Wheels turning: CHO cell modeling moves into a digital biomanufacturing era: Subtitle: CHO Metabolic Modeling

Area of Science:

• Biotechnology
• Bioprocess Engineering
• Computational Biology

Background:

• CHO cell culture advancements rely on understanding environmental impacts on metabolism and productivity.
• In-silico models are crucial for predicting process parameter effects.
• Industry 4.0 drives digitalization and smart manufacturing in biopharmaceutical development.

Purpose of the Study:

• To review mechanistic modeling of CHO cell metabolism, including glycosylation and secretory pathways.
• To discuss the integration of multi-omics data for deeper intracellular process understanding.
• To explore the application of machine learning and AI in biopharmaceutical development for optimization and control.

Main Methods:

• Mechanistic modeling (kinetic, constraint-based) of CHO central metabolism.
• Integration of multi-omics data (genomics, transcriptomics, proteomics, metabolomics).
• Application of data-driven analysis, self-learning systems, digital twins, and artificial intelligence.

Main Results:

• Mechanistic models incorporating omics data provide insights into product quality and pathway dynamics.
• Digitalization and machine learning facilitate real-time process control and predictive capabilities.
• Hybrid models show promise for efficient and cost-effective drug development.

Conclusions:

• Mechanistic and data-driven modeling approaches are essential for advancing CHO bioprocesses.
• Machine learning and AI accelerate the development and manufacturing of biologics.
• Optimized bioprocesses lead to more efficient and affordable drug production.