Multivariate PAT solutions for biopharmaceutical cultivation: current progress and limitations

Area of Science:

• Biopharmaceutical Manufacturing
• Process Analytical Technology (PAT)
• Data Science in Biotechnology

Background:

• The biopharmaceutical industry generates large, complex datasets, posing challenges for traditional process monitoring.
• Existing methods struggle to extract meaningful information from multi-factorial and multi-collinear data.
• Process Analytical Technology (PAT) and Quality by Design (QbD) principles require advanced data handling capabilities.

Purpose of the Study:

• To discuss the critical role of Multivariate Data Analysis (MVDA) in industrial bioprocessing.
• To evaluate the progress and limitations of MVDA as a PAT solution for biopharmaceutical cell cultivation.
• To highlight the necessity of MVDA for effective bioprocess data management.

Main Methods:

• Review and discussion of Multivariate Data Analysis (MVDA) techniques applied to biopharmaceutical datasets.
• Focus on the application of MVDA in biopharmaceutical cultivation processes.
• Analysis of the effectiveness of MVDA-based models for process understanding and control.

Main Results:

• MVDA is essential for delineating relevant process information from large, complex datasets.
• MVDA-based models have demonstrated utility and should be routinely implemented in bioprocesses.
• Current limitations of MVDA as a PAT solution for biopharmaceutical cultivation are identified.

Conclusions:

• MVDA is a key enabler for managing and interpreting large datasets in the (bio)pharmaceutical industry.
• Routine implementation of MVDA models is recommended for bioprocesses.
• MVDA is central to achieving advanced PAT objectives, including real-time process control within the design space for cell cultivations.