Control Strategy for Excipient Variability in the Quality by Design Approach Using Statistical Analysis and...

Area of Science:

• Pharmaceutical Sciences
• Materials Science

Background:

• Quality by Design (QbD) is crucial for robust drug formulation.
• The impact of excipient variability on the QbD design space remains unclear.

Purpose of the Study:

• To investigate the effect of microcrystalline cellulose (MCC) variability on the design space and quality of amlodipine besylate immediate-release tablets.
• To assess how MCC manufacturer and grade influence drug product quality attributes.

Main Methods:

• Employed a Quality by Design (QbD) approach and D-optimal mixture design for formulation optimization.
• Evaluated 36 different MCCs to assess variability's impact on the design space.
• Utilized statistical analysis and an artificial neural network (ANN) to correlate MCC properties with critical quality attributes (CQAs).

Main Results:

• MCC variability, based on manufacturer and grade, significantly shifted the established design space.
• Established associations between MCC physicochemical properties and critical quality attributes (CQAs).
• Developed an accurate ANN model to predict dissolution based on MCC properties, demonstrating low prediction errors.

Conclusions:

• Excipient variability must be considered within the QbD framework to ensure a reliable design space.
• Statistical analysis and predictive modeling (ANN) are effective tools for managing excipient variability's impact on drug product quality.
• This study provides a comprehensive approach to understanding and controlling excipient variability in pharmaceutical development.