Applications of population approaches in toxicology

Bois, Frédéric Y.

doi:10.1016/s0378-4274(01)00270-3

Cited by 33 publications

(26 citation statements)

References 46 publications

(44 reference statements)

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“…We have shown through limited simulations that the proposed method estimated the overall mean exposure well with tight CIs. Further investigations especially on the impact of particular functional form as suggested in Bernillon and Bois (2000) and Bois (2001), are needed for applications in field studies. Third, depending on the metabolic half-life of the investigated chemical, optimal design may need to be addressed prior to biological sample collection (Bois et al 1999).…”

Section: Discussionmentioning

confidence: 99%

Exposure estimation using repeated blood concentration measurements

Chen

Shih

2009

Stoch Environ Res Risk Assess

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Physiologically based toxicokinetic (PBTK) modeling has been well established to study the distributions of chemicals in target tissues. In addition, the hierarchical Bayesian statistical approach using Markov Chain Monte Carlo (MCMC) simulations has been applied successfully for parameter estimation. The aim was to estimate the constant inhalation exposure concentration (assumed) using a PBTK model based on repeated measurements in venous blood, so that exposures could be estimated. By treating the constant exterior exposure as an unknown parameter of a four-compartment PBTK model, we applied MCMC simulations to estimate the exposure based on a hierarchical Bayesian approach. The dataset on 16 volunteers exposed to 100 ppm (%0.538 mg/L) trichloroethylene vapors for 4 h was reanalyzed as an illustration. Cases of time-dependent exposures with a constant mean were also studied via 100 simulated datasets. The posterior geometric mean of 0.571, with narrow 95% posterior confidence interval (CI) (0.506, 0.645), estimated the true trichloroethylene inhalation concentration (0.538 mg/L) with very high precision. Also, the proposed method estimated the overall constant mean of the simulated time-dependent exposure scenarios well with slightly wider 95% CIs. The proposed method justifies the accuracy of exposure estimation from biomonitoring data using PBTK model and MCMC simulations from a real dataset and simulation studies numerically, which provides a starting point for future applications in occupational exposure assessment.

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Section: Discussionmentioning

confidence: 99%

Exposure estimation using repeated blood concentration measurements

Chen

Shih

2009

Stoch Environ Res Risk Assess

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“…Most existing PBPK applications were applied to well-understood or characterized studies of an individual or a small cohort. The new challenge is how to apply PBPK models to larger and more poorly characterized human populations that have highly variable exposures, activities, physiology, and pharmacokinetics (Bois, 2001). An important research question here is whether PBPK models are broadly applicable as tools for relating dose biomarkers to measures of population exposure and health risk.…”

Section: Introductionmentioning

confidence: 99%

“…However, biomarkers obtained from these surveys are inherently variable as a result of the inter-and intraindividual variability in both the exposures to the population and the physiology of the individuals in the population (Bois, 2001). The key questions are whether and how well we can quantify the variation in source-to-dose relationships against the noise contributed by these other variable and uncertain factors.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing population exposures from dose biomarkers: inhalation of trichloroethylene (TCE) as a case study

Sohn

McKone

Blancato

2004

J Expo Sci Environ Epidemiol

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Physiologically based pharmacokinetic (PBPK) modeling is a well-established toxicological tool designed to relate exposure to a target tissue dose. The emergence of federal and state programs for environmental health tracking and the availability of exposure monitoring through biomarkers creates the opportunity to apply PBPK models to estimate exposures to environmental contaminants from urine, blood, and tissue samples. However, reconstructing exposures for large populations is complicated by often having too few biomarker samples, large uncertainties about exposures, and large interindividual variability. In this paper, we use an illustrative case study to identify some of these difficulties, and for a process for confronting them by reconstructing population-scale exposures using Bayesian inference. The application consists of interpreting biomarker data from eight adult males with controlled exposures to trichloroethylene (TCE) as if the biomarkers were random samples from a large population with unknown exposure conditions. The TCE concentrations in blood from the individuals fell into two distinctly different groups even though the individuals were simultaneously in a single exposure chamber. We successfully reconstructed the exposure scenarios for both subgroups F although the reconstruction of one subgroup is different than what is believed to be the true experimental conditions. We were however unable to predict with high certainty the concentration of TCE in air.

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“…Monte Carlo methods are based on a Bayesian statistical approach that involves the use of experimental data to update estimates of a hypothesized "prior" probability distribution for one or more model parameters. Examples of Monte Carlo methods applied to PBPK models can be found in [22][23][24][25][26][27][28][29][30][31].…”

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

“…The chamber air concentration C c (t) is specified as part of the experiment and is used as a forcing function in the arterial blood equation (24). For the results we present in this paper, we set the chamber air concentration to 2000 parts per million TCE for one hour, followed by zero ppm TCE until the final time t f (in hours).…”

Section: Overview Of the Tce Modelmentioning

confidence: 99%

Probabilistic methods for addressing uncertainty and variability in biological models: application to a toxicokinetic model

Banks

Potter

2004

Mathematical Biosciences

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Population variability and uncertainty are important features of biological systems that must be considered when developing mathematical models for these systems. In this paper we present probability-based parameter estimation methods that account for such variability and uncertainty. Theoretical results that establish well-posedness and stability for these methods are discussed. A probabilistic parameter estimation technique is then applied to a toxicokinetic model for trichloroethylene using several types of simulated data. Comparison with results obtained using a standard, deterministic parameter estimation method suggests that the probabilistic methods are better able to capture population variability and uncertainty in model parameters.

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Applications of population approaches in toxicology

Cited by 33 publications

References 46 publications

Exposure estimation using repeated blood concentration measurements

Exposure estimation using repeated blood concentration measurements

Reconstructing population exposures from dose biomarkers: inhalation of trichloroethylene (TCE) as a case study

Probabilistic methods for addressing uncertainty and variability in biological models: application to a toxicokinetic model

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