Variability of physiologically based pharmacokinetic (PBPK) model parameters and their effects on PBPK model predictions in a risk assessment for perchloroethylene (PCE)

doi:10.1016/0378-4274(93)90126-I

Toxicology Letters

Volume 68, Issues 1–2, May 1993, Pages 131-144

https://doi.org/10.1016/0378-4274(93)90126-I Get rights and content

Abstract

When used in the risk assessment process, the output from physiologically based pharmacokinetic (PBPK) models has usually been considered as an exact estimate of dose, ignoring uncertainties in the parameter values used in the model and their impact on model predictions. We have collected experimental data on the variability of key parameters in a PBPK model for tetrachloroethylene (PCE) and have used Monte Carlo analysis to estimate the resulting variability in the model predictions. Blood/air and tissue/ blood partition coefficients and the interanimal variability of these data were determined for tetrachloroethylene (PCE). The mean values and variability for these and other published model parameters were incorporated into a PBPK model for PCE and a Monte Carlo analysis (n = 600) was performed to determine the effect on model predicted dose surrogates for a PCE risk assessment. For a typical dose surrogate, area under the blood time curve for metabolite in the liver (AUCLM), the coefficient of variation was 25% and the mean value for AUCLM was within a factor of two of the maximum and minimum values generated in the 600 simulations. These calculations demonstrate that parameter uncertainty is not a significant potential source of variability in the use of PBPK models in risk assessment. However, we did not in this study consider uncertainties as to metabolic pathways, mechanism of carcinogenicity, or appropriateness of dose surrogates.

References (16)

M.E. Andersen et al.
Physiologically based pharmacokinetics and the risk assessment process for methylene chloride
Toxicol. Appl. Pharmacol.
(1987)
R.H. Reitz et al.
Development of a physiologically based pharmacokinetic model for risk assessment with 1,4-dioxane
Toxicol. Appl. Pharmacol.
(1990)
C.B. Frederick et al.
A physiologically based pharmacokinetic and pharmacodynamic model to describe the oral dosing of rats with ethyl acrylate and its implications for risk assessment
Toxicol. Appl. Pharmacol.
(1992)
F.Y. Bois et al.
Precision and sensitivity of pharmacokinetic models for cancer risk assessment: tetrachloroethylene in mice, rats, and humans
Toxicol. Appl. Pharmacol.
(1990)
D. Farrar et al.
Evaluation of uncertainity in input parameters to pharmacokinetic models and the resulting uncertainity in output
Toxicol. Lett.
(1989)
M.L. Gargas et al.
Partition coefficients of low-molecular weight volatile chemicals in various liquids and tissues
Toxicol. Appl. Pharmacol.
(1989)
C-W. Lam et al.
Mechanism of transport and distribution of organic solvents in blood
Toxicol. Appl. Pharmacol.
(1990)
J.A. Buben et al.
Delineation of the role of metabolism in the hepatotoxicity of trichloroethylene and perchloroethylene: A dose-effect study
Toxicol. Appl. Pharmacol.
(1985)

There are more references available in the full text version of this article.

