Integration of stochastic simulation with multivariate analysis: short-term facility fit prediction.

Area of Science:

• Biopharmaceutical Manufacturing
• Process Engineering
• Data Science

Background:

• Sub-optimal short-term facility fit issues can significantly impact biopharmaceutical production efficiency and cost.
• Identifying the root causes of these issues is crucial for process optimization and successful technology transfer.

Purpose of the Study:

• To develop and present a decision-support tool for pinpointing the root causes of sub-optimal facility fit in biopharmaceutical facilities.
• To demonstrate the tool's application in addressing technology transfer challenges for therapeutic antibodies.

Main Methods:

• Integrated stochastic simulation (Monte Carlo, discrete-event simulation) with advanced multivariate statistical analysis (Principal Component Analysis, clustering algorithms).
• Utilized hierarchical and k-means clustering, and stacked parallel coordinate plots for data visualization and analysis.
• Applied the tool to industrial batch process datasets for biopharmaceuticals, including case studies on therapeutic antibody technology transfer.

Main Results:

• The developed tool successfully identified process fingerprints and cluster characteristics associated with sub-optimal facility fit.
• Case studies demonstrated that reallocating product pool tanks can alleviate facility fit issues.
• The impact of operational changes was assessed through updated process fingerprint analysis.

Conclusions:

• The integrated simulation and statistical analysis tool provides a robust method for diagnosing facility fit problems in biopharmaceutical manufacturing.
• This approach aids in optimizing processes, mitigating risks during technology transfer, and improving cost of goods.
• The tool facilitates informed decision-making for enhancing facility fit and overall production efficiency.