An Interlaboratory Comparison on the Characterization of a Sub-micrometer Polydisperse Particle Dispersion

Glass Silicone Oil Free Pre-filled Syringe as Primary Container in Autoinjector

OBJECTIVE

Pre-filled syringes (PFSs) have become popular as a convenient and cost-effective container closure system for delivering biotherapeutics. However, standard siliconized PFSs may compromise the stability of therapeutic proteins due to their exposure to the silicone oil-water interface. To address this concern, silicone oil-free (SOF) glass syringes coupled with silicone-oil free plunger stoppers have been developed. This study aims to compare the impact of silicone oil-free (SOF) and siliconized syringes as primary container on protein stability and device functionality of the combination products.

METHODS

The stability of proteins with different modalities was assessed in SOF and siliconized 1 mL glass syringes for up to 6 months at 5℃, 25℃, and 40℃ with levels of subvisible particles and soluble aggregate determined by micro-flow imaging (MFI) and ultra performance size-exclusion chromatography (UP-SEC). The functionality of SOF glass syringes, including break loose force, extrusion force and delivery time in autoinjectors, was evaluated at different time points during the stability study. Additionally, SOF glass syringes were filled with viscosity surrogate ranging from 1 to 90 cP to understand the impact of solution viscosity on break loose force, extrusion force, and autoinjector delivery time.

RESULTS

SOF demonstrates compatibility with proteins and exhibited significantly low particle counts compared to siliconized PFS. SOF syringes show significantly higher break-loose and extrusion forces. However, unlike siliconized syringes where silicone oil migration increases extrusion force, no significant change in functionality was observed in SOF glass syringe during stability testing. Overall, SOF glass syringes showed great potential as an alternative package for biologics with comparable performance on functionality as siliconized PFS.

CONCLUSIONS

The combination of SOF glass and its PTFE coated stopper presents a new primary container closure system with both adequate protein stability and desired functionality features.

Features in Backgrounds of Microscopy Images Introduce Biases in Machine Learning Analyses

Subvisible particles may be encountered throughout the processing of therapeutic protein formulations. Flow imaging microscopy (FIM) and backgrounded membrane imaging (BMI) are techniques commonly used to record digital images of these particles, which may be analyzed to provide particle size distributions, concentrations, and identities. Although both techniques record digital images of particles within a sample, FIM analyzes particles suspended in flowing liquids, whereas BMI records images of dry particles after collection by filtration onto a membrane. This study compared the performance of convolutional neural networks (CNNs) in classifying images of subvisible particles recorded by both imaging techniques. Initially, CNNs trained on BMI images appeared to provide higher classification accuracies than those trained on FIM images. However, attribution analyses showed that classification predictions from CNNs trained on BMI images relied on features contributed by the membrane background, whereas predictions from CNNs trained on FIM features were based largely on features of the particles. Segmenting images to minimize the contributions from image backgrounds reduced the apparent accuracy of CNNs trained on BMI images but caused minimal reduction in the accuracy of CNNs trained on FIM images. Thus, the seemingly superior classification accuracy of CNNs trained on BMI images compared to FIM images was an artifact caused by subtle features in the backgrounds of BMI images. Our findings emphasize the importance of examining machine learning algorithms for image analysis with attribution methods to ensure the robustness of trained models and to mitigate potential influence of artifacts within training data sets.

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.

Optimization of Flow Imaging Microscopy Setting Using Spherical Beads with Optical Properties Similar to Those of Biopharmaceuticals

Flow imaging microscopy (FIM) is widely used to characterize biopharmaceutical subvisible particles (SVPs). The segmentation threshold, which defines the boundary between the particle and the background based on pixel intensity, should be properly set for accurate SVP quantification. However, segmentation thresholds are often subjectively and empirically set, potentially leading to variations in measurements across instruments and operators. In the present study, we developed an objective method to optimize the FIM segmentation threshold using poly(methyl methacrylate) (PMMA) beads with a refractive index similar to that of biomolecules. Among several candidate particles that were evaluated, 2.5-µm PMMA beads were the most reliable in size and number, suggesting that the PMMA bead size analyzed by FIM could objectively be used to determine the segmentation threshold for SVP measurements. The PMMA bead concentrations measured by FIM were highly consistent with the indicative concentrations, whereas the PMMA bead size analyzed by FIM decreased with increasing segmentation threshold. The optimal segmentation threshold where the analyzed size was closest to the indicative size differed between an instrument with a black-and-white camera and that with a color camera. Inter-instrument differences in SVP concentrations in acid-stressed recombinant adeno-associated virus (AAV) and protein aggregates were successfully minimized by setting an optimized segmentation threshold specific to the instrument. These results reveal that PMMA beads can aid in determining a more appropriate segmentation threshold to evaluate biopharmaceutical SVPs using FIM.

Variables Impacting Silicone Oil Migration and Biologics in Prefilled Syringes

Prefilled syringes (PFS) as a primary container for parenteral drug products offer significant advantages, such as fast delivery time, ease of self-administration and fewer dosing errors. Despite the benefits that PFS can provide to patients, the silicone oil pre-coated on the glass barrels has shown migration into the drug product, which can impact particle formation and syringe functionality. Health authorities have urged product developers to better understand the susceptibility of drug products to particle formation in PFS due to silicone oil. In the market, there are multiple syringe sources provided by various PFS suppliers. Due to current supply chain shortages and procurement preferences for commercial products, the PFS source may change in the middle of development. Additionally, health authorities require establishing source duality. Therefore, it is crucial to understand how different syringe sources and formulation compositions impact the drug product quality. Here, several design of experiments (DOE) are executed that focus on the risk of silicone oil migration induced by syringe sources, surfactants, protein types, stress, etc. We utilized Resonant Mass Measurement (RMM) and Micro Flow Imaging (MFI) to characterize silicone oil and proteinaceous particle distribution in both micron and submicron size ranges, as well as ICP-MS to quantify silicon content. The protein aggregation and PFS functionality were also monitored in the stability study. The results show that silicone oil migration is impacted more by syringe source, siliconization process and surfactant (type & concentration). The break loose force and extrusion force across all syringe sources increase significantly as protein concentration and storage temperature increase. Protein stability is found to be impacted by its molecular properties and is less impacted by the presence of silicone oil, which is the same inference drawn in other literatures. A detailed evaluation described in this paper enables a thorough and optimal selection of primary container closure and de-risks the impact of silicone oil on drug product stability.

Number Concentration Measurements of Polystyrene Submicrometer Particles

The number of techniques to measure number concentrations and size distributions of submicrometer particles has recently increased. Submicrometer particle standards are needed to improve the accuracy and reproducibility of these techniques. The number concentrations of fluorescently labeled polystyrene submicrometer sphere suspensions with nominal 100 nm, 200 nm and 500 nm diameters were measured using seven different techniques. Diameter values were also measured where possible. The diameter values were found to agree within 20%, but the number concentration values differed by as much as a factor of two. Accuracy and reproducibility related with the different techniques are discussed with the goal of using number concentration standards for instrument calibration. Three of the techniques were used to determine SI-traceable number concentration values, and the three independent values were averaged to give consensus values. This consensus approach is proposed as a protocol for certifying SI-traceable number concentration standards.