Calculating the mass of subvisible protein particles with improved accuracy using microflow imaging data

Area of Science:

• Biopharmaceutical manufacturing
• Protein therapeutics stability
• Particle characterization

Background:

• Subvisible particles (1-100 μm) are a critical stability issue for protein therapeutics.
• Quantifying the protein mass within these particles is challenging due to low particle numbers.

Purpose of the Study:

• To develop a novel, accurate, and easy-to-implement method for calculating subvisible protein particle mass.
• To utilize microflow imaging (MFI) data (number, size, morphology) for mass estimation.

Main Methods:

• Developed a calculation method integrating particle number, size, and morphology from MFI.
• Validated the method with polystyrene standards and stressed IgG1 mAb solutions.
• Incorporated literature-based protein density and composition for improved volume modeling.

Main Results:

• Calculated particle mass showed good agreement with experimental measurements for stressed mAb samples.
• The novel method demonstrated improved accuracy and precision compared to existing techniques.
• Morphological data enhanced particle volume modeling, leading to better mass estimations.

Conclusions:

• The developed method accurately estimates subvisible protein particle mass, even at low experimental levels.
• This approach enhances the understanding of protein particle formation and therapeutic stability.
• Accounting for particle mass alongside number provides a more comprehensive stability assessment.