Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT...

Area of Science:

• Pharmaceutical Sciences
• Chemical Engineering
• Materials Science

Background:

• Optimizing pharmaceutical manufacturing processes is crucial for ensuring drug product quality and reducing development costs.
• Understanding the interplay between blend properties, critical quality attributes (CQA), and critical process parameters (CPP) is essential for robust process design.
• Challenges in blend processability and Process Analytical Technology (PAT) implementation can hinder efficient manufacturing.

Purpose of the Study:

• To develop an empirical predictive model correlating blend properties with CQAs and CPPs for blending and tableting.
• To investigate the impact of blend properties and process parameters on blending performance and tablet quality.
• To identify challenges in PAT implementation and continuous direct compression (CDC) platform performance.

Main Methods:

• Quantitative analysis of relationships between blend properties, CQAs, and CPPs.
• Evaluation of thirty diverse ternary blends on a continuous direct compression line (ConsiGma™ CDC-50).
• Assessment of impeller configuration, impeller speed, and blend composition effects on blending and compression.

Main Results:

• Impeller configuration and speed significantly influenced blending performance, while blend properties had a limited impact.
• Blend properties significantly affected tablet quality during compression.
• An empirical predictive model was successfully developed to guide process configuration selection.

Conclusions:

• The developed predictive model can reduce trial runs, development time, and costs for new drug products.
• Blend properties present significant challenges for PAT implementation and CDC platform performance.
• Further process development is necessary to address remaining challenges in blend properties and PAT integration.