Literature DB >> 21409407

A population approach to in vitro-in vivo correlation modelling for compounds with nonlinear kinetics.

Clare Gaynor1, Adrian Dunne, Cian Costello, John Davis.   

Abstract

Developing an In Vitro-In Vivo Correlation (IVIVC) model is becoming an important part of the drug development process. Traditional methods such as deconvolution and convolution make the assumption of linearity of the system being studied and are, therefore, unsuitable for use with compounds exhibiting nonlinear kinetics. This study proposes the use of a compartmental approach which may be based on systems of differential equations, a method which can comfortably accommodate nonlinearity. This technique can easily be implemented using existing NONMEM libraries and is an accurate, fast and straightforward method of developing an IVIVC model.

Mesh:

Year:  2011        PMID: 21409407     DOI: 10.1007/s10928-011-9195-3

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  8 in total

1.  In vivo-in vitro correlation (IVIVC) modeling incorporating a convolution step.

Authors:  T O'Hara; S Hayes; J Davis; J Devane; T Smart; A Dunne
Journal:  J Pharmacokinet Pharmacodyn       Date:  2001-06       Impact factor: 2.745

2.  Direct, differential-equation-based in-vitro-in-vivo correlation (IVIVC) method.

Authors:  Peter Buchwald
Journal:  J Pharm Pharmacol       Date:  2003-04       Impact factor: 3.765

3.  A comparison of the prediction accuracy of two IVIVC modelling techniques.

Authors:  Clare Gaynor; Adrian Dunne; John Davis
Journal:  J Pharm Sci       Date:  2008-08       Impact factor: 3.534

4.  A nonlinear mixed effects IVIVC model for multi-release drug delivery systems.

Authors:  S Rossenu; C Gaynor; A Vermeulen; A Cleton; A Dunne
Journal:  J Pharmacokinet Pharmacodyn       Date:  2008-09-09       Impact factor: 2.745

5.  Draft guidance for industry extended-release solid oral dosage forms. Development, evaluation and application of in vitro-in vivo correlations.

Authors:  H Malinowski; P Marroum; V R Uppoor; W Gillespie; H Y Ahn; P Lockwood; J Henderson; R Baweja; M Hossain; N Fleischer; L Tillman; A Hussain; V Shah; A Dorantes; R Zhu; H Sun; K Kumi; S Machado; V Tammara; T E Ong-Chen; H Mahayni; L Lesko; R Williams
Journal:  Adv Exp Med Biol       Date:  1997       Impact factor: 2.622

6.  Ethanol pharmacokinetics in white women: nonlinear model fitting versus zero-order elimination analyses.

Authors:  M S Mumenthaler; J L Taylor; J A Yesavage
Journal:  Alcohol Clin Exp Res       Date:  2000-09       Impact factor: 3.455

7.  The elimination of phenytoin in man.

Authors:  M J Eadie; J H Tyrer; F Bochner; W D Hooper
Journal:  Clin Exp Pharmacol Physiol       Date:  1976 May-Jun       Impact factor: 2.557

8.  Kinetic evaluation of nonlinear drug elimination by a disposition decomposition analysis. Application to the analysis of the nonlinear elimination kinetics of erythropoietin in adult humans.

Authors:  P Veng-Pedersen; J A Widness; L M Pereira; C Peters; R L Schmidt; L S Lowe
Journal:  J Pharm Sci       Date:  1995-06       Impact factor: 3.534

  8 in total
  4 in total

1.  Bioavailability, bioequivalence, and in vitro-in vivo correlation of oxybutynin transdermal patch in rabbits.

Authors:  Achyut Khire; Pradeep Vavia
Journal:  Drug Deliv Transl Res       Date:  2014-04       Impact factor: 4.617

2.  Deconvolution Analysis by Non-linear Regression Using a Convolution-Based Model: Comparison of Nonparametric and Parametric Approaches.

Authors:  Roberto Gomeni; Françoise Bressolle-Gomeni
Journal:  AAPS J       Date:  2019-12-09       Impact factor: 4.009

3.  Comparison of Alternative Population Modeling Approaches for Implementing a Level A IVIVC and for Assessing the Time-Scaling Factor Using Deconvolution and Convolution-Based Methods.

Authors:  Roberto Gomeni; Françoise Bressolle-Gomeni
Journal:  AAPS J       Date:  2020-04-15       Impact factor: 4.009

Review 4.  In Vitro Dissolution and in Silico Modeling Shortcuts in Bioequivalence Testing.

Authors:  Moawia M Al-Tabakha; Muaed J Alomar
Journal:  Pharmaceutics       Date:  2020-01-04       Impact factor: 6.321

  4 in total

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