Instrumented roll technology for the design space development of roller compaction process.

Area of Science:

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

Background:

• Roller compaction is a critical unit operation in pharmaceutical manufacturing for producing granules and tablets.
• Accurate measurement and control of process parameters are essential for consistent product quality.
• Traditional methods for assessing compaction quality can be time-consuming and may not provide real-time data.

Purpose of the Study:

• To develop and validate instrumented roll technology for measuring normal stress during roller compaction.
• To investigate the effects of key process parameters on normal stress and ribbon density.
• To establish a model for predicting ribbon density and assess the feasibility of on-line monitoring.

Main Methods:

• Utilized an Alexanderwerk WP120 roller compactor equipped with instrumented rolls featuring three force transducers.
• Studied placebo and active pharmaceutical ingredient (API) pre-blends under varying roll speed, feed screw speed, and hydraulic roll pressure.
• Analyzed normal stress, ribbon density, compressibility, and gap using developed models.

Main Results:

• Normal stress was directly proportional to hydraulic pressure and inversely related to the screw-to-roll speed ratio.
• Ribbon density increased with normal stress in placebo blends and was also dependent on the gap in active blends.
• Models developed using placebo data accurately predicted ribbon densities of active blends, with prediction error decreasing as API concentration decreased.
• On-line prediction of ribbon density was demonstrated using normal stress and porosity-pressure data.

Conclusions:

• Instrumented roll technology provides valuable real-time data on normal stress during roller compaction.
• This technology enhances fundamental understanding, enabling more accurate prediction of ribbon density and optimization of process parameters.
• The developed models show potential for reducing the number of Design of Experiments (DOE) runs required for process development and control.