Diffusion and sedimentation interaction parameters for measuring the second virial coefficient and their utility as predictors of protein aggregation

Hydrodynamic and thermodynamic analysis of PEGylated human serum albumin

Covalent labeling of therapeutic drugs and proteins with polyethylene glycol (PEGylation) is an important modification for improving stability, solubility, and half-life. PEGylation alters protein solution behavior through its impact on thermodynamic nonideality by increasing the excluded volume, and on hydrodynamic nonideality by increasing the frictional drag. To understand PEGylation's impact, we investigated the thermodynamic and hydrodynamic properties of a model system consisting of PEGylated human serum albumin derivatives using analytical ultracentrifugation (AUC) and dynamic light scattering (DLS). We constructed PEGylated human serum albumin derivatives of single, linear 5K, 10K, 20K, and 40K PEG chains and a single branched-chain PEG of 40K (2 × 20K). Sedimentation velocity (SV) experiments were analyzed using SEDANAL direct boundary fitting to extract ideal sedimentation coefficients so, hydrodynamic nonideality ks, and thermodynamic nonideality 2BM1SV terms. These quantities allow the determination of the Stokes radius Rs, the frictional ratio f/fo, and the swollen or entrained volume Vs/v, which measure size, shape, and solvent interaction. We performed sedimentation equilibrium experiments to obtain independent measurements of thermodynamic nonideality 2BM1SE. From DLS measurements, we determined the interaction parameter, kD, the concentration dependence of the apparent diffusion coefficient, D, and from extrapolation of D to c = 0 a second estimate of Rs. Rs values derived from SV and DLS measurements and ensemble model calculations (see complementary study) are then used to show that ks + kD = theoretical 2B22M1. In contrast, experimental BM1 values from SV and sedimentation equilibrium data collectively allow for similar analysis for protein-PEG conjugates and show that ks + kD = 1.02-1.07∗BM1, rather than the widely used ks + kD = 2BM1 developed for hard spheres. The random coil behavior of PEG dominates the colloidal properties of PEG-protein conjugates and exceeds the sum of a random coil and hard-sphere volume due to excess entrained water.

Investigation on the Combined Effect of Hydroxypropyl Beta-Cyclodextrin (HPβCD) and Polysorbate in Monoclonal Antibody Formulation

Monoclonal antibodies require careful formulation due to their inherent stability limitations. Polysorbates are commonly used to stabilize mAbs, but they are prone to degradation, which results in unwanted impurities. KLEPTOSE® HPβCD (hydroxypropyl beta-cyclodextrin) has functioned as a stable stabilizer for protein formulations in our previous research. The current study investigates the collaborative impact of combining polysorbates and HPβCD as excipients in protein formulations. The introduction of HPβCD in formulations showed it considerably reduced aggregation in two model proteins, bevacizumab and ipilimumab, following exposure to various stress conditions. The diffusion interaction parameter revealed a reduction in protein-protein interactions by HPβCD. In bevacizumab formulations, the subvisible particle counts per 0.4 mL of samples in commercial formulations vs. formulations containing both HPβCD and polysorbates subjected to distinct stressors were as follows: agitation, 87,308 particles vs. 15,350 particles; light, 25,492 particles vs. 6765 particles; and heat, 1775 particles vs. 460 particles. Isothermal titration calorimetry (ITC) measurement indicated a weak interaction between PS 80 and HPβCD, with a KD value of 74.7 ± 7.5 µM and binding sites of 5 × 10-3. Surface tension measurements illustrated that HPβCD enhanced the surface activity of polysorbates. The study suggests that combining these excipients can improve mAb stability in formulations, offering an alternative for the biopharmaceutical industry.

