Electrostatically Mediated Attractive Self-Interactions and Reversible Self-Association of Fc-Fusion Proteins

Attractive self-interactions and reversible self-association are implicated in many problematic solution behaviors for therapeutic proteins, such as irreversible aggregation, elevated viscosity, phase separation, and opalescence. Protein self-interactions and reversible oligomerization of two Fc-fusion proteins (monovalent and bivalent) and the corresponding fusion partner protein were characterized experimentally with static and dynamic light scattering as a function of pH (5 and 6.5) and ionic strength (10 mM to at least 300 mM). The fusion partner protein and monovalent Fc-fusion each displayed net attractive electrostatic self-interactions at pH 6.5 and net repulsive electrostatic self-interactions at pH 5. Solutions of the bivalent Fc-fusion contained higher molecular weight species that prevented quantification of typical interaction parameters (B22 and kD). All three of the proteins displayed reversible self-association at pH 6.5, where oligomers dissociated with increased ionic strength. Coarse-grained molecular simulations were used to model the self-interactions measured experimentally, assess net self-interactions for the bivalent Fc-fusion, and probe the specific electrostatic interactions between charged amino acids that were involved in attractive electrostatic self-interactions. Mayer-weighted pairwise electrostatic energies from the simulations suggested that attractive electrostatic self-interactions at pH 6.5 for the two Fc-fusion proteins were due to cross-domain interactions between the fusion partner domain(s) and the Fc domain.

Differential Surface Adsorption Phenomena for Conventional and Novel Surfactants Correlates with Changes in Interfacial mAb Stabilization

Protein adsorption on surfaces can result in loss of drug product stability and efficacy during the production, storage, and administration of protein-based therapeutics. Surface-active agents (excipients) are typically added in protein formulations to prevent undesired interactions of proteins on surfaces and protein particle formation/aggregation in solution. The objective of this work is to understand the molecular-level competitive adsorption mechanism between the monoclonal antibody (mAb) and a commercially used excipient, polysorbate 80 (PS80), and a novel excipient, N-myristoyl phenylalanine-N-polyetheramine diamide (FM1000). The relative rate of adsorption of PS80 and FM1000 was studied by pendant bubble tensiometry. We find that FM1000 saturates the interface faster than PS80. Additionally, the surface-adsorbed amounts from X-ray reflectivity (XRR) measurements show that FM1000 blocks a larger percentage of interfacial area than PS80, indicating that a lower bulk FM1000 surface concentration is sufficient to prevent protein adsorption onto the air/water interface. XRR models reveal that with an increase in mAb concentration (0.5-2.5 mg/mL: IV based formulations), an increased amount of PS80 concentration (below critical micelle concentration, CMC) is required, whereas a fixed value of FM1000 concentration (above its relatively lower CMC) is sufficient to inhibit mAb adsorption, preventing mAb from co-existing with surfactants on the surface layer. With this observation, we show that the CMC of the surfactant is not the critical factor to indicate its ability to inhibit protein adsorption, especially for chemically different surfactants, PS80 and FM1000. Additionally, interface-induced aggregation studies indicate that at minimum surfactant concentration levels in protein formulations, fewer protein particles form in the presence of FM1000. Our results provide a mechanistic link between the adsorption of mAbs at the air/water interface and the aggregation induced by agitation in the presence of surfactants.

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids

Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer-protein hybrid materials.

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids

Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer-protein hybrid materials.

Effect of Two Unique Nanoparticle Formulations on the Efficacy of a Broadly Protective Vaccine Against Pseudomonas Aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen responsible for a wide range of infections in humans. In addition to its innate antibiotic resistance, P. aeruginosa is very effective in acquiring resistance resulting in the emergence of multi-drug resistance strains and a licensed vaccine is not yet available. We have previously demonstrated the protective efficacy of a novel antigen PaF (Pa Fusion), a fusion of the type III secretion system (T3SS) needle tip protein, PcrV, and the first of two translocator proteins, PopB. PaF was modified to provide a self-adjuvanting activity by fusing the A1 subunit of the heat-labile enterotoxin from Enterotoxigenic E. coli to its N-terminus to give L-PaF. In addition to providing protection against 04 and 06 serotypes of P. aeruginosa, L-PaF elicited opsonophagocytic killing and stimulated IL-17A secretion, which have been predicted to be required for a successful vaccine. While monomeric recombinant subunit vaccines can be protective in mice, this protection often does not transfer to humans where multimeric formulations perform better. Here, we use two unique formulations, an oil-in-water (o/w) emulsion and a chitosan particle, as well as the addition of a unique TLR4 agonist, BECC438 (a detoxified lipid A analogue designated Bacterial Enzymatic Combinatorial Chemistry 438), as an initial step in optimizing L-PaF for use in humans. The o/w emulsion together with BECC438 provided the best protective efficacy, which correlated with high levels of opsonophagocytic killing and IL-17A secretion, thereby reducing the lung burden among all the vaccinated groups tested.

Time-Dependent Multi-Light-Source Image Classification Combined With Automated Multidimensional Protein Phase Diagram Construction for Protein Phase Behavior Analysis

Image-based protein phase diagram analysis is key for understanding and exploiting protein phase behavior in the biopharmaceutical field. However, required data analysis has become a notorious time-consuming task since high-throughput screening approaches were implemented. A variety of computational tools have been developed to support analysis, but these tools primarily use end point visible light images. This study investigates the combined effect of end point and time-dependent image features obtained from cross-polarized and ultraviolet light features, supplementary to visible light, on protein phase diagram image classification. In addition, external validation was performed to evaluate the classification algorithm's applicability to support protein phase diagram scoring. The predicted protein phase behavior classes were subsequently used to automatically construct multidimensional protein phase diagrams to prevent image information loss without complicating the used image classification algorithm. Combining end point and time-dependent features from 3 light sources resulted in a balanced accuracy of 86.4 ± 4.3%, which is comparable to or better than more complex classifiers reported in literature. External validation resulted in a correct formulation classification rate of 91.7%. Subsequent automated construction of the multidimensional protein phase diagrams, using predicted classes, allowed visualization of details such as crystallization rate and protein phase behavior type coexistence.

Aerodynamic Droplet Stream Expansion for the Production of Spray Freeze-Dried Powders

In spray freeze-srying (SFD), a solution is sprayed into a refrigerant medium, frozen, and subsequently sublimation dried, which allows the production of flowable lyophilized powders. SFD allows commonly freeze-dried active pharmaceutical ingredients (e.g., proteins and peptides) to be delivered using new applications such as needle-free injection and nasal or pulmonary drug delivery. In this study, a droplet stream was injected into a vortex of cold gas in order to reduce the risk of droplet collisions and therefore droplet growth before congelation, which adversely affects the particle size distribution. Droplets with initial diameters of about 40-50 μm were frozen quickly in a swirl tube at temperatures around -75°C and volumetric gas flow rates between 17 and 34 L/min. Preliminary studies that were focused on the evaluation of spray cone footprints were performed prior to SFD. A 2³ factorial design with a model solution of mannitol (1.5% m/V) and maltodextrin (1.5% m/V) was used to create flowable, low density (0.01-0.03 g/cm³) spherical lyophilisate powders. Mean particle diameter sizes of the highly porous particles ranged between 49.8 ± 6.6 and 88.3 ± 5.5 μm. Under optimal conditions, the mean particle size was reduced from 160 to 50 μm (decrease of volume by 96%) compared to non-expanded streams, whereas the SPAN value did not change significantly. This method is suitable for the production of lyophilized powders with small particle sizes and narrow particle size distributions, which is highly interesting for needle-free injection or nasal delivery of proteins and peptides.