Literature DB >> 17620347

Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs.

Stefan S De Buck1, Vikash K Sinha, Luca A Fenu, Marjoleen J Nijsen, Claire E Mackie, Ron A H J Gilissen.   

Abstract

The aim of this study was to evaluate different physiologically based modeling strategies for the prediction of human pharmacokinetics. Plasma profiles after intravenous and oral dosing were simulated for 26 clinically tested drugs. Two mechanism-based predictions of human tissue-to-plasma partitioning (P(tp)) from physicochemical input (method Vd1) were evaluated for their ability to describe human volume of distribution at steady state (V(ss)). This method was compared with a strategy that combined predicted and experimentally determined in vivo rat P(tp) data (method Vd2). Best V(ss) predictions were obtained using method Vd2, providing that rat P(tp) input was corrected for interspecies differences in plasma protein binding (84% within 2-fold). V(ss) predictions from physicochemical input alone were poor (32% within 2-fold). Total body clearance (CL) was predicted as the sum of scaled rat renal clearance and hepatic clearance projected from in vitro metabolism data. Best CL predictions were obtained by disregarding both blood and microsomal or hepatocyte binding (method CL2, 74% within 2-fold), whereas strong bias was seen using both blood and microsomal or hepatocyte binding (method CL1, 53% within 2-fold). The physiologically based pharmacokinetics (PBPK) model, which combined methods Vd2 and CL2 yielded the most accurate predictions of in vivo terminal half-life (69% within 2-fold). The Gastroplus advanced compartmental absorption and transit model was used to construct an absorption-disposition model and provided accurate predictions of area under the plasma concentration-time profile, oral apparent volume of distribution, and maximum plasma concentration after oral dosing, with 74%, 70%, and 65% within 2-fold, respectively. This evaluation demonstrates that PBPK models can lead to reasonable predictions of human pharmacokinetics.

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Year:  2007        PMID: 17620347     DOI: 10.1124/dmd.107.015644

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  54 in total

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3.  Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS).

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Journal:  Pharm Res       Date:  2015-07-09       Impact factor: 4.200

4.  Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis.

Authors:  Sheila Annie Peters
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5.  Prediction of modified release pharmacokinetics and pharmacodynamics from in vitro, immediate release, and intravenous data.

Authors:  Viera Lukacova; Walter S Woltosz; Michael B Bolger
Journal:  AAPS J       Date:  2009-05-09       Impact factor: 4.009

Review 6.  Mechanistic approaches to predicting oral drug absorption.

Authors:  Weili Huang; Sau Lawrence Lee; Lawrence X Yu
Journal:  AAPS J       Date:  2009-04-21       Impact factor: 4.009

Review 7.  The role of transporters in the pharmacokinetics of orally administered drugs.

Authors:  Sarah Shugarts; Leslie Z Benet
Journal:  Pharm Res       Date:  2009-06-30       Impact factor: 4.200

Review 8.  Towards quantitative prediction of oral drug absorption.

Authors:  Jennifer B Dressman; Kirstin Thelen; Ekarat Jantratid
Journal:  Clin Pharmacokinet       Date:  2008       Impact factor: 6.447

9.  Practical anticipation of human efficacious doses and pharmacokinetics using in vitro and preclinical in vivo data.

Authors:  Tycho Heimbach; Suresh B Lakshminarayana; Wenyu Hu; Handan He
Journal:  AAPS J       Date:  2009-08-26       Impact factor: 4.009

Review 10.  Predicting pharmacokinetics of drugs using physiologically based modeling--application to food effects.

Authors:  N Parrott; V Lukacova; G Fraczkiewicz; M B Bolger
Journal:  AAPS J       Date:  2009-01-29       Impact factor: 4.009

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