Particle Metrology Approach to Understanding How Storage Conditions Affect Long-Term Liposome Stability.

Lipid nanoparticles are a generic type of nanomaterial with broad applicability in medicine as drug delivery vehicles. Liposomes are a subtype of lipid nanoparticles and, as a therapeutic platform, can be loaded with a genetic material or pharmaceutical agents for use as drug treatments. An open question for these types of lipid nanoparticles is what factor(s) affect the long-term stability of the particles. The stability of the particle is of great interest to understand and predict the effective shelf-life and storage requirements. In this report, we detail a one-year study of liposome stability as a function of lipid composition, buffer composition/pH, and storage temperature. This was done in aqueous solution without freezing. The effect of lipid composition is shown to be a critical factor when evaluating stability of the measured particle size and number concentration. Other factors (i.e., storage temperature and buffer pH/composition) were shown to be less critical but still have some effect. The stability of these particles informs formulation and optimal storage requirements and assists with future developmental planning of a NIST liposome-based reference material. This work also highlights the complex nature of long-term soft particle storage in biopharmaceutical applications.

An Interlaboratory Study to Identify Potential Visible Protein-Like Particle Standards.

Visible protein-like particle standards may improve visual inspection and/or appearance testing practices used in the biotechnology industry. They may improve assay performance resulting in better alignment and more standardized training among different companies. The National Institute of Standards and Technology (NIST) has conducted an interlaboratory study to test whether the standards under development mimic typical proteinaceous particles found in biotherapeutics and if they can be implemented during the visual inspection process. Fourteen organizations from industry and government have participated. A total of 20 labs from these 14 organizations participated with analysts from 6 formulation, 7 analytical, 4 quality control, and 3 manufacturing labs. The circulated samples consisted of abraded ethylene tetrafluoroethylene (ETFE) particles or photolithographic particles. The results consist of qualitative ratings, which varied substantially among organizations and within labs. Polydisperse ETFE particle suspensions, containing particles enriched in greater than 150 µm in size, were rated more favorably than the photolithographic particles by formulation and analytical scientists. The largest monodisperse photolithographic particles (approximately 300 µm in size) were favored equally compared to ETFE by all scientists. Solution modifications to decrease the settling rate or to alter optical properties of the ETFE solutions yielded lower ratings by the analysts. Both particle types received mixed ratings for their usability and for their application for visual inspection and for training purposes. Industry feedback will assist NIST in developing reference material(s) for visible protein-like particles.

Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.

OBJECTIVE: Digital microscopy is used to monitor particulates such as protein aggregates within biopharmaceutical products. The images that result encode a wealth of information that is underutilized in pharmaceutical process monitoring. For example, images of particles in protein drug products typically are analyzed only to obtain particle counts and size distributions, even though the images also reflect particle characteristics such as shape and refractive index. Multiple groups have demonstrated that convolutional neural networks (CNNs) can extract information from images of protein aggregates allowing assignment of the likely stress at the "root-cause" of aggregation. A practical limitation of previous CNN-based approaches is that the potential aggregation-inducing stresses must be known a priori, disallowing identification of particles produced by unknown stresses. METHODS: We demonstrate an expanded CNN analysis of flow imaging microscopy (FIM) images incorporating judiciously chosen particle standards within a recently proposed "fingerprinting algorithm" (Biotechnol. & Bioeng. (2020) 117:3322) that allows detection of particles formed by unknown root-causes. We focus on ethylene tetrafluoroethylene (ETFE) microparticles as standard surrogates for protein aggregates. We quantify the sensitivity of the new algorithm to experimental parameters such as microscope focus and solution refractive index changes, and explore how FIM sample noise affects statistical testing procedures. RESULTS & CONCLUSIONS: Applied to real-world microscopy images of protein aggregates, the algorithm reproducibly detects complex, distinguishing "textural features" of particles that are not easily described by standard morphological measurements. This offers promise for quality control applications and for detecting shifts in protein aggregate populations due to stresses resulting from unknown process upsets.

Rapid evaporation at the superheat limit of methanol, ethanol, butanol and n-heptane on platinum films supported by low-stress SiN membranes.

The bubble nucleation temperatures of several organic liquids (methanol, ethanol, butanol, n-heptane) on stress-minimized platinum (Pt) films supported by SiN membranes is examined by pulse-heating the membranes for times ranging from 1 µs to 10 µs. The results show that the nucleation temperatures increase as the heating rates of the Pt films increase. Measured nucleation temperatures approach predicted superheat limits for the smallest pulse times which correspond to heating rates over 108 K/s, while nucleation temperatures are significantly lower for the longest pulse times. The microheater membranes were found to be robust for millions of pulse cycles, which suggests their potential in applications for moving fluids on the microscale and for more fundamental studies of phase transitions of metastable liquids.

Particle shape effects on subvisible particle sizing measurements.

Particle analysis tools for the subvisible (<100 µm) size range, such as light obscuration, flow imaging (FI), and electrical sensing zone (ESZ), often produce results that do not agree with one another, despite their general agreement when characterizing polystyrene latex spheres of different sizes. To include the effect of shape in comparison studies, we have used the methods of photolithography to create rods and disks. Although the rods are highly monodisperse, the instruments produce broadened peaks and report mean size parameters that are different for different instruments. We have fabricated a microfluidic device that simultaneously performs ESZ and FI measurements on each particle to elucidate the causes of discrepancies and broadening. Alignment of the rods with flow causes an oversizing by FI and undersizing by ESZ. FI also oversizes rods because of the incorrect edge definition that results from diffraction and imperfect focus. We present an improved correction algorithm for this effect that reduces discrepancies for rod-shaped particles. Tumbling of particles is observed in the microfluidic ESZ/FI and results in particle oversizing and breadth of size distribution for the monodisperse rods.