Predicting particle size during fluid bed granulation using process measurement data.

Area of Science:

• Chemical Engineering
• Pharmaceutical Sciences

Background:

• Fluid bed granulation is a key pharmaceutical process for particle design.
• Accurate real-time particle size monitoring is crucial for process control and product quality.

Purpose of the Study:

• To introduce a novel concept for predicting particle size during fluid bed granulation.
• To develop predictive models for particle size during the spraying and drying phases.

Main Methods:

• Utilized process measurements from a design of experiments study.
• Developed partial least squares (PLS) models using 41 process parameters as factors.
• Employed in-line particle size data (d50) obtained via spatial filtering as the response.
• Tested combinations of 2 to 6 process parameters for model development (11 batches) and testing (4 batches).

Main Results:

• Predictive PLS models demonstrated good particle size (d50) prediction, particularly during the spraying phase (Q2=0.86).
• Predicted particle size data showed greater consistency compared to measured in-line data, which were sensitive to process failures like poor fluidization.
• The models effectively captured the relationship between process parameters and particle size.

Conclusions:

• The developed concept offers a robust method for particle size prediction in fluid bed granulation.
• Successful implementation requires a well-instrumented granulation environment and reliable real-time particle size data for model development.
• This predictive approach can enhance process understanding and control in pharmaceutical manufacturing.