Literature DB >> 2690411

Evaluation of uncertainty in input parameters to pharmacokinetic models and the resulting uncertainty in output.

D Farrar1, B Allen, K Crump, A Shipp.   

Abstract

Physiologically-based pharmacokinetic (PBPK) models may be used to predict the concentrations of parent chemical or metabolites in tissues, resulting from specified chemical exposures. An important application of PBPK modeling is in assessment of carcinogenic risks to humans, based on animal data. The parameters of a PBPK model may include metabolic parameters, blood/air and tissue/blood partition coefficients, and physiological parameters, such as organ weights and blood flow rates. Uncertainty in estimates of these parameters results in uncertainty regarding tissue concentrations and resulting risks. Data are reviewed relevant to the quantification of these uncertainties, for a PBPK model-based risk assessment for tetrachloroethylene. Probability distributions are developed to express uncertainty in model parameters, and uncertainties are propagated by a sequence of operations that simulates processes recognized as contributing to estimates of human risk. Distributions of PBPK model output and human risk estimates are used to characterize uncertainty resulting from uncertainty in model parameters.

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Year:  1989        PMID: 2690411     DOI: 10.1016/0378-4274(89)90044-1

Source DB:  PubMed          Journal:  Toxicol Lett        ISSN: 0378-4274            Impact factor:   4.372


  8 in total

Review 1.  Whole body pharmacokinetic models.

Authors:  Ivan Nestorov
Journal:  Clin Pharmacokinet       Date:  2003       Impact factor: 6.447

2.  Population-based analysis of methadone distribution and metabolism using an age-dependent physiologically based pharmacokinetic model.

Authors:  Feng Yang; Xianping Tong; D Gail McCarver; Ronald N Hines; Daniel A Beard
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-06-07       Impact factor: 2.745

3.  Fuzzy simulation of pharmacokinetic models: case study of whole body physiologically based model of diazepam.

Authors:  Ivelina I Gueorguieva; Ivan A Nestorov; Malcolm Rowland
Journal:  J Pharmacokinet Pharmacodyn       Date:  2004-06       Impact factor: 2.745

4.  Reducing whole body physiologically based pharmacokinetic models using global sensitivity analysis: diazepam case study.

Authors:  Ivelina Gueorguieva; Ivan A Nestorov; Malcolm Rowland
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-12-20       Impact factor: 2.745

5.  Effect of various exposure scenarios on the biological monitoring of organic solvents in alveolar air. I. Toluene and m-xylene.

Authors:  S Laparé; R Tardif; J Brodeur
Journal:  Int Arch Occup Environ Health       Date:  1993       Impact factor: 3.015

6.  Incorporation of stochastic variability in mechanistic population pharmacokinetic models: handling the physiological constraints using normal transformations.

Authors:  Nikolaos Tsamandouras; Thierry Wendling; Amin Rostami-Hodjegan; Aleksandra Galetin; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-05-26       Impact factor: 2.745

Review 7.  Applications of physiologic pharmacokinetic modeling in carcinogenic risk assessment.

Authors:  D Krewski; J R Withey; L F Ku; M E Andersen
Journal:  Environ Health Perspect       Date:  1994-12       Impact factor: 9.031

Review 8.  Development of a physiologically based pharmacokinetic model of trichloroethylene and its metabolites for use in risk assessment.

Authors:  H J Clewell; P R Gentry; T R Covington; J M Gearhart
Journal:  Environ Health Perspect       Date:  2000-05       Impact factor: 9.031

  8 in total

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