Literature DB >> 10664535

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

P Poulin1, F P Theil.   

Abstract

The tissue:plasma (P(t:p)) partition coefficients (PCs) are important drug-specific input parameters in physiologically based pharmacokinetic (PBPK) models used to estimate the disposition of drugs in biota. Until now the use of PBPK models in early stages of the drug discovery process was not possible, since the estimation of P(t:p) of new drug candidates by using conventional in vitro and/or in vivo methods is too time and cost intensive. The objectives of the study were (i) to develop and validate two mechanistic equations for predicting a priori the rabbit, rat and mouse P(t:p) of non-adipose and non-excretory tissues (bone, brain, heart, intestine, lung, muscle, skin, spleen) for 65 structurally unrelated drugs and (ii) to evaluate the adequacy of using P(t:p) of muscle as predictors for P(t:p) of other tissues. The first equation predicts P(t:p) at steady state, assuming a homogenous distribution and passive diffusion of drugs in tissues, from a ratio of solubility and macromolecular binding between tissues and plasma. The ratio of solubility was estimated from log vegetable oil:water PCs (K(vo:w)) of drugs and lipid and water levels in tissues and plasma, whereas the ratio of macromolecular binding for drugs was estimated from tissue interstitial fluid-to-plasma concentration ratios of albumin, globulins and lipoproteins. The second equation predicts P(t:p) of drugs residing predominantly in the interstitial space of tissues. Therefore, the fractional volume content of interstitial space in each tissue replaced drug solubilities in the first equation. Following the development of these equations, regression analyses between P(t:p) of muscle and those of the other tissues were examined. The average ratio of predicted-to-experimental P(t:p) values was 1.26 (SD = 1.40, r = 0.90, n = 269), and 85% of the 269 predicted values were within a factor of three of the corresponding literature values obtained under in vivo and in vitro conditions. For predicted and experimental P(t:p), linear relationships (r > 0.9 in most cases) were observed between muscle and other tissues, suggesting that P(t:p) of muscle is a good predictor for the P(t:p) of other tissues. The two previous equations could explain the mechanistic basis of these linear relationships. The practical aim of this study is a worthwhile goal for pharmacokinetic screening of new drug candidates. Copyright 2000 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 89:16-35, 2000

Entities:  

Mesh:

Substances:

Year:  2000        PMID: 10664535     DOI: 10.1002/(SICI)1520-6017(200001)89:1<16::AID-JPS3>3.0.CO;2-E

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


  97 in total

1.  Computation of log BB values for compounds transported through carrier-mediated mechanisms using in vitro permeability data from brain microvessel endothelial cell (BMEC) monolayers.

Authors:  Helen H Usansky; Patrick J Sinko
Journal:  Pharm Res       Date:  2003-03       Impact factor: 4.200

2.  A physiologic model for simulating gastrointestinal flow and drug absorption in rats.

Authors:  Stefan Willmann; Walter Schmitt; Jörg Keldenich; Jennifer B Dressman
Journal:  Pharm Res       Date:  2003-11       Impact factor: 4.200

Review 3.  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

4.  Model-based approaches for ivabradine development in paediatric population, part I: study preparation assessment.

Authors:  Sophie Peigné; François Bouzom; Karl Brendel; Charlotte Gesson; Sylvain Fouliard; Marylore Chenel
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-11-12       Impact factor: 2.745

Review 5.  Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Authors:  Jennifer E Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2015-08-21       Impact factor: 3.922

6.  Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid.

Authors:  Pyry A J Välitalo; Koen Griffioen; Matthew L Rizk; Sandra A G Visser; Meindert Danhof; Gaori Rao; Piet H van der Graaf; J G Coen van Hasselt
Journal:  Pharm Res       Date:  2015-12-01       Impact factor: 4.200

7.  Preliminary physiologically based pharmacokinetic models for benzo[a]pyrene and dibenzo[def,p]chrysene in rodents.

Authors:  Susan Ritger Crowell; Shantu G Amin; Kim A Anderson; Gowdahalli Krishnegowda; Arun K Sharma; Jolen J Soelberg; David E Williams; Richard A Corley
Journal:  Toxicol Appl Pharmacol       Date:  2011-09-29       Impact factor: 4.219

8.  A physiologically-based pharmacokinetic model of drug detoxification by nanoparticles.

Authors:  Marissa S Fallon; Manoj Varshney; Donn M Dennis; Anuj Chauhan
Journal:  J Pharmacokinet Pharmacodyn       Date:  2004-10       Impact factor: 2.745

9.  Physiologically based pharmacokinetic modelling: a sound mechanistic basis is needed.

Authors:  L Aarons
Journal:  Br J Clin Pharmacol       Date:  2005-12       Impact factor: 4.335

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.