Population Toxicokinetics of Benzene

Bois, Frédéric Y.; Jackson, Elise T.; Pekari, Kaija; Smith, Martyn T.

doi:10.2307/3433197

Cited by 26 publications

(39 citation statements)

References 14 publications

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“…Some a priori knowledge of p and 12 is available in the form of "standard" values for some parameters. Uncertainty in these averages and variances (8)(9)(10)). An exception is the volume of the tracheobronchial compartment, quite uncertain and for which S was set to correspond to 200% CV.…”

Section: Methods Datamentioning

confidence: 99%

“…At the group level, various concentrations or quantities (y) were measured. The expected values of these measurements are a function (f) of exposure level (E), time (t), a set of physiological parameters of unknown values (9), and a set of measured, covariate parameters (q>) such as body weight. E, t, 0, and q are experiment specific.…”

Section: Methods Datamentioning

confidence: 99%

“…Finally, most of the time, prior physiological information is simply about population averages and is not directly applicable to any particular individual for which data were obtained. Fortunately, all these problems can be solved in a unified way though a Bayesian population toxicokinetic approach, which is worth implementing even in the case of small numbers of study subjects (7)(8)(9)(10). Bayesian statistics provides a natural way of merging a priori knowledge gained by implementing a physiological model, with the in vivo experimental data.…”

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confidence: 99%

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Statistical analysis of Clewell et al. PBPK model of trichloroethylene kinetics.

Bois

2000

Environ Health Perspect

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A physiologically based pharmacokinetic model for trichloroethylene (TCE) in rodents and humans was calibrated with published toxicokinetic data sets. A Bayesian statistical framework was used to combine previous information about the model parameters with the data likelihood, to yield posterior parameter distributions. The use of the hierarchical statistical model yielded estimates of both variability between experimental groups and uncertainty in TCE toxicokinetics. After adjustment of the model by Markov chain Monte Carlo sampling, estimates of variability for the animal or human metabolic parameters ranged from a factor of 1.5-2 (geometric standard deviation [GSDI). Uncertainty was of the same order as variability for animals and higher than variability for humans. The model was used to make posterior predictions for several measures of cancer risk. These predictions were affected by both uncertainties and variability and exhibited GSDs ranging from 2 to 6 in mice and rats and from 2 to 10 for humans. The recent development of a comprehensive physiologically based pharmacokinetic (PBPK) model of trichloroethylene (TCE) disposition and metabolism in mice, rats, and humans (1) offers us the opportunity to examine issues of variability and uncertainty for that solvent. In particular, uncertainties in prediction of various cancer dose metrics deserve to be computed, since they could be directly used as input for improved risk assessments.PBPK modeling provides a strong mechanistic basis for prediction of disposition and metabolism of toxicants. Yet much uneasiness remains with the use of these models in toxicology (2). Similarly, as discussed in a recent review and an accompanying commentary, PBPK modeling has not seen the development it promised for therapeutic compounds (3,4). The reason for this essentially lies in the lack of statistical methods for calibrating these models. Because of individual variability and uncertainty, many parameters are difficult to measure accurately even for inbred animal strains. Using input parameters or presenting results in the form of a single value can therefore be very misleading (5). In the absence of rigorous statistical treatment, inference presented by PBPK modeling is largely empirical, hypotheses are left unvalidated, and predictions lack realistic measures of uncertainty. This state of affairs is unfortunate, when considering the consequences (for public health and national welfare) of the decisions made using these models.Obviously, correct statistical treatment of PBPK models is difficult, since these are large nonlinear models with relatively small data sets and a high degree of uncertainty and biological variability (6). It is also essential to respect the fundamental specificity of PBPK models, i.e., their high prior information content, which they provide through the opportunity to use physiological information on parameter values. Yet, although several parameters have physiological meaning and a narrow range of possible values, others-often specific of the com...

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Section: Methods Datamentioning

confidence: 99%

Section: Methods Datamentioning

confidence: 99%

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confidence: 99%

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Statistical analysis of Clewell et al. PBPK model of trichloroethylene kinetics.

Bois

2000

Environ Health Perspect

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“…A typical population TK model is presented in Figure 1 (Bois et al, 1996a;Bois et al, 1996b;Wakefield, 1996) . The "hierarchical" or "multilevel" structure of the model is apparent, with two major components: the subject level and the population level.…”

Section: Population Modelsmentioning

confidence: 99%

“…The same arguments as for animal experiments can be made in favour or the use of population models in that context. For further discussion, the reader is referred to our recent population TK analyses of volunteer exposures to tetrachloroethylene (Bois et al, 1996a; , benzene (Bois et al, 1996b) , trichloroethylene (Bois, 2000b;Bois, 2000a) , or 1,3-butadiene . The inclusion of genotype information in such analyses is straightforward (Johanson et al, 1999) , and should see considerable development in the near future.…”

Section: Human Volunteers Studiesmentioning

confidence: 99%

Applications of population approaches in toxicology

Bois

2001

Toxicology Letters

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Many experimental or observational studies in toxicology are best analysed in a population framework.Recent examples include investigations of the extent and origin of intra-individual variability in toxicity studies, incorporation of genotypic information to address intra-individual variability, optimal design of experiments, and extension of toxicokinetic modelling to the analysis of biomarker studies in industrial hygiene or epidemiological contexts. Bayesian statistics provide powerful numerical methods for fitting population models, particularly when complex mechanistic toxicokinetic or toxicodynamic models are involved. Challenges and limitations to the use of population models, in terms of basic structure, computational burden, ease of implementation and data accessibility, are identified and discussed.

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Benzene and non-Hodgkin's lymphoma

1999

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Incidence rates for non‐Hodgkin's lymphoma (NHL) have been rising throughout the world for several decades, and no convincing explanation exists for the majority of this increase. The commonest subtypes of NHL have no well‐defined aetiological factors but lymphoma development has been linked with exposure to a variety of chemicals, including nitrates, pesticides, herbicides, and solvents. Benzene, a solvent and important constituent of petrochemical products, is a potent lymphomagen in experimental animals and high‐dose exposure in humans is associated with both acute myeloid leukaemia and NHL. Much current interest centres on the possibility that environmental benzene exposure in the general public may underlie a proportion of the increase in NHL. Seventy per cent of benzene exposure in the environment is derived from vehicle exhaust emissions, whose increase has closely paralleled the rise in frequency of the disease. Mathematical modelling has been used to calculate an acceptable concentration of benzene in air based on risk estimates derived from industrial exposure, but the recommended target concentration in the U.K. of 1 ppb is regularly exceeded in urban locations. Detailed investigation of the health effects of low‐level benzene exposure awaits an accurate assay for quantifying long‐term human exposure. The 32P post‐labelling technique for the detection of toxin‐specific DNA adducts is extremely sensitive and has been applied in the biomonitoring of exposure to a number of carcinogens, but benzene–DNA adducts have to date proved elusive of detection. Copyright © 1999 John Wiley & Sons, Ltd.

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Population Toxicokinetics of Benzene

Cited by 26 publications

References 14 publications

Statistical analysis of Clewell et al. PBPK model of trichloroethylene kinetics.

Statistical analysis of Clewell et al. PBPK model of trichloroethylene kinetics.

Applications of population approaches in toxicology

Benzene and non-Hodgkin's lymphoma

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