Sensitivity analysis, dominant factors, and robustness of the ECETOC TRA v3, Stoffenmanager 4.5, and ART 1.5 occupational exposure models

Occupational exposure modeling is widely used in the context of the E.U. regulation on the registration, evaluation, authorization, and restriction of chemicals (REACH). First tier tools, such as European Centre for Ecotoxicology and TOxicology of Chemicals (ECETOC) targeted risk assessment (TRA) or Stoffenmanager, are used to screen a wide range of substances. Those of concern are investigated further using second tier tools, e.g., Advanced REACH Tool (ART). Local sensitivity analysis (SA) methods are used here to determine dominant factors for three models commonly used within the REACH framework: ECETOC TRA v3, Stoffenmanager 4.5, and ART 1.5. Based on the results of the SA, the robustness of the models is assessed. For ECETOC, the process category (PROC) is the most important factor. A failure to identify the correct PROC has severe consequences for the exposure estimate. Stoffenmanager is the most balanced model and decision making uncertainties in one modifying factor are less severe in Stoffenmanager. ART requires a careful evaluation of the decisions in the source compartment since it constitutes ∼75% of the total exposure range, which corresponds to an exposure estimate of 20-22 orders of magnitude. Our results indicate that there is a trade off between accuracy and precision of the models. Previous studies suggested that ART may lead to more accurate results in well-documented exposure situations. However, the choice of the adequate model should ultimately be determined by the quality of the available exposure data: if the practitioner is uncertain concerning two or more decisions in the entry parameters, Stoffenmanager may be more robust than ART.

Occupational respiratory exposures to irritating and sensitizing volatile compounds deriving from cleaning products

