2010 - Berlin - Germany

PAGE 2010: Physiology-based modelling
Martin Bergstrand

Semi-mechanistic modeling of absorption from extended release formulations - linking in vitro to in vivo

Martin Bergstrand (1), Erik Söderlind (2), Ulf Eriksson (2), Werner Weitschies (3), Mats O. Karlsson (1)

(1) Department of Pharmaceutical Biosciences, Uppsala University, Sweden. (2) AstraZeneca R&D, Mölndal, Sweden. (3) Institute of Pharmacy, University of Greifswald, Greifswald, Germany.

Background: The FDA guidance on extended release (ER) formulations (1) states that "Whatever the method used to establish a Level A IVIVC, the model should predict the entire in vivo time course from the in vitro data." Common methods for establishment of IVIVC do not utilize the available relevant information in order to do so. One example is information on regional absorption properties that is frequently obtained in drug development to guide development of ER formulations. A relatively new method for clinical assessment of tablet GI transit, regional absorption and in vivo drug release is Magnetic Marker Monitoring (MMM) (2).

Aim: A new framework to incorporate relevant clinical data and in vitro data to establish IVIVC.

Methods: Data from an ongoing drug development program has been used for development and testing of the suggested approach.  The model building data consisted of in vitro data from a family of HPMC gel matrix tablets and in vivo data from: an MMM study with one solid formulation (tablet transit, in vivo drug release and plasma concentration) and plasma concentration data from other studies following local infusion in colon (Bioperm capsule), i.v. dosing and administration of oral solution. A model validation dataset included plasma concentrations for three formulations for which no in vivo data had been used during model building.
A model describing drug release as a function of experimental conditions (pH, RPM and ionic strength) and formulation characteristics (API, tablet size etc) was developed.  The in vitro model was later applied to the in vivo drug release data from the MMM study together with prior knowledge on physiological properties throughout the GI tract and the observed tablet position. The model was used to estimate the extent of mechanic stress in different parts of the GI tract, expressed as corresponding RPM in the in vitro experiments.  The drug release model was subsequently used as an input function for a PK model including regional absorption and disposition throughout the GI tract. The PK model and a Markov model describing tablet transit patterns for different regiments of concomitant food intake was constructed based on a previously described principle (2).

Results: Modeling of the in vivo drug release rendered estimates of regional mechanic stress; upper stomach 93 RPM, lower stomach 130 RPM, small intestine 63 RPM, colon 45 RPM. Furthermore it was found that the mechanic stress was significantly lower during the night (-55%). Differences in rate and extent of absorbed substance over the different parts of the GI tract were described with the PK model. A satisfying predictive performance was demonstrated for both drug release and plasma concentrations with respect to the formulation used for model building. Prospective prediction of formulations not used for model building was in parts successful and in other parts informative on how the model could be further developed.

[1]   Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations; Guidance for Industry; U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), U.S. Government Printing Office: Washington, DC, September 1997.
[2]   Bergstrand M et al. Mechanistic modeling of a magnetic marker monitoring study linking gastrointestinal tablet transit, in vivo drug release, and pharmacokinetics. Clin Pharmacol Ther2009 Jul;86(1):77-83.

Reference: PAGE 19 (2010) Abstr 1847 [www.page-meeting.org/?abstract=1847]
Oral presentation: Physiology-based modelling
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