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Systematic Design of Biologics Harvest Processes Using Hybrid Filtration Models

Systematic Design of Biologics Harvest Processes Using Hybrid Filtration Models

This video summarizes how combining descriptive and predictive modeling improves depth filtration processes for biologics. It highlights the use of hybrid models that enhance process design and robustness suited for biologic harvests.

November 2, 2025

198 views


Advances in CHO Cell Research and Bioprocessing Techniques 2025

Advances in CHO Cell Research and Bioprocessing Techniques 2025

This video summarizes recent research on CHO cells used in antibody drug production, highlighting methods to improve bioprocessing and targeted gene integration, and introduces hybrid modeling techniques for optimizing biologic harvest processes.

November 4, 2025

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Abstract Synopsis

  • The article discusses how increased cell debris in biologic harvest processes has made traditional depth filtration less effective, causing a need for more robust, predictive tools to improve process development.
  • Historically, process development relied on resource-heavy screening and semiempirical models to understand filter fouling, but these methods are limited in guiding product development due to their descriptive nature.
  • To overcome these limitations, a hybrid modeling approach was developed that combines machine learning (partial least squares regression) with mechanistic fouling models, enabling better prediction and interpretation of filter performance based on historical process data.]