Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Stabilization of an Infectious Enveloped Virus by Spray-Drying and Lyophilization.

Enveloped viruses are attractive candidates for use as gene- and immunotherapeutic agents due to their efficacy at infecting host cells and delivering genetic information. They have also been used in vaccines as potent antigens to generate strong immune responses, often requiring fewer doses than other vaccine platforms as well as eliminating the need for adjuvants. However, virus instability in liquid formulations may limit their shelf life and require that these products be transported and stored under stringently controlled temperature conditions, contributing to high cost and limiting patient access. In this work, spray-drying and lyophilization were used to embed an infectious enveloped virus within dry, glassy polysaccharide matrices. No loss of viral titer was observed following either spray-drying (at multiple drying gas temperatures) or lyophilization. Furthermore, viruses embedded in the glassy formulations showed enhanced thermal stability, retaining infectivity after exposure to elevated temperatures as high as 85 °C for up to one hour, and for up to 10 weeks at temperatures as high as 30 °C. In comparison, viruses in liquid formulations lost infectivity within an hour at temperatures above 40 °C, or after incubation at 25 °C for longer periods of time.

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.

Supervised and unsupervised machine learning approaches for monitoring subvisible particles within an aluminum-salt adjuvanted vaccine formulation.

Suspensions of protein antigens adsorbed to aluminum-salt adjuvants are used in many vaccines and require mixing during vial filling operations to prevent sedimentation. However, the mixing of vaccine formulations may generate undesirable particles that are difficult to detect against the background of suspended adjuvant particles. We simulated the mixing of a suspension containing a protein antigen adsorbed to an aluminum-salt adjuvant using a recirculating peristaltic pump and used flow imaging microscopy to record images of particles within the pumped suspensions. Supervised convolutional neural networks (CNNs) were used to analyze the images and create "fingerprints" of particle morphology distributions, allowing detection of new particles generated during pumping. These results were compared to those obtained from an unsupervised machine learning algorithm relying on variational autoencoders (VAEs) that were also used to detect new particles generated during pumping. Analyses of images conducted by applying both supervised CNNs and VAEs found that rates of generation of new particles were higher in aluminum-salt adjuvant suspensions containing protein antigen than placebo suspensions containing only adjuvant. Finally, front-face fluorescence measurements of the vaccine suspensions indicated changes in solvent exposure of tryptophan residues in the protein that occurred concomitantly with new particle generation during pumping.

Effect of mechanical stresses on viral capsid disruption during droplet formation and drying.

Identification of the mechanisms by which viruses lose activity during droplet formation and drying is of great importance to understanding the spread of infectious diseases by virus-containing respiratory droplets and to developing thermally stable spray dried live or inactivated viral vaccines. In this study, we exposed suspensions of baculovirus, an enveloped virus, to isolated mechanical stresses similar to those experienced during respiratory droplet formation and spray drying: fluid shear forces, osmotic pressure forces, and surface tension forces at interfaces. DNA released from mechanically stressed virions was measured by SYBR Gold staining to quantify viral capsid disruption. Theoretical estimates of the force exerted by fluid shear, osmotic pressures and interfacial tension forces during respiratory droplet formation and spray drying suggest that osmotic and interfacial stresses have greater potential to mechanically destabilize viral capsids than forces associated with shear stresses. Experimental results confirmed that rapid changes in osmotic pressure, such as those associated with drying of virus-containing droplets, caused significant viral capsid disruption, whereas the effect of fluid shear forces was negligible. Surface tension forces were sufficient to provoke DNA release from virions adsorbed at air-water interfaces, but the extent of this disruption was limited by the time required for virions to diffuse to interfaces. These results demonstrate the effect of isolated mechanical stresses on virus particles during droplet formation and drying.

Interfacial Adsorption Controls Particle Formation in Antibody Formulations Subjected to Extensional Flows and Hydrodynamic Shear.