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Accounting for metabolism in a physiologically based pharmacokinetic (PBPK) model is important from a pharmacokinetics perspective. Hepatic and extrahepatic enzymatic biotransformations occur across species and are usually dependent upon age and perhaps sex. Metabolism is either linked causally to toxicity or detoxification. In this chapter, brief descriptions of experimental methods are given for how quantitative metabolic studies are conducted and how the information is used in PBPK models. A challenging simulation exercise is provided for the chemical bisphenol A (BPA), which undergoes extensive hepatic and extrahepatic Phase II conjugation. Phase II conjugation is an inactivation step for BPA.
Sensitivity and Monte Carlo analysis techniques and their use in uncertainty, variability, and population analysis
2020, Physiologically Based Pharmacokinetic (PBPK) Modeling: Methods and Applications in Toxicology and Risk Assessment
Sensitivity and Monte Carlo analyses are integral parts of uncertainty and variability analysis of physiologically-based pharmacokinetic (PBPK) models. Both may be used to assess the impact of parameter uncertainty and variability on model output; however, a sensitivity analysis typically determines changes in output by changing input parameters one at a time by a set amount while Monte Carlo analysis generally consists of random sampling from one or more distributions for the input parameters of the model. This chapter details the steps in constructing sensitivity and Monte Carlo analysis for a PBPK model and includes what modifications may need to be made to the model and how to select/define the distributions. Some discussion on how these analyses are used to assess uncertainty and variability in a PBPK model is also included.
Incorporation of the glutathione conjugation pathway in an updated physiologically-based pharmacokinetic model for perchloroethylene in mice
2018, Toxicology and Applied Pharmacology
Citation Excerpt :
The current model predictions for perc and TCA were also compared with MLE results of previous model. The current model predictions of the internal toxicokinetics of perc at both population and strain-specific levels are consistent with previous model predictions and in vivo data on perc and TCA in blood in male B6C3F1 mice at three different single doses of oil based oral gavage from Gearhart et al. (1993) (Fig. 6). The updated model at population level underestimated blood perc, especially at low dose (100 mg/kg) group, while it a bit overestimated blood TCA in the same dose group.
Perchloroethylene (perc) induced target organ toxicity has been associated with tissue-specific metabolic pathways. Previous physiologically-based pharmacokinetic (PBPK) modeling of perc accurately predicted oxidative metabolites but suggested the need to better characterize glutathione (GSH) conjugation as well as toxicokinetic uncertainty and variability.
We updated the previously published “harmonized” perc PBPK model in mice to better characterize GSH conjugation metabolism as well as the uncertainty and variability of perc toxicokinetics.
The updated PBPK model includes expanded models for perc and its oxidative metabolite trichloroacetic acid (TCA), and physiologically-based sub-models for conjugative metabolites. Previously compiled mouse kinetic data in B6C3F1 and Swiss-Webster mice were augmented to include data from a recent study in male C57BL/6J mice that measured perc and metabolites in serum and multiple tissues. Hierarchical Bayesian population analysis using Markov chain Monte Carlo was conducted to characterize uncertainty and inter-strain variability in perc metabolism.
The updated model fit the data as well or better than the previously published “harmonized” PBPK model. Tissue dosimetry for both oxidative and conjugative metabolites was successfully predicted across the three strains of mice, with estimated residuals errors of 2-fold for majority of data. Inter-strain variability across three strains was evident for oxidative metabolism; GSH conjugation data were only available for one strain.
This updated PBPK model fills a critical data gap in quantitative risk assessment by predicting the internal dosimetry of perc and its oxidative and GSH conjugation metabolites and lays the groundwork for future studies to better characterize toxicokinetic variability.
Modelling ecological and human exposure to POPs in Venice lagoon – Part II: Quantitative uncertainty and sensitivity analysis in coupled exposure models
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The study is focused on applying uncertainty and sensitivity analysis to support the application and evaluation of large exposure models where a significant number of parameters and complex exposure scenarios might be involved. The recently developed MERLIN-Expo exposure modelling tool was applied to probabilistically assess the ecological and human exposure to PCB 126 and 2,3,7,8-TCDD in the Venice lagoon (Italy). The ‘Phytoplankton’, ‘Aquatic Invertebrate’, ‘Fish’, ‘Human intake’ and PBPK models available in MERLIN-Expo library were integrated to create a specific food web to dynamically simulate bioaccumulation in various aquatic species and in the human body over individual lifetimes from 1932 until 1998. MERLIN-Expo is a high tier exposure modelling tool allowing propagation of uncertainty on the model predictions through Monte Carlo simulation. Uncertainty in model output can be further apportioned between parameters by applying built-in sensitivity analysis tools. In this study, uncertainty has been extensively addressed in the distribution functions to describe the data input and the effect on model results by applying sensitivity analysis techniques (screening Morris method, regression analysis, and variance-based method EFAST). In the exposure scenario developed for the Lagoon of Venice, the concentrations of 2,3,7,8-TCDD and PCB 126 in human blood turned out to be mainly influenced by a combination of parameters (half-lives of the chemicals, body weight variability, lipid fraction, food assimilation efficiency), physiological processes (uptake/elimination rates), environmental exposure concentrations (sediment, water, food) and eating behaviours (amount of food eaten). In conclusion, this case study demonstrated feasibility of MERLIN-Expo to be successfully employed in integrated, high tier exposure assessment.
