Recent Developments in Pharmaceutical Spray Drying: Modeling, Process Optimization, and Emerging Trends with Machine...

Area of Science:

• Pharmaceutical technology
• Chemical engineering
• Computational modeling

Background:

• Spray drying is crucial for producing pharmaceutical powders, with process parameters significantly impacting product quality.
• Traditional modeling methods like Computational Fluid Dynamics (CFD) face limitations in accuracy and computational cost for complex spray drying scenarios.

Purpose of the Study:

• To review the progress and challenges in modeling the spray drying process for pharmaceutical applications.
• To explore the potential of Machine Learning (ML) and hybrid modeling approaches.

Main Methods:

• Review of existing literature on spray drying modeling techniques.
• Analysis of Computational Fluid Dynamics (CFD) limitations.
• Evaluation of Machine Learning (ML) models for spray drying optimization.
• Discussion of emerging hybrid ML-CFD models, digital twins, transfer learning, and explainable AI (XAI).

Main Results:

• CFD models, while established, have limitations including high computational expense and accuracy issues under complex conditions.
• ML models offer promising accuracy for spray drying but require high-quality data and may struggle with novel parameter predictions.
• Hybrid models integrating ML and CFD, along with techniques like digital twins and XAI, are emerging as powerful tools.

Conclusions:

• Machine learning presents an emerging technique to enhance pharmaceutical spray drying processes.
• Hybrid modeling approaches combining ML and CFD offer a path to overcome individual method limitations.
• Further research into advanced techniques like digital twins and XAI is warranted for optimizing pharmaceutical spray drying.