Literature DB >> 17372687

Mechanistic approaches to volume of distribution predictions: understanding the processes.

Trudy Rodgers1, Malcolm Rowland.   

Abstract

PURPOSE: To use recently developed mechanistic equations to predict tissue-to-plasma water partition coefficients (Kpus), apply these predictions to whole body unbound volume of distribution at steady state (Vu(ss)) determinations, and explain the differences in the extent of drug distribution both within and across the various compound classes.
MATERIALS AND METHODS: Vu(ss) values were predicted for 92 structurally diverse compounds in rats and 140 in humans by two approaches. The first approach incorporated Kpu values predicted for 13 tissues whereas the second was restricted to muscle.
RESULTS: The prediction accuracy was good for both approaches in rats and humans, with 64-78% and 82-92% of the predicted Vu(ss) values agreeing with in vivo data to within factors of +/-2 and 3, respectively.
CONCLUSIONS: Generic distribution processes were identified as lipid partitioning and dissolution where the former is higher for lipophilic unionised drugs. In addition, electrostatic interactions with acidic phospholipids can predominate for ionised bases when affinities (reflected by binding to constituents within blood) are high. For acidic drugs albumin binding dominates when plasma protein binding is high. This ability to explain drug distribution and link it to physicochemical properties can help guide the compound selection process.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17372687     DOI: 10.1007/s11095-006-9210-3

Source DB:  PubMed          Journal:  Pharm Res        ISSN: 0724-8741            Impact factor:   4.580


  102 in total

1.  Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases.

Authors:  Trudy Rodgers; David Leahy; Malcolm Rowland
Journal:  J Pharm Sci       Date:  2005-06       Impact factor: 3.534

2.  Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions.

Authors:  Trudy Rodgers; Malcolm Rowland
Journal:  J Pharm Sci       Date:  2006-06       Impact factor: 3.534

3.  Pharmacokinetics of ketorolac tromethamine in humans after intravenous, intramuscular and oral administration.

Authors:  D Jung; E Mroszczak; L Bynum
Journal:  Eur J Clin Pharmacol       Date:  1988       Impact factor: 2.953

4.  Pharmacokinetics of ketanserin and its metabolite ketanserin-ol in man after intravenous, intramuscular and oral administration.

Authors:  J Heykants; A Van Peer; R Woestenborghs; S Gould; J Mills
Journal:  Eur J Clin Pharmacol       Date:  1986       Impact factor: 2.953

5.  Quantitative structure-pharmacokinetic relationships for systemic drug distribution kinetics not confined to a congeneric series.

Authors:  R A Herman; P Veng-Pedersen
Journal:  J Pharm Sci       Date:  1994-03       Impact factor: 3.534

6.  Extensive binding of the bioflavonoid quercetin to human plasma proteins.

Authors:  D W Boulton; U K Walle; T Walle
Journal:  J Pharm Pharmacol       Date:  1998-02       Impact factor: 3.765

7.  Pharmacokinetics of famotidine, a new H2-receptor antagonist, in relation to renal function.

Authors:  T Takabatake; H Ohta; M Maekawa; Y Yamamoto; Y Ishida; H Hara; S Nakamura; Y Ushiogi; M Kawabata; N Hashimoto
Journal:  Eur J Clin Pharmacol       Date:  1985       Impact factor: 2.953

8.  Prediction of human volume of distribution values for neutral and basic drugs. 2. Extended data set and leave-class-out statistics.

Authors:  Franco Lombardo; R Scott Obach; Marina Y Shalaeva; Feng Gao
Journal:  J Med Chem       Date:  2004-02-26       Impact factor: 7.446

9.  Absorption and disposition of furosemide in healthy volunteers, measured with a metabolite-specific assay.

Authors:  D E Smith; E T Lin; L Z Benet
Journal:  Drug Metab Dispos       Date:  1980 Sep-Oct       Impact factor: 3.922

Review 10.  Prediction of the disposition of nine weakly acidic and six weakly basic drugs in humans from pharmacokinetic parameters in rats.

Authors:  Y Sawada; M Hanano; Y Sugiyama; T Iga
Journal:  J Pharmacokinet Biopharm       Date:  1985-10
View more
  107 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

Review 2.  Coexistence of passive and carrier-mediated processes in drug transport.

Authors:  Kiyohiko Sugano; Manfred Kansy; Per Artursson; Alex Avdeef; Stefanie Bendels; Li Di; Gerhard F Ecker; Bernard Faller; Holger Fischer; Grégori Gerebtzoff; Hans Lennernaes; Frank Senner
Journal:  Nat Rev Drug Discov       Date:  2010-08       Impact factor: 84.694

3.  Use of dried blood spots in drug development: pharmacokinetic considerations.

Authors:  Malcolm Rowland; Gary T Emmons
Journal:  AAPS J       Date:  2010-04-10       Impact factor: 4.009

Review 4.  Quantitative clinical pharmacology is transforming drug regulation.

Authors:  Carl C Peck
Journal:  J Pharmacokinet Pharmacodyn       Date:  2010-10-27       Impact factor: 2.745

Review 5.  Physiologically-based pharmacokinetic modeling for absorption, transport, metabolism and excretion.

Authors:  K Sandy Pang; Matthew R Durk
Journal:  J Pharmacokinet Pharmacodyn       Date:  2010-12-14       Impact factor: 2.745

6.  Evaluating a physiologically based pharmacokinetic model for predicting the pharmacokinetics of midazolam in Chinese after oral administration.

Authors:  Hong-yun Wang; Xia Chen; Ji Jiang; Jun Shi; Pei Hu
Journal:  Acta Pharmacol Sin       Date:  2015-11-23       Impact factor: 6.150

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

Authors:  Max von Kleist; Wilhelm Huisinga
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-09-25       Impact factor: 2.745

8.  Herb-drug interactions: challenges and opportunities for improved predictions.

Authors:  Scott J Brantley; Aneesh A Argikar; Yvonne S Lin; Swati Nagar; Mary F Paine
Journal:  Drug Metab Dispos       Date:  2013-12-11       Impact factor: 3.922

9.  Physiologically based pharmacokinetic modelling and in vivo [I]/K(i) accurately predict P-glycoprotein-mediated drug-drug interactions with dabigatran etexilate.

Authors:  Yuansheng Zhao; Zhe-Yi Hu
Journal:  Br J Pharmacol       Date:  2014-02       Impact factor: 8.739

10.  Drug Distribution Part 2. Predicting Volume of Distribution from Plasma Protein Binding and Membrane Partitioning.

Authors:  Ken Korzekwa; Swati Nagar
Journal:  Pharm Res       Date:  2016-12-13       Impact factor: 4.200

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.