How Well Do Low- and High-Concentration Protein Interactions Predict Solution Viscosities of Monoclonal Antibodies?

Flow Activation Energy of High-Concentration Monoclonal Antibody Solutions and Protein-Protein Interactions Influenced by NaCl and Sucrose

The solution viscosity and protein-protein interactions (PPIs) as a function of temperature (4-40 °C) were measured at a series of protein concentrations for a monoclonal antibody (mAb) with different formulation conditions, which include NaCl and sucrose. The flow activation energy (Eη) was extracted from the temperature dependence of solution viscosity using the Arrhenius equation. PPIs were quantified via the protein diffusion interaction parameter (kD) measured by dynamic light scattering, together with the osmotic second virial coefficient and the structure factor obtained through small-angle X-ray scattering. Both viscosity and PPIs were found to vary with the formulation conditions. Adding NaCl introduces an attractive interaction but leads to a significant reduction in the viscosity. However, adding sucrose enhances an overall repulsive effect and leads to a slight decrease in viscosity. Thus, the averaged (attractive or repulsive) PPI information is not a good indicator of viscosity at high protein concentrations for the mAb studied here. Instead, a correlation based on the temperature dependence of viscosity (i.e., Eη) and the temperature sensitivity in PPIs was observed for this specific mAb. When kD is more sensitive to the temperature variation, it corresponds to a larger value of Eη and thus a higher viscosity in concentrated protein solutions. When kD is less sensitive to temperature change, it corresponds to a smaller value of Eη and thus a lower viscosity at high protein concentrations. Rather than the absolute value of PPIs at a given temperature, our results show that the temperature sensitivity of PPIs may be a more useful metric for predicting issues with high viscosity of concentrated solutions. In addition, we also demonstrate that caution is required in choosing a proper protein concentration range to extract kD. In some excipient conditions studied here, the appropriate protein concentration range needs to be less than 4 mg/mL, remarkably lower than the typical concentration range used in the literature.

Simultaneous Protein Adsorption and Viscosity Measurement using Micropillar-Enhanced Acoustic Wave (μPAW) Device for Pharmaceutical Applications

At the early stages of drug development, the amount of drug materials is rather limited. In this case, viscosity measurement is often postponed to the later stages, where grams of proteins can be produced. Therefore, it is necessary to develop a viscometer capable of measuring the viscosity with high accuracy while requiring low sample volume. This study presents a novel viscosity measurement technique based on measuring the resonance frequency and motional resistance of a micropillar-enhanced acoustic wave (μPAW) device. The μPAW was developed by fabricating micropillars on the quartz crystal microbalance substrate in order to achieve ultra-high sensitivity, thanks to a unique coupling between the micropillar and the resonator. The experimental measurements demonstrated a nonlinear relationship between the density and viscosity of the fluid and the response of μPAW. A calibration correlation was developed using the response of μPAW in aqueous glycerol and sucrose solutions. The measurements were then extended using high-concentration BSA solutions as the model of protein solution. The main advantage of the μPAW device in this work over other viscometers is the ability to simultaneously measure solution viscosity and protein adsorption on the surface. This is a huge step forward in the development of sensing systems for the pharmaceutical industry, where real-time sensing of target biological proteins and measuring the viscosity of a solution is required.

Rose Bengal Labeled Bovine Serum Albumin for Protein Transport Imaging in Subcutaneous Tissues Using Computed Tomography and Fluorescence Microscopy

Subcutaneous (SC) injection of protein-based therapeutics is a convenient and clinically established drug delivery method. However, progress is needed to increase the bioavailability. Transport of low molecular weight (Mw) biotherapeutics such as insulin and small molecule contrast agents such as lipiodol has been studied using X-ray computed tomography (CT). This analysis, however, does not translate to the investigation of higher Mw therapeutics, such as monoclonal antibodies (mAbs), due to differences in molecular and formulation properties. In this study, an iodinated fluorescein analog rose bengal (RB) was used as a radiopaque and fluorescent label to track the distribution of bovine serum albumin (BSA) compared against unconjugated RB and sodium iodide (NaI) via CT and confocal microscopy following injection into ex vivo porcine SC tissue. Importantly, the high concentration BSA-RB exhibited viscosities more like that of viscous biologics than the small molecule contrast agents, suggesting that the labeled protein may serve as a more suitable formulation for the investigation of injection plumes. Three-dimensional (3D) renderings of the injection plumes showed that the BSA-RB distribution was markedly different from unconjugated RB and NaI, indicating the need for direct visualization of large protein therapeutics using conjugated tags rather than using small molecule tracers. Whereas this proof-of-concept study shows the novel use of RB as a label for tracking BSA distribution, our experimental approach may be applied to high Mw biologics, including mAbs. These studies could provide crucial information about diffusion in SC tissue and the influence of injection parameters on distribution, transport, and downstream bioavailability.

Impact of Glycosylation on Protein-Protein Self-Interactions of Monoclonal Antibodies

Protein self-interactions measured via second osmotic virial coefficients (B22) and dynamic light scattering interaction parameter values (kD) are often used as metrics for assessing the favorability of protein candidates and different formulations during monoclonal antibody (MAb) product development. Model predictions of B22 or kD typically do not account for glycans, though glycosylation can potentially impact experimental MAb self-interactions. To the best of our knowledge, the impact of MAb glycosylation on the experimentally measured B22 and kD values has not yet been reported. B22 and kD values of two fully deglycosylated MAbs and their native (i.e., fully glycosylated) counterparts were measured by light scattering over a range of pH and ionic strength conditions. Significant differences between B22 and kD of the native and deglycosylated forms were observed at a range of low to high ionic strengths used to modulate the effect of electrostatic contributions. Differences were most pronounced at low ionic strength, indicating that electrostatic interactions are a contributing factor. Though B22 and kD values were statistically equivalent at high ionic strengths where electrostatics were fully screened, we observed protein-dependent qualitative differences, which indicate that steric interactions may also play a role in the observed B22 and kD differences. A domain-level coarse-grained molecular model accounting for charge differences was considered to potentially provide additional insight but was not fully predictive of the behavior across all of the solution conditions investigated. This highlights that both the level of modeling and lack of inclusion of glycans may limit existing models in making quantitatively accurate predictions of self-interactions.

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.

Antibody association in solution: cluster distributions and mechanisms

Understanding factors that affect the clustering and association of antibodies molecules in solution is critical to their development as therapeutics. For 19 different monoclonal antibody (mAb) solutions, we measured the viscosities, the second virial coefficients, the Kirkwood-Buff integrals, and the cluster distributions of the antibody molecules as functions of protein concentration. Solutions were modeled using the statistical-physics Wertheim liquid-solution theory, representing antibodies as Y-shaped molecular structures of seven beads each. We found that high-viscosity solutions result from more antibody molecules per cluster. Multi-body properties such as viscosity are well predicted experimentally by the 2-body Kirkwood-Buff quantity, G22, but not by the second virial coefficient, B22, and well-predicted theoretically from the Wertheim protein-protein sticking energy. Weakly interacting antibodies are rate-limited by nucleation; strongly interacting ones by propagation. This approach gives a way to relate micro to macro properties of solutions of associating proteins.

