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in Vitro Dissolution Profile Comparison and IVIVR

Carbamazepine Case
  • Pradeep Sathe
  • Yi Tsong
  • Vinod P. Shah
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 423)

Abstract

USP1 describes the in-vivo/in-vitro correlation as the “establishment of a rational relationship between a biological property, or a parameter derived from a biological property produced by a dosage form and a physicochemical property or characteristic of the same dosage form”. In relation to a formulation, the most commonly used biological properties are the pharmacokinetic parameters such as Cmax or AUC, obtained following the administration of the dosage form while the physicochemical property is the dosage form’s in-vitro dissolution performance such as percent of drug released under a given set of conditions. The relationship between these two biological and physicochemical properties, is then expressed quantitatively. For rapidly dissolving (less than 15 minutes) immediate release formulations, an in-vitro characterization could be made using a single dissolution point. This however cannot be said for immediate release formulations consisting either low solubility actives or slowly dissolving matrix or for modified release formulations. In these cases, a dissolution profile and not a single dissolution specification point, is more appropriate. If an adequate in-vitro/in-vivo relationship is established using a suitable technique such as statistical moments or deconvolution, an insight into the pharmacokinetics could be obtained from the formulation’s in-vitro dissolution performance. More importantly, in case of an alteration in the dissolution profile characteristics, it allows one to raise a flag about the possible alteration in the in-vivo performance and pharmacokinetics.

Keywords

Dosage Form Confidence Region Dissolution Profile Similarity Region Statistical Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1997

Authors and Affiliations

  • Pradeep Sathe
    • 1
  • Yi Tsong
    • 1
  • Vinod P. Shah
    • 1
  1. 1.Office of Pharmaceutical Sciences Center for Drug Evaluation and ResearchU.S. Food and Drug AdministrationRockvilleUSA

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