Pharmacokinetic modeling as a tool for biological monitoring

Air samples versus biomarkers for epidemiology

BACKGROUND

It has been speculated on theoretical grounds that biomarkers are superior surrogates for chemical exposures to air samples in epidemiology studies.

METHODS AND RESULTS

Biomarkers were classified according to their position in the exposure-disease continuum-that is, parent compound, reactive intermediate, stable metabolite, macromolecular adduct, or measure of cellular damage. Because airborne exposures and these different biomarkers are time series that vary within and between persons in a population, they are all prone to measurement error effects when used as surrogates for true chemical exposures. It was shown that the attenuation bias in the estimated slope characterising a log exposure-log disease relation should decrease as the within- to between-person variance ratio of a given set of air or biomarker measurements decreases. To gauge the magnitudes of these variance ratios, a database of 12,077 repeated observations was constructed from 127 datasets, including air and biological measurements from either occupational or environmental settings. The within- and between-person variance components (in log scale, after controlling for fixed effects of time) and the corresponding variance ratios for each set of air and biomarker measurements were estimated. It was shown that estimated variance ratios of biomarkers decreased in the order short term (residence time < or =2 days) > intermediate term (2 days < residence time < or =2 months) > long term biomarkers (residence time >2 months). Overall, biomarkers had smaller variance ratios than air measurements, particularly in environmental settings. This suggests that a typical biomarker would provide a less biasing surrogate for exposure than would a typical air measurement.

CONCLUSION

Epidemiologists are encouraged to consider the magnitudes of variance ratios, along with other factors related to practicality and cost, in choosing among candidate surrogate measures of exposure.

Physiologically based modeling of n-hexane kinetics in humans following inhalation exposure at rest and under physical exertion: impact on free 2,5-hexanedione in urine and on n-hexane in alveolar air

We used a modified physiologically based pharmacokinetic (PBPK) to describe/predict n-hexane (HEX) alveolar air concentrations and free 2,5-HD urinary concentrations in humans exposed to n-HEX by inhalation during a typical workweek. The effect of an increase in workload intensity on these two exposure indicators was assessed and, using Monte Carlo simulation, the impact of biological variability was investigated. The model predicted HEX alveolar air concentrations at rest of 19.0 ppm (25 ppm exposure) and 38.7 ppm (50 ppm exposure) at the end of the last working day (day 5), while free 2,5-HD urinary concentrations of 3.4 micromol/L (25 ppm) and 6.3 micromol/L (50 ppm) were predicted for the same period (last 4.5 hours of Day 5). Monte Carlo simulations showed that the range of values expected to occur in a group of 1000 individuals exposed to 50 ppm of HEX (95% confidence interval) for free 2,5-HD (1.7-14.7 micromol/L) is much higher compared with alveolar air HEX (33.4-46 ppm). Simulations of exposure at 50 ppm with different workloads predicted that an increase in workload intensity would not greatly affect both indicators studied. However, the alveolar air HEX concentration is more sensitive to modifications of workload intensity and time of sampling, after the end of exposure, compared with 2,5-HD. The PBPK model successfully described the HEX alveolar air concentrations and free 2,5-HD urinary concentrations measured in human volunteers and is the first, to our knowledge, to describe the excretion kinetics of free 2,5-HD in humans over a 5-day period.

