Uncertainties in physiologically based pharmacokinetic models caused by several input parameters

Abstract

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.

Biological monitoring of workers exposed to ethylbenzene and co-exposed to xylene

“…Urinary metabolite concentrations were calculated assuming a creatinine excretion rate of 1.4 g/day for a 70 kg, 170 cm man (Droz et al 1999). Table 3 presents the physiological parameters used in the model (Droz et al , 1999Droz 1996, 1997;Jang et al 1999a). …”

Analysis of Algorithms Predicting Blood:Air and Tissue:Blood Partition Coefficients from Solvent Partition Coefficients for Prevalent Components of JP-8 Jet Fuel

“…Differences in tissue and blood PCs used in a PBPK model can influence predictions (Jang et al, 1999). Overall, solvent PC equations predicting rat and human blood:air PCs for selected JP-8 components were not consistent in their performance.…”

In silico toxicology: simulating interaction thresholds for human exposure to mixtures of trichloroethylene, tetrachloroethylene, and 1,1,1-trichloroethane.

“…Biomarkers of exposure such as generated metabolites and/or parent compound blood levels are measures frequently used to assess past and current exposures in occupational and environmental settings. Although not the only source of variability in the observed biological monitoring results, metabolic interactions due to mixed exposures can significantly complicate their interpretation (Jang et al 1999a). Coexposure to another chemical can either increase or decrease the biomarker levels (compared with single exposure) by altering metabolism and/or elimination.…”