Model-based design space determination of peptide chromatographic purification processes

Area of Science:

• Chemical Engineering
• Process Chemistry
• Pharmaceutical Manufacturing

Background:

• Fixed operating conditions in chemical processes often result in suboptimal performance.
• Process flexibility is crucial for adapting to changes in feed composition, product requirements, and economic factors.
• The U.S. FDA's Process Analytical Technology (PAT) initiative promotes adaptive manufacturing processes.

Purpose of the Study:

• To implement Quality-by-Design (QbD) principles in developing a chromatographic purification process.
• To establish a procedure for determining the process design space using chromatographic modeling.
• To assess batch failure risks and identify critical process parameters (CPPs) through modeling.

Main Methods:

• Utilized chromatographic modeling to define the process design space.
• Assessed risks of batch failure and identified critical process parameters (CPPs).
• Employed an ideal cut strategy, focusing on yield and productivity as critical quality attributes (CQAs).

Main Results:

• Modeled trends in CQAs within the design space were analyzed.
• Process disturbances were shown to significantly reduce the design space.
• Batch failures were linked to simultaneous and specific changes in multiple CPPs.
• Model predictions were validated against experimental data, confirming reliability.

Conclusions:

• Model-based approaches reliably support process development for chromatographic purification.
• Implementing QbD and modeling enhances process flexibility and performance.
• Understanding the impact of disturbances on the design space is critical for robust manufacturing.