A time scaling approach to develop an in vitro-in vivo correlation (IVIVC) model using a convolution-based technique

Area of Science:

• Pharmacokinetics
• Drug Development
• Computational Modeling

Background:

• In vitro-in vivo correlation (IVIVC) models are crucial for drug formulation, dissolution specification setting, and bio-waiver applications.
• Traditional deconvolution methods for IVIVC present statistical challenges.
• A convolution-based population approach offers a promising alternative.

Purpose of the Study:

• To develop a robust IVIVC model for a drug with disparate in vitro and in vivo time scales.
• To employ a time-scaling approach within a convolution-based framework.
• To assess the utility and predictability of the developed IVIVC model.

Main Methods:

• Utilized a time-scaling approach with a convolution-based population technique.
• Applied the NONMEM package for model development and fitting.
• Analyzed longitudinal in vitro and in vivo data, accounting for between-subject variability.
• Assessed model performance through comparison of predicted vs. observed plasma concentration-time profiles.

Main Results:

• Successfully developed and fitted an IVIVC model using the convolution-based population approach.
• The model demonstrated strong internal and external predictability, meeting regulatory validation criteria.
• The time-scaling method proved effective for data with significantly different in vitro and in vivo time scales.

Conclusions:

• A time-scaling approach combined with a convolution-based population method is effective for developing IVIVC models, especially with differing time scales.
• This versatile approach can successfully model drug behavior across distinct in vitro and in vivo profiles.
• The developed IVIVC model supports regulatory applications like bio-waiver assessments.