Modeling the coating layer thickness in a pharmaceutical coating process

Although mechanistic numerical simulations can offer great insights into a process, they are limited with respect to resolved process time. While statistical models provide long-term predictability, determining the underlying probability distributions is often challenging. In this work, detailed CFD-DEM simulations of a pharmaceutical Wurster coating process for microspheres are used to evaluate the input parameters for a novel Monte-Carlo simulation approach. The combined strengths of both modeling approaches make it possible to predict the coating mass and thickness distributions over the entire process time. It was observed that smaller beads receive a thicker coating layer since they pass the spray zone closer to the nozzle. Moreover, it was established that, in contrast to the airflow rate, the spray rate has a great impact on the inter-particle coating variability. A stochastic model was developed to investigate the relative contribution of coating layer variability and fill weight variability to the product non-uniformity in a capsule filling process of Multiple Unit Pellet Systems (MUPS).

Automated summary

In this study, detailed CFD-DEM simulations were used to evaluate input parameters for a novel Monte-Carlo simulation approach in predicting coating mass and thickness distributions over the entire process time for pharmaceutical microspheres. The research found that smaller beads receive thicker coating layers and that the spray rate significantly impacts inter-particle coating variability. A stochastic model was developed to investigate the contribution of coating layer and fill weight variability to product non-uniformity in a capsule filling process of Multiple Unit Pellet Systems (MUPS).