Proteomics-based method to comprehensively model the removal of host cell protein impurities

Area of Science:

• Biopharmaceutical Manufacturing
• Proteomics
• Chromatography

Background:

• Mechanistic models often overlook host cell proteins (HCPs), which are critical impurities in biopharmaceutical purification.
• Comprehensive understanding of HCP binding to chromatography resins is lacking, hindering process optimization.
• Existing methods do not cover the entire HCP proteome for binding parameter determination.

Purpose of the Study:

• To develop a novel method for determining binding parameters of individual HCPs from large-scale proteomics data.
• To enable comprehensive modeling of HCP-resin interactions for improved bioprocess understanding.
• To optimize purification strategies for removing problematic HCP impurities.

Main Methods:

• Applied large-scale proteomics to analyze an Escherichia coli harvest sample.
• Developed a method to determine binding parameters for all detected HCPs.
• Validated the model by predicting retention times for 15 abundant and problematic HCPs.

Main Results:

• Successfully determined binding parameters for individual HCPs across various concentration ranges.
• The developed model accurately predicted measured retention times during validation gradients.
• In silico optimization of anion exchange chromatography was achieved using determined HCP isotherm parameters.

Conclusions:

• The novel method provides a comprehensive approach to characterizing HCP-resin interactions.
• Accurate modeling of HCP binding facilitates the development of effective strategies for impurity removal.
• This work advances biopharmaceutical process understanding and optimization for target antigen purification.