The LINK software platform is being utilized by Pfizer for S-factor optimization and application of particle classification by distinction of proteinaceous particulate vs. silicone oil.
Pfizer has presented on their research and collaboration with Lumetics LINK™ software last year at the annual Breckenridge, Colorado Protein Aggregation and Immunogenicity conference (poster available below). This year at the 2019 Protein Stability Conference, Pfizer has built on their work with a co-authored poster presentation focusing on; “Aspects of silicone separation in protein-silicone mixtures using S-factor-style electronic filter”. The research explores the effect of refractive index upon the effectiveness of S-factor style electronic filters. Pfizer highlights the requirement for consideration of newly optimized S-factor cutoff functions not only when using different microscopy optical configurations, but also with differences in the optical properties of the particle/fluid system.
The LINK software was utilized for rapid data processing during the S Factor cut-off function optimization process, including application of new S Factor cut-off functions and evaluation of their effectiveness. The LINK software also helped achieve research results quicker by providing an automated importing, analysis, and reporting process. The abstract is available below with the 2018 & 2019 posters available for download.
ABSTRACT: Aspects of silicone separation in protein-silicone mixtures using S-factor-style electronic filter
AUTHORS: E. Torres 1, A. Mehta 1 M. Lisowski 1, L. Rosner 1, P. Kolhe 1, C. Merchant 2, D. Thomas 2
1 Pfizer Biotherapeutics Pharmaceutical R & D; 2 Lumetics Inc.
Distinguishing between silicone oil vs. non-silicone oil particulates in protein formulations is important both in terms of accurately detecting protein aggregation during stress/stability, as well as for studying the protein-silicone oil interactions.
Strehl et al first reported a multi-parametric S-factor function capable of electronically filtering particles from data generated using ProteinSimple MFI 4100 flow microscopy system with a 400 µm flow cell. However, any function permitting distinction between silicone oil and proteinaceous particulates is dependent both on the optics and image processing of the flow microscopy system utilized, as well as the optical properties of the sample (i.e. difference in refractive Index between fluid and particulate). Therefore, time-consuming offline data processing may be needed for S-factor optimization in each instance. To simplify the time consuming assessment and address optical properties challenges, we took an approach of understanding refractive properties of silicone oil/protein mixtures and used this to create an effective silicone oil filter using the Lumetics LINK software package.
In this poster an S-factor style filter was generated and applied to silicone oil and protein mixtures from data collected with a MFI 4200/5200 system and a 100 µm flow cell. A discussion on silicone oil quantification limitations due to a shift in solution refractive properties and linearity observed within silicone oil/protein mixtures as a function of specific particulate size ranges, will be presented.
S-Factor optimization for MFI using
2018 Pfizer and Lumetics co-publication
Aspects of silicone separation in protein-silicone mixtures using S-factor-style electronic filter
2019 Pfizer and Lumetics co-publication
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