Improved Diffusion Interaction Parameter Measurement to Predict the Viscosity of Concentrated mAb Solutions

During the developability assessment of therapeutic monoclonal antibody (mAb) candidates, utilization of robust high-throughput predictive assays enables rapid selection of top candidates with low risks for late-stage development. Predicting the viscosities of highly concentrated mAbs using limited materials is an important aspect of developability assessment because high viscosity can complicate manufacturability, stability, and administration. Here, we report a high-throughput assay measuring protein-protein interactions to predict mAb viscosity. The diffusion interaction parameter (kD) measures colloidal self-association in dilute solutions and has been reported to be predictive of the mAb viscosity at high concentrations. However, kD of Amgen early stage IgG1 mAb candidates measured in 10 mM acetate at pH 5.2 containing sucrose and polysorbate (denoted A52SuT) shows only weak correlation to their viscosities at 140 mg/mL in A52SuT. We hypothesize that kD measured in A52SuT reflects primarily long-range electrostatic repulsions because most of these mAb candidates carry strong net positive charges in this low ionic strength formulation with pH (5.2) well below pI values of mAb candidates. However, the viscosities of high concentration mAbs depend heavily on short-range molecular interactions. We propose an improved kD method in which salt is added to suppress charge repulsions and to allow for detection of key short-range interactions in dilute solutions. Salt types and salt concentrations were screened, and an optimal salt condition was identified. This optimized method was further validated using two test mAb sets. Overall, the method improves the Pearson R2 between kD and viscosity (6-230 cP) from 0.24 to 0.80 for a data set consisting of 37 mAbs.

Protein Association in Solution: Statistical Mechanical Modeling

Protein molecules associate in solution, often in clusters beyond pairwise, leading to liquid phase separations and high viscosities. It is often impractical to study these multi-protein systems by atomistic computer simulations, particularly in multi-component solvents. Instead, their forces and states can be studied by liquid state statistical mechanics. However, past such approaches, such as the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, were limited to modeling proteins as spheres, and contained no microscopic structure-property relations. Recently, this limitation has been partly overcome by bringing the powerful Wertheim theory of associating molecules to bear on protein association equilibria. Here, we review these developments.

Characterizing Protein-Protein Interactions and Viscosity of a Monoclonal Antibody from Low to High Concentration Using Small-Angle X-ray Scattering and Molecular Dynamics Simulations

Understanding protein-protein interactions and formation of reversible oligomers (clusters) in concentrated monoclonal antibody (mAb) solutions is necessary for designing stable, low viscosity (η) concentrated formulations for processing and subcutaneous injection. Here we characterize the strength (K) of short-range anisotropic attractions (SRA) for 75-200 mg/mL mAb2 solutions at different pH and cosolute conditions by analyzing structure factors (Seff(q)) from small-angle X-ray scattering (SAXS) using coarse-grained molecular dynamics simulations. Best fit simulations additionally provide cluster size distributions, fractal dimensions, cluster occluded volume, and mAb coordination numbers. These equilibrium properties are utilized in a model to account for increases in viscosity caused by occluded volume in the clusters (packing effects) and dissipation of stress across lubricated fractal clusters. Seff(q) is highly sensitive to K at 75 mg/mL where mAbs can mutually align to form SRA contacts but becomes less sensitive at 200 mg/mL as steric repulsion due to packing becomes dominant. In contrast, η at 200 mg/mL is highly sensitive to SRA and the average cluster size from SAXS/simulation, which is observed to track the cluster relaxation time from shear thinning. By analyzing the distribution of sub-bead hot spots on the 3D mAb surface, we identify a strongly attractive hydrophobic patch in the complementarity determining region (CDR) at pH 4.5 that contributes to the high K and consequently large cluster sizes and high η. Adding NaCl screens electrostatic interactions and increases the impact of hydrophobic attraction on cluster size and raises η, whereas nonspecific binding of Arg attenuates all SRA, reducing η. The hydrophobic patch is absent at higher pH values, leading to smaller K, smaller clusters, and lower η. This work constitutes a first attempt to use SAXS and CG modeling to link both structural and rheological properties of concentrated mAb solutions to the energetics of specific hydrophobic patches on mAb surfaces. As such, our work opens an avenue for future research, including the possibility of designing coarse-grained models with physically meaningful interacting hot spots.

Raman Spectroscopic Analysis of Highly-Concentrated Antibodies under the Acid-Treated Conditions

PURPOSE

Antibody drugs are usually formulated as highly-concentrated solutions, which would easily generate aggregates, resulting in loss of efficacy. Although low pH increases the colloidal dispersion of antibodies, acid denaturation can be an issue. Therefore, knowing the physical properties at low pH under high concentration conditions is important.

METHODS

Raman spectroscopy was used to investigate pH-induced conformational changes of antibodies at 50 mg/ml. Experiments in pH 3 to 7 were performed for human serum IgG and recombinant rituximab.

RESULTS

We detected the evident changes at pH 3 in Tyr and Trp bands, which are the sensitive markers of intermolecular interactions. Thermal transition analysis over the pH range demonstrated that the thermal transition temperature (Tm) was highest at pH 3. Acid-treated and neutralized one showed higher Tm than that of pH 7, indicating that their extent of intermolecular interactions correlated with the Tm values. Onset temperature was clearly different between concentrated and diluted samples. Colloidal analyses confirmed the findings of the Raman analysis.

CONCLUSION

Our studies demonstrated the positive correlation between Raman analysis and colloidal information, validating as a method for evaluating antibody conformation associated with aggregation propensities.

A multi-step machine learning approach for accelerating QbD-based process development of protein spray drying

This study proposes a new material-efficient multi-step machine learning (ML) approach for the development of a design space (DS) for spray drying proteins. Typically, a DS is developed by performing a design of experiments (DoE) with the spray dryer and the protein of interest, followed by deriving the DoE models via multi-variate regression. This approach was followed as a benchmark to the ML approach. The more complex the process and required accuracy of the final model is, the more experiments are necessary. However, most biologics are expensive and thus experiments should be kept to a minimum. Therefore, the suitability of using a surrogate material and ML for the development of a DS was investigated. To this end, a DoE was performed with the surrogate and the data used for training the ML approach. The ML and DoE model predictions were compared to measurements of three protein-based validation runs. The suitability of using lactose as surrogate was investigated and advantages of the proposed approach were demonstrated. Limitations were identified at protein concentrations >35mg/ml and particle sizes of x₅₀>6µm. Within the investigated DS protein secondary structure was preserved, and most process settings, resulted in yields >75% and residual moisture <10wt.%.