During their manufacturing and delivery to patients, therapeutic proteins are commonly exposed to various interfaces and to hydrodynamic shear forces. Although adsorption of proteins to solid-liquid interfaces is known to foster formation of protein aggregates and particles, the impact of shear remains controversial, in part because of experimental challenges in separating the effects of shear from those caused by simultaneous exposure to interfaces. Extensional flows (occurring when solutions flow through sudden contractions) exert localized elongational forces that have been suspected to be damaging to proteins. In this work, we measured aggregation and particle formation in formulations of polyclonal and monoclonal antibodies subjected to extensional flow, high shear (105 s-1) and exposure to stainless-steel/water interfaces. Modification of the surface charge at the stainless steel/water interface changed protein adsorption characteristics without altering shear profiles, enabling shear and interfacial interactions to be separated. Even under conditions where antibodies were subjected to high hydrodynamic shear and extensional flow, production of subvisible particles could be inhibited by modifying the stainless-steel surface charge to minimize antibody adsorption. Digital images of particles recorded by flow imaging microscopy (FIM) and analyzed with machine learning algorithms were consistent with a particle formation mechanism by which antibodies adsorb and aggregate at the stainless-steel/water interface and subsequently form particles when shear displaces the interfacial aggregates, transporting them into the bulk solution. Topographical differences measured using atomic force microscopy (AFM) supported the proposed mechanism by showing reduced levels of protein adsorption on surface-charge-modified stainless-steel.

A Single Dose, Thermostable, Trivalent Human Papillomavirus Vaccine Formulated Using Atomic Layer Deposition.

Formulations of human papillomavirus (HPV) 16, 18, and 31 L1 capsomere protein antigens were spray dried to obtain glassy microspheres that were then coated by atomic layer deposition (ALD) with nanometer-thin protective layers of alumina. Spray-drying was used to formulate human papillomavirus (HPV) 16, 18, and 31 L1 capsomere protein antigens within glassy microspheres to which nanoscopic protective layers of alumina were applied using ALD. Suspensions of alumina-coated, capsomere-containing microparticles were administered in a single dose to mice. ALD-deposited alumina coatings provided thermostability and a delayed in vivo release of capsomere antigens, incorporating both a prime and a boost dose in one injection. Total serotype-specific antibody titers as well as neutralizing titers determined from pseudovirus infectivity assays were unaffected by incubation of the ALD-coated vaccines for at 4, 50, or 70 °C for three months prior to administration. In addition, even after incubation for three months at 70 °C, single doses of ALD-coated vaccines produced both higher total antibody responses and higher neutralizing responses than control immunizations that used two doses of conventional liquid formulations stored at 4 °C.

Machine learning approaches to root cause analysis, characterization, and monitoring of subvisible particles in monoclonal antibody formulations.

Processing stresses on therapeutic proteins may cause formation of subvisible particles. Different stress mechanisms generate particle populations with characteristic morphological "fingerprints," and machine learning techniques like convolutional neural networks (CNNs) allow classification of microscopy images of these particles according to known stresses at their root cause. Using CNNs to classify novel particle types not included during network training may lead to inaccurate classification, however, using CNNs to monitor the presence of particulate matter not explicitly used in training could serve as a useful process analytical technology. We used CNNs to classify and identify the root cause of particles generated by subjecting three monoclonal antibodies (mAbs) to various common manufacturing stresses. We probed the generality of particles generated by stressing different mAbs in different formulations and showed that CNN analyses were sensitive not only to the applied stress, but also the buffer conditions and the particular mAb that generated particle populations. Thus, models trained on images of particles created with one mAb and buffer system may not provide accurate root cause analysis when applied to particles generated by other mAb and buffer systems. A lever-rule analysis of CNN-derived fingerprints was used to characterize the composition of mixtures of particle types. Finally, we monitored the temporal evolution of CNN-derived fingerprints when novel populations of particles, which were not included during training, were generated by pumping mAb solutions through a peristaltic pump.

Machine Learning Analysis Provides Insight into Mechanisms of Protein Particle Formation Inside Containers During Mechanical Agitation.

Container choice can influence particle generation within protein formulations. Incompatibility between proteins and containers can manifest as increased particle concentrations, shifts in particle size distributions and changes in particle morphology distributions. In this study, flow imaging microscopy (FIM) combined with machine learning-based goodness-of-fit hypothesis testing algorithms were used in accelerated stability studies to investigate the impact of containers on particle formation. Containers in four major container categories subdivided into eleven container types were filled with monoclonal antibody formulations and agitated with and without headspace, producing subvisible particles. Digital images of the particles were recorded using flow imaging microscopy and analyzed with machine learning algorithms. Particle morphology distributions depended on container category and type, revealing differences that would not have been obvious by analysis of particle concentrations or container surface characteristics alone. Additionally, the algorithm was used to compare morphologies of particles generated in containers against those generated using isolated stresses at air-liquid and container-air-liquid interfaces. These comparisons showed that the morphology distributions of particles formed during agitation most closely resemble distributions that result from exposure of proteins to moving triple interface lines at points where container-air-liquid interfaces intersect. The approach described here can be used to identify dominant causes of particle generation due to protein-container interactions.