Recent findings suggest an association between exposure to cleaning products and respiratory dysfunctions including asthma. However, little information is available about quantitative airborne exposures of professional cleaners to volatile organic compounds deriving from cleaning products. During the first phases of the study, a systematic review of cleaning products was performed. Safety data sheets were reviewed to assess the most frequently added volatile organic compounds. It was found that professional cleaning products are complex mixtures of different components (compounds in cleaning products: 3.5 ± 2.8), and more than 130 chemical substances listed in the safety data sheets were identified in 105 products. The main groups of chemicals were fragrances, glycol ethers, surfactants, solvents; and to a lesser extent phosphates, salts, detergents, pH-stabilizers, acids, and bases. Up to 75% of products contained irritant (Xi), 64% harmful (Xn) and 28% corrosive (C) labeled substances. Hazards for eyes (59%), skin (50%) and by ingestion (60%) were the most reported. Monoethanolamine, a strong irritant and known to be involved in sensitizing mechanisms as well as allergic reactions, is frequently added to cleaning products. Monoethanolamine determination in air has traditionally been difficult and air sampling and analysis methods available were little adapted for personal occupational air concentration assessments. A convenient method was developed with air sampling on impregnated glass fiber filters followed by one step desorption, gas chromatography and nitrogen phosphorous selective detection. An exposure assessment was conducted in the cleaning sector, to determine airborne concentrations of monoethanolamine, glycol ethers, and benzyl alcohol during different cleaning tasks performed by professional cleaning workers in different companies, and to determine background air concentrations of formaldehyde, a known indoor air contaminant. The occupational exposure study was carried out in 12 cleaning companies, and personal air samples were collected for monoethanolamine (n=68), glycol ethers (n=79), benzyl alcohol (n=15) and formaldehyde (n=45). All but ethylene glycol mono-n-butyl ether air concentrations measured were far below (<1/10) of the Swiss eight hours occupational exposure limits, except for butoxypropanol and benzyl alcohol, where no occupational exposure limits were available. Although only detected once, ethylene glycol mono-n-butyl ether air concentrations (n=4) were high (49.5 mg/m3 to 58.7 mg/m3), hovering at the Swiss occupational exposure limit (49 mg/m3). Background air concentrations showed no presence of monoethanolamine, while the glycol ethers were often present, and formaldehyde was universally detected. Exposures were influenced by the amount of monoethanolamine in the cleaning product, cross ventilation and spraying. The collected data was used to test an already existing exposure modeling tool during the last phases of the study. The exposure estimation of the so called Bayesian tool converged with the measured range of exposure the more air concentrations of measured exposure were added. This was best described by an inverse 2nd order equation. The results suggest that the Bayesian tool is not adapted to predict low exposures. The Bayesian tool should be tested also with other datasets describing higher exposures. Low exposures to different chemical sensitizers and irritants should be further investigated to better understand the development of respiratory disorders in cleaning workers. Prevention measures should especially focus on incorrect use of cleaning products, to avoid high air concentrations at the exposure limits. - De récentes études montrent l'existence d'un lien entre l'exposition aux produits de nettoyages et les maladies respiratoires telles que l'asthme. En revanche, encore peu d'informations sont disponibles concernant la quantité d'exposition des professionnels du secteur du nettoyage aux composants organiques volatiles provenant des produits qu'ils utilisent. Pendant la première phase de cette étude, un recueil systématique des produits professionnels utilisés dans le secteur du nettoyage a été effectué. Les fiches de données de sécurité de ces produits ont ensuite été analysées, afin de répertorier les composés organiques volatiles les plus souvent utilisés. Il a été mis en évidence que les produits de nettoyage professionnels sont des mélanges complexes de composants chimiques (composants chimiques dans les produits de nettoyage : 3.5 ± 2.8). Ainsi, plus de 130 substances listées dans les fiches de données de sécurité ont été retrouvées dans les 105 produits répertoriés. Les principales classes de substances chimiques identifiées étaient les parfums, les éthers de glycol, les agents de surface et les solvants; dans une moindre mesure, les phosphates, les sels, les détergents, les régulateurs de pH, les acides et les bases ont été identifiés. Plus de 75% des produits répertoriés contenaient des substances décrites comme irritantes (Xi), 64% nuisibles (Xn) et 28% corrosives (C). Les risques pour les yeux (59%), la peau (50%) et par ingestion (60%) était les plus mentionnés. La monoéthanolamine, un fort irritant connu pour être impliqué dans les mécanismes de sensibilisation tels que les réactions allergiques, est fréquemment ajouté aux produits de nettoyage. L'analyse de la monoéthanolamine dans l'air a été habituellement difficile et les échantillons d'air ainsi que les méthodes d'analyse déjà disponibles étaient peu adaptées à l'évaluation de la concentration individuelle d'air aux postes de travail. Une nouvelle méthode plus efficace a donc été développée en captant les échantillons d'air sur des filtres de fibre de verre imprégnés, suivi par une étape de désorption, puis une Chromatographie des gaz et enfin une détection sélective des composants d'azote. Une évaluation de l'exposition des professionnels a été réalisée dans le secteur du nettoyage afin de déterminer la concentration atmosphérique en monoéthanolamine, en éthers de glycol et en alcool benzylique au cours des différentes tâches de nettoyage effectuées par les professionnels du nettoyage dans différentes entreprises, ainsi que pour déterminer les concentrations atmosphériques de fond en formaldéhyde, un polluant de l'air intérieur bien connu. L'étude de l'exposition professionnelle a été effectuée dans 12 compagnies de nettoyage et les échantillons d'air individuels ont été collectés pour l'éthanolamine (n=68), les éthers de glycol (n=79), l'alcool benzylique (n=15) et le formaldéhyde (n=45). Toutes les substances mesurées dans l'air, excepté le 2-butoxyéthanol, étaient en-dessous (<1/10) de la valeur moyenne d'exposition aux postes de travail en Suisse (8 heures), excepté pour le butoxypropanol et l'alcool benzylique, pour lesquels aucune valeur limite d'exposition n'était disponible. Bien que détecté qu'une seule fois, les concentrations d'air de 2-butoxyéthanol (n=4) étaient élevées (49,5 mg/m3 à 58,7 mg/m3), se situant au-dessus de la frontière des valeurs limites d'exposition aux postes de travail en Suisse (49 mg/m3). Les concentrations d'air de fond n'ont montré aucune présence de monoéthanolamine, alors que les éthers de glycol étaient souvent présents et les formaldéhydes quasiment toujours détectés. L'exposition des professionnels a été influencée par la quantité de monoéthanolamine présente dans les produits de nettoyage utilisés, par la ventilation extérieure et par l'emploie de sprays. Durant la dernière phase de l'étude, les informations collectées ont été utilisées pour tester un outil de modélisation de l'exposition déjà existant, l'outil de Bayesian. L'estimation de l'exposition de cet outil convergeait avec l'exposition mesurée. Cela a été le mieux décrit par une équation du second degré inversée. Les résultats suggèrent que l'outil de Bayesian n'est pas adapté pour mettre en évidence les taux d'expositions faibles. Cet outil devrait également être testé avec d'autres ensembles de données décrivant des taux d'expositions plus élevés. L'exposition répétée à des substances chimiques ayant des propriétés irritatives et sensibilisantes devrait être investiguée d'avantage, afin de mieux comprendre l'apparition de maladies respiratoires chez les professionnels du nettoyage. Des mesures de prévention devraient tout particulièrement être orientées sur l'utilisation correcte des produits de nettoyage, afin d'éviter les concentrations d'air élevées se situant à la valeur limite d'exposition acceptée.

