An example of model structure differences using sensitivity analyses in physiologically based pharmacokinetic models of trichloroethylene in humans
Trichloroethylene (TCE) is an industrial chemical and an environmental contaminant. TCE and its metabolites may be carcinogenic and affect human health. Physiologically based pharmacokinetic (PBPK) models that differ in compartmentalization are developed for TCE metabolism in humans, and the focus o...
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Veröffentlicht in: | Bulletin of mathematical biology 2007-11, Vol.69 (8), p.2591-2625 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Trichloroethylene (TCE) is an industrial chemical and an environmental contaminant. TCE and its metabolites may be carcinogenic and affect human health. Physiologically based pharmacokinetic (PBPK) models that differ in compartmentalization are developed for TCE metabolism in humans, and the focus of this investigation is to evaluate alternative models. The two models formulated differ in the compartmentalization of metabolites; more specifically, one model has compartments for all chemicals and the other model has only a generalized body compartment for each the metabolites and contains multiple compartments for the parent, TCE. The models are compared through sensitivity analyses in order to selectively discriminate with regards to model structure. Sensitivities to a parameter of cardiac output (Qcc) are calculated, and the more compartmentalized model predictions for excretion show lower sensitivity to changes in this parameter. Values of Qcc used in the sensitivity analyses are specifically chosen to be applicable to adults of ages into the low 60s. Since information about cardiac output across a population is not often incorporated into a PBPK model, the more compartmentalized ("full") model is probably a more appropriate mathematical description of TCE metabolism, but further study may be necessary to decide which model is a more reasonable option if distributional information about Qcc is used. The study is intended to illustrate how sensitivity analysis can be used in order to make appropriate decisions about model development when considering physiological parameters than vary across the population. |
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ISSN: | 0092-8240 1522-9602 |
DOI: | 10.1007/s11538-007-9233-x |