Literature DB >> 10407257

A new approach to modelling the relationship between in vitro and in vivo drug dissolution/absorption.

A Dunne1, T O'Hara, J Devane.   

Abstract

A major goal of the pharmaceutical scientist is finding a relationship between an in vitro characteristic of an oral dosage form and its in vivo performance. One such relationship between drug dissolution (or absorption) in vivo and that in vitro is known as an 'in vitro-in vivo correlation' (IVIVC) whose importance stems from the fact that it may be used to minimize the number of human studies required during product development, assist in setting meaningful in vitro dissolution specifications and justify biowaivers for scale-up and post approval changes. A number of ways of describing an IVIVC have been reported with 'level A' being the most informative and therefore most desirable. In the majority of cases reported to date, both the model and the statistical methods employed for level A IVIVC are very simplistic. The model assumes that the rate and extent of dissolution in vivo are the same as those in vitro. The statistical methods ignore the repeated measures nature of the data and use a response variable as an independent variable without accounting for measurement error. This paper describes some new models which include the simple model as a special case. The modelling approach is based on considering the time at which a drug molecule enters solution (in vitro or in vivo) to be a random variable. The in vitro and in vivo distributions are then related to one another using a proportional odds, proportional hazards or proportional reversed hazards model. The models can be extended by adding a linear time component which describes a time varying relationship. Following the addition of random effects to these structural models in order to account for the repeated measures nature of the data collected, the models may be described as generalized linear mixed effects models. The models were fitted to some data sets using a maximum likelihood based method and the results indicate that these models have potential for describing an in vitro-in vivo relationship which cannot be described using the currently available models. Copyright 1999 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Substances:

Year:  1999        PMID: 10407257

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  8 in total

1.  Predictive ability of level A in vitro-in vivo correlation for ringcap controlled-release acetaminophen tablets.

Authors:  J T Dalton; A B Straughn; D A Dickason; G P Grandolfi
Journal:  Pharm Res       Date:  2001-12       Impact factor: 4.200

2.  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

3.  Modeling heterogeneity of properties and random effects in drug dissolution.

Authors:  P Lánský; M Weiss
Journal:  Pharm Res       Date:  2001-07       Impact factor: 4.200

4.  A time scaling approach to develop an in vitro-in vivo correlation (IVIVC) model using a convolution-based technique.

Authors:  Cian Costello; Stefaan Rossenu; An Vermeulen; Adriaan Cleton; Adrian Dunne
Journal:  J Pharmacokinet Pharmacodyn       Date:  2011-07-07       Impact factor: 2.745

5.  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

6.  Evaluating In Vivo-In Vitro Correlation Using a Bayesian Approach.

Authors:  Junshan Qiu; Marilyn Martinez; Ram Tiwari
Journal:  AAPS J       Date:  2016-02-19       Impact factor: 4.009

7.  From drug delivery systems to drug release, dissolution, IVIVC, BCS, BDDCS, bioequivalence and biowaivers.

Authors:  Vangelis Karalis; Eleni Magklara; Vinod P Shah; Panos Macheras
Journal:  Pharm Res       Date:  2010-07-16       Impact factor: 4.200

Review 8.  Inhaled chemotherapy in lung cancer: future concept of nanomedicine.

Authors:  Paul Zarogoulidis; Ekaterini Chatzaki; Konstantinos Porpodis; Kalliopi Domvri; Wolfgang Hohenforst-Schmidt; Eugene P Goldberg; Nikos Karamanos; Konstantinos Zarogoulidis
Journal:  Int J Nanomedicine       Date:  2012-03-22
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.