfully integrated in a systematic alternative to current skin notations for detecting chemicals associated with potential dermal risk in the workplace.

Effects of bioaerosol exposure on work-related symptoms among Swiss sawmill workers.

OBJECTIVE: Exposure to bioaerosols in the occupational environment of sawmills could be associated with a wide range of health effects, in particular respiratory impairment, allergy and organic dust toxic syndrome. The objective of the study was to assess the frequency of medical respiratory and general symptoms and their relation to bioaerosol exposure. METHOD: Twelve sawmills in the French part of Switzerland were investigated and the relationship between levels of bioaerosols (wood dust, airborne bacteria, airborne fungi and endotoxins), medical symptoms and impaired lung function was explored. A health questionnaire was distributed to 111 sawmill workers. RESULTS: The concentration of airborne fungi exceeded the limit recommended by the Swiss National Insurance (SUVA) in the twelve sawmills. This elevated fungi level significantly influenced the occurrence of bronchial syndrome (defined by cough and expectorations). No other health effects (irritations or respiratory effects) could be associated to the measured exposures. We observed that junior workers showed significantly more irritation syndrome (defined by itching/running nose, snoring and itching/red eyes) than senior workers. Lung function tests were not influenced by bioaerosol levels nor dust exposure levels. CONCLUSION: Results suggest that occupational exposure to wood dust in a Swiss sawmill does not promote a clinically relevant decline in lung function. However, the occurrence of bronchial syndrome is strongly influenced by airborne fungi levels.

A biologically based dynamic model for predicting the disposition of methanol and its metabolites in animals and humans.

A multicompartment biologically based dynamic model was developed to describe the time evolution of methanol and its metabolites in the whole body and in accessible biological matrices of rats, monkeys, and humans following different exposure scenarios. The dynamic of intercompartment exchanges was described mathematically by a mass balance differential equation system. The model's conceptual and functional representation was the same for rats, monkeys, and humans, but relevant published data specific to the species of interest served to determine the critical parameters of the kinetics. Simulations provided a close approximation to kinetic data available in the published literature. The average pulmonary absorption fraction of methanol was estimated to be 0.60 in rats, 0.69 in monkeys, and 0.58-0.82 in human volunteers. The corresponding average elimination half-life of absorbed methanol through metabolism to formaldehyde was estimated to be 1.3, 0.7-3.2, and 1.7 h. Saturation of methanol metabolism appeared to occur at a lower exposure in rats than in monkeys and humans. Also, the main species difference in the kinetics was attributed to a metabolism rate constant of whole body formaldehyde to formate estimated to be twice as high in rats as in monkeys. Inversely, in monkeys and in humans, a larger fraction of body burden of formaldehyde is rapidly transferred to a long-term component. The latter represents the formaldehyde that (directly or after oxidation to formate) binds to various endogenous molecules or is taken up by the tetrahydrofolic-acid-dependent one-carbon pathway to become the building block of synthetic pathways. This model can be used to quantitatively relate methanol or its metabolites in biological matrices to the absorbed dose and tissue burden at any point in time in rats, monkeys, and humans for different exposures, thus reducing uncertainties in the dose-response relationship, and animal-to-human and exposure scenario comparisons. The model, adapted to kinetic data in human volunteers exposed acutely to methanol vapors, predicts that 8-h inhalation exposures ranging from 500 to 2000 ppm, without physical activities, are needed to increase concentrations of blood formate and urinary formic acid above mean background values reported by various authors (4.9-10.3 and 6.3-13 mg/liter, respectively). This leaves blood and urinary methanol concentrations as the most sensitive biomarkers of absorbed methanol.

Biological monitoring of workers exposed to ethylbenzene and co-exposed to xylene.

