Literature DB >> 19280352

Modelling and PBPK simulation in drug discovery.

Hannah M Jones1, Iain B Gardner, Kenny J Watson.   

Abstract

Physiologically based pharmacokinetic (PBPK) models are composed of a series of differential equations and have been implemented in a number of commercial software packages. These models require species-specific and compound-specific input parameters and allow for the prediction of plasma and tissue concentration time profiles after intravenous and oral administration of compounds to animals and humans. PBPK models allow the early integration of a wide variety of preclinical data into a mechanistic quantitative framework. Use of PBPK models allows the experimenter to gain insights into the properties of a compound, helps to guide experimental efforts at the early stages of drug discovery, and enables the prediction of human plasma concentration time profiles with minimal (and in some cases no) animal data. In this review, the application and limitations of PBPK techniques in drug discovery are discussed. Specific reference is made to its utility (1) at the lead development stage for the prioritization of compounds for animal PK studies and (2) at the clinical candidate selection and "first in human" stages for the prediction of human PK.

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Year:  2009        PMID: 19280352      PMCID: PMC2664888          DOI: 10.1208/s12248-009-9088-1

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  60 in total

1.  A compartmental absorption and transit model for estimating oral drug absorption.

Authors:  L X Yu; G L Amidon
Journal:  Int J Pharm       Date:  1999-09-20       Impact factor: 5.875

Review 2.  Prediction of hepatic metabolic clearance based on interspecies allometric scaling techniques and in vitro-in vivo correlations.

Authors:  T Lavé; P Coassolo; B Reigner
Journal:  Clin Pharmacokinet       Date:  1999-03       Impact factor: 6.447

3.  Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance.

Authors:  Dermot F McGinnity; Matthew G Soars; Richard A Urbanowicz; Robert J Riley
Journal:  Drug Metab Dispos       Date:  2004-07-30       Impact factor: 3.922

4.  The binding of drugs to hepatocytes and its relationship to physicochemical properties.

Authors:  Rupert P Austin; Patrick Barton; Sarfraz Mohmed; Robert J Riley
Journal:  Drug Metab Dispos       Date:  2004-12-22       Impact factor: 3.922

Review 5.  Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices.

Authors:  J B Houston; D J Carlile
Journal:  Drug Metab Rev       Date:  1997-11       Impact factor: 4.518

6.  Animal scale-up.

Authors:  R L Dedrick
Journal:  J Pharmacokinet Biopharm       Date:  1973-10

7.  Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches.

Authors:  Kiyomi Ito; J Brian Houston
Journal:  Pharm Res       Date:  2005-01       Impact factor: 4.200

8.  Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics.

Authors:  H Boxenbaum
Journal:  J Pharmacokinet Biopharm       Date:  1982-04

9.  Interspecies pharmacokinetic comparisons and allometric scaling of napsagatran, a low molecular weight thrombin inhibitor.

Authors:  T Lavé; R Portmann; G Schenker; A Gianni; A Guenzi; M A Girometta; M Schmitt
Journal:  J Pharm Pharmacol       Date:  1999-01       Impact factor: 3.765

10.  A physiological model for the estimation of the fraction dose absorbed in humans.

Authors:  Stefan Willmann; Walter Schmitt; Jörg Keldenich; Jörg Lippert; Jennifer B Dressman
Journal:  J Med Chem       Date:  2004-07-29       Impact factor: 7.446

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  51 in total

1.  BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems.

Authors:  John F Graf; Bernhard J Scholz; Maria I Zavodszky
Journal:  J Pharmacokinet Pharmacodyn       Date:  2011-12-10       Impact factor: 2.745

2.  Physiologically based modeling of pravastatin transporter-mediated hepatobiliary disposition and drug-drug interactions.

Authors:  Manthena V S Varma; Yurong Lai; Bo Feng; John Litchfield; Theunis C Goosen; Arthur Bergman
Journal:  Pharm Res       Date:  2012-05-26       Impact factor: 4.200

3.  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 4.  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

Review 5.  Pharmacometric Models for Characterizing the Pharmacokinetics of Orally Inhaled Drugs.

Authors:  Jens Markus Borghardt; Benjamin Weber; Alexander Staab; Charlotte Kloft
Journal:  AAPS J       Date:  2015-04-07       Impact factor: 4.009

6.  Utility of physiologically based absorption modeling in implementing Quality by Design in drug development.

Authors:  Xinyuan Zhang; Robert A Lionberger; Barbara M Davit; Lawrence X Yu
Journal:  AAPS J       Date:  2011-01-05       Impact factor: 4.009

7.  Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions.

Authors:  Weize Huang; Mariko Nakano; Jennifer Sager; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2017-08-31       Impact factor: 3.922

8.  Physiologically-Based Pharmacokinetic Modeling of Macitentan: Prediction of Drug-Drug Interactions.

Authors:  Ruben de Kanter; Patricia N Sidharta; Stéphane Delahaye; Carmela Gnerre; Jerome Segrestaa; Stephan Buchmann; Christopher Kohl; Alexander Treiber
Journal:  Clin Pharmacokinet       Date:  2016-03       Impact factor: 6.447

Review 9.  Computational approaches to analyse and predict small molecule transport and distribution at cellular and subcellular levels.

Authors:  Kyoung Ah Min; Xinyuan Zhang; Jing-yu Yu; Gus R Rosania
Journal:  Biopharm Drug Dispos       Date:  2013-12-10       Impact factor: 1.627

10.  Quinolone Amides as Antitrypanosomal Lead Compounds with In Vivo Activity.

Authors:  Georg Hiltensperger; Nina Hecht; Marcel Kaiser; Jens-Christoph Rybak; Alexander Hoerst; Nicole Dannenbauer; Klaus Müller-Buschbaum; Heike Bruhn; Harald Esch; Leane Lehmann; Lorenz Meinel; Ulrike Holzgrabe
Journal:  Antimicrob Agents Chemother       Date:  2016-07-22       Impact factor: 5.191

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