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Related Experiment Videos

In vivo-in vitro correlation (IVIVC) modeling incorporating a convolution step.

T O'Hara1, S Hayes, J Davis

  • 1Elan Corporation plc, Athlone, Ireland.

Journal of Pharmacokinetics and Pharmacodynamics
|July 27, 2001
PubMed
Summary
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In vivo-in vitro correlation (IVIVC) models can be improved by incorporating a convolution step. The odds model, including convolution, better describes plasma drug concentration data than the traditional identity model.

Area of Science:

  • Pharmacokinetics and Pharmacodynamics
  • Drug Development and Formulation
  • Biostatistics and Modeling

Background:

  • In vivo-in vitro correlation (IVIVC) models are crucial for predicting in vivo drug performance from in vitro data.
  • Traditional IVIVC models often rely on deconvoluted data, which can be unstable and may not directly address primary interests in plasma drug concentration.
  • The focus is typically on plasma drug concentration-time profiles (e.g., AUC, Cmax), not solely on the fraction dissolved.

Purpose of the Study:

  • To describe and evaluate IVIVC models that incorporate a convolution step to directly model plasma drug concentration data.
  • To compare the performance of the odds model (with convolution) against the identity model for IVIVC.
  • To demonstrate an improved modeling approach for drug absorption and disposition.

Related Experiment Videos

Main Methods:

  • Development and description of IVIVC models incorporating a convolution step, including odds, hazards, and reversed hazards models.
  • Fitting the odds model and the identity model to plasma drug concentration/time data from two different products.
  • Utilizing nonlinear mixed-effects modeling software for model fitting and comparison.

Main Results:

  • The odds model, which includes a convolution step, demonstrated a reasonable fit to the plasma drug concentration data for both products.
  • The odds model provided a significantly better fit compared to the identity model in both data sets.
  • Incorporating a convolution step overcomes the limitations associated with analyzing deconvoluted data.

Conclusions:

  • The odds model, incorporating a convolution step, offers a more robust and accurate approach for IVIVC compared to the identity model.
  • This modeling strategy directly addresses the primary interest in plasma drug concentration and its functions.
  • The findings support the use of convolution-incorporating models for improved prediction of in vivo drug behavior.