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.

Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

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.

Prediction of Infant Drug Exposure Through Breastfeeding: Population PK Modeling and Simulation of Fluoxetine Exposure.

The likelihood of significant exposure to drugs in infants through breast milk is poorly defined, given the difficulties of conducting pharmacokinetics (PK) studies. Using fluoxetine (FX) as an example, we conducted a proof-of-principle study applying population PK (popPK) modeling and simulation to estimate drug exposure in infants through breast milk. We simulated data for 1,000 mother-infant pairs, assuming conservatively that the FX clearance in an infant is 20% of the allometrically adjusted value in adults. The model-generated estimate of the milk-to-plasma ratio for FX (mean: 0.59) was consistent with those reported in other studies. The median infant-to-mother ratio of FX steady-state plasma concentrations predicted by the simulation was 8.5%. Although the disposition of the active metabolite, norfluoxetine, could not be modeled, popPK-informed simulation may be valid for other drugs, particularly those without active metabolites, thereby providing a practical alternative to conventional PK studies for exposure risk assessment in this population.

Using toxicokinetic modeling approach to determine biological reference values (BRVs) and to assess human exposure to pesticides

Exposure to various pesticides has been characterized in workers and the general population, but interpretation and assessment of biomonitoring data from a health risk perspective remains an issue. For workers, a Biological Exposure Index (BEI®) has been proposed for some substances, but most BEIs are based on urinary biomarker concentrations at Threshold Limit Value - Time Weighted Average (TLV-TWA) airborne exposure while occupational exposure can potentially occurs through multiple routes, particularly by skin contact (i.e.captan, chlorpyrifos, malathion). Similarly, several biomonitoring studies have been conducted to assess environmental exposure to pesticides in different populations, but dose estimates or health risks related to these environmental exposures (mainly through the diet), were rarely characterized. Recently, biological reference values (BRVs) in the form of urinary pesticide metabolites have been proposed for both occupationally exposed workers and children. These BRVs were established using toxicokinetic models developed for each substance, and correspond to safe levels of absorption in humans, regardless of the exposure scenario. The purpose of this chapter is to present a review of a toxicokinetic modeling approach used to determine biological reference values. These are then used to facilitate health risk assessments and decision-making on occupational and environmental pesticide exposures. Such models have the ability to link absorbed dose of the parent compound to exposure biomarkers and critical biological effects. To obtain the safest BRVs for the studied population, simulations of exposure scenarios were performed using a conservative reference dose such as a no-observed-effect level (NOEL). The various examples discussed in this chapter show the importance of knowledge on urine collections (i.e. spot samples and complete 8-h, 12-h or 24-h collections), sampling strategies, metabolism, relative proportions of the different metabolites in urine, absorption fraction, route of exposure and background contribution of prior exposures. They also show that relying on urinary measurements of specific metabolites appears more accurate when applying this approach to the case of occupational exposures. Conversely, relying on semi-specific metabolites (metabolites common to a category of pesticides) appears more accurate for the health risk assessment of environmental exposures given that the precise pesticides to which subjects are exposed are often unknown. In conclusion, the modeling approach to define BRVs for the relevant pesticides may be useful for public health authorities for managing issues related to health risks resulting from environmental and occupational exposures to pesticides.

