Direct, differential-equation-based in-vitro-in-vivo correlation (IVIVC) method.

Area of Science:

• Pharmacokinetics and Drug Delivery
• Mathematical Modeling in Pharmacology

Background:

• Existing in-vitro-in-vivo correlation (IVIVC) models often rely on integral transforms, limiting transparency and flexibility.
• Accurate prediction of in-vivo drug performance from in-vitro data is crucial for pharmaceutical development.

Purpose of the Study:

• To introduce a new differential equation-based IVIVC method for directly correlating in-vitro dissolution and in-vivo plasma concentrations.
• To enhance the transparency, performance, and flexibility of IVIVC modeling.

Main Methods:

• Utilized one- or multi-compartment pharmacokinetic models and systems of differential equations.
• Connected in-vivo input rate to in-vitro dissolution rate via a general functional dependency with time scaling and shifting.
• Incorporated a multiplying factor for absorption variability and step-down functions for dosage form transit.

Main Results:

• Applied the method to two datasets with varying release formulations (slow, medium, fast).
• Achieved good or acceptable fits and predictions using a leave-one-formulation-out approach.
• Demonstrated realistic fitted parameter values and validated predictive power through mean squared errors and percent AUC errors.

Conclusions:

• The proposed differential equation-based IVIVC method offers a transparent and flexible alternative to integral transform-based models.
• The method successfully predicts in-vivo drug performance from in-vitro dissolution data, showing good accuracy.
• Incorporating factors for absorption variability and dosage form transit further improves model performance.