Integrated Process Model Applications Linking Bioprocess Development to Quality by Design Milestones.

Area of Science:

• Biopharmaceutical Process Development
• Computational Modeling in Biotechnology
• Process Analytical Technology (PAT)

Background:

• Maximizing data value in bioprocess development is crucial for reducing time-to-market and experimental costs.
• Advanced in silico methods offer a promising avenue for enhancing process understanding and efficiency.
• Current workflows often require extensive wet-lab experiments, increasing development timelines and costs.

Purpose of the Study:

• To propose a novel integrated process model procedure to optimize Stage 1 process validation using available development data.
• To automate key Quality by Design (QbD) activities, including risk assessment and control strategy establishment.
• To demonstrate the utility of the proposed methods in a case study for a monoclonal antibody bioprocess.

Main Methods:

• Generation of an integrated process model based on existing bioprocess data.
• Application of two Monte Carlo simulation-based parameter sensitivity analysis linearization techniques.
• Automation of risk assessment severity rankings and preliminary control strategy determination for critical process parameters.

Main Results:

• The integrated model and simulation techniques were successfully applied to a monoclonal antibody bioprocess case study.
• Results supported Stage 1 process validation milestones.
• Demonstrated potential to reduce investigated parameters in process characterization by up to 24%.

Conclusions:

• The proposed procedure effectively maximizes the use of development data for bioprocess optimization and validation.
• The methodology facilitates iterative updates of risk assessments and process controls throughout the process lifecycle.
• This approach ensures a robust and efficient drug supply by enhancing process understanding and control.