Silicone Microdroplets in Protein Formulations—Detection and Enumeration - Pharmaceutical Technology

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Silicone Microdroplets in Protein Formulations—Detection and Enumeration
The authors describe a novel analytical approach that uses the shape-analysis capabilities of MFI to detect and enumerate silicone oil microdroplets in protein formulations that also contain aggregates of similar size and in a similar concentration.

Pharmaceutical Technology
Volume 33, Issue 4, pp. 74-79

Figure 5: Mixed population of silicone-oil droplets and protein aggregates. (ALL FIGURES ARE COURTESY OF THE AUTHORS.)
Differentiating silicone oil and protein particles. The mixed siliconeoil droplet and aggregated protein sample was measured using MFI. Visual analysis of the captured images showed that the two particle types could easily be resolved for particles ≥5 μm ECD. Because silicone-oil droplets have a consistently higher aspect ratio compared with aggregated protein particles of the same size (see Figure 5), a simple software filter with an aspect ratio ≥0.85 and ECD ≥5 μm cutoff was applied to the mixed population. The images obtained in the two individual populations prior to mixing were visually examined to assess the accuracy of this filter. This comparison showed an accuracy of 96% for pure samples (i.e., 4% of the particles were incorrectly labeled as either silicone oil or protein aggregates).


Silicone-oil-induced particle formation in therapeutic proteins can be an issue for commercialization of the product. It is critical to characterize the nature of the particles resulting from the phenomenon of silicone-induced aggregation of proteins and antibodies. The results obtained by MFI analysis of mixed silicone-oil droplet and aggregate protein populations show that the two populations can be resolved with a high degree of accuracy using a simple software filter that uses aspect ratio and ECD limits. This level of accuracy would normally be sufficient when the concentration of the two particle types is comparable. If one population was much smaller than the other, then higher levels of accuracy could be achieved by including more morphological parameters in the analysis. If it was desired to extend the analysis to smaller particles, then the analysis could be carried out at higher magnification. These results clearly show the advantage of the MFI analysis over the light obscuration and filter-based techniques when attempting to isolate subpopulations.

The ability of MFI to resolve and independently measure populations of silicone-oil droplets and protein aggregates/particulates that are simultaneously present in heterogeneous samples can be used in a number of applications from packaging to formulation development. Some of these applications include:
  • Orthogonal technique to other particle characterization techniques
  • Selection of silicone type and application techniques to minimize droplet formation
  • Qualification of silicone-oil microdroplets levels from coated containers
  • Formulation optimization to minimize silicone-induced protein aggregation and particulation
  • Development of advanced container or enclosures that do not shed silicone oil microdroplets into the protein formulation.


MFI with automated particle classification is an emerging technology that can play a useful role in understanding and controlling silicone-oil-droplet-induced aggregation of proteins in parenteral pharmaceuticals. More generally, the ability of the technology to resolve and independently characterize mixed particle populations, including a wide range of subvisible and visible particle types, offers a rapid and powerful means of evaluating subvisible and visible particle populations in parenteral products.

Deepak K. Sharma*, PhD, is a senior scientist in R&D, and Peter Oma is the director of R&D at Brightwell Technologies Inc., 115 Terence Matthews Crescent, Ottawa, Ontario, K2M 2B2, Canada, tel. 613.591.7715, fax 613.591.7716,
. Sampath Krishnan, PhD, is a senior scientist in Process & Product Development at Amgen Inc.

*To whom all correspondence should be addressed.

Submitted: July 21, 2008; Accepted: Aug. 28, 2008. Published online January 2009.

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