Antibodies Adsorbed to the Air-Water Interface Form Soft Glasses

When monoclonal antibodies are exposed to an air-water interface, they form aggregates, which negatively impacts their performance. Until now, the detection and characterization of interfacial aggregation have been difficult. Here, we exploit the mechanical response imparted by interfacial adsorption by measuring the interfacial shear rheology of a model antibody, anti-streptavidin immunoglobulin-1 (AS-IgG1), at the air-water interface. Strong viscoelastic layers of AS-IgG1 form when the protein is adsorbed from the bulk solution. Creep experiments correlate the compliance of the interfacial protein layer with the subphase solution pH and bulk concentration. These, along with oscillatory strain amplitude and frequency sweeps, show that the viscoelastic behavior of the adsorbed layers is that of a soft glass with interfacial shear moduli on the order of 10-3 Pa m. Shifting the creep compliance curves under different applied stresses forms master curves consistent with stress-time superposition of soft interfacial glasses. The interfacial rheology results are discussed in the context of the interface-mediated aggregation of AS-IgG1.

Subclass Effects on Self-Association and Viscosity of Monoclonal Antibodies at High Concentrations

The effects of a subclass of monoclonal antibodies (mAbs) on protein-protein interactions, formation of reversible oligomers (clusters), and viscosity (η) are not well understood at high concentrations. Herein, we quantify a short-range anisotropic attraction between the complementarity-determining region (CDR) and CH3 domains (K_CDR-CH3) for vedolizumab IgG1, IgG2, or IgG4 subclasses by fitting small-angle X-ray scattering (SAXS) structure factor S_eff(q) data with an extensive library of 12-bead coarse-grained (CG) molecular dynamics simulations. The K_CDR-CH3 bead attraction strength was isolated from the strength of long-range electrostatic repulsion for the full mAb, which was determined from the theoretical net charge and a scaling parameter ψ to account for solvent accessibility and ion pairing. At low ionic strength (IS), the strongest short-range attraction (K_CDR-CH3) and consequently the largest clusters and highest η were observed with IgG1, the subclass with the most positively charged CH3 domain. Furthermore, the trend in K_CDR-CH3 with the subclass followed the electrostatic interaction energy between the CDR and CH3 regions calculated with the BioLuminate software using the 3D mAb structure and molecular interaction potentials. Whereas the equilibrium cluster size distributions and fractal dimensions were determined from fits of SAXS with the MD simulations, the degree of cluster rigidity under flow was estimated from the experimental η with a phenomenological model. For the systems with the largest clusters, especially IgG1, the inefficient packing of mAbs in the clusters played the largest role in increasing η, whereas for other systems, the relative contribution from stress produced by the clusters was more significant. The ability to relate η to short-range attraction from SAXS measurements at high concentrations and to theoretical characterization of electrostatic patches on the 3D surface is not only of fundamental interest but also of practical value for mAb discovery, processing, formulation, and subcutaneous delivery.

Droplet-based microfluidic platform for viscosity measurement over extended concentration range

Rheology of concentrated protein solutions is crucial for the understanding of macromolecular crowding dynamics as well as the formulation of protein therapeutics. The cost and scarcity of most protein samples prevents wide-scale rheological studies as conventional viscosity measurement methods require large sample volume. There is a growing need for a precise and robust viscosity measurement tool that minimizes consumption and simplifies the handling of highly concentrated protein solutions. This objective is achieved by combining microfluidics and microrheology: we developed a specific microsystem to study the viscosity of aqueous solutions at high concentrations. The PDMS chip allows in situ production, storing and monitoring of water-in-oil nanoliter droplets. We perform precise viscosity measurements inside individual droplets by particle-tracking microrheology of fluorescent probes. Pervaporation of water through a PDMS membrane induces aqueous droplet shrinking, concentrating the sample up to 150 times, thus allowing viscosity measurements along an extended concentration range in just one experiment. The methodology is precisely validated by studying the viscosity of sucrose solutions. Two model proteins are also studied with sample consumption reduced to as little as 1 μL of diluted solution, showcasing the viability of our approach for the study of biopharmaceuticals.

Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model

Attractive protein-protein interactions in concentrated monoclonal antibody (mAb) solutions may lead to the formation of clusters that increase viscosity. Here, we propose an analytical model that relates mAb solution viscosity to clustering by accounting for the contributions of suboptimal mAb packing within a cluster and cluster fractal dimension. The influence of short-range, anisotropic attractions and long-range Coulombic repulsion on cluster properties is investigated by analyzing the cluster-size distributions, cluster fractal dimensions, radial distribution functions, and static structure factors from a library of coarse-grained molecular dynamics simulations. The library spans a vast range of mAb charges and attractive interactions in solutions of varying ionic strength. We present a framework for combining the viscosity model and simulation library to successfully characterize the attraction, repulsion, and clustering of an experimental mAb in three different pH and cosolute conditions by fitting the measured viscosity or structure factor from small-angle X-ray scattering. At low ionic strength, the cluster-size distribution is impacted by strong charges, and both the viscosity and net charge or structure factor and net charge must be considered to deconvolute the effects of short-range attraction and long-range repulsion.

Design of a microfluidic mixer channel: First steps into creating a fluorescent dye-based biosensor for mAb aggregate detection

A major challenge in the transition to continuous biomanufacturing is the lack of process analytical technology (PAT) tools which are able to collect real-time information on the process and elicit a response to facilitate control. One of the critical quality attributes (CQAs) of interest during monoclonal antibodies production is aggregate formation. The development of a real-time PAT tool to monitor aggregate formation is then crucial to have immediate feedback and process control. Miniaturized sensors placed after each unit operation can be a powerful solution to speed up an analytical measurement due to their characteristic short reaction time. In this work, a micromixer structure capable of mixing two streams is presented, to be employed in the detection of mAb aggregates using fluorescent dyes. Computational fluid dynamics (CFD) simulations were used to compare the mixing performance of a series of the proposed designs. A final design of a zigzag microchannel with 45° angle was reached and this structure was subsequently fabricated and experimentally validated with colour dyes and, later, with a FITC-IgG molecule. The designed zigzag micromixer presents a mixing index of around 90%, obtained in less than 30 seconds. Therefore, a micromixer channel capable of a fast and efficient mixing is hereby demonstrated, to be used as a real-time PAT tool for a fluorescence based detection of protein aggregation.

Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks

Solutions of monoclonal antibodies (mAbs) can show increased viscosity at high concentration, which can be a disadvantage during protein purification, filling, and administration. The viscosity is determined by protein-protein-interactions, which are influenced by the antibody's sequence as well as solution conditions, like pH, buffer type, or the presence of salts and other excipients. To predict viscosity, experimental parameters, like the diffusion interaction parameter (kD), or computational tools harnessing information derived from primary sequence, are often used, but a reliable predictive tool is still missing. We present a modeling approach employing artificial neural networks (ANNs) using experimental factors combined with simulation-derived parameters plus viscosity data from 27 highly concentrated (180 mg/mL) mAbs. These ANNs can be used to predict if mAbs exhibit problematic viscosity at distinct concentrations or to model viscosity-concentration-curves.

Technology development to evaluate the effectiveness of viscosity reducing excipients

Addition of pharmaceutical excipients is a commonly used approach to decrease the viscosity of highly concentrated protein formulations, which otherwise could not be subcutaneously injected or processed. The variety of protein-protein interactions, which are responsible for increased viscosities, makes a portfolio approach necessary. Screening of several excipients to develop such a portfolio is time and money consuming in industrial settings. Responsible protein-protein interactions were investigated using the interaction parameter k_D obtained from dynamic light scattering measurements in the studies presented herein. Together with in-silico calculated excipient parameter, k_D could be used as a screening tool accelerating screening and formulation development as k_D is suitable to high-throughput formats using small quantities of protein and low concentrations. A qualitative correlation between k_D and high-concentration viscosity behavior could be shown in our case.

CENTAUR-The small- and wide-angle neutron scattering diffractometer/spectrometer for the Second Target Station of the Spallation Neutron Source

CENTAUR has been selected as one of the eight initial instruments to be built at the Second Target Station (STS) of the Spallation Neutron Source at Oak Ridge National Laboratory. It is a small-angle neutron scattering (SANS) and wide-angle neutron scattering (WANS) instrument with diffraction and spectroscopic capabilities. This instrument will maximally leverage the high brightness of the STS source, the state-of-the-art neutron optics, and a suite of detectors to deliver unprecedented capabilities that enable measurements over a wide range of length scales with excellent resolution, measurements on smaller samples, and time-resolved investigations of evolving structures. Notably, the simultaneous WANS and diffraction capability will be unique among neutron scattering instruments in the United States. This instrument will provide much needed capabilities for soft matter and polymer sciences, geology, biology, quantum condensed matter, and other materials sciences that need in situ and operando experiments for kinetic and/or out-of-equilibrium studies. Beam polarization and a high-resolution chopper will enable detailed structural and dynamical investigations of magnetic and quantum materials. CENTAUR's excellent resolution makes it ideal for low-angle diffraction studies of highly ordered large-scale structures, such as skyrmions, shear-induced ordering in colloids, and biomembranes. Additionally, the spectroscopic mode of this instrument extends to lower momentum transfers than are currently possible with existing spectrometers, thereby providing a unique capability for inelastic SANS studies.

Effects of Monovalent Salt on Protein-Protein Interactions of Dilute and Concentrated Monoclonal Antibody Formulations

In this study, we used sodium chloride (NaCl) to extensively modulate non-specific protein-protein interactions (PPI) of a humanized anti-streptavidin monoclonal antibody class 2 molecule (ASA-IgG2). The changes in PPI with varying NaCl (C_NaCl) and monoclonal antibody (mAb) concentration (C_mAb) were assessed using the diffusion interaction parameter k_D and second virial coefficient B₂₂ measured from solutions with low to moderate C_mAb. The effective structure factor S(q)_eff measured from concentrated mAb solutions using small-angle X-ray and neutron scattering (SAXS/SANS) was also used to characterize the PPI. Our results found that the nature of net PPI changed not only with C_NaCl, but also with increasing C_mAb. As a result, parameters measured from dilute and concentrated mAb samples could lead to different predictions on the stability of mAb formulations. We also compared experimentally determined viscosity results with those predicted from interaction parameters, including k_D and S(q)_eff. The lack of a clear correlation between interaction parameters and measured viscosity values indicates that the relationship between viscosity and PPI is concentration-dependent. Collectively, the behavior of flexible mAb molecules in concentrated solutions may not be correctly predicted using models where proteins are considered to be uniform colloid particles defined by parameters derived from low C_mAb.

Utility of High Resolution 2D NMR Fingerprinting in Assessing Viscosity of Therapeutic Monoclonal Antibodies

Purpose

The viscosity of highly concentrated therapeutic monoclonal antibody (mAb) formulations at concentrations ≥ 100 mg/mL can significantly affect the stability, processing, and drug product development for subcutaneous delivery. An early identification of a viscosity prone mAb during candidate selection stages are often beneficial for downstream processes. Higher order structure of mAbs may often dictate their viscosity behavior at high concentration. Thus it is beneficial to gauge or rank-order their viscosity behavior using noninvasive structural fingerprinting methods and to potentially screen for suitable viscosity lowering excipients.

Methods

In this study, Dynamic Light Scattering (DLS) and 2D NMR based methyl fingerprinting were used to correlate viscosity behavior of a set of Pfizer mAbs. The viscosities of mAbs were determined. Respective Fab and Fc domains were generated for studies.

Result

Methyl fingerprinting of intact mAbs allows for differentiation of viscosity prone mAbs from well behaved ones even at 30–40 mg/ml, where bulk viscosity of the solutions are near identical. For viscosity prone mAbs, peak broadening and or distinct chemical shift changes were noted in intact and fragment fingerprints, unlike the well-behaved mAbs, indicative of protein protein interactions (PPI).

Conclusion

Fab-Fab or Fab-Fc interactions may lead to formation of protein networks at high concentration. The early transients to these network formation may be manifested through peak broadening or peak shift in the 2D NMR spectrum of mAb/mAb fragments. Such insights go beyond rank ordering mAbs based on viscosity behavior, which can be obtained by other methods as well..

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-022-03200-6.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

Measuring Self-Association of Antibody Lead Candidates with Dynamic Light Scattering

In this method chapter, we provide a brief overview of the key methods available to measure self-association of monoclonal antibodies (mAbs) and explain for which experimental throughputs they are usually applied. We then focus on dynamic light scattering (DLS) and describe experimental details on how to measure the diffusion interaction parameter (k_D) which is occasionally referred to as the gold standard for measuring self-association of proteins. The k_D is a well-established parameter to predict solution viscosity, which is one of the most critical developability parameters of mAbs. Finally, we present a pH and excipient screen that is designed to measure self-association with DLS under conditions that are relevant for bioprocessing and formulation of mAbs. The presented light scattering methods are well suited for lead candidate selections where it is essential to select mAbs with high developability potential for progression toward first human dose.