OBJECTIVE: Ethylbenzene is an important constituent of widely used solvent mixtures in industry. The objective of the present study was to provide information about biological monitoring of occupational exposure to ethylbenzene, and to review the biological limit values corresponding to the threshold limit value of ethylbenzene. METHODS: A total of 20 male workers who had been exposed to a mixture of ethylbenzene and xylene, through painting and solvent mixing with commercial xylene in a metal industry, were recruited into this study. Environmental and biological monitoring were performed during an entire week. The urinary metabolites monitored were mandelic acid for ethylbenzene and methylhippuric acid for xylene. Correlations were analyzed between urinary metabolites and environmental exposure for ethylbenzene and xylene. The interaction effects of a binary exposure to ethylbenzene and xylene were also investigated using a physiologically based pharmacokinetic (PBPK) model. RESULTS: The average environmental concentration of organic solvents was 12.77 ppm for xylene, and 3.42 ppm for ethylbenzene. A significant correlation (R2 = 0.503) was found between environmental xylene and urinary methylhippuric acid. Urinary level of methylhippuric acid corresponding to 100 ppm of xylene was 1.96 g/g creatinine in the worker study, whereas it was calculated as 1.55 g/g creatinine by the PBPK model. Urinary level of mandelic acid corresponding to 100 ppm of ethylbenzene was found to be 0.7 g/g creatinine. PBPK results showed that the metabolism of ethylbenzene was highly depressed by co-exposure to high concentrations of xylene leading to a non-linear behavior. CONCLUSIONS: At low exposures, both methylhippuric acid and mandelic acid can be used as indicators of commercial xylene exposures. However at higher concentrations mandelic acid cannot be recommended as a biological indicator due to the saturation of mandelic acid produced by the co-exposure to xylene.

[Risk management in the hospital milieu: needs and implications]. / La gestion des risques en milieu hospitalier: exigences et implications.

Medicine can be dangerous for the patients, the caregivers, the visitors and the environment. Technological progress provides devices and drugs that are always more powerful, more efficacious, but at the same time able to lead to severe side effects. This paper describes the system set up in a university hospital to fulfill legal requirements. Specialists in specific fields build up commissions, which are united in a coordination office. A general policy for the hospital has been decided, but each commission is responsible for managing the risks in its field. The overall philosophy moved from a quality assurance to a quality management system, in which the employee involved in an incident or an accident is no longer considered the only culprit except in cases of obvious violation of established procedures. In order to be efficient, the system must be as simple as possible, and well known, so that collaborators gain confidence in it. Once this cultural revolution is accomplished, quality but also security of the procedures will be improved. Its impact on cost is more questionable, as the system generates running costs which might be higher than the savings it might bring.

Toxicokinetic modelling of methyl formate exposure and implications for biological monitoring.

A toxicokinetic (TK) model was developed to describe the inhalation exposure in humans to methyl formate (MF), a catalyst used in foundries, and to discuss biological monitoring. The TK model consisted of four compartments: MF, the metabolites--methanol (MeOH) and formic acid (FA)--and, in addition, a urinary compartment describing the saturable reabsorption of FA. Levels of MeOH and FA in urine, from an experimental study (100 ppm MF, 8 h at rest), validated the present model. The TK model describes well the general behaviour of MeOH and FA in urine after MF exposure. A nonlinear and a linear relationship respectively, was predicted between MF exposure and FA or MeOH excretion in urine, and this has previously been seen after occupational MF exposure. The present model has been modified to simulate MeOH exposure as well. Generally low exposures (concentration or exercise) produce only marginal increases in FA urinary excretions, but when exposure is elevated, urinary FA excretion increases because of saturation in the mechanism of reabsorption. Using FA urinary excretion as the critical indicator, because of its link to health effects, an occupational exposure limit value for MF of no greater than 50 ppm should be selected (based on predictions with the TK model). MeOH in urine can be considered as a biomarker for MF at low exposure, because of lower background values and of a linear relationship with exposure. At higher exposures, however, FA could be used as a biomarker as it becomes progressively more sensitive. But the use of biological monitoring for MF is difficult because of individual variations in background values. Under the present state of knowledge both FA and MeOH should be used to estimate only group exposures, rather than individual exposures.

Propylene glycol monomethyl ether (PGME) occupational exposure. 1. Biomonitoring by analysis of PGME in urine.

An analytical method was developed for the determination of free and conjugated PGME-alpha in urine. The method involves a solid-phase extraction on LC-18 columns and a GC/FID analysis after derivatization with trimethysilylimidazole. The assay was linear (least-squares regression coefficient 0.996), specific, reproducible (intraassay variability 10%, interassay variability 10%), and allowed a high level of PGME recovery (more than 90%). The assay was applied to the analysis of urine samples from three workers who were occupationally exposed to PGME to estimate their exposure. The highest value of PGME concentration in urine was 7.78 mg/l. Air concentrations of PGME ranged between 20 and 40 ppm. A statistically significant correlation was found between measurements of external exposure and PGME in urine. An important fraction of PGME in urine was found to be conjugated.

Propylene glycol monomethyl ether (PGME) exposure. 2. Identification of products containing PGME, their importance and their use in Switzerland.

