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

Gearhart, Jeff; Mahle, Deirdre A.; Greene, Robert J.; Seckel, C.; Flemming, Carlyle D.; Fisher, Jeffery; Clewell, Harvey J.

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

Cited by 67 publications

(43 citation statements)

References 13 publications

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“…In addition, DCA is produced in a roughly linear fashion from PERC (28,125) at levels consistent with production from TCA, the principal metabolite of PERC. An analysis of data on the dose-response and elimination kinetics of DCA formed from TCE led to the conclusion that another source of DCA was required in addition to TCA, but that the data were inconsistent with the second source being the initial oxidation step (126).…”

Section: Metabolism Oftcementioning

confidence: 92%

Development of a Physiologically-Based Pharmacokinetic Model of Trichloroethylene and Its Metabolites for Use in Risk Assessment

Covington¹,

Clewell²,

Fisher³

2004

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A physiologically based pharmacokinetic (PBPK) model was developed that provides a comprehensive description of the kinetics of trichloroethylene (TCE) and its metabolites, trichloroethanol (TCOH), trichloroacetic acid (TCA), and dichloroacetic acid (DCA), in the mouse, rat, and human for both oral and inhalation exposure. The model includes descriptions of the three principal target tissues for cancer identified in animal bioassays: liver, lung, and kidney. Cancer dose metrics provided in the model include the area under the concentration curve (AUC) for TCA and DCA in the plasma, the peak concentration and AUC for chloral in the tracheobronchial region of the lung, and the production of a thioacetylating intermediate from dichlorovinylcysteine in the kidney. Additional dose metrics provided for noncancer risk assessment include the peak concentrations and AUCs for TCE and TCOH in the blood, as well as the total metabolism of TCE divided by the body weight. Sensitivity and uncertainty analyses were performed on the model to evaluate its suitability for use in a pharmacokinetic risk assessment for TCE. Model predictions of TCE, TCA, DCA, and TCOH concentrations in rodents and humans are in good agreement with a variety of experimental data, suggesting that the model should provide a useful basis for evaluating crossspecies differences in pharmacokinetics for these chemicals. In the case of the lung and kidney target tissues, however, only limited data are available for establishing cross-species pharmacokinetics. As a result, PBPK model calculations of target tissue dose for lung and kidney should be used with caution.

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Section: Metabolism Oftcementioning

confidence: 92%

Development of a Physiologically-Based Pharmacokinetic Model of Trichloroethylene and Its Metabolites for Use in Risk Assessment

Covington¹,

Clewell²,

Fisher³

2004

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“…The usual procedure for incorporating measures of variability and uncertainty into pharmacokinetic modeling is Monte Carlo (MC 1 ) simulation (Farrar et al, 1989;Bois et al, 1991;Hattis et al, 1990;Gearhart et al, 1993;Krewski et al, 1995). The core of this procedure is the specification of prior model parameter probability distribution functions (pdfs).…”

Section: Methodsmentioning

confidence: 99%

“…This practice is a source of the following concerns. First, handling single values in the presence of significant variability and uncertainty is inappropriate and may be misleading (Farrar et al, 1989;Bois et al, 1991;Hattis et al, 1990;Gearhart et al, 1993;Krewski et al, 1995). Usually derived from a small sample, the mean may not be representative or even meaningful.…”

mentioning

confidence: 99%

Incorporating Measures of Variability and Uncertainty into the Prediction of in Vivo Hepatic Clearance from in Vitro Data

