Predictive Drug Release Modeling Across Dissolution Apparatuses I and II using Computational Fluid Dynamics.

Area of Science:

• Pharmaceutical Sciences
• Drug Delivery Systems
• Computational Fluid Dynamics

Background:

• Dissolution testing is crucial for pharmaceutical product development and quality control.
• Predicting active pharmaceutical ingredient (API) release across different United States Pharmacopeia (USP) dissolution apparatuses (I and II) traditionally requires extensive experimental data.
• Existing methods may not fully capture formulation-specific release behaviors and apparatus hydrodynamics.

Purpose of the Study:

• To develop and validate a predictive modeling process for API release in USP dissolution apparatuses.
• To enable accurate predictions using minimal experimental dissolution data.
• To reduce the number of dissolution experiments and chemical materials required during method development.

Main Methods:

• Developed a modeling process integrating formulation-specific drug release behavior and apparatus hydrodynamics.
• Measured experimental mass transfer coefficients using a conventional mass balance.
• Utilized computational fluid dynamics (CFD) to relate mass transfer coefficients to hydrodynamics and apparatus settings, establishing a novel 1-D model.

Main Results:

• The 1-D model successfully predicted mass transfer coefficients and corresponding drug release for various apparatus configurations.
• Validation against five erosion-based formulations demonstrated high accuracy, within 8% labelled amount (LA) of API.
• Achieved an average root mean square deviation of 3% LA, confirming the model's predictive power.

Conclusions:

• The developed modeling process accurately predicts API release in USP dissolution apparatuses I and II.
• This predictive capability significantly minimizes the need for extensive experimental dissolution studies.
• The approach offers a feasible strategy for reducing material consumption and optimizing drug development timelines.