Literature DB >> 11038432

Carbamazepine level-A in vivo-in vitro correlation (IVIVC): a scaled convolution based predictive approach.

P Veng-Pedersen1, J V Gobburu, M C Meyer, A B Straughn.   

Abstract

A method is presented for prediction of the systemic drug concentration profile from in vitro release/dissolution data for a drug formulation. The method is demonstrated using four different tablet formulations containing 200 mg carbamazepine (CZM), each administered in a four way cross-over manner to 20 human subjects, with 15 blood samples drawn to determine the resulting concentration profile. Amount versus time dissolution data were obtained by a 75 rpm paddle method for each formulation. Differentiation, with respect to time, of a monotonic quadratic spline fitted to the dissolution data provided the dissolution rate curve. The dissolution curve was through time and magnitude scaling mapped into a drug concentration curve via a convolution by a single exponential, and the estimated unit impulse response function. The method was tested by cross-validation, where the in vivo concentration profiles for each formulation were predicted based on correlation parameters determined from in vivo-in vitro data from the remaining three formulations. The mean prediction error (MPE), defined as the mean value of 100% x(observed-predicted)/observed was calculated for all 240 cross-validation predictions. The mean values of MPE were in the range of 10-36% (average 22%) with standard deviations (S.D.s) in the range of 9-33% (average 13%), indicating a good prediction performance of the proposed in vivo-in vitro correlation (IVIVC) method. Copyright 2000 John Wiley & Sons, Ltd.

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Year:  2000        PMID: 11038432     DOI: 10.1002/1099-081x(200001)21:1<1::aid-bdd207>3.0.co;2-d

Source DB:  PubMed          Journal:  Biopharm Drug Dispos        ISSN: 0142-2782            Impact factor:   1.627


  6 in total

Review 1.  Utilisation of pharmacokinetic-pharmacodynamic modelling and simulation in regulatory decision-making.

Authors:  J V Gobburu; P J Marroum
Journal:  Clin Pharmacokinet       Date:  2001       Impact factor: 6.447

2.  In silico prediction of optimal in vivo delivery properties using convolution-based model and clinical trial simulation.

Authors:  Roberto Gomeni; Carla Dangeli; Alan Bye
Journal:  Pharm Res       Date:  2002-01       Impact factor: 4.200

3.  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

Review 4.  The science of USP 1 and 2 dissolution: present challenges and future relevance.

Authors:  Vivian Gray; Gregg Kelly; Min Xia; Chris Butler; Saji Thomas; Stephen Mayock
Journal:  Pharm Res       Date:  2009-01-23       Impact factor: 4.200

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.  Examining the Use of a Mechanistic Model to Generate an In Vivo/In Vitro Correlation: Journey Through a Thought Process.

Authors:  Bipin Mistry; Nikunjkumar Patel; Masoud Jamei; Amin Rostami-Hodjegan; Marilyn N Martinez
Journal:  AAPS J       Date:  2016-06-16       Impact factor: 4.009

  6 in total

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