Predicting tablet properties using In-Line measurements and evolutionary equation Discovery: A high shear wet...

Area of Science:

• Pharmaceutical Manufacturing
• Process Analytical Technology (PAT)
• Granulation Engineering

Background:

• High shear wet granulation (HSWG) is crucial for tablet manufacturing, enhancing powder properties and process efficiency.
• In-line process analytical technology (PAT) is vital for real-time monitoring of granulation dynamics and process control.
• Traditional methods often lack the sensitivity to capture the intricate details of granule formation.

Purpose of the Study:

• To investigate the use of in-line force measurements as a sensitive indicator for predicting granule and tablet properties during HSWG.
• To develop a predictive model for tablet tensile strength based on real-time granulation data.
• To explore the advantages of a closed-form analytical model for process control compared to AI methodologies.

Main Methods:

• Utilized a novel force probe for in-line measurement of powder bed dynamics during HSWG.
• Employed a face-centered surface response design of experiments (DoE) to explore key process parameters.
• Developed a closed-form analytical model using discovery of evolutionary equations from DoE data.

Main Results:

• In-line force measurements proved more sensitive to granulation processes than torque measurements.
• Characteristic force profiles provided a fingerprint of HSWG, revealing granule evolution and binder distribution.
• The developed closed-form model accurately predicted tablet tensile strength from in-line data.

Conclusions:

• In-line force sensing offers a powerful tool for understanding and controlling HSWG.
• The closed-form analytical model enables real-time prediction and process optimization, surpassing limitations of AI methods.
• This approach facilitates rapid adjustment of compression machine settings for consistent tablet quality and reduced waste.