OBJECTIVE: In order to identify users of PGME and potential exposures, a chemical registration database maintained in Switzerland was analysed. METHOD: The database contains information on the composition of products (qualitative and quantitative), the field of use, the year of registration and the domain of commercial applications (public or professional). RESULTS: Identification of potential exposures in Switzerland was carried out. Out of a total of 150,000 products, 2334 were found to contain PGME and most contained between 1% and 10% PGME. There was a great increase in the number of products declared between 1983 and 1991. The principal fields of use were in inks, varnishes and paints.

Uncertainties in physiologically based pharmacokinetic models caused by several input parameters.

OBJECTIVE: One of the problems in the application of physiologically based pharmacokinetic (PB-PK) models is that authors often use different input parameters, with unknown influence on the results. Differences in the simulation results obtained with various sets of parameters are examined herein. METHOD: Chemicals considered were perchloroethylene, toluene, and styrene. Simulations of alveolar concentrations, blood concentrations, and urinary metabolite excretions were performed for the three solvents. The input parameters discussed herein are physiological values, metabolic constants, and partition coefficients. The influence of metabolic constants and partition coefficients is studied by comparison of models against one another. RESULTS: Metabolic parameters such as Vmax and K(m) varied considerably between authors. Tissue-gas partition coefficients, especially for the fat compartment, also differed according to the authors. Such differences in input parameter values proved to have a large influence on PB-PK model results and, therefore, increased their uncertainties. Uncertainties were much more significant in urinary metabolite concentration than in alveolar and blood concentration for chemicals that are poorly metabolized. On the other hand, uncertainties were more significant in alveolar and blood concentrations than in urinary metabolite excretions for chemicals that are well metabolized. CONCLUSION: Careful attention is necessary in the selection and/or citation of values from published data. The validity of PB-PK models should be simultaneously confirmed with both the blood and/or alveolar concentration and urinary metabolite concentrations.

Biological monitoring of tetrahydrofuran: contribution of a physiologically based pharmacokinetic model.

A seven-compartment physiologically based pharmacokinetic (PBPK) model was developed to predict biological levels of tetrahydrofuran under various exposure scenarios. Affinities for the tissue were estimated from measurements of liquid-gas partition coefficients for water, olive oil, and blood. Metabolism was assumed to follow a rapid first order reaction. urinary excretion was simulated considering passive reabsorption of tetrahydrofuran in the tubules. The validity of the model was tested by comparison with available experimental and field data. Agreement was satisfactory with all studies available except one, which showed much higher results than expected. The source of this difference could not be identified, but cannot be explained by different exposure conditions, such as duration, concentration, or physical work load. However, it is recommended that this particular study not be used in the establishment of a biological exposure index. Simulation of repeated occupational exposure with the PBPK model allowed the prediction of biological levels that would be reached after repeated exposure at the American Conference of Governmental Industrial Hygienists' threshold limit value, time-weighted average of 200 ppm. For samples taken at the end of the shift, the PBPK model predicts 5.1 ppm for breath, 57 mumol/L (4.1 mg/L) for venous blood, and 100 mumol/L (7.2 mg/L) for urine.

Assessment of occupational exposure to diesel fumes--parameter optimization of the thermal coulometric measurement method for carbon.

'Elemental' carbon (EC) is used as a surrogate to assess occupational exposure to diesel soot. EC thermal analysis needs complete desorption of organic compounds from the soot particles prior to analysis in order to minimize positive interferences and artefacts. The desorption of the organic compounds can be considered as the major step which influences the reliability of the EC determination. A systematic study was carried out to investigate the different parameters of influence such as desorption temperature, desorption duration, heating rate and type of the sample on the desorption efficiency. It was found that temperature and duration are the major parameters of influence on the desorption efficiency. The influence of the sample load can be seen as a measure of the pyrolysis susceptibility of the sample. An optimized temperature program is proposed.

Ethnic differences in biological monitoring of several organic solvents. I. Human exposure experiment.

OBJECTIVES: In order to improve the reliability of biological monitoring and the development of biological limit values, ethnic differences for several organic solvents were studied in Orientals and Caucasians. METHODS: Six Caucasian and six Oriental volunteers were exposed to each organic solvent in an exposure chamber for 6 h. Exposure concentration to each organic solvent studied was 50 ppm for perchloroethylene, 50 ppm for styrene and 100 ppm for m-xylene, respectively. Biological monitoring was carried out for the parent organic solvents in exhaled air and in blood, and for the metabolites in urine during and after exposure. RESULTS: Caucasians showed higher concentrations of perchloroethylene in exhaled air than Orientals after exposure. But Caucasians showed lower concentrations of styrene in the exhaled air than Orientals during the second half of exposure and after it. Orientals showed lower concentrations of urinary metabolites than Caucasians except for mandelic acid. There were no statistically significant differences in the concentrations of solvent in blood for all three solvents. CONCLUSIONS: Implications of these differences in biological levels, under identical exposure conditions, are discussed in the context of biological monitoring.