Designing Robust Monoclonal Antibody Drug Products: Pitfalls of Simplistic Approaches for Stability Prediction

Thermal stability attributes including unfolding onset (Tonset) and mid-point (Tm) are often utilized for efficient development of monoclonal antibody (mAb) products during lead selection and formulation screening workflows. An assumption of direct correlation between thermal and kinetic physical stability underpins this basic approach. While literature reports have substantiated this general approach under specific conditions, clear exceptions have been highlighted alongside. Herein, a set of mAbs formulated under diverse solution conditions to generate a broad array of thermal and kinetic stability profiles were systematically analyzed. Sequence modifications in the Fc region were purposefully engineered to generate a set of low-melting mAbs. A diverse set of excipients were subsequently utilized and shown to modulate the Tm over a wide range. While a general correlation between high Tm and low aggregation rate was observed under accelerated conditions, the predictive utility of Tm under relevant product storage conditions was inadequate at best. Critically, Tm data did not correlate with long-term aggregation rates under refrigerated or room temperature conditions. Even under accelerated conditions, Tm appeared to be a poor predictor of aggregation once it exceeded the solution storage temperature (40°C) by ∼15°C, similar to conditions routinely encountered in the development of canonical mAbs (Tm > 60°C). Pitfalls of simplistic correlative approaches are discussed in the context of practical biologics product development.

Computational Screening for mAb Colloidal Stability with Coarse-Grained, Molecular-Scale Simulations

Monoclonal antibodies (mAbs) are an important modality of protein therapeutics with broad applications for numerous diseases. However, colloidal instabilities occurring at high protein concentrations can limit the ability to develop stable, high-concentration liquid dosage forms that are required for patient-centric, device-mediated products. Therefore, it is advantageous to identify colloidally stable mAbs early in the discovery process to ensure that they are selected for development. Experimental screening for colloidal stability can be time- and resource-consuming and is most feasible at the later stages of drug development due to material requirements. Alternatively, computational approaches have emerging potential to provide efficient screening and focus developmental efforts on mAbs with the greatest developability potential, while providing mechanistic relationships for colloidal instability. In this work, coarse-grained, molecular-scale models were fine-tuned to screen for colloidal stability at amino-acid resolution. This model parameterization provides a framework to screen for mAb self-interactions and extrapolate to bulk solution behavior. This approach was applied to a wide array of mAbs under multiple buffer conditions, demonstrating the utility of the presented computational approach to augment early candidate screening and later formulation strategies for protein therapeutics.

Biophysical and biochemical characterization of a recombinant Lyme disease vaccine antigen, CspZ-YA

Lyme disease, caused by Lyme Borrelia spirochetes, is the most common vector-borne illness in the United States. Despite its global significance, with an estimated 14.5 % seroprevalence, there is currently no licensed vaccine. Previously, we demonstrated that CspZ-YA protein conferred protection against Lyme Borrelia infection, making it a promising vaccine candidate. However, such a protein was tagged with hexahistidine, and thus not preferred for vaccine development; furthermore, the formulation to stabilize the protein was understudied. In this work, we developed a two-step purification process for tag-free E. coli-expressed recombinant CspZ-YA. We further utilized various bioassays to analyze the protein and determine the suitable buffer system for long-term storage and formulation as a vaccine immunogen. The results indicated that a buffer with a pH between 6.5 and 8.5 stabilized CspZ-YA by reducing its surface hydrophobicity and colloidal interactions. Additionally, low pH values induced a change in local spatial conformation and resulted in a decrease in α-helix content. Lastly, an optimal salinity of 22-400 mM at pH 7.5 was found to be important for its stability. Collectively, this study provides a fundamental biochemical and biophysical understanding and insights into the ideal stabilizing conditions to produce CspZ-YA recombinant protein for use in vaccine formulation and development.

The origins of nonideality exhibited by monoclonal antibodies and Fab fragments in human serum