Biological exposure indicators: quantification of biological variability using toxicokinetic modeling

Compartmental and physiologically based toxicokinetic modeling coupled with Monte Carlo simulation were used to quantify the impact of biological variability (physiological, biochemical, and anatomic parameters) on the values of a series of bio-indicators of metal and organic industrial chemical exposures. A variability extent index and the main parameters affecting biological indicators were identified. Results show a large diversity in interindividual variability for the different categories of biological indicators examined. Measurement of the unchanged substance in blood, alveolar air, or urine is much less variable than the measurement of metabolites, both in blood and urine. In most cases, the alveolar flow and cardiac output were identified as the prime parameters determining biological variability, thus suggesting the importance of workload intensity on absorbed dose for inhaled chemicals.

Distribution of metals exposure and associations with cardiometabolic risk factors in the "Modeling the Epidemiologic Transition Study".

BACKGROUND: Metals are known endocrine disruptors and have been linked to cardiometabolic diseases via multiple potential mechanisms, yet few human studies have both the exposure variability and biologically-relevant phenotype data available. We sought to examine the distribution of metals exposure and potential associations with cardiometabolic risk factors in the "Modeling the Epidemiologic Transition Study" (METS), a prospective cohort study designed to assess energy balance and change in body weight, diabetes and cardiovascular disease risk in five countries at different stages of social and economic development. METHODS: Young adults (25-45 years) of African descent were enrolled (N = 500 from each site) in: Ghana, South Africa, Seychelles, Jamaica and the U.S.A. We randomly selected 150 blood samples (N = 30 from each site) to determine concentrations of selected metals (arsenic, cadmium, lead, mercury) in a subset of participants at baseline and to examine associations with cardiometabolic risk factors. RESULTS: Median (interquartile range) metal concentrations (μg/L) were: arsenic 8.5 (7.7); cadmium 0.01 (0.8); lead 16.6 (16.1); and mercury 1.5 (5.0). There were significant differences in metals concentrations by: site location, paid employment status, education, marital status, smoking, alcohol use, and fish intake. After adjusting for these covariates plus age and sex, arsenic (OR 4.1, 95% C.I. 1.2, 14.6) and lead (OR 4.0, 95% C.I. 1.6, 9.6) above the median values were significantly associated with elevated fasting glucose. These associations increased when models were further adjusted for percent body fat: arsenic (OR 5.6, 95% C.I. 1.5, 21.2) and lead (OR 5.0, 95% C.I. 2.0, 12.7). Cadmium and mercury were also related with increased odds of elevated fasting glucose, but the associations were not statistically significant. Arsenic was significantly associated with increased odds of low HDL cholesterol both with (OR 8.0, 95% C.I. 1.8, 35.0) and without (OR 5.9, 95% C.I. 1.5, 23.1) adjustment for percent body fat. CONCLUSIONS: While not consistent for all cardiometabolic disease markers, these results are suggestive of potentially important associations between metals exposure and cardiometabolic risk. Future studies will examine these associations in the larger cohort over time.

Toxicokinetic modeling of folpet fungicide and its ring-biomarkers of exposure in humans.