US FDA-approved therapeutic antibodies with high-concentration formulation: summaries and perspectives

Thirty four (34) of the total US FDA approved 103 therapeutic antibody drugs, accounts for one third of the total approved mAbs, are formulated with high protein concentration (100 mg/mL or above) which are the focus of this article. The highest protein concentration of these approved mAbs is 200 mg/mL. The dominant administration route is subcutaneous (76%). Our analysis indicates that it may be rational to implement a platform formulation containing polysorbate, histidine and sucrose to accelerate high concentration formulation development for antibody drugs. Since 2015, the FDA approval numbers are significantly increased which account for 76% of the total approval numbers, i.e., 26 out of 34 highly concentrated antibodies. Thus, we believe that the high concentration formulations of antibody drugs will be the future trend of therapeutic antibody formulation development, regardless of the challenges of highly concentrated protein formulations.

Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: Experimental assessment and computational predictions of domain interactions

Human/humanized IgG4 antibodies have reduced effector function relative to IgG1 antibodies, which is desirable for certain therapeutic purposes. However, the developability and biophysical properties for IgG4 antibodies are not well understood. This work focuses on the head-to-head comparison of key biophysical properties, such as self-interaction and viscosity, for 14 human/humanized, and chimeric IgG1 and IgG4 S228P monoclonal antibody pairs that contain the identical variable regions. Experimental measurements showed that the IgG4 S228P antibodies have similar or higher self-interaction and viscosity than that of IgG1 antibodies in 20 mM sodium acetate, pH 5.5. We report sequence and structural drivers for the increased viscosity and self-interaction detected in IgG4 S228P antibodies through a combination of experimental data and computational models. Further, we applied and extended a previously established computational model for IgG1 antibodies to predict the self-interaction and viscosity behavior for each antibody pair, providing insight into the structural characteristics and differences of these two isotypes. Interestingly, we observed that the IgG4 S228P swapped variants, where the CH3 domain was swapped for that of an IgG1, showed reduced self-interaction behavior. These domain swapped IgG4 S228P molecules also showed reduced viscosity from experiment and coarse-grained simulations. We also observed that experimental diffusion interaction parameter (kD) values have a high correlation with computational diffusivity prediction for both IgG1 and IgG4 S228P isotypes.

Abbreviations: AHc, constant region Hamaker constant; AHv, variable region Hamaker constant; CDRs, Complementarity-determining regions; CG, Coarse-grained model; CH1, Constant heavy chain 1; CH2 Constant heavy chain 2; CH3 Constant heavy chain 3; chgCH3 Effective charge on the CH3 region; CL Constant light chain; cP, Centipoise; DLS, Dynamic light scattering; Fab, Fragment antigen-binding; Fc, Fragment crystallizable; Fv, Variable domaing; (r) Radial distribution function; H1 CDR1 of Heavy Chain; H2 CDR2 of Heavy Chain; H3 CDR3 of Heavy Chain; HVI, High viscosity index; IgG1 human immunoglobulin of IgG1 subclass; IgG4 human immunoglobulin of IgG4 subclass; kD, Diffusion interaction parameter; L1 CDR1 of Light Chain; L2 CDR2 of Light Chain; L3 CDR3 of Light Chain; mAb, Monoclonal antibody; MD, Molecular dynamics; PPI Protein–protein interactions; SCM, Spatial charge map; UP-SEC, Ultra-high-performance size-exclusion chromatography; VH, Variable domain of Heavy Chain; VL, Variable domain of Light Chain

Revisit PEG-Induced Precipitation Assay for Protein Solubility Assessment of Monoclonal Antibody Formulations

PURPOSE

Protein solubility is an important attribute of pharmaceutical monoclonal antibody (MAb) formulations, particularly at high MAb concentrations. PEG-induced protein precipitation has been routinely used to assess protein solubility. To provide insights for better understanding and implementation of PEG-induced protein precipitation assay, this work compares different solubility measures and examines their relevance to loss of protein solubility in concentrated formulations.

METHODS

Solubility of a MAb in 15 formulations was evaluated using PEG-induced precipitation assay. Three apparent protein solubility measures, the middle-point and onset PEG concentrations (c_mid and c_onset) as well as the binding free energy (μ_B), were obtained from the PEG-induced protein precipitation assay and compared to the DLS protein interaction parameter (k_D). Visual inspection of loss of protein solubility in concentrated formulations during storage was used to further examine the discrepancy of protein solubility ranking by these measures.

RESULTS

PEG-induced precipitation assay predicted overall protein solubility ranking similar to that by DLS k_D. However, for three formulations with ionic excipients NaCl, Arg·Cl, and Arg·Glu·Cl, PEG-induced precipitation assay yielded more accurate predictions compared to DLS k_D measurements. Furthermore, μ_B showed superior ability in distinguishing protein solubility for these formulations.

CONCLUSIONS

This study demonstrated good correlations between the protein solubility measures obtained from PEG-induced precipitation experiments and DLS k_D measurement. It also provides one example in which protein solubility ranking by binding free energy is more accurate than the other measures. The results support the theoretical proposition that μ_B has a potential to serve as standard protein solubility measure.

Concentration trend study on foaming properties for native soy protein isolate treated by ultrasound and heating

Given the non-linearity of many protein properties with a short range of concentration which cannot be predicted a priori, and due to the lack of references in the food industry, we proceeded to analyze the foaming ones. The existing bibliography belongs to other fields of research but it is scarcely found for this area. For the food industry, ultrasound is considered one of the most environment-friendly processing. In addition, heating combination would alter their results considerably by synergistic or additive phenomena. Native soy protein isolate was obtained in our laboratory to use it as starting material; ultrasound with temperature was applied at 2, 4 and 6%w/w protein concentrations. Therefore, the objective of this paper was to determine the effect of ultrasound+temperature (50 or 90 °C) simultaneously applied, on the foamability by relating with the relative viscoelasticity, aggregates particle size distribution and their surface charge by zeta potential. The results indicated that treatments promoted changes on the functional parameters depending on the protein concentration. The analysis showed that at 4%wt/wt was adequate to improve foam formation and stability at same time. Dynamic rheology of continuous phase was relation with foamability showing the higher relative viscoelasticity at 4% of concentration after the combined treatment. Light scattering studies could partially explain this observation, taking into account both, the bulk viscosity and the low number of large particles formed after treating. Surface charge was increased for all concentrations equally leading to the aggregates formation of greater colloidal stability for all concentration and treatment conditions investigated.

Supplementary Information

The online version contains supplementary material available at (10.1007/s13197-020-04954-w).

