Modeling the coating layer thickness in a pharmaceutical coating process

Area of Science:

• Pharmaceutical engineering
• Computational fluid dynamics
• Discrete element method

Background:

• Mechanistic simulations offer insights but are limited by process time.
• Statistical models predict long-term behavior but require accurate probability distributions.
• Wurster coating of microspheres is crucial for drug delivery systems.

Purpose of the Study:

• To develop a hybrid simulation approach combining CFD-DEM and Monte Carlo methods.
• To predict coating mass and thickness distributions over extended process times.
• To investigate factors influencing coating variability and product non-uniformity in MUPS.

Main Methods:

• Detailed Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) simulations of Wurster coating.
• Development and application of a novel Monte Carlo simulation approach.
• Stochastic modeling to analyze variability contributions in capsule filling.

Main Results:

• Smaller beads receive thicker coating layers due to proximity to the spray nozzle.
• Spray rate has a greater impact on inter-particle coating variability than airflow rate.
• Quantified the relative contributions of coating layer and fill weight variability to MUPS non-uniformity.

Conclusions:

• The hybrid simulation approach accurately predicts coating distributions over the entire process time.
• Optimizing spray rate is critical for minimizing coating variability in pharmaceutical processes.
• Understanding variability sources is essential for improving MUPS product uniformity.