In-Line Film Coating Thickness Estimation of Minitablets in a Fluid-Bed Coating Equipment

Area of Science:

• Pharmaceutical Technology
• Process Analytical Technology (PAT)
• Materials Science

Background:

• Accurate in-line monitoring of film coating thickness is crucial for pharmaceutical product quality.
• Traditional methods for thickness measurement are often off-line, leading to delays and potential batch rejection.
• Existing in-line techniques may be limited by formulation or color variations.

Purpose of the Study:

• To develop and validate a visual imaging method for in-line estimation of minitablet film coating thickness.
• To assess the accuracy and robustness of the developed method compared to established techniques.
• To demonstrate the utility of the method for real-time process control and endpoint determination.

Main Methods:

• Utilized a commercially available image acquisition system integrated with a fluid-bed coating apparatus.
• Developed dedicated image and data analysis algorithms to process acquired visual data.
• Validated the method against simulated images, laboratory-scale coating runs, and optical microscopy measurements.
• Implemented a novel observation window cleaning mechanism to ensure measurement accuracy.

Main Results:

• The visual imaging method accurately estimated minitablet coating thickness, with the average band coating thickness showing the highest accuracy (RMSE of 1.30 μm) compared to microscopy.
• The window cleaning mechanism significantly improved measurement accuracy (reducing RMSE from 9.52 μm to 1.30 μm for band coating thickness).
• The developed approach demonstrated robustness against variations in coating formulation and color, achieving an accuracy of at least 2 μm.

Conclusions:

• In-line visual imaging provides a highly accurate and reliable method for estimating minitablet film coating thickness.
• The developed system, including the window cleaning mechanism, is a promising alternative to existing techniques for real-time pharmaceutical coating process monitoring.
• This technology supports enhanced process understanding and control, potentially leading to improved manufacturing efficiency and product quality.