Towards an improved prediction of concentrated antibody solution viscosity using the Huggins coefficient

The viscosity of a monoclonal antibody solution must be monitored and controlled as it can adversely affect product processing, packaging and administration. Engineering low viscosity mAb formulations is challenging as prohibitive amounts of material are required for concentrated solution analysis, and it is difficult to predict viscosity from parameters obtained through low-volume, high-throughput measurements such as the interaction parameter, k_D, and the second osmotic virial coefficient, B₂₂. As a measure encompassing the effect of intermolecular interactions on dilute solution viscosity, the Huggins coefficient, k_h, is a promising candidate as a parameter measureable at low concentrations, but indicative of concentrated solution viscosity. In this study, a differential viscometry technique is developed to measure the intrinsic viscosity, [η], and the Huggins coefficient, k_h, of protein solutions. To understand the effect of colloidal protein-protein interactions on the viscosity of concentrated protein formulations, the viscometric parameters are compared to k_D and B₂₂ of two mAbs, tuning the contributions of repulsive and attractive forces to the net protein-protein interaction by adjusting solution pH and ionic strength. We find a strong correlation between the concentrated protein solution viscosity and the k_h but this was not observed for the k_D or the b₂₂, which have been previously used as indicators of high concentration viscosity. Trends observed in [η] and k_h values as a function of pH and ionic strength are rationalised in terms of protein-protein interactions.

Lowering the viscosity of a high-concentration antibody solution by protein-polyelectrolyte complex

High-concentration and low-viscosity antibody formulations are necessary when administering these solutions subcutaneously (SC) due to limitations on injection volume. Here we show a method to decrease the viscosity of monoclonal antibody solution by protein-polyelectrolyte complex (PPC) with poly-l-glutamic acid (polyE). The viscosity of omalizumab solutions was 90 cP at the concentration of 150 mg/mL. In the presence of 20-50 mM polyE, the viscosity of PPC solution of 150 mg/mL omalizumab dramatically decreased below 10 cP due to the formation of crowded solution. The crowded state of PPC, named aggregated PPC (A-PPC), contained water droplets with a diameter of 10 μm or larger with low antibody concentrations. In the presence of 60 mM or more polyE, the omalizumab solution was transparent with the viscosity of 40 cP or less, named soluble PPC (S-PPC). More importantly, the solutions of both A-PPC and S-PPC were fully redissolved by the addition of phosphate saline buffer confirmed by secondary structure, the amount of aggregates, and binding activity to antigen.

The Protein Folding Problem: The Role of Theory

The protein folding problem was first articulated as question of how order arose from disorder in proteins: How did the various native structures of proteins arise from interatomic driving forces encoded within their amino acid sequences, and how did they fold so fast? These matters have now been largely resolved by theory and statistical mechanics combined with experiments. There are general principles. Chain randomness is overcome by solvation-based codes. And in the needle-in-a-haystack metaphor, native states are found efficiently because protein haystacks (conformational ensembles) are funnel-shaped. Order-disorder theory has now grown to encompass a large swath of protein physical science across biology.

Cluster Percolation Causes Shear Thinning Behavior in Concentrated Solutions of Monoclonal Antibodies

High-concentration (>100 g/L) solutions of monoclonal antibodies (mAbs) are typically characterized by anomalously large solution viscosity and shear thinning behavior for strain rates ≥10³ s^-1. Here, the link between protein-protein interactions (PPIs) and the rheology of concentrated solutions of COE-03 and COE-19 mAbs is studied by means of static and dynamic light scattering and microfluidic rheometry. By comparing the experimental data with predictions based on the Baxter sticky hard-sphere model, we surprisingly find a connection between the observed shear thinning and the predicted percolation threshold. The longest shear relaxation time of mAbs was much larger than that of model sticky hard spheres within the same region of the phase diagram, which is attributed to the anisotropy of the mAb PPIs. Our results suggest that not only the strength but also the patchiness of short-range attractive PPIs should be explicitly accounted for by theoretical approaches aimed at predicting the shear rate-dependent viscosity of dense mAb solutions.

Toward a Suite of Coarse-Grained Models for Molecular Simulation of Monoclonal Antibodies and Therapeutic Proteins

A series of coarse-grained models for molecular simulation of proteins are considered, with emphasis on the application of predicting protein-protein self-interactions for monoclonal antibodies (MAbs). As an illustrative example and for quantitative comparison, the models are used to predict osmotic virial coefficients over a broad range of attractive and repulsive self-interactions and solution conditions for a series of MAbs where the second osmotic virial coefficient has been experimentally determined in prior work. The models are compared based on how well they can predict experimental behavior, their computational burdens, and scalability. An intermediate-resolution model is also introduced that can capture specific electrostatic interactions with improved efficiency and similar or improved accuracy when compared to the previously published models. Guidance is included for the selection of coarse-grained models more generally for capturing a balance of electrostatic, steric, and short-ranged nonelectrostatic interactions for proteins from low to high concentrations.

Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters

High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein-protein interactions among many antibodies. Molecular simulation is a promising method to study protein-protein interactions; however, all-atom models do not allow the simulation of multiple molecules, which is necessary to compute viscosity directly. Coarse-grained models, on the other hand can do this. In this work, a 12-bead coarse-grained model based on Swan and coworkers (J. Phys. Chem. B 2018, 122, 2867-2880) was applied to study antibody interactions. Two adjustable parameters related to the short-range interactions on the variable and constant regions were determined by fitting experimental data of 20 IgG1 monoclonal antibodies at 150 mg/mL. The root-mean-square deviation improved from 1 to 0.68, and the correlation coefficient improved from 0.63 to 0.87 compared to that of a previous model that assumed the short-range interactions were the same for all the beads. Our model is also able to calculate the viscosity over a wide range of concentrations without additional parameters. A tabulated viscosity based on our model is provided to facilitate antibody screening in early-stage design.

Protein-protein interactions at high concentrations: Effects of ArgHCl and NaCl on the stability, viscosity and aggregation mechanisms of protein solution

The aim of this work was to use the diffusion coefficient ration (D_m/D_line) as a parameter to characterize the stability of protein at high concentration, to compare the effects of ArgHCl and NaCl on the interaction of highly concentrated proteins under different pH conditions, and to explore the correlation with protein stability. For this purpose, a high-concentration bovine serum albumin solution (BSA) was selected as the model system, and the diffusion coefficient, aggregation degree, conformational stability, and solution viscosity of the protein were studied by dynamic light scattering (DLS) and spectral detection techniques. The result showed that there was a significant correlation between the D_m/D_line and the protein aggregation. The D_m/D_line of the protein was minimum at pH 7.4, which corresponded to the maximum degree of aggregation and the highest solution viscosity. At pH 7.4, the hydrophobic interactions and the increased conformational stability of ArgHCl maximized the stability of the protein and reduced the viscosity of the solution by 69.3%. At pH 3.0, the strong charge shielding effect of ArgHCl and NaCl and the decreased conformational stability induced protein aggregation and the gel formation. These findings provided valuable insights into the mechanism of protein aggregation and the diffusion coefficient ration (D_m/D_line) could be a potential tool for the pre-formulation studies.