A human in vivo toxicokinetic model was built to allow a better understanding of the toxicokinetics of folpet fungicide and its key ring biomarkers of exposure: phthalimide (PI), phthalamic acid (PAA) and phthalic acid (PA). Both PI and the sum of ring metabolites, expressed as PA equivalents (PAeq), may be used as biomarkers of exposure. The conceptual representation of the model was based on the analysis of the time course of these biomarkers in volunteers orally and dermally exposed to folpet. In the model, compartments were also used to represent the body burden of folpet and experimentally relevant PI, PAA and PA ring metabolites in blood and in key tissues as well as in excreta, hence urinary and feces. The time evolution of these biomarkers in each compartment of the model was then mathematically described by a system of coupled differential equations. The mathematical parameters of the model were then determined from best fits to the time courses of PI and PAeq in blood and urine of five volunteers administered orally 1 mg kg(-1) and dermally 10 mg kg(-1) of folpet. In the case of oral administration, the mean elimination half-life of PI from blood (through feces, urine or metabolism) was found to be 39.9 h as compared with 28.0 h for PAeq. In the case of a dermal application, mean elimination half-life of PI and PAeq was estimated to be 34.3 and 29.3 h, respectively. The average final fractions of administered dose recovered in urine as PI over the 0-96 h period were 0.030 and 0.002%, for oral and dermal exposure, respectively. Corresponding values for PAeq were 24.5 and 1.83%, respectively. Finally, the average clearance rate of PI from blood calculated from the oral and dermal data was 0.09 ± 0.03 and 0.13 ± 0.05 ml h(-1) while the volume of distribution was 4.30 ± 1.12 and 6.05 ± 2.22 l, respectively. It was not possible to obtain the corresponding values from PAeq data owing to the lack of blood time course data.

A flow cytometry based oligotrophic pollutant exposure test to detect bacterial growth inhibition and cell injury.

Toxicity of chemical pollutants in aquatic environments is often addressed by assays that inquire reproductive inhibition of test microorganisms, such as algae or bacteria. Those tests, however, assess growth of populations as a whole via macroscopic methods such as culture turbidity or colony-forming units. Here we use flow cytometry to interrogate the fate of individual cells in low-density populations of the bacterium Pseudomonas fluorescens SV3 exposed or not under oligotrophic conditions to a number of common pollutants, some of which derive from oil contamination. Cells were stained at regular time intervals during the exposure assay with fluorescent dyes that detect membrane injury (i.e., live-dead assay). Reduction of population growth rates was observed upon toxicant insult and depended on the type of toxicant. Modeling and cell staining indicate that population growth rate decrease is a combined effect of an increased number of injured cells that may or may not multiply, and live cells dividing at normal growth rates. The oligotrophic assay concept presented here could be a useful complement for existing biomarker assays in compliance with new regulations on chemical effect studies or, more specifically, for judging recovery after exposure to fluctuating toxicant conditions.

Ganciclovir exposure under a 450mg daily dosage of valganciclovir for cytomegalovirus prevention in kidney transplantation: a prospective study.

Manuel O, Pascual M, Perrottet N, Lamoth F, Venetz J-P, Decosterd LA, Buclin T, Meylan PR. Ganciclovir exposure under a 450 mg daily dosage of valganciclovir for cytomegalovirus prevention in kidney transplantation: a prospective study. 
Clin Transplant 2010: 24: 794-800. Abstract:  This prospective study aimed at determining the ganciclovir exposure observed under a daily dosage of 450 mg valganciclovir routinely applied to kidney transplant recipients with a GFR above 25 mL/min at risk for cytomegalovirus (CMV) disease. Ganciclovir levels at trough (C(trough) ) and at peak (C(3 h) ) were measured monthly. Ganciclovir exposure (area under the curve [AUC(0-24) ]) was estimated using Bayesian non-linear mixed-effect modeling (NONMEM). Thirty-six patients received 450 mg of valganciclovir daily for three months. Median ganciclovir C(3 h) was 3.9 mg/L (range: 1.3-7.1), and C(trough) was 0.4 mg/L (range 0.1-2.7). Median AUC(0-24) of ganciclovir was 59.3 mg h/L (39.0-85.3) in patients with GFR(MDRD) 26-39 mL/min, 35.8 mg h/L (24.9-55.8) in patients with GFR(MDRD) 40-59 mL/min, and 29.6 mg h/L (22.0-43.2) in patients with GFR(MDRD)  ≥ 60 mL/min. No major differences in adverse events according to ganciclovir exposure were observed. CMV viremia was not detected during prophylaxis. After discontinuing prophylaxis, CMV viremia was seen in 8/36 patients (22%), and 4/36 patients (11%) developed CMV disease. Ganciclovir exposure after administration of valganciclovir 450 mg daily in recipients with GFR ≥60 mL/min was comparable to those previously reported with oral ganciclovir. A routine daily dose of 450 mg valganciclovir appears to be acceptable for CMV prophylaxis in most kidney transplant recipients.