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Quantifying Silicone Droplets in Protein Therapeutics with Imaging

FlowCam characterization of silicone dropletsThe recent Workshop on Protein Aggregation and Immunogenicity held in Breckenridge, Colorado, is dedicated to increasing the understanding of the role of aggregates in the immunogenicity of therapeutic protein products. According to their website, this knowledge is critical because immune responses due to aggregates can have serious consequences for patients, and immunogenicity can be major stumbling block to product development.

During the workshop, we presented data from a recent study done by our Technical Director, Lew Brown. The study involved analyzing a protein therapeutic sample containing both protein aggregates and silicone droplets using the FlowCAM dynamic imaging particle analysis (DIPA) system. Once the data was acquired by FlowCAM, several different mathematical approaches to identifying and quantifying silicone droplets in the sample were implemented.

Protein or Silicone Droplet? Seeing is Believing.

Many protein-based therapeutics, particularly pre-filled syringes, contain silicone droplets for lubrication. Since they are not considered harmful, it is beneficial to remove these droplets from particle counts when reporting under USP <788> or other protocols.  Therefore, having an effective means of characterizing silicone droplets in biologic formulations is important.

The typical method for determining the number and size of particles is light obscuration. While this method can count and estimate the size of particles, it offers little insight on the nature of the particles. You are not able to see the particle, so it’s impossible to tell a protein from a silicone droplet. 

Dynamic imaging particle analysis (DIPA) is increasingly being used to differentiate proteins from non-protein particulates, including silicone drops, in these biologic formulations. The ability of DIPA to measure particle shape parameters and automatically filter data based on those parameters presents an opportunity to automatically detect and remove silicone droplets from a data set. Using this method as a tool for characterizing silicone droplets in protein therapeutics could significantly increase the speed and accuracy of data collection and reporting.  

Optimizing Silicone Droplet Characterization

DIPA systems measure a variety of particle shape properties using computer algorithms. Because proteins are irregular in shape and silicone drops are predominantly round, filters are typically built using some function of circularity. The simplest calculation for circularity is aspect ratio (particle width divided by length). However, aspect ratio is a relatively crude measurement, so even though protein aggregates are generally irregular in shape, some may have a high aspect ratio causing the computer to characterize them incorrectly.

The FlowCAM measures over 30 particle properties, including 10 related to circularity alone. The study sought to determine which measurement, or combination of measurements, would provide the best results.

First he analyzed a protein therapeutic sample containing both protein aggregates and silicone droplets with the FlowCAM. Then he analyzed the data set visually to create a baseline number of protein aggregates and silicone droplets, and assigned every particle image a particle-type. Finally using the FlowCAM’s VisualSpreadsheet® software, he subjected the original data set to automated, algorithmic analysis using different methods to try to quantify the silicone droplet content. For each algorithm, he recorded the total number of silicone droplets identified and the number of false positives and false negatives as compared to the baseline manually classified data set.

FlowCam used to detect protein aggregates and silicone droplets - VisualSpreadsheet

He found that having more advanced measurements, such as Hu Circularity, and more advanced methods, such as statistical filtering, significantly increased the accuracy of silicone droplet quantification in protein-based therapeutics compared to using simple aspect ratio filters. Using a system that has a variety of particle properties to choose from can greatly increase your chances of successfully distinguishing the particle type you are looking for.

Show Me the Images!

The quantitative analysis discussed in Lew’s poster would not have been possible without being able to actually see each particle image. The ability to hand-classify them is a critical component of understanding how well the software filters can segment out silicone droplets.

When performing automated particle analysis studies, it’s always important to verify your results for accuracy using a secondary method. Being able to actually look at the particle image makes this verification easy.

While the human eye/brain remains the gold standard for classification of any particle, the DIPA method shows great promise for optimizing the process. This study is just the start of developing a methodology to help better understand the use of DIPA in characterizing biologics. Stay tuned!

For full details of the study and findings, download the full poster:
A Comparison of Methods for Quantifying Silicone Droplets in Biologics Using Dynamic Imaging Particle Analysis >>



Learn More!

Discover how you can improve protein aggregate characterization in parental drug formulations with DIPA in our informative eBook, The Ultimate Guide to Dynamic Imaging Particle Analysis.



Topics: Protein Therapeutics, Biopharmaceutical Research