Nestorov

Gueorguieva²,

Jones³

et al. 2002

Drug Metab Dispos

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ABSTRACT:The existing procedures for quantitative in vitro-in vivo clearance prediction can be significantly biased either by totally neglecting the existing variability and uncertainty by using mean parameter values or by implementing Monte Carlo simulation with statistical distribution of the parameters reconstructed from very small sets of data. The aim of the present study is to develop a methodology for the prediction of in vivo hepatic clearance in the presence of semiquantitative or qualitative data and accounting for the existing uncertainty and variability. The method consists of two steps: 1) transformation of the information available into fuzzy sets (fuzzification); and 2) computation of the in vivo clearance using arithmetic operations with fuzzy sets. To illustrate the approach, rat hepatocyte and microsomal data for eight benzodiazepine compounds are used. A comparison with a standard Monte Carlo procedure is made. The methodology proposed can be used when Monte Carlo simulation may be biased or cannot be implemented. The obtained fuzzy in vivo clearance can be used subsequently in fuzzy simulations of pharmacokinetic models.The ability to predict pharmacokinetic events in vivo, especially in humans, from in vitro data and other relevant information is of great importance both to academia in providing a quantitative framework for identification and investigation of the key processes involved and to industry in facilitating drug selection and development. Of all the pharmacokinetic parameters, most work in this endeavor has concentrated on the prediction of hepatic (metabolic) clearance. It usually comprises a series of sequential steps, starting from the estimation of intrinsic clearance in the in vitro system, through the identification of appropriate scaling coefficients, and ending with the implementation of a liver model. The ultimate goal is to achieve as accurate a quantitative prediction of the in vivo hepatic clearance as possible.The information used and generated throughout the in vitro-in vivo clearance prediction process is characterized by a large degree of uncertainty and significant variation in the experimental values Iwatsubo et al., 1997; Lavè et al., 1997;Obach et al., 1997) due to a number of highly related reasons. These include the complexity and sometimes unknown nature of the phenomena involved, the imperfect instrumentation and information processing tools, and the high inherent variability of the biological systems. The terms variability and uncertainty are used almost interchangeably in the pharmacokinetic and metabolism literature. It should be noted, however, that variability is an inherent property of the system of interest; it can be observed and recorded but not changed. In contrast, uncertainty relates to variations due to errors in assumptions, hypotheses, observations, experiments, and handling of the system studied. Accordingly, uncertainty in the information available can be decreased and theoretically eliminated by implementing "ideal" experiments and data-pro...

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“…The solvents studied were perchloroethylene (PCE; Dallas et al 1994a, b;Gearhart et al 1993), styrene (Andersen and Ramsey 1983;Csanady et al 1994;Filser et al 1993;LoÈ f and Johanson 1993;Paterson and Mackay 1986;Perbellini et al 1988;Ramsey and Andersen 1984), and toluene (Droz et al 1989;Purcell et al 1990;Tardif et al 1993a, b), which have been widely used in industry and for which PB-PK models have been reported by several authors.…”

Section: Methodsmentioning

confidence: 99%

“…The tissue-gas partition coe cient of the fat compartment for perchloroethylene is 3020 in one model (Dallas et al 1994a), corresponding to 2-fold the values of 1450±1455 used in other models (Droz et al 1989;Gearhart et al 1993). For toluene, there is no big difference among the authors.…”

Section: Methodsmentioning

confidence: 99%

Uncertainties in physiologically based pharmacokinetic models caused by several input parameters

Jang¹,

Droz²,

Chung³

1999

International Archives of Occupational and Environmental Health

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Objective: One of the problems in the application of physiologically based pharmacokinetic (PB-PK) models is that authors often use di erent input parameters, with unknown in¯uence on the results. Di erences in the simulation results obtained with various sets of parameters are examined herein. Method: Chemicals considered were perchloroethylene, toluene, and styrene. Simulations of alveolar concentrations, blood concentrations, and urinary metabolite excretions were performed for the three solvents. The input parameters discussed herein are physiological values, metabolic constants, and partition coe cients. The inuence of metabolic constants and partition coe cients is studied by comparison of models against one another. Results: Metabolic parameters such as V max and K m varied considerably between authors. Tissue-gas partition coe cients, especially for the fat compartment, also di ered according to the authors. Such di erences in input parameter values proved to have a large in¯uence on PB-PK model results and, therefore, increased their uncertainties. Uncertainties were much more signi®cant in urinary metabolite concentration than in alveolar and blood concentration for chemicals that are poorly metabolized. On the other hand, uncertainties were more signi®cant in alveolar and blood concentrations than in urinary metabolite excretions for chemicals that are well metabolized. Conclusion: Careful attention is necessary in the selection and/or citation of values from published data. The validity of PB-PK models should be simultaneously con®rmed with both the blood and/or alveolar concentration and urinary metabolite concentrations.

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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)

Cited by 67 publications

References 13 publications

Development of a Physiologically-Based Pharmacokinetic Model of Trichloroethylene and Its Metabolites for Use in Risk Assessment

Development of a Physiologically-Based Pharmacokinetic Model of Trichloroethylene and Its Metabolites for Use in Risk Assessment

Incorporating Measures of Variability and Uncertainty into the Prediction of in Vivo Hepatic Clearance from in Vitro Data

Uncertainties in physiologically based pharmacokinetic models caused by several input parameters

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