Comparison of models for the estimation of biological partition coefficients

Several models have been published for calculating blood-air, tissue-air, or tissue-blood partition coefficients of volatile organic chemicals in human or rat tissues, from functions of their octanol-water partition coefficients or solubilities in vegetable oil and water. In this work, the relative accuracy, strengths, and limitations of the various models are examined. Comparison of predicted human tissue-air and tissue-blood partition coefficients with experimental values has been made for 12 chemicals, covering a wide range of lipophilicity (acetone, isopropanol, diethylether, methylene dichloride, benzene, toluene, trichloroethylene, trichloroethane, n-pentane, cyclohexane, n-hexane, and n-heptane). Seven published models for human tissue-air and 10 models for tissue-blood partition coefficients have been compared. Fewer models are available for predicting rat tissue-air and rat tissue-blood partition coefficients, but a similar comparison has been made. The ratio of predicted to experimental partition coefficients and their mean, R(mean), and the mean magnitude of the difference between predicted and experimental values of log(10) P, E, were used to assess the accuracy of each model. For the test set the most accurate for human blood-air partition coefficients were the empirical equations of Meulenberg and Vijverberg (R(mean) = 1.1 +/- 0.46, E = 0.156) and the empirical solvation equation of Abraham and Weathersby (1994) (R(mean) = 0.93 +/- 0.38, E = 0.166). For rat blood, predictions are much less accurate due to difficulties in modeling the effects of protein binding, which are much larger. Overall, for rat blood-air partition coefficients the equation of Meulenberg and Vijverberg (1999) (R(mean) = 0.74 +/- 0.50, E = 0.236) was the most accurate. The tissue-composition-based equations of Poulin and Krishnan, using solubilities in vegetable oil, performed well for human liver-air partition coefficients (R(mean) = 1.21 +/- 0.28, E = 0.079) for log(octanol-water partition coefficients) > 0.7 and for fat-air partition coefficients, but overestimated solubilities in human kidney and brain tissues (e.g., for kidney tissue, R = 1.88 +/- 0.58, E = 0.255). The equations of Meulenberg and Vijverberg (2000a), Abraham and Weathersby (1994), and Paterson and Mackay (1989) also performed moderately well for human tissue-air partition coefficients. For rat muscle-air, liver-air, and fat-air partition coefficients the model of Poulin and Krishnan (1996a) gave the most accurate predictions. For tissue-blood partition coefficients, generally good agreement with experimental values is obtained by the empirical model of Balaz and Lukacova (1999) (e.g., for human kidney, R(mean) = 1.15 +/- 0.38, E = 0.085) and, if solubility in fat is known, by the equations of Fiserova-Bergerova and Diaz (1986) (e.g., for human muscle, R(mean) = 1.10 +/- 0.39, E = 0.107). The equations of DeJongh et al. (1997) gave the most accurate predictions for rat muscle-blood, liver-blood and fat-blood partition coefficients (e.g., for rat muscle R(mean) = 1.03 +/- 0.39, E = 0.149), but predictions were less accurate than for human tissue-blood partition coefficients, attributable to difficulties in modeling the effect of protein binding. The choice of equation for use in physiologically based pharmacokinetic (PBPK) models depends on the species, tissue, and chemical lipophilicity.

Are current biomarkers suitable for the assessment of manganese exposure in individual workers?

BACKGROUND

Whole blood and urinary manganese have been measured in occupational and environmental studies for the assessment of exposure. The aim of this study was to assess the relationship between the airborne concentrations of manganese and these biological indicators.

METHODS

Environmental and biological monitoring was performed in a group of 94 employees in a ferroalloy production, who were exposed to manganese (Mn) oxides (MnO(2) and Mn(3)O(4)). The results were compared with those from a control group of 87 subjects not exposed to Mn.

RESULTS

Mn exposure levels ranged between 5 and 740 micrograms/m(3), with arithmetic and geometric mean and median values being 202.6, 97.6, and 150 micrograms/m(3), respectively. Arithmetic and geometric means for Mn in total blood (MnB) were, respectively, 10.3+/-3.8 and 9.7 micrograms/L in the exposed and 5.9+/-1.7 and 5.7 micrograms/L in the controls. For urinary Mn (MnU), arithmetic and geometric means were, respectively, 4.9+/-3.6 and 3. 8 micrograms/L in the exposed and 1.2+/-1.4 and 0.7 micrograms/L in the controls. On a group comparison, a significant relationship was found between high and low exposed subgroups, identified according to Mn atmospheric concentrations (MnA), for both MnB (F value=38.0, P > 0.0001) and MnU (F value=36.1, P > 0.0001). On a linear relationship, a correlation was observed between MnA and MnB (r=0. 34; r(2)=0.112; P=0.001), whereas no association was found between MnA and MnU. A significant relationship emerged also between MnB and MnU (r=0.48, r(2)=0.23, P < 0.0001). No association was observed between an index of cumulative exposure and the biological indicators of exposure.