Ethnic differences in biological monitoring of several organic solvents. II. A simulation study with a physiologically based pharmacokinetic model.

To improve the reliability of biological monitoring and the development of biological limit values, ethnic differences in the biological monitoring of several organic solvents were studied in Orientals and Caucasians. Six Caucasian and six Oriental volunteers were exposed to each organic solvent in an exposure chamber for 6 h at rest. The exposure concentrations were 50 ppm for perchloroethylene, 50 ppm for styrene, and 100 ppm for m-xylene, respectively. Experimental results were compared with simulation results of a physiologically based pharmacokinetic (PB-PK) model. Differences between Orientals and Caucasians under occupational exposure were also estimated by extrapolation. The simulation results obtained for the Caucasian group showed good agreement with the experimental results. However, the Oriental group did not show good agreement when the same metabolic parameters values applied to Caucasians were used in the PB-PK model. By modification of the metabolic parameters it was possible to get a good fit between the model and the results of the Oriental group. The simulation results obtained for occupational exposure also showed differences in biological levels between the two ethnic groups. Implications of these differences between experimental and simulation results are discussed in the context of the application of biological monitoring and in the development of biological limit values.

A physiologically based pharmacokinetic model for arsenic exposure. II. Validation and application in humans.

A physiologically based pharmacokinetic model (PB-PK) for inorganic arsenic exposure in humans has been developed. This model is an extension of a PB-PK model for hamsters and rabbits, with adjustments for body weight, metabolic rates, and absorption rates. It describes the absorption, distribution, metabolism, and excretion of arsenate, arsenite (As(III)), methyl arsonate, and dimethyl arsinate, the four major metabolites of inorganic arsenic. The routes of intake considered are inhalation of arsenic dust and fumes and oral intake of arsenic via drinking water and food. The PB-PK model for the oral exposure route is validated using data on urinary excretion after repeated oral exposure to As(III) as well as after exposure to inorganic As via drinking water. Absorption by inhalation is validated using data on urinary excretion after occupational exposure to arsenic trioxide dust and fumes. In both cases, the model gives satisfactory results for urinary excretion of the four As metabolites. The PB-PK model is also used in the description of the effects on the kinetics of exposure via different routes and for the simulation of various realistic exposure scenarios.

A physiologically based pharmacokinetic model for arsenic exposure. I. Development in hamsters and rabbits.

A physiologically based pharmacokinetic model for exposure to inorganic arsenic in hamsters and rabbits has been developed. The model in its present state simulates three routes of exposure to inorganic arsenic: oral intake, intravenous injection, and intratracheal instillation. It describes the tissue concentrations and the urinary and fecal excretions of the four arsenic metabolites: inorganic As(III) and As(V), methylarsonic acid, and dimethylarsinic acid. The model consists of five tissue compartments, chosen according to arsenic affinities: liver, kidneys, lungs, skin, and others. The model is based on physiological parameters, which were scaled according to body weight. When physiological parameters were not available, the data for the model were obtained by fitting (tissue affinity, absorption rate, and metabolic rate constants). The excretions of the arsenic metabolites in urine and feces are well simulated with the model for both species. Further validation of the arsenic metabolite concentrations in the tissues and in vitro measurements of the tissue affinity constants are discussed.

Pharmacokinetic modeling as a tool for biological monitoring.

The relationships between biological indicators and exposure or tissue burdens are determined by the pharmacokinetic behaviour of the chemical. They can be studied by pharmacokinetic models of various types. Simple pharmacokinetic models are used here to describe general relationships valid for large groups of chemicals or situations. Important parameters to consider are the half-life of the biological indicator, the individual variability and the exposure variability. Biological sampling strategies are presented for monitoring of groups of workers, or individual workers. For specific chemicals, mainly solvents, more elaborate models can be developed, i.e., physiologically-based pharmacokinetic models including physiological, metabolic and physicochemical parameters. Such models are useful to describe the influence of confounding factors. Physiologically-based pharmacokinetic models can also be developed for metals and metalloids. Antimony is presented here as an example. In conclusion, pharmacokinetic modeling brings much information on sampling time, sample size, limit values, effect of physical workload and of individual physiological parameters.