Lyophilized Filovirus Glycoprotein Vaccines: Peroxides in a Vaccine Formulation with Polysorbate 80-Containing Adjuvant are Associated with Reduced Neutralizing Antibody Titers in Both Mice and Non-Human Primates.

Zaire ebolavirus, Sudan ebolavirus, and Marburg marburgvirus are the filoviruses most commonly associated with human disease. Previously, we administered a three-dose regimen of trivalent vaccines comprising glycoprotein antigens from each virus in mice and non-human primates (NHPs). The vaccines, which contained a polysorbate 80-stabilized squalane-in-water emulsion adjuvant and were lyophilized from a solution containing trehalose, produced high antibody levels against all three filovirus antigens. Subsequently, single-vial formulations containing a higher concentration of adjuvant were generated for testing in NHPs, but these vaccines elicited lower neutralizing antibody titers in NHPs than previously tested formulations. In order to explain these results, in the current work we measured the size of adjuvant emulsion droplets and the peroxide levels present in the vaccines after lyophilization and reconstitution and tested the effects of these variables on the immune response in mice. Increases in squalane droplet sizes were observed when the ratio of adjuvant to trehalose was increased beyond a critical value, but antibody and neutralizing antibody titers in mice were independent of the droplet size. Higher levels of peroxides in the vaccines correlated with higher concentrations of adjuvant in the formulations, and higher peroxide levels were associated with increased levels of oxidative damage to glycoprotein antigens. Neutralizing titers in mice were inversely correlated with peroxide levels in the vaccines, but peroxide levels could be reduced by adding free methionine, resulting in retention of high neutralizing antibody titers. Overall, the results suggest that oxidation of glycoprotein antigens by peroxides in the polysorbate 80-stabilized squalane-in-water emulsion adjuvant, but not lyophilization-induced increases in adjuvant emulsion droplet size may have been responsible for the decreased neutralizing titers seen in formulations containing higher amounts of adjuvant.

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.

Atomic-Layer Deposition Processes Applied to Phage λ and a Phage-like Particle Platform Yield Thermostable, Single-Shot Vaccines.

Especially in developing countries, the impact of vaccines can be limited by logistical obstacles associated with multiple dose regimens, pathogen variants, and challenges imposed by requirements for maintaining vaccines at low temperatures during shipping and storage. Thus, there is a need for vaccines that can be flexibly modified to address evolving pathogen landscapes, are stable outside of narrow "cold-chain" temperatures and require administration of only single doses. Here we demonstrate in proof-of-concept studies a vaccine platform that addresses these impediments to more widespread use of vaccines. The platform relies on bacteriophage-derived phage-like-particles (PLPs) that utilize a "plug-and-play" antigen delivery system that allows for fast, easy alteration of antigens on the surface of the PLPs. Thermostability of PLP-based vaccines can be achieved by embedding the PLPs within glassy particles produced by spray drying, and nanoscopic aluminum oxide layers applied using atomic layer deposition (ALD) can serve to control release of antigen in vivo, yielding vaccine formulations that elicit strong immune responses after administration of single doses. Bacteriophage λ was stabilized by spray drying to form powders that were incubated at 37 °C for up to a year without loss of infectious activity. PLPs derived from bacteriophage λ were expressed and purified from E. coli cultures, and an in vitro conjugation strategy was used to decorate specific PLP surface sites with T4-lysozyme, a model vaccine antigen. The resulting T4-lysozyme:PLP complexes (Lys-PLPs) were embedded in glassy dry powders formed by spray drying and coated with nanometer-thick layers of alumina deposited by ALD in a fluidized bed reactor. Alumina-coated Lys-PLP vaccines were stable for a least a month at 50 °C, and single doses of the alumina-coated vaccines elicited immune responses that were indistinguishable from responses generated by conventional two-dose, prime-and-boost dosing regimens of alum-adjuvanted Lys-PLP vaccines.

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

The measurement of polydisperse protein aggregates and particles in biotherapeutics remains a challenge, especially for particles with diameters of ≈ 1 µm and below (sub-micrometer). This paper describes an interlaboratory comparison with the goal of assessing the measurement variability for the characterization of a sub-micrometer polydisperse particle dispersion composed of five sub-populations of poly(methyl methacrylate) (PMMA) and silica beads. The study included 20 participating laboratories from industry, academia, and government, and a variety of state-of-the-art particle-counting instruments. The received datasets were organized by instrument class to enable comparison of intralaboratory and interlaboratory performance. The main findings included high variability between datasets from different laboratories, with coefficients of variation from 13 % to 189 %. Intralaboratory variability was, on average, 37 % of the interlaboratory variability for an instrument class and particle sub-population. Drop-offs at either end of the size range and poor agreement on maximum counts of particle sub-populations were noted. The mean distributions from an instrument class, however, showed the size-coverage range for that class. The study shows that a polydisperse sample can be used to assess performance capabilities of an instrument set-up (including hardware, software, and user settings) and provides guidance for the development of polydisperse reference materials.

