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Area of Science:

  • Pharmacokinetics and Drug Delivery
  • Pharmaceutical Sciences
  • Biopharmaceutics

Background:

  • Modified-release (MR) drug products require understanding in vitro-in vivo correlations for consistent performance.
  • In vitro-in vivo correlation (IVIVC) is crucial for optimizing MR formulations and supporting regulatory decisions.
  • Tofacitinib was used as a model compound to evaluate deconvolution methodologies.

Purpose of the Study:

  • To evaluate three deconvolution methodologies (numerical, compartmental, mechanistic) within GPX™ for IVIVC.
  • To assess the ability of these models to predict in vivo drug absorption from in vitro dissolution data.
  • To demonstrate the utility of deconvolution-based IVIVC in a physiologically-based pharmacokinetic (PBPK) framework.

Main Methods:

  • Developed prototype MR formulations of tofacitinib with varying release rates.
  • Conducted a randomized crossover study in healthy volunteers to obtain plasma concentration-time data.
  • Applied numerical, compartmental, and mechanistic deconvolution methods to derive in vivo fraction absorbed profiles.
  • Simulated plasma concentration-time profiles using convoluted data and compared them to observed clinical data.
  • Calculated prediction errors and confidence intervals for pharmacokinetic parameters (Cmax, AUC) to assess bioequivalence.

Main Results:

  • Deconvolution methodologies successfully derived in vivo fraction absorbed profiles.
  • Simulated plasma profiles showed good agreement with observed clinical data.
  • Prediction errors for Cmax and AUC were within acceptable limits, indicating successful bioequivalence assessment.
  • The study demonstrated the utility of deconvolution-based IVIVC within a PBPK framework.

Conclusions:

  • Deconvolution-based IVIVC models are valuable tools for modified-release drug product development.
  • These models facilitate formulation optimization and support regulatory flexibility.
  • The approach provides a robust strategy for managing formulation lifecycles and assessing dissolution variability.