Literature DB >> 15858851

Tissue distribution of basic drugs: accounting for enantiomeric, compound and regional differences amongst beta-blocking drugs in rat.

Trudy Rodgers1, David Leahy, Malcolm Rowland.   

Abstract

The purpose of this research was to identify the major factors controlling the distribution of beta-blockers (acebutolol, betaxolol, bisoprolol, metoprolol, oxprenolol, pindolol, propranolol and timolol) in rats, across tissues, compounds and enantiomers. Tissue distribution was assessed at steady state by infusing cassette doses of beta-blockers into the jugular vein via an indwelling catheter at a constant rate. Blood was sampled via an indwelling catheter in the carotid artery, and 12 tissues excised at the end of dose infusion (4 or 8 h). Drug concentrations were quantified using a novel chiral LC-MS method and the tissue-to-plasma (Kp) and tissue-to-plasma water (Kpu) values were calculated for each tissue. Differences between Kp were observed between many enantiomeric pairs, and largely explained by enantiomeric differences in plasma protein binding. Across compounds, Kpu values were generally highest in lung and lowest in adipose, and were higher for the more lipophilic drugs betaxolol and propranolol. For any tissue, Kpu differences between the individual beta-blockers correlated well with the corresponding affinity for blood cells. For all compounds, regional tissue distribution correlated well with tissue acidic phospholipid concentrations, with phosphatidylserine appearing to have the strongest influence. This information may be used as the basis for predicting the tissue distribution of basic drugs. (c) 2005 Wiley-Liss, Inc.

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Year:  2005        PMID: 15858851     DOI: 10.1002/jps.20323

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  36 in total

1.  Optimal design for multivariate response pharmacokinetic models.

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2.  Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes.

Authors:  Marylore Chenel; François Bouzom; Leon Aarons; Kayode Ogungbenro
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3.  Prediction of Tissue-to-Plasma Ratios of Basic Compounds in Mice.

Authors:  Prashant B Nigade; Jayasagar Gundu; K Sreedhara Pai; Kumar V S Nemmani
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-10       Impact factor: 2.441

4.  Predictions of metabolic drug-drug interactions using physiologically based modelling: Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil.

Authors:  Nathalie Perdaems; Helene Blasco; Cedric Vinson; Marylore Chenel; Sarah Whalley; Fanny Cazade; François Bouzom
Journal:  Clin Pharmacokinet       Date:  2010-04       Impact factor: 6.447

5.  Revisiting a physiologically based pharmacokinetic model for cocaine with a forensic scope.

Authors:  María Elena Bravo-Gómez; Laura Nayeli Camacho-García; Luz Alejandra Castillo-Alanís; Miguel Ángel Mendoza-Meléndez; Alejandra Quijano-Mateos
Journal:  Toxicol Res (Camb)       Date:  2019-03-13       Impact factor: 3.524

6.  Incorporation of the Time-Varying Postprandial Increase in Splanchnic Blood Flow into a PBPK Model to Predict the Effect of Food on the Pharmacokinetics of Orally Administered High-Extraction Drugs.

Authors:  Rachel H Rose; David B Turner; Sibylle Neuhoff; Masoud Jamei
Journal:  AAPS J       Date:  2017-05-19       Impact factor: 4.009

Review 7.  A physiologically based pharmacokinetic model of the minipig: data compilation and model implementation.

Authors:  Claudia Suenderhauf; Neil Parrott
Journal:  Pharm Res       Date:  2012-11-21       Impact factor: 4.200

8.  Physiologically Based Pharmacokinetic (PBPK) Modeling of Pitavastatin and Atorvastatin to Predict Drug-Drug Interactions (DDIs).

Authors:  Peng Duan; Ping Zhao; Lei Zhang
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-08       Impact factor: 2.441

9.  Influence of lipophilicity on drug partitioning into sclera, choroid-retinal pigment epithelium, retina, trabecular meshwork, and optic nerve.

Authors:  Rajendra S Kadam; Uday B Kompella
Journal:  J Pharmacol Exp Ther       Date:  2009-11-19       Impact factor: 4.030

10.  A workflow example of PBPK modeling to support pediatric research and development: case study with lorazepam.

Authors:  A R Maharaj; J S Barrett; A N Edginton
Journal:  AAPS J       Date:  2013-01-24       Impact factor: 4.009

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