HP-β-CD for the formulation of IgG and Ig-based biotherapeutics

The main challenge to develop HCF for IgG and Ig-based therapeutics is to achieve essential solubility, viscosity and stability of these molecules in order to maintain product quality and meet regulatory requirement during manufacturing, production, storage, shipment and administration processes. The commonly used and FDA approved excipients for IgG and Ig -based therapeutics may no longer fulfil the challenge of HCF development for these molecules to certain extent, especially for some complex Ig-based platforms. 2-Hydroxypropyl beta-cyclodextrin (HP-β-CD) is one of the promising excipients applied recently for HCF development of IgG and Ig-based therapeutics although it has been used for formulation of small synthesized chemical drugs for more than thirty years. This review describes essential aspects about application of HP-β-CD as excipient in pharmaceutical formulation, including physico-chemical properties of HP-β-CD, supply chain, regulatory, patent landscape, marketed drugs with HP-β-CD, analytics and analytical challenges, stability and control strategies, and safety concerns. It also provides an overview of different studies, and outcomes thereof, regarding formulation development for IgGs and Ig-based molecules in liquid and solid (lyophilized) dosage forms with HP-β-CD. The review specifically highlights the challenges for formulation manufacturing of IgG and Ig-based therapeutics with HP-β-CD and identifies areas for future work in pharmaceutical and formulation development.

Machine Learning Feature Selection for Predicting High Concentration Therapeutic Antibody Aggregation

Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation behaviors of 21 approved monospecific monoclonal antibodies at high concentration (150 mg/mL), yielding a correlation coefficient of 0.71 on validation tests with only two features using a linear model. The nearest neighbors and support vector regression models further improved the performance, which have correlation coefficients of 0.86 and 0.80, respectively. This framework can be extended to train other models that predict different physical properties.

Product-Specific Impact of Viscosity Modulating Formulation Excipients During Ultra-High Concentration Biotherapeutics Drug Product Development

Developing ultra-high concentration biotherapeutics drug products can be challenging due to increased viscosity, processing, and stability issues. Excipients used to alleviate these concerns are traditionally evaluated at lower protein concentrations. This study investigates whether classically known modulators of stability and viscosity at low (<50 mg/mL) to high (>50 - 150 mg/mL) protein concentrations are beneficial in ultra-high (>150 mg/mL) concentration protein formulations and drug products. This study evaluates the effect of arginine monohydrochloride, proline, and lysine monohydrochloride on viscosity and concentratability at different high and ultra-high protein concentrations using a monoclonal antibody, mAbN, formulation as a candidate protein system. The effect of excipients on the viscosity and concentratability (rate and extent) was different at high versus ultra-high protein concentrations. These results highlight that classical excipients in literature known to modulate protein interactions at low protein concentrations and reduce viscosity at high protein concentrations may need to be evaluated at target protein concentrations in a product-specific manner while developing ultra-high concentration biologics drug products.

Temperature Dependence of Protein Solution Viscosity and Protein-Protein Interactions: Insights into the Origins of High-Viscosity Protein Solutions

Protein solution viscosity (η) as a function of temperature was measured at a series of protein concentrations under a range of formulation conditions for two monoclonal antibodies (MAbs) and a globular protein (aCgn). Based on theoretical arguments, a strong temperature dependence for protein-protein interactions (PPI) indicates highly anisotropic, short-ranged attractions that could lead to higher solution viscosities. The semi-empirical Ross-Minton model was used to determine the apparent intrinsic viscosity, shape, and "crowding" factors for each protein as a function of temperature and formulation conditions. The apparent intrinsic viscosity was independent of temperature for aCgn, while a slight decrease with increasing temperature was observed for the MAbs. The temperature dependence of solution viscosity was analyzed using the Andrade-Eyring equation to determine the effective activation energy of viscous flow (E_a,η). While E_a,η values were different for each protein, they were independent of formulation conditions for a given protein. PPI were quantified via the osmotic second virial coefficient (B₂₂) and the protein diffusion interaction parameter (k_D) as a function of temperature under the same formulation conditions as the viscosity measurements. Net interactions ranged from strongly attractive to repulsive by changing formulation pH and ionic strength for each protein. Overall, larger activation energies for PPI corresponded to larger activation energies for η, and those were predictive of the highest η values at higher protein concentrations.

Development challenges of high concentration monoclonal antibody formulations

High concentration monoclonal antibody drug products represent a special segment of biopharmaceuticals. In contrast to other monoclonal antibody products, high concentration monoclonal antibodies are injected subcutaneously helping increase patient compliance and reduce the number of hospital patient visits. It is important to note that a high protein concentration (≥50 mg/mL) poses a challenge from a product development perspective. Colloidal properties, physical and chemical protein stability should be considered during formulation, primary packaging and manufacturing process development as well as optimization of other dosage form-related parameters. The aim of such development work is to obtain a drug product capable of maintaining appropriate protein structure throughout its shelf-life and ensure proper and accurate dosage upon administration.

A Rapid, Small-Volume Approach to Evaluate Protein Aggregation at Air-Water Interfaces

Non-native protein aggregation is a common concern for biopharmaceuticals. A given protein may aggregate through a variety of mechanisms that depend on solution and physico-chemical stress conditions. A thorough evaluation of aggregation behavior for a protein under all conditions of interest is necessary to ensure drug safety and efficacy. This work introduces a rapid, small-volume approach to evaluate protein aggregation propensity upon exposure to air-water interfaces (AWI). A microtensiometer apparatus is used to aerate a small volume of a protein solution with microbubbles for short periods of time (≤10 s). Sub-visible particles that form are captured and analyzed using backgrounded membrane imaging. This allows one to capture all particles in the solution while being sample sparing. The surface-mediated aggregation of two model monoclonal antibodies (MAbs) and a globular protein (aCgn) was tested as a function of pH and temperature. Temperature had a negligible effect under the rapid interface turnover time scales with this technique. Electrostatic protein-protein interactions, mediated by pH changes, were more influential for particle formation via AWI. Nonionic surfactants substantially reduced particle formation for all MAb solutions, but not aCgn. The results are contrasted with expectations when exposing samples to much larger air-water interfacial stress.

