Figure 2: Results of a FlowCAM run with protein-based biologic sample, including (left) graphs and summary statistics and
(right) representative particle images including, protein aggregates and silicone droplets.
To demonstrate how dynamic imaging particle analysis can characterize subvisible particulates in biologics, the author analyzed
a sample protein-based therapeutic using the FlowCAM (Fluid Imaging Technologies) particle imaging system. Figure 2 shows
the result of the analysis, including summary statistics and representative particle images. This particular sample contained
a large amount of silicone droplets. To enable the system to differentiate between various types of particles automatically,
one can build a digital filter to distinguish the silicone droplets from other particulates. The filter is created by selecting
a few examples of silicone droplets and instructing the software to find similar particles. The system uses a sophisticated
method known as statistical pattern recognition to classify every particle automatically as being one of the class (i.e.,
silicone droplets) or not (3).
Table I: Number of particles found in each size category before and after silicone droplets were removed.
In this example, out of the 2844 original particles in the run, 1788 were identified as silicone droplets. Because USP <788> addresses particles larger than 10 µm and larger than 25 µm, the particles were sorted into two categories (see Table
I). One can see a dramatic difference between the particle counts for the two categories once the silicone droplets have been
removed. If the silicone droplets are considered to be a harmless byproduct of the delivery method, then excluding them from
the analysis will yield different results than would be found using light obscuration according to <788>.
Figure 3: Conceptual diagram of object and image space in an optical system.
The first factor to consider when using dynamic imaging particle analysis is resolution. Unlike the human eye, which sees
the world as continuous, digital images are sampled versions of the real world. This means that any given image is divided
into discrete picture elements or pixels that form the whole. Thus, the imaging sensor has a discrete number of samples of
the object being captured. The easiest way to visualize this is to project the sensor geometry onto the object being captured,
as shown in Figure 3. In this diagram, the overall system magnification is 200%, so that the object's image is two times larger
than the actual object. A more meaningful way to express this is in terms of a calibration factor equal to the size of one
pixel of the sensor projected onto the object. In this case the calibration factor would be 2.5 µm/pixel. Therefore, one pixel
of the image covers 2.5 µm in length or width. Since a pixel is the smallest unit of a digital image, this means that, in
theory, the system can measure down to 2.5 µm.