Because the filtering operations that automatically separate various particle types in imaging particle analysis are based
on the image measurements, it seems to follow that less-than-sharp images with inaccurate measurements would not produce good-quality
particle characterization. To demonstrate this idea with protein aggregates, the author analyzed a sample protein-based therapeutic
with the FlowCAM using a deeper-than-normal flow cell to yield particle images of varying sharpness. Two image libraries were
created for use with a statistical pattern-recognition algorithm. One contained eight aggregate images in sharp focus, and
the other contained eight aggregate images in less sharp focus. Each library was then used to perform a statistical pattern
match on the entire run of aggregate particles.
The results are shown in Figure 6. Sixty percent fewer aggregates were found using the less focused library images. In statistical
filtering, this occurs because the library particles form a looser, more ambiguous cluster in the n-dimensional pattern-recognition space because of the higher variance in measurements (3). This cluster, in turn, yields lower
statistical confidence in the identity of the library particles, and therefore a lower match rate.
Figure 6: Results of statistical pattern recognition with two libraries of various image quality. Results on left (152 particles/mL)
used the sharp library particles, and results on the right (91 particles/mL) used the less sharp library particles.
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Not only will the resulting match rate be lower for less sharp particle libraries, but the inaccuracy of the measurements
can also lead to false positives. To summarize this discussion of image quality: Fuzzy Images = Fuzzy Measurements = Fuzzy
Classifications.
For the characterization of protein aggregates and other particulates in biologics, volumetric techniques such as light obscuration,
laser diffraction, and electrozone (Coulter) sensing have limited effectiveness because of the fact that each must assume
that particles are spherical in shape. This assumption means that the techniques cannot distinguish between protein aggregates
and other particulates, such as silicone droplets, found in biologics.
Imaging particle analysis can overcome the limitations of these volumetric techniques by measuring shape and gray-scale parameters
of particles. It also has the benefit of detecting transparent particles, such as protein aggregates, where techniques such
as light obscuration fail or mischaracterize the particles. However, as with any other method of particle analysis, it is
important to understand the limitations and factors that affect particle measurements using this method. This article has
shown that three factors must be kept in mind when evaluating the efficacy of an imaging particle analysis solution for characterizing
biologics: resolution, thresholding, and image quality. Once these factors are understood, one can have higher confidence
in one's interpretation of results based on this technique.
LEW BROWN is the director of marketing at Fluid Imaging Technologies, 65 Forest Falls Dr., Yarmouth, ME 04096, lew@fluidimaging.com
.
REFERENCES
1. S. Aldrich, US Pharmacopeia Workshop on Particle Size: Particle Detection and Measurement (Rockville, MD, 2010).
2. J.F. Carpenter et al., J. Pharm. Sci. 98 (4), 1201–1205 (2009)
3. L. Brown, "Particle Image Understanding–A Primer,"
http://www.fluidimaging.com/resource-center-whitepapers.htm, accessed July 12, 2011.
4. L.Brown, "Imaging Particle Analysis: Resolution and Sampling Considerations,"
http://www.fluidimaging.com/resource-center-whitepapers.htm, accessed July 12, 2011.
5. J.S. Pedersen, Bio-Process International European Conference and Exposition (Nice, France, 2011).
6. L. Brown, "Imaging Particle Analysis: The Importance of Image Quality,"
http://www.fluidimaging.com/resource-center-whitepapers.htm, accessed July 12, 2011.
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