Development and evaluation of a harmonized physiologically based pharmacokinetic (PBPK) model for perchloroethylene toxicokinetics in mice, rats, and humans
2011, Toxicology and Applied Pharmacology
Citation Excerpt :
Reitz et al. (1996) performed in vitro and in vivo experiments to support their PBPK model in mice and rats, and used a “parallelogram approach” to estimate metabolic parameters in humans. Clewell et al. (2005) updated the Gearhart et al. (1993) model, including consideration of human toxicokinetic data published by Volkel et al. (1998). In addition, a number of PBPK models were developed only in humans, primarily to characterize uncertainty and/or human variability.
This article reports on the development of a “harmonized” PBPK model for the toxicokinetics of perchloroethylene (tetrachloroethylene or perc) in mice, rats, and humans that includes both oxidation and glutathione (GSH) conjugation of perc, the internal kinetics of the oxidative metabolite trichloroacetic acid (TCA), and the urinary excretion kinetics of the GSH conjugation metabolites N-Acetylated trichlorovinyl cysteine and dichloroacetic acid. The model utilizes a wider range of in vitro and in vivo data than any previous analysis alone, with in vitro data used for initial, or “baseline,” parameter estimates, and in vivo datasets separated into those used for “calibration” and those used for “evaluation.” Parameter calibration utilizes a limited Bayesian analysis involving flat priors and making inferences only using posterior modes obtained via Markov chain Monte Carlo (MCMC). As expected, the major route of elimination of absorbed perc is predicted to be exhalation as parent compound, with metabolism accounting for less than 20% of intake except in the case of mice exposed orally, in which metabolism is predicted to be slightly over 50% at lower exposures. In all three species, the concentration of perc in blood, the extent of perc oxidation, and the amount of TCA production is well-estimated, with residual uncertainties of ~ 2-fold. However, the resulting range of estimates for the amount of GSH conjugation is quite wide in humans (~ 3000-fold) and mice (~ 60-fold). While even high-end estimates of GSH conjugation in mice are lower than estimates of oxidation, in humans the estimated rates range from much lower to much higher than rates for perc oxidation. It is unclear to what extent this range reflects uncertainty, variability, or a combination. Importantly, by separating total perc metabolism into separate oxidative and conjugative pathways, an approach also recommended in a recent National Research Council review, this analysis reconciles the disparity between those previously published PBPK models that concluded low perc metabolism in humans and those that predicted high perc metabolism in humans. In essence, both conclusions are consistent with the data if augmented with some additional qualifications: in humans, oxidative metabolism is low, while GSH conjugation metabolism may be high or low, with uncertainty and/or interindividual variability spanning three orders of magnitude. More direct data on the internal kinetics of perc GSH conjugation, such as trichlorovinyl glutathione or tricholorvinyl cysteine in blood and/or tissues, would be needed to better characterize the uncertainty and variability in GSH conjugation in humans.
Evaluation of the impact of the exposure route on the human kinetic adjustment factor
2011, Regulatory Toxicology and Pharmacology
The objective of this study was to assess the impact of the exposure route on the human kinetic adjustment factor (HKAF), for which a default value of 3.16 is used in non-cancer risk assessment. A multi-route PBPK model was modified from the literature and used for computing the internal dose metrics in adults, neonates, children, elderly and pregnant women following three route-specific scenarios to chloroform, bromoform, tri- or per-chloroethylene (TCE or PERC). These include 24-h inhalation exposure, body-weight adjusted oral exposure and 30 min dermal exposure to contaminated drinking water. Distributions for body weight (BW), height (BH) and hepatic cytochrome P450 2E1 (CYP2E1) content were obtained from the literature, whereas model parameters (flows, volumes) were calculated from BW and BH. Monte Carlo simulations were performed and the HKAF was calculated as the ratio of the 95th percentile value of internal dose metrics in subpopulation to the 50th percentile value in adults. On the basis of the area under the parent compound’s arterial blood concentration vs time curve (AUC_pc), highest HKAFs were obtained in neonates for every scenario considered, and were the highest for bromoform (range: 3.6–7.4). Exceedance of the default value based on AUC_PC was also observed for an oral exposure to chloroform in neonates (4.9). In all other cases, HKAFs remained below the default value. Overall, this study has pointed out the dependency of the HKAF on the exposure route, dose metrics and subpopulation considered, as well as characteristics of the chemicals investigated.

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Variability of physiologically based pharmacokinetic (PBPK) model parameters and their effects on PBPK model predictions in a risk assessment for perchloroethylene (PCE)

Abstract

Toxicol. Appl. Pharmacol.

Toxicol. Appl. Pharmacol.

Toxicol. Appl. Pharmacol.

Toxicol. Appl. Pharmacol.

Toxicol. Lett.

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Toxicol. Appl. Pharmacol.