The Strengths of Total Holographic Video Microscopy in Detecting Sub-Visible Protein Particles in Biopharmaceuticals: A Comparison to Flow Imaging and Resonant Mass Measurement

Monitoring polysorbate 80 degradation in protein solutions using Total Holographic Characterization

The degradation of polysorbate surfactants can limit the shelf life of biologic pharmaceutical products. Polysorbate is susceptible to degradation via either oxidation or hydrolysis pathways which releases free fatty acids (FFA) and other complex polymers. Degradants from Polysorbate 80 (PS80) can form particles and impact drug product quality. PS80 degradation products appear at low concentrations, and their refractive indexes are similar to that of the buffer, making them very challenging to detect. Furthermore, aggregates of FFA are similar in size and refractive index to protein aggregates adding complexity to characterizing these particles in protein solutions. Total Holographic Characterization (THC) is used in this work to characterize FFA particles of oleic acid and linoleic acid, the two most common degradation products of PS80. We demonstrate that the characteristic THC profile of the FFA oleic acid emulsion droplets can be used to monitor the degradation of PS80. THC can detect oleic acid at a concentration down to less than 100 ng/mL. Using the characteristic THC signal of oleic acid as a marker, the degradation of PS80 in protein solutions can be monitored quantitatively even in the presence of other contaminants of the same size, including silicone oil emulsion droplets and protein aggregates.

Screening techniques for monitoring the sub-visible particle formation of free fatty acids in biopharmaceuticals

Free fatty acid (FFA) particles that originate from the enzymatic hydrolysis of polysorbate (PS) via co-purified host cell proteins generally appear abruptly in drug products during real-time (long-term) storage. Efforts were taken to understand the kinetics of FFA particle formation, aiming for a mitigation strategy. However, it is rather challenging particularly in the sub-visible particle (SVP) range, due to either the insufficient sensitivity of the analytical techniques used or the interference of the formulation matrices of proteinaceous drug products. In this study, we examined the feasibility of Raman microscopy, backgrounded membrane imaging (BMI) and total holographic characterization (THC) on the detection of FFA sub-visible particles (SVPs). The results indicate that THC is the most sensitive technique to track their occurrence during the course of PS hydrolysis. Moreover, with this technique we are able to distinguish different stages of FFA particle formation in the medium. In addition, a real time stability study of a biopharmaceutical was analyzed, demonstrating the viability of THC to monitor SVPs in a real sample and correlate it to the visible particles (VPs) occurrence.

Machine learning enables precise holographic characterization of colloidal materials in real time

Holographic particle characterization uses in-line holographic video microscopy to track and characterize individual colloidal particles dispersed in their native fluid media. Applications range from fundamental research in statistical physics to product development in biopharmaceuticals and medical diagnostic testing. The information encoded in a hologram can be extracted by fitting to a generative model based on the Lorenz-Mie theory of light scattering. Treating hologram analysis as a high-dimensional inverse problem has been exceptionally successful, with conventional optimization algorithms yielding nanometer precision for a typical particle's position and part-per-thousand precision for its size and index of refraction. Machine learning previously has been used to automate holographic particle characterization by detecting features of interest in multi-particle holograms and estimating the particles' positions and properties for subsequent refinement. This study presents an updated end-to-end neural-network solution called CATCH (Characterizing and Tracking Colloids Holographically) whose predictions are fast, precise, and accurate enough for many real-world high-throughput applications and can reliably bootstrap conventional optimization algorithms for the most demanding applications. The ability of CATCH to learn a representation of Lorenz-Mie theory that fits within a diminutive 200 kB hints at the possibility of developing a greatly simplified formulation of light scattering by small objects.