CONCLUSIONS

These results confirm that MnB and MnU can discriminate groups of occupationally exposed workers from groups of nonexposed subjects. MnB is also related to the intensity of external exposure on a linear relationship, but given a high variability, it is not suitable for individual biological monitoring. Therefore, further research should focus on more accurate biomarkers of Mn exposure.

Physiologically based models of metal kinetics

The issues confronting the modeler of metals kinetics are somewhat different from those with which the modeler of organic chemical behavior is faced. Particularly important features of metals kinetics include metal-protein binding and metal-metal interactions. Reduction, and for some metals oxidation, is frequently an intrinsic part of metal metabolism. Alkylation/dealkylation reactions may or may not render the metal less active, and the behavior of alkylated or dealkylated metabolites must often be included in a complete kinetic model. Despite these complexities, the kinetics of metals are as amenable to the techniques of physiologically based modeling as are the kinetics of organic chemicals. Like all models, those for metals kinetics have the potential to organize a variety of observations, sometimes including apparently inconsistent observations, into a coherent framework of behavior, to identify needs for more complete experimental information, and to assist the risk assessor in making judgments concerning dose-response relationships. Development of physiologically based models of the kinetic behavior of metals is in its very early stages. The kinetics of only four metals, arsenic, chromium, mercury, and lead, have been modeled with any degree of completeness. Of these, the lead model is the most fully realized at the present time. The chromium and mercury models are still in the process of development, and experimental data are being gathered to support further development and refinement of the arsenic model. We may expect to see continued progress made on these models and their practical applications, as well as the development of new models for other toxicologically significant metals such as cadmium, manganese, nickel, and aluminum.

Effect of various exposure scenarios on the biological monitoring of organic solvents in alveolar air. II. 1,1,1-Trichloroethane and trichloroethylene

The purpose of the present study was to investigate the influence of different exposure scenarios on the elimination of trichloroethylene (TRI) and 1,1,1-trichloroethane (1,1,1-TRI) in alveolar air and other biological fluids in human volunteers. In addition, it was sought to establish an interactive process between experimental data gathering and simulation modeling in an attempt to predict the influence of the different scenarios of exposure to TRI and 1,1,1-TRI on their respective biological monitoring indices and thus to establish the flexibility and validity of simulation models. Two adult male and two adult female Caucasian volunteers were exposed by inhalation, in a dynamic controlled exposure chamber, to various concentrations of TRI (12.5-25 ppm) or 1,1,1-TRI (87.5-175 ppm) in order to establish the influence of exposure concentration, duration of exposure, variation of concentration within day, and work load on biological exposure indices. The concentrations of unchanged solvents in end-exhaled air and in blood as well as the urinary excretion of trichloroethanol (TCE) and trichloroacetic acid (TCA) were determined. The results show that doubling the exposure concentration for both solvents led to a proportional increase in the concentrations of unchanged solvents in alveolar air and blood at the end of a 7-h exposure period; this proportionality was still observable in 1,1,1-TRI expired air samples 16 h after the end of the third exposure day. In the case of urinary excretion of TCE and TCA, the proportionality between excretion and exposure concentration was not as good. It was once again observed that the slow excretion of both metabolites leads to progressive cumulation and that their urinary determination is subject to considerable interindividual variations. After adjustment (lowering) of the exposure concentration to account for a prolongation of the duration of exposure (from 8 to 12 h) it was observed that the concentrations of TRI or 1,1,1-TRI towards the end of both exposure periods are more a reflection of the actual exposure concentration than of the exposure duration. Despite important interindividual variations, these adjusted and nonadjusted exposures led to almost identical average total urinary excretion over 24 h) of TCE and TCA after exposure to 1,1,1-TRI, as was also the case for TCE but not for TCA after exposure to TRI. Induced within-day variations in the exposure concentration led to corresponding but not proportional changes in alveolar air concentrations for both solvents. After exposure to peak concentrations there was a lag period before alveolar air concentrations returned to prepeak levels. At the end of repeated 10-min periods of physical exercise at 50 W, alveolar air concentrations of TRI were increased by 50% while those of 1,1,1-TRI increased by only 12%. After optimization of the physiologically based toxicokinetic model parameters with experimental data collected during the first exposure scenario, results pertaining to the three other scenarios were adequately simulated by the optimized models. Overall, the results of the present study suggest that alveolar air solvent concentration is a reliable index of exposure to both TRI and 1,1,1-TRI under various experimental exposure scenarios. These results also suggest that urinary excretion of TCE and TCA must be interpreted with caution when assessing exposure to either solvents. For exposure situations likely to be encountered in the workplace, physiologically based toxicokinetic modeling appears to be a useful tool both for developing strategies of biological monitoring of exposure to volatile organic solvents and for predicting alveolar air concentrations under a given set of exposure conditions.

