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Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body

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Published on: January 11, 2018

Unmasking translucent protein particles by improved micro-flow imaging™ algorithms.

Jesper Søndergaard Pedersen¹, Malin Persson

¹Diabetes Protein Engineering, Novo Nordisk A/S, Måløv DK-2760, Denmark.

Journal of Pharmaceutical Sciences

|November 28, 2013

Summary

Statistical tests and a new algorithm improve micro-flow imaging (MFI) for detecting challenging protein particles in biopharmaceuticals. This enhances quality control for subvisible particle analysis.

Keywords:

image analysis imaging methods microparticles microscopy particle sizing protein aggregation protein formulation subvisible particles

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Area of Science:

Biopharmaceutical Development
Analytical Chemistry
Particle Characterization

Background:

Micro-flow imaging (MFI™) is crucial for characterizing subvisible particles in biopharmaceuticals.
Protein particles present challenges due to variability in size, appearance, and translucency.
Existing optical detection methods struggle with these heterogeneous particles.

Purpose of the Study:

To develop statistical tests for routine evaluation of MFI™ particle dataset quality.
To introduce an improved particle detection algorithm for enhanced sensitivity to translucent particles.
To address limitations in optical detection of subvisible protein aggregates.

Main Methods:

Development of standard statistical tests for spatial randomness (nearest neighbor, quadrat analysis).
Application of tests to stressed antibody samples to identify artifacts.
Implementation of a novel local pixel intensity variance algorithm for particle detection.

Main Results:

Statistical tests effectively uncovered fragmentation artifacts and detector sensitivity issues.
The new algorithm significantly reduced fragmentation artifacts.
The improved algorithm enhanced detection sensitivity for translucent protein particles.

Conclusions:

Current MFI™ techniques have limitations in detecting variable protein particles.
Statistical quality control and advanced algorithms can improve subvisible particle analysis.
Further advancements in optical detection are needed for robust biopharmaceutical quality assessment.