Literature DB >> 17899329

Physiologically based pharmacokinetic modelling: a sub-compartmentalized model of tissue distribution.

Max von Kleist1, Wilhelm Huisinga.   

Abstract

We present a sub-compartmentalized model of drug distribution in tissue that extends existing approaches based on the well-stirred tissue model. It is specified in terms of differential equations that explicitly account for the drug concentration in erythrocytes, plasma, interstitial and cellular space. Assuming, in addition, steady state drug distribution and by lumping the different sub-compartments, established models to predict tissue-plasma partition coefficients can be derived in an intriguingly simple way. This direct link is exploited to explicitly construct and parameterize the sub-compartmentalized model for moderate to strong bases, acids, neutrals and zwitterions. The derivation highlights the contributions of the different tissue constituents and provides a simple and transparent framework for the construction of novel tissue distribution models.

Mesh:

Year:  2007        PMID: 17899329     DOI: 10.1007/s10928-007-9071-3

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  28 in total

1.  A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery.

Authors:  P Poulin; F P Theil
Journal:  J Pharm Sci       Date:  2000-01       Impact factor: 3.534

2.  Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs.

Authors:  P Poulin; K Schoenlein; F P Theil
Journal:  J Pharm Sci       Date:  2001-04       Impact factor: 3.534

3.  Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition.

Authors:  Patrick Poulin; Frank-Peter Theil
Journal:  J Pharm Sci       Date:  2002-05       Impact factor: 3.534

Review 4.  ADMET in silico modelling: towards prediction paradise?

Authors:  Han van de Waterbeemd; Eric Gifford
Journal:  Nat Rev Drug Discov       Date:  2003-03       Impact factor: 84.694

Review 5.  Whole body pharmacokinetic models.

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

6.  An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery.

Authors:  Neil Parrott; Nicolas Paquereau; Philippe Coassolo; Thierry Lavé
Journal:  J Pharm Sci       Date:  2005-10       Impact factor: 3.534

7.  Lumping of whole-body physiologically based pharmacokinetic models.

Authors:  I A Nestorov; L J Aarons; P A Arundel; M Rowland
Journal:  J Pharmacokinet Biopharm       Date:  1998-02

8.  A physiologically based pharmacokinetic model of zidovudine (AZT) in the mouse: model development and scale-up to humans.

Authors:  H H Chow
Journal:  J Pharm Sci       Date:  1997-11       Impact factor: 3.534

Review 9.  Physiologically-based pharmacokinetic simulation modelling.

Authors:  George M Grass; Patrick J Sinko
Journal:  Adv Drug Deliv Rev       Date:  2002-03-31       Impact factor: 15.470

Review 10.  Human extrahepatic cytochromes P450: function in xenobiotic metabolism and tissue-selective chemical toxicity in the respiratory and gastrointestinal tracts.

Authors:  Xinxin Ding; Laurence S Kaminsky
Journal:  Annu Rev Pharmacol Toxicol       Date:  2002-01-10       Impact factor: 13.820

View more
  10 in total

1.  Lumping of physiologically-based pharmacokinetic models and a mechanistic derivation of classical compartmental models.

Authors:  Sabine Pilari; Wilhelm Huisinga
Journal:  J Pharmacokinet Pharmacodyn       Date:  2010-07-27       Impact factor: 2.745

2.  Defining the role of macrophages in local moxifloxacin tissue concentrations using biopsy data and whole-body physiologically based pharmacokinetic modelling.

Authors:  Andrea N Edginton; Gertrud Ahr; Stefan Willmann; Heino Stass
Journal:  Clin Pharmacokinet       Date:  2009       Impact factor: 6.447

3.  Explicit reformulations of the Lambert W-omega function for calculations of the solutions to one-compartment pharmacokinetic models with Michaelis-Menten elimination kinetics.

Authors:  Marko Goličnik
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2011-05-01       Impact factor: 2.441

Review 4.  Fluorescence techniques for determination of the membrane potentials in high throughput screening.

Authors:  Magda Przybylo; Tomasz Borowik; Marek Langner
Journal:  J Fluoresc       Date:  2010-11       Impact factor: 2.217

5.  Physiology-based pharmacokinetics of caspofungin for adults and paediatrics.

Authors:  Felix Stader; Gudrun Wuerthwein; Andreas H Groll; Joerg-Janne Vehreschild; Oliver A Cornely; Georg Hempel
Journal:  Pharm Res       Date:  2014-12-19       Impact factor: 4.200

6.  Drug-class specific impact of antivirals on the reproductive capacity of HIV.

Authors:  Max von Kleist; Stephan Menz; Wilhelm Huisinga
Journal:  PLoS Comput Biol       Date:  2010-03-26       Impact factor: 4.475

Review 7.  Physiologically-based PK/PD modelling of therapeutic macromolecules.

Authors:  Peter Thygesen; Panos Macheras; Achiel Van Peer
Journal:  Pharm Res       Date:  2009-10-22       Impact factor: 4.200

8.  Mechanistic framework predicts drug-class specific utility of antiretrovirals for HIV prophylaxis.

Authors:  Sulav Duwal; Laura Dickinson; Saye Khoo; Max von Kleist
Journal:  PLoS Comput Biol       Date:  2019-01-30       Impact factor: 4.475

9.  Predicting plasma concentrations of bisphenol A in children younger than 2 years of age after typical feeding schedules, using a physiologically based toxicokinetic model.

Authors:  Andrea N Edginton; Len Ritter
Journal:  Environ Health Perspect       Date:  2008-11-14       Impact factor: 9.031

10.  Hybrid stochastic framework predicts efficacy of prophylaxis against HIV: An example with different dolutegravir prophylaxis schemes.

Authors:  Sulav Duwal; Laura Dickinson; Saye Khoo; Max von Kleist
Journal:  PLoS Comput Biol       Date:  2018-06-14       Impact factor: 4.475

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.