When performing sub-visible particle analysis of protein therapeutics, you need to be able to separate protein aggregates from silicone droplets and other contaminants. The ability to properly identify and enumerate protein aggregates, especially in the 2 to 10 micron range, is of utmost importance.
One challenge with protein aggregate characterization stems from their semi-transparency. For this reason, they are often mis-characterized or not even seen by light obscuration systems.
With dynamic imaging particle analysis (DIPA) you can differentiate transparent protein aggregates from other particles in your formulation, such as silicone droplets. However, to maximize effectiveness, it is essential to start with the proper settings.
With semi-transparent particles, like protein aggregates, it’s difficult to determine where the particle actually begins and ends. The correct threshold settings help you overcome this challenge.
Thresholding is a common method of image segmentation used in image processing in order to define the boundaries of a particle. Basically it helps the instrument isolate the protein particle from the background.
When using DIPA, this is important because these settings define the representative image from which all particle properties are measured.
How Thresholding Impacts Sensitivity
In a DIPA system, an opaque particle is typically darker than the background because the illumination is behind the flow cell as viewed by the camera. That’s why most imaging particle analyzers only threshold based on the presence of a value darker than the background.
With semi-transparent particles like protein aggregates, portions of the particle image may actually be lighter than the background. That is why when simple dark-only thresholding is used, transparent particles can often be cut up into smaller particle pieces (known as fractionation).
Therefore threshold settings for characterizing particles in a protein formulation must be more sensitive to subtle gray-scale changes than is necessary for other types of samples. If you have the ability to set both dark and light thresholding values, you will have better particle measurements, and improved count and concentration calculations.
A Real-World Comparison
Now that the FDA requests studying sub-visible particles between 2 and 10 microns in protein therapeutics using a quantitative method, much focus has been put on which instrument can best meet these requirements. A recent presentation concluded that:
* Applying orthogonal methods, such as the FlowCAM® can provide detailed analysis of protein aggregation.
* Morphological information from the DIPA technique can differentiate types of aggregates even in the sample with high protein concentration.
* The ability to differentiate proteinaceous particles from silicone oil proves to be critical for demonstration of product quality.
In one example presented, the impact of container material on particle concentration in a formulation that was subjected to shaking was studied.
Samples were analyzed using two different DIPA instruments, including the FlowCAM (see results below). In all cases, more particles were found with the FlowCAM, and the coefficient of variation was typically lower.
It was concluded that results from the FlowCAM were more reliable because of the ability to see all images captured and visually verify that the data was correct.
Source: Protein Aggregation and Emerging Tools to Support Development and Characterization, Danny K. Chou, PharmD, PhD, Gilead. Presented at PEG Summit China, Shanghai, China, April 2014.
Webinar: The Importance of Particle Characterization in Therapeutic Proteins
In this informative webinar, available on-demand, special guest presenters Dr. John Carpenter and Dr. Jeff Schwegman discuss the importance of sub-visible particle characterization in therapeutic protein formulations.