Biokinetics and stability aspects of biomarkers: recommendations for application in population studies

The knowledge of the toxicokinetics of chemicals is an important prerequisite of biological monitoring of exposure. Kinetic data allow to determine whether the test is likely to reflect the recent exposure or to integrate the exposure over a certain period of time. They are necessary to select the appropriate parameter, biological specimen and sampling time by taking into account the type of exposure and the sensitivity of the analytical method. Various mathematical models have been developed to provide a global and quantitative description of the behavior of the chemical in the organism. In practice, however, the parameter to which one usually refers is the elimination half-life of the biomarker which reflects both the affinity of the chemical for the biological matrix and the efficiency of excretory or metabolic processes. Since chemically-induced diseases usually develop following repeated exposures over many years, markers which are the most useful for population studies are those integrating the dose received by the organism or by the target organ(s) over a toxicologically relevant period of time exposure. For a reliable application of biological monitoring in population studies, information must be collected on the stability of the biomarker and the precautions to be taken during transportation and storage of samples. The factors most likely to affect the stability of the parameter are evaporation, chemical deterioration, precipitation, adsorption on vessel surfaces and contamination. This paper formulates a series of practical recommendations to prevent or minimize variations in the pre-analytical phase which might be caused by these factors.

Assessment of long-term styrene exposure: a comparative study of a logbook method and biological monitoring

In a recent joint European research project "Biomonitoring of human populations exposed to genotoxic environmental chemicals: biomonitoring of styrene exposed individuals", a logbook method for assessment of long-term styrene exposure was applied in two Danish factories manufacturing glass fibre-reinforced polyester. The method was based on work process identification, assignment of work process concentrations and logbook keeping. Measures of exposure calculated by this method were compared with results from simultaneous measurements of styrene in blood and the metabolites mandelic acid and phenylglyoxylic acid in urine. Correlations were comparable with those obtained by use of personal samplers as published in the literature. Styrene in blood, however, only correlated with logbook concentrations at the time of sampling. Exposures were moderate to low. Mean personal 8-h time-weighted average concentration (8hTWAC) was 76 mg/m3 styrene (SD 54 mg/m3, range 2-230 mg/m3). The Danish 8hTWAC threshold limit value for styrene in air, 105 mg/m3 (25 ppm), was exceeded on 17% of personal days. The summed urinary metabolites, mandelic acid and phenylglyoxylic acid, had a mean personal value of 138 mg/g creatinine (SD 84 mg/g creatinine) on the day of sampling. Blood styrene mean value was 129 micrograms/l (SD 74 micrograms/l, range 66-358 micrograms/l). It is concluded that the logbook method offers a technique for testing whether measurements are performed on representative days and may be recommended as a tool supplementary to biological monitoring in the assessment of long-term exposure.