Analysis of antibody self-interaction by bio-layer interferometry as tool to support lead candidate selection during preformulation and developability assessments

Developability assessment of therapeutic mAb candidates before entering CMC development mitigates the risk of later failure because of manufacturing and stability issues. For mAbs derived from library based screenings, such evaluation starts with the first panning and ends with the selection of a lead candidate. This candidate should show, amongst others, high affine target binding and beneficial conformational as well as chemical stability. In addition, colloidal stability, reflected by the self-interaction propensity, should be superior in order to reduce aggregate formation and unacceptably high viscosity at elevated protein concentrations. Here, we present a study demonstrating the application of self-interaction bio-layer interferometry (SI-BLI) in a developability assessment, including the evaluation of preformulations. We reveal that the formulation rankings based on SI-BLI, DLS and viscosity measurements correlate. SI-BLI provides a deeper understanding of influencing factors on mAb self-interaction such as ionic strength or cation species. The attractive mAb self-interaction propensity was significantly more suppressed by Mg²⁺ compared to Na⁺. SI-BLI can be performed in high throughput with minimal material and sample preparation needs. Therefore, it can be applied in early stages of developability assessment going beyond the use of a platform formulation and a small number of analysis, to screen more parameters before proceeding with candidate selection and further extensive development.

Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach

Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein-protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient-protein interaction and its effect on non-specific PPI is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient-protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is indicated to be predictive of relative protein stability. Comparison of arginine, trehalose, and sucrose, all of which give the highest viscosity in combination with analysis of B₂₂ and k_D and the SILCS metrics, indicates that higher viscosities are associated with a low number of predicted binding sites, with lower binding affinity of arginine leading to its anomalously high impact on viscosity. The present study indicates the potential for the SILCS-Biologics approach to be of utility in the rational design of excipients during biologics formulation.

Application of a High Throughput and Automated Workflow to Therapeutic Protein Formulation Development

Rapid and efficient formulation development is critical to successfully bringing therapeutic protein drug products into a competitive market under increasingly aggressive timelines. Conventional application of high throughput techniques for formulation development have been limited to lower protein concentrations, which are not applicable to late stage development of high concentration therapeutics. In this work, we present a high throughput (HT) formulation workflow that enables screening at representative concentrations via integration of a micro-buffer exchange system with automated analytical instruments. The operational recommendations associated with the use of such HT systems as well as the efficiencies gained (reduction in hands-on time and run time by over 70% and 30%, respectively), which enable practical characterization of an expanded formulation design space, are discussed. To demonstrate that the workflow is fit for purpose, the formulation properties and stability profiles (SEC and CEX) from samples generated by the HT workflow were compared to those processed by ultrafiltration/diafiltration, and the results were shown to be in good agreement. This approach was further applied to two case studies, one focused on a formulation screen that studied the effects of pH and excipient on viscosity and stability, and the other focused on selection of an appropriate viscosity mimic solution for a protein product.

Resolving Liquid-Liquid Phase Separation for a Peptide Fused Monoclonal Antibody by Formulation Optimization

Liquid-liquid phase separation (LLPS) of protein solutions has been usually related to strong protein-protein interactions (PPI) under certain conditions. For the first time, we observed the LLPS phenomenon for a novel protein modality, peptide-fused monoclonal antibody (pmAb). LLPS emerged within hours between pH 6.0 to 7.0 and disappeared when solution pH values decreased to pH 5.0 or lower. Negative values of interaction parameter (k_D) and close to zero values of zeta potential (ζ) were correlated to LLPS appearance. However, between pH 6.0 to 7.0, a strong electrostatic repulsion force was expected to potentially avoid LLPS based on the sequence predicted pI value, 8.35. Surprisingly, this is significantly away from experimentally determined pI, 6.25, which readily attributes the LLPS appearances of pmAb to the attenuated electrostatic repulsion force. Such discrepancy between experiment and prediction reminds the necessity of actual measurement for a complicated modality like pmAb. Furthermore, significant protein degradation took place upon thermal stress at pH 5.0 or lower. Therefore, the effects of pH and selected excipients on the thermal stability of pmAb were further assessed. A formulation consisting of arginine at pH 6.5 successfully prevented the appearance of LLPS and enhanced its thermal stability at 40 °C for pmAb. In conclusion, we have reported LLPS for a pmAb and successfully resolved the issue by optimizing formulation with aids from PPI characterization.

Potential and limits of a colloid approach to protein solutions

Looking at globular proteins with the eyes of a colloid scientist has a long tradition, in fact a significant part of the early colloid literature was focused on protein solutions. However, it has also been recognized that proteins are much more complex than the typical hard sphere-like synthetic model colloids. Proteins are not perfect spheres, their interaction potentials are in general not isotropic, and using theories developed for such particles are thus clearly inadequate in many cases. In this perspective article, we now take a closer look at the field. In particular, we reflect on the fact that modern colloid science has been undergoing a tremendous development, where a multitude of novel systems have been developed in the lab and in silico. During the last decade we have seen a rapidly increasing number of reports on the synthesis of anisotropic, patchy and/or responsive synthetic colloids, that start to resemble their complex biological counterparts. This experimental development is also reflected in a corresponding theoretical and simulation effort. The experimental and theoretical toolbox of colloid science has thus rapidly expanded, and there is obviously an enormous potential for an application of these new concepts to protein solutions, which has already been realized and harvested in recent years. In this perspective article we make an attempt to critically discuss the exploitation of colloid science concepts to better understand protein solutions. We not only consider classical applications such as the attempt to understand and predict solution stability and phase behaviour, but also discuss new challenges related to the dynamics, flow behaviour and liquid-solid transitions found in concentrated or crowded protein solutions. It not only aims to provide an overview on the progress in experimental and theoretical (bio)colloid science, but also discusses current shortcomings in our ability to correctly reproduce and predict the structural and dynamic properties of protein solutions based on such a colloid approach.

Identifying Key Residues That Drive Strong Electrostatic Attractions between Therapeutic Antibodies

Attractive electrostatic protein-protein interactions (PPI) necessarily involve identifying oppositely charged regions of the protein surface that interact favorably. This cannot be done reliably if one only considers a single protein in isolation unless there are obvious charge "patches" that result in extreme molecular dipoles. Prior work [ J. Pharm. Sci. 2019 , 108 , 120 - 132 ] identified three monoclonal antibodies (MAbs) that displayed experimental behavior ranging from net repulsive to strongly attractive electrostatic interactions. The present work provides a systematic computational approach for identifying the origin of diverse PPI, in terms of which sets of amino acids or individual amino acids are most influential, and determining if there are different patterns of pairwise amino acid interaction "maps" that result in different behaviors. The charge was eliminated computationally, one by one, for each charged residue in the wild-type sequences, which resulted in predicted changes in the second osmotic virial coefficient. The results highlight interaction "maps" that correspond to cases with qualitatively different net electrostatic PPI for the different MAbs and solution conditions, as well as key sets of residues that contribute to strongly attractive PPI. A more computationally efficient method is also proposed to identify key amino acids based on Mayer-weighted interaction energies.