Dynamic optimization of an integrated cultivation-aggregation model for mAb production

Area of Science:

• Biotechnology
• Biopharmaceutical Manufacturing
• Process Engineering

Background:

• Monoclonal antibodies (mAbs) are susceptible to aggregation due to their protein structure, impacting drug efficacy and patient safety.
• Minimizing aggregation while maximizing mAb throughput is crucial for biopharmaceutical production under current good manufacturing practices (cGMP).

Purpose of the Study:

• To develop and analyze an integrated dynamic model for mAb cultivation and aggregation in Chinese Hamster Ovary (CHO) cells.
• To investigate the impact of temperature and feed flow manipulation on mAb production and aggregation.
• To perform dynamic optimization for maximizing mAb throughput and minimizing irreversible aggregate content simultaneously.

Main Methods:

• Formulation and analysis of an integrated dynamic model for mAb cultivation and aggregation.
• Simulation studies involving temperature manipulation in batch reactors.
• Simulation studies involving feed flow manipulation in isothermal fed-batch reactors.
• Dynamic optimization for single and dual objectives (throughput maximization, aggregation minimization).

Main Results:

• Identified key insights into the tradeoffs associated with simultaneous temperature and feed flow rate manipulation.
• Demonstrated the influence of these manipulated variables on mAb throughput and aggregation within bioreactors.
• Provided a basis for optimizing bioprocesses to balance productivity and product quality.

Conclusions:

• Dynamic modeling and optimization are effective tools for addressing conflicting objectives in mAb production.
• Strategic manipulation of temperature and feed flow can mitigate aggregation while enhancing mAb throughput.
• The study offers valuable guidance for improving biopharmaceutical manufacturing processes.