Single-vial filovirus glycoprotein vaccines: Biophysical characteristics and immunogenicity after co-lyophilization with adjuvant.

Zaire ebolavirus (EBOV), Sudan ebolavirus (SUDV), and Marburg marburgvirus (MARV) are the most prevalent and pathogenic species of filovirus. Previously, we showed that glycoprotein antigens from each virus could be lyophilized to create thermostable monovalent subunit vaccines. However, cross-protection is not expected from the monovalent vaccines and therefore developing a trivalent filovirus vaccine would be desirable. Subunit protein vaccines often require the addition of an adjuvant to sufficiently boost the immunogenicity. Typically, liquid suspensions or emulsions of adjuvants and lyophilized antigens are stored in separate vials to avoid destabilizing interactions and are only mixed immediately before administration. Herein, we describe the development and characterization of monovalent and trivalent filovirus vaccines that are co-lyophilized with a squalane-in-water emulsion adjuvant. We found that the single-vial presentation retained adjuvant particle diameter and zeta potential after lyophilization and reconstitution. Furthermore, the trivalent vaccines elicited high antibody levels against all three antigens in mice and non-human primates. These results advance the prospect of developing a single-vial trivalent filovirus vaccine, which would enable easier distribution and administration of the vaccine to resource-poor areas.

Thermostability of a trivalent, capsomere-based vaccine for human papillomavirus infection.

Currently licensed vaccines require a cold-chain to maintain efficacy. This cold-chain requirement reduces the availability of vaccines in resource-poor areas of the world. Commercially available human papillomavirus (HPV) vaccines protect against the most common HPV types related to cervical cancer; however, their impact is limited in many regions due to cold-chain requirements. The goal of this study was to test the thermostability of an adjuvanted, trivalent HPV L1 capsomere-based vaccine (containing HPV types 16, 18, and 31) that was formulated by using lyophilization to embed the antigens within a solid, glassy matrix. Thermal stabilities were determined by storing the vaccine formulations for 3 months at 50 °C, followed by immunization of BALB/c mice and measurement of antibody responses. Antibody responses to capsomere vaccines formulated with alum were unchanged after storage for 3 months at 50 °C. Neutralizing responses to these vaccines were unchanged by high-temperature storage, and were equivalent to those generated after administration of the commercially available liquid HPV vaccine Gardasil®9.

Insulin Fibril Formation Caused by Mechanical Shock and Cavitation.

Cavitation can occur when liquids are exposed to pressure waves of sufficient amplitude, producing rapidly expanding and collapsing gas bubbles that generate localized regions of high energy dissipation. When vials containing insulin were subjected to mechanical shock or when ultrasound was applied to the vials, the resulting cavitation events induced formation of insulin amyloid fibril nuclei that were detected by transmission electron microscopy and quantified by fluorescence spectroscopy following staining with the amyloid-sensitive dye thioflavin-T. Dropping insulin solutions in glass vials produced only minute amounts of insulin fibril nuclei, which could be detected by allowing the nuclei to grow. Cavitation-induced formation of amyloid aggregates may be relevant for iatrogenic insulin deposition disease, where insulin fibrils formed in vitro prior to administration to patients could serve as nuclei for growing fibril deposits in vivo.

Machine Learning and Accelerated Stress Approaches to Differentiate Potential Causes of Aggregation in Polyclonal Antibody Formulations During Shipping.

Therapeutic proteins are among the most widely prescribed medications, with wide distribution and complex supply chains. Shipping exposes protein formulations to stresses that can trigger aggregation, although the exact mechanism(s) responsible for aggregation are unknown. To better understand how shipping causes aggregation, we compared populations of aggregates that were formed in a polyclonal antibody formulation during live shipping studies to populations observed in accelerated stability studies designed to mimic both the sporadic high g-force and continuous low g-force stresses encountered during shipping. Additionally, we compared the effects on aggregation levels generated in two types of secondary packaging, one of which was designed to mitigate the effects of large g-force stresses. Aggregation was quantified using fluorescence intensity of 4,4'-dianilino-1,1'-binaphthyl-5,5'-disulfonic acid (bis-ANS) dye, size exclusion high performance liquid chromatography (SECHPLC), and flow imaging microscopy (FIM). FIM was also combined with machine learning methods to analyze particle morphology distributions. These comparisons revealed that the morphology distributions of aggregates formed during live shipping resemble distributions that result from low g-force events, but not those observed following high g-force events, suggesting that low g-force stresses play a predominant role in shipping-induced aggregation.