The development of therapeutic antibodies remains challenging, time-consuming, and expensive. A key contributing factor is a lack of understanding of how proteins are affected by complex biological environments such as serum and plasma. Nonideality due to attractive or repulsive interactions with cosolutes can alter the stability, aggregation propensity, and binding interactions of proteins in solution. Fluorescence correlation spectroscopy (FCS) can be used to measure apparent second virial coefficient (B2,app ) values for therapeutic and model monoclonal antibodies (mAbs) that capture the nature and strength of interactions with cosolutes directly in undiluted serum and similar complex biological media. Here, we use FCS-derived B2,app measurements to identify the components of human serum responsible for nonideal interactions with mAbs and Fab fragments. Most mAbs exhibit neutral or slightly attractive interactions with intact serum. Generally, mAbs display repulsive interactions with albumin and mildly attractive interactions with IgGs in the context of whole serum. Crucially, however, these attractive interactions are much stronger with pooled IgGs isolated from other serum components, indicating that the effects of serum nonideality can only be understood by studying the intact medium (rather than isolated components). Moreover, Fab fragments universally exhibited more attractive interactions than their parental mAbs, potentially rendering them more susceptible to nonideality-driven perturbations. FCS-based B2,app measurements have the potential to advance our understanding of how physiological environments impact protein-based therapeutics in general. Furthermore, incorporating such assays into preclinical biologics development may help de-risk molecules and make for a faster and more efficient development process.

On the utility of microfluidic systems to study protein interactions: advantages, challenges, and applications

Within the complex milieu of a cell, which comprises a large number of different biomolecules, interactions are critical for function. In this post-reductionist era of biochemical research, the 'holy grail' for studying biomolecular interactions is to be able to characterize them in native environments. While there are a limited number of in situ experimental techniques currently available, there is a continuing need to develop new methods for the analysis of biomolecular complexes that can cope with the additional complexities introduced by native-like solutions. We think approaches that use microfluidics allow researchers to access native-like environments for studying biological problems. This review begins with a brief overview of the importance of studying biomolecular interactions and currently available methods for doing so. Basic principles of diffusion and microfluidics are introduced and this is followed by a review of previous studies that have used microfluidics to measure molecular diffusion and a discussion of the advantages and challenges of this technique.

Chemometrics in Protein Formulation: Stability Governed by Repulsion and Protein Unfolding

Therapeutic proteins can be challenging to develop due to their complexity and the requirement of an acceptable formulation to ensure patient safety and efficacy. To date, there is no universal formulation development strategy that can identify optimal formulation conditions for all types of proteins in a fast and reliable manner. In this work, high-throughput characterization, employing a toolbox of five techniques, was performed on 14 structurally different proteins formulated in 6 different buffer conditions and in the presence of 4 different excipients. Multivariate data analysis and chemometrics were used to analyze the data in an unbiased way. First, observed changes in stability were primarily determined by the individual protein. Second, pH and ionic strength are the two most important factors determining the physical stability of proteins, where there exists a significant statistical interaction between protein and pH/ionic strength. Additionally, we developed prediction methods by partial least-squares regression. Colloidal stability indicators are important for prediction of real-time stability, while conformational stability indicators are important for prediction of stability under accelerated stress conditions at 40 °C. In order to predict real-time storage stability, protein-protein repulsion and the initial monomer fraction are the most important properties to monitor.

In vitro measurement of concentration of unlabeled protein within a hyaluronic acid matrix

There are currently more than 560 therapeutic monoclonal antibodies (mAbs) at various stages of research and clinical testing, including candidates for administration by subcutaneous (SC) injection. Preclinical studies based on in vitro measurements of high molecular weight proteins within simulated SC matrices are assisting laboratory studies of interactions of injectable biotherapeutic proteins within the SC environment in relation to bioavailability. We report a new method for directly measuring diffusion of unlabeled, high molecular weight proteins injected into an in vitro matrix that simulates the negatively charged environment of the SC. The matrix consists of 10 mg/ml HA in a repurposed cell culture chamber. The measurement consists of pipetting triplicate 20 μl protein samples into the matrix, placing the chamber in a laboratory scanner, activating tryptophan residues in the protein at 280 nm, and imaging the resulting protein fluorescence at 384 nm over a 0.5-4 h time period thus tracking protein movement. This facile approach enables mapping of protein concentration as a function of time and distance within the matrix, and determination of diffusion coefficients, D, within ±10%. Bovine IgG and BSA gave D = 2.3 ± 0.2*10^-7 and 4.6 ± 0.2*10^-7 cm² /s at 24°C, respectively, for initial protein concentrations of 21 mg/mL.

