Literature DB >> 12803771

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

Peter Buchwald1.   

Abstract

A new, differential equation-based in-vitro-in-vivo correlation (IVIVC) method is proposed that directly relates the time-profiles of in-vitro dissolution rates and in-vivo plasma concentrations by using one- or multi-compartment pharmacokinetic models and a corresponding system of differential equations. The rate of in-vivo input is connected to the rate of in-vitro dissolution through a general functional dependency that allows for time scaling and time shifting. A multiplying factor that accounts for the variability of absorption conditions as the drug moves along is also incorporated. Two data sets incorporating slow-, medium-, and fast-release formulations were used to test the applicability of the method, and predictive powers were assessed with a leave-one-formulation-out approach. All fitted parameters had realistic values, and good or acceptable fits and predictions were obtained as measured by plasma concentration mean squared errors and percent AUC errors. Introduction of step-down functions that account for the transit of the dosage form past the intestinal sites of absorption proved useful. By avoiding the integral transforms used in the existing deconvolution- or convolution-based IVIVC models, the present method can provide increased transparency, improved performance, and greater modelling flexibility.

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Year:  2003        PMID: 12803771     DOI: 10.1211/002235702847

Source DB:  PubMed          Journal:  J Pharm Pharmacol        ISSN: 0022-3573            Impact factor:   3.765


  20 in total

1.  In vitro-in vivo correlations: tricks and traps.

Authors:  J-M Cardot; B M Davit
Journal:  AAPS J       Date:  2012-05-01       Impact factor: 4.009

2.  An automated process for building reliable and optimal in vitro/in vivo correlation models based on Monte Carlo simulations.

Authors:  Steven C Sutton; Mingxiu Hu
Journal:  AAPS J       Date:  2006-05-05       Impact factor: 4.009

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4.  Model-based decision making in early clinical development: minimizing the impact of a blood pressure adverse event.

Authors:  Mark Stroh; Carol Addy; Yunhui Wu; S Aubrey Stoch; Nazaneen Pourkavoos; Michelle Groff; Yang Xu; John Wagner; Keith Gottesdiener; Craig Shadle; Hong Wang; Kimberly Manser; Gregory A Winchell; Julie A Stone
Journal:  AAPS J       Date:  2009-02-06       Impact factor: 4.009

5.  Population in vitro-in vivo correlation model for pramipexole slow-release oral formulations.

Authors:  Elena Soto; Sebastian Haertter; Michael Koenen-Bergmann; Alexander Staab; Iñaki F Trocóniz
Journal:  Pharm Res       Date:  2009-12-29       Impact factor: 4.200

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

Authors:  Clare Gaynor; Adrian Dunne; Cian Costello; John Davis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2011-03-16       Impact factor: 2.745

7.  A novel beads-based dissolution method for the in vitro evaluation of extended release HPMC matrix tablets and the correlation with the in vivo data.

Authors:  Uroš Klančar; Boštjan Markun; Saša Baumgartner; Igor Legen
Journal:  AAPS J       Date:  2012-11-28       Impact factor: 4.009

8.  In vitro- in vivo correlation's dissolution limits setting.

Authors:  B Roudier; B M Davit; E Beyssac; J-M Cardot
Journal:  Pharm Res       Date:  2014-03-28       Impact factor: 4.200

9.  Statistical comparison of dissolution profiles to predict the bioequivalence of extended release formulations.

Authors:  J D Gomez-Mantilla; U F Schaefer; V G Casabo; T Lehr; C M Lehr
Journal:  AAPS J       Date:  2014-05-23       Impact factor: 4.009

10.  Regulatory Experience with In Vivo In Vitro Correlations (IVIVC) in New Drug Applications.

Authors:  Sandra Suarez-Sharp; Min Li; John Duan; Heta Shah; Paul Seo
Journal:  AAPS J       Date:  2016-08-01       Impact factor: 4.009

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