Stability of lyophilized and spray dried vaccine formulations.

Liquid formulations of vaccines are subject to instabilities that result from degradation processes that proceed via a variety of physical and chemical pathways. In dried formulations, such as those prepared by lyophilization or spray drying, many of these degradation pathways may be avoided or inhibited. Thus, the stability of vaccine formulations can be enhanced significantly in the absence of bulk water. Potential advantages of dry vaccine formulations include extended shelf lives and less stringent cold-chain storage requirements, both of which offer possibilities of reduced vaccine wastage and facilitated distribution to resource-poor areas. Lyophilization and spray drying represent the most common methods of stabilizing vaccines through drying. This article reviews several lyophilized and spray dried vaccines that address a diverse set of pathogens, as well as some of the assays used to quantify their stability. Recent dry vaccine trends include needle-free delivery of dry powder via non-parenteral routes of administration and the incorporation of advanced vaccine adjuvants into formulations, which further contribute to the goal of increasing vaccine distribution to resource-poor areas. Challenges associated with development of these newer technologies are also discussed.

Hydrogel Coatings on Container Surfaces Reduce Protein Aggregation Caused by Mechanical Stress and Cavitation.

This work reports the ability of hydrogel coatings to protect therapeutic proteins from cavitation-induced aggregation caused by mechanical stress. Here, we show that polyacrylamide hydrogel coatings on container surfaces suppress mechanical shock-induced cavitation and the associated aggregation of intravenous immunoglobulin (IVIg). First, crosslinked polyacrylamide hydrogels were grown on the surfaces of borosilicate glass vials. Treatment with ultrasound showed that these hydrogel surfaces suppressed cavitation events to levels below those found for unfunctionalized borosilicate glass. Next, IVIg solutions were loaded into these vials and subjected to tumbling, horizontal shaking, and drop testing. Aggregation was quantified by bisANS fluorescence staining and particle counting by flow imaging microscopy (FIM). In all cases, the presence of polyacrylamide hydrogels on the vial surfaces reduced the amount of IVIg aggregation and the number of particulates. In addition, the polyacrylamide appeared to have a protective effect that prevented additional aggregates from forming at extended tumbling times. Finally, drop test studies showed that the polyacrylamide coatings suppressed detectable cavitation. This work reveals how even a simple hydrogel vial coating can have a profound effect on stabilizing protein therapeutics.

Preservation of Quaternary Structure in Thermostable, Lyophilized Filovirus Glycoprotein Vaccines: A Search for Stability-Indicating Assays.

The filoviruses Zaire ebolavirus (EBOV), Marburg marburgvirus (MARV), and Sudan ebolavirus (SUDV) are some of the most lethal infectious agents known. To date, the Zaire ebolavirus vaccine (ERVEBO®) is the only United States Food and Drug Administration (FDA) approved vaccine available for any species of filovirus. However, the ERVEBO® vaccine requires cold-chain storage not to exceed -60 °C. Such cold-chain requirements are difficult to maintain in low- and middle-income countries where filovirus outbreaks originate. To improve the thermostability of filovirus vaccines in order to potentially relax or eliminate these cold-chain requirements, monovalent subunit vaccines consisting of glycoproteins from EBOV, MARV, and SUDV were stabilized within amorphous disaccharide glasses through lyophilization. Lyophilized formulations and liquid controls were incubated for up to 12 weeks at 50 °C to accelerate degradation. To identify a stability-indicating assay appropriate for monitoring protein degradation and immunogenicity loss during these accelerated stability studies, filovirus glycoprotein secondary, tertiary, and quaternary structures and vaccine immunogenicity were measured. Size-exclusion chromatography was the most sensitive indicator of glycoprotein stability in the various formulations for all three filovirus immunogens. Degradation of the test vaccines during accelerated stability studies was reflected in changes in quaternary structure, which were discernible with size-exclusion chromatography. Filovirus glycoproteins in glassy lyophilized formulations retained secondary, tertiary, and quaternary protein structure over the incubation period, whereas the proteins within liquid controls both aggregated to form higher molecular weight species and dissociated from their native quaternary structure to form a variety of structurally-perturbed lower molecular weight species.