General procedure to aid the development of continuous pharmaceutical processes using multivariate statistical...

Area of Science:

• Pharmaceutical Manufacturing
• Chemical Engineering
• Process Analytical Technology

Background:

• Streamlining pharmaceutical manufacturing is crucial for reducing costs and enhancing safety.
• Early-stage development often yields sparse and unstructured data, hindering process understanding.
• Continuous manufacturing offers advantages but requires robust data analysis for development.

Purpose of the Study:

• To propose a general procedure for applying latent variable statistical methods to support continuous pharmaceutical process development with limited data.
• To identify and rank key process drivers in continuous manufacturing.
• To enhance the understanding of how various factors impact process operation and product quality.

Main Methods:

• Application of latent variable statistical methods.
• Analysis of sparse and unstructured experimental data.
• Industrial case study on paracetamol tablet manufacturing.

Main Results:

• Identification and ranking of main driving forces affecting process variability.
• Elucidation of the impact of active pharmaceutical ingredient (API) pretreatments, formulation modes, and processing unit settings.
• Improved understanding of how these factors influence intermediate and final product properties.

Conclusions:

• The proposed procedure effectively utilizes limited data for continuous process development.
• Results provide a foundation for science-based quality risk assessment and robust control strategy definition.
• The approach supports the potential integration of a design space for continuous pharmaceutical manufacturing.