Model based strategies towards protein A resin lifetime optimization and supervision

Area of Science:

• Biopharmaceutical Manufacturing
• Chromatography
• Process Engineering

Background:

• Protein A resins are critical but expensive materials in biopharmaceutical purification.
• Resin aging, caused by various processes, limits their operational lifetime and increases manufacturing costs.
• Maximizing resin lifetime is crucial for economic viability in the biopharmaceutical industry.

Purpose of the Study:

• To develop and evaluate model-based strategies for controlling and enhancing the lifetime of protein A resins.
• To provide methods for predicting column performance and understanding resin aging mechanisms.
• To reduce experimental effort and manufacturing costs associated with resin usage.

Main Methods:

• A purely statistical, model-based approach for qualitative monitoring and performance prediction using on-line chromatographic data (e.g., UV signal).
• A hybrid modeling approach utilizing a lumped kinetic model with two aging parameters, fitted using experimental data from varying cleaning procedures (CPs).
• Analysis of aging parameters related to binding capacity deterioration and mass transfer rate reduction.

Main Results:

• The statistical model enables prediction of key performance indicators like yield, purity, and dynamic binding capacity.
• The hybrid model identifies prevailing aging mechanisms influenced by different cleaning procedures.
• The hybrid model facilitates model-based optimization of cleaning procedures and accurate yield forecasting with on-line corrections.

Conclusions:

• Both developed model-based strategies show significant promise in extending protein A resin lifetime.
• These approaches enhance process understanding, reduce experimental needs, lower cost of goods, and improve process robustness.
• Implementing these strategies can lead to more efficient and cost-effective biopharmaceutical manufacturing.