Literature DB >> 11323201

Modelling and simulation of variability and uncertainty in toxicokinetics and pharmacokinetics.

I Nestorov1.   

Abstract

Two important methodological issues within the framework of the variability and uncertainty analysis of toxicokinetic and pharmacokinetic systems are discussed: (i) modelling and simulation of the existing physiologic variability in a population; and (ii) modelling and simulation of variability and uncertainty when there is insufficient or not well defined (e.g. small sample, semiquantitative, qualitative and vague) information available. Physiologically based pharmacokinetic models are especially suited for separating and characterising the physiologic variability from the overall variability and uncertainty in the system. Monte Carlo sampling should draw from multivariate distributions, which reflect all levels of existing dependencies in the intact organism. The population characteristics should be taken into account. A fuzzy simulation approach is proposed to model variability and uncertainty when there is semiquantitative, qualitative and vague information about the model parameters and their statistical distributions cannot be defined reliably.

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Year:  2001        PMID: 11323201     DOI: 10.1016/s0378-4274(01)00273-9

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


  15 in total

Review 1.  Whole body pharmacokinetic models.

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2.  Spline functions in convolutional modeling of verapamil bioavailability and bioequivalence. I: conceptual and numerical issues.

Authors:  J Popović
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2006 Apr-Jun       Impact factor: 2.441

3.  Simulating pharmacokinetic and pharmacodynamic fuzzy-parameterized models: a comparison of numerical methods.

Authors:  Kok-Yong Seng; Ivan Nestorov; Paolo Vicini
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-08-21       Impact factor: 2.745

Review 4.  Combining the 'bottom up' and 'top down' approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data.

Authors:  Nikolaos Tsamandouras; Amin Rostami-Hodjegan; Leon Aarons
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

5.  Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim.

Authors:  Periklis Tsiros; Frederic Y Bois; Aristides Dokoumetzidis; Georgia Tsiliki; Haralambos Sarimveis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-04-04       Impact factor: 2.745

6.  Prediction of drug disposition in diabetic patients by means of a physiologically based pharmacokinetic model.

Authors:  Jia Li; Hai-Fang Guo; Can Liu; Zeyu Zhong; Li Liu; Xiao-Dong Liu
Journal:  Clin Pharmacokinet       Date:  2015-02       Impact factor: 6.447

7.  Assessing drug distribution in tissues expressing P-glycoprotein using physiologically based pharmacokinetic modeling: identification of important model parameters through global sensitivity analysis.

Authors:  Frederique Fenneteau; Jun Li; Fahima Nekka
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-10-22       Impact factor: 2.745

Review 8.  Facilitation of drug evaluation in children by population methods and modelling.

Authors:  Michel Tod; Vincent Jullien; Gérard Pons
Journal:  Clin Pharmacokinet       Date:  2008       Impact factor: 6.447

9.  A Bayesian population PBPK model for multiroute chloroform exposure.

Authors:  Yuching Yang; Xu Xu; Panos G Georgopoulos
Journal:  J Expo Sci Environ Epidemiol       Date:  2009-05-27       Impact factor: 5.563

10.  Simulation of the pharmacokinetics of bisoprolol in healthy adults and patients with impaired renal function using whole-body physiologically based pharmacokinetic modeling.

Authors:  Guo-fu Li; Kun Wang; Rui Chen; Hao-ru Zhao; Jin Yang; Qing-shan Zheng
Journal:  Acta Pharmacol Sin       Date:  2012-10-22       Impact factor: 6.150

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