Predicting Experimental B22 Values and the Effects of Histidine Charge States for Monoclonal Antibodies Using Coarse-Grained Molecular Simulations

Static light scattering (SLS) was used to characterize five monoclonal antibodies (MAbs) as a function of total ionic strength (TIS) at pH values between 5.5 and 7.0. Second osmotic virial coefficient (B₂₂) values were determined experimentally for each MAb as a function of TIS using low protein concentration SLS data. Coarse-grained molecular simulations were performed to predict the B₂₂ values for each MAb at a given pH and TIS. To include the effect of charge fluctuations of titratable residues in the B₂₂ calculations, a statistical approach was introduced in the Monte Carlo algorithm based on the protonation probability based on a given pH value and the Henderson-Hasselbalch equation. The charged residues were allowed to fluctuate individually, based on the sampled microstates and the influence of electrostatic interactions on net protein-protein interactions during the simulations. Compared to static charge simulations, the new approach provided improved results compared to experimental B₂₂ values at pH conditions near the pK_a of titratable residues.

Effect of the ADCC-modulating mutations and the selection of human IgG isotypes on physicochemical properties of Fc

Monoclonal antibodies, particularly IgGs and Ig-based molecules, are a well-established and growing class of biotherapeutic drugs. In order to improve efficacy, potency and pharmacokinetics of these therapeutic drugs, pharmaceutical industries have investigated significantly in engineering fragment crystallizable (Fc) domain of these drugs to optimize the interactions of these drugs and Fc gamma receptors (FcγRs) in recent ten years. The biological function of the therapeutics with the antibody-dependent cellular cytotoxicity (ADCC) enhanced double mutation (S239D/I332E) of isotype IgG1, the ADCC reduced double mutation (L234A/L235A) of isotype IgG1, and ADCC reduced isotype IgG4 has been well understood. However, limited information regarding the effect of these mutations or isotype difference on physicochemical properties (PCP), developability, and manufacturability of therapeutics bearing these different Fc regions is available. In this report, we systematically characterize the effects of the mutations and IgG4 isotype on conformation stability, colloidal stability, solubility, and storage stability at accelerated conditions in two buffer systems using six Fc variants. Our results provide a basis for selecting appropriate Fc region during development of IgG or Ig-based therapeutics and predicting effect of the mutations on CMC development process.

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.

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.

An accelerated surface-mediated stress assay of antibody instability for developability studies

High physical stability is required for the development of monoclonal antibodies (mAbs) into successful therapeutic products. Developability assays are used to predict physical stability issues such as high viscosity and poor conformational stability, but protein aggregation remains a challenging property to predict. Among different types of stresses, air–water and solid–liquid interfaces are well known to potentially trigger protein instability and induce aggregation. Yet, in contrast to the increasing number of developability assays to evaluate bulk properties, there is still a lack of experimental methods to evaluate antibody stability against interfaces. Here, we investigate the potential of a hydrophobic nanoparticle surface-mediated stress assay to assess the stability of mAbs during the early stages of development. We evaluate this surface-mediated accelerated stability assay on a rationally designed library of 14 variants of a humanized IgG4, featuring a broad span of solubility values and other developability properties. The assay could identify variants characterized by high instability against agitation in the presence of air–water interfaces. Remarkably, for the set of investigated molecules, we observe strong correlations between the extent of aggregation induced by the surface-mediated stress assay and other developability properties of the molecules, such as aggregation upon storage at 45°C, self-association (evaluated by affinity-capture self-interaction nanoparticle spectroscopy) and nonspecific interactions (estimated by cross-interaction chromatography, stand-up monolayer chromatography (SMAC), SMAC*). This highly controlled surface-mediated stress assay has the potential to complement and increase the ability of the current set of screening techniques to assess protein aggregation and developability potential of mAbs during the early stages of drug development.

Abbreviations:AC-SINS: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy; AMS: Ammonium sulfate precipitation; ANS: 1-anilinonaphtalene-8-sulfonate; CIC: Cross-interaction chromatography; DLS: Dynamic light scattering; HIC: Hydrophobic interaction chromatography; HNSSA: Hydrophobic nanoparticles surface-stress assay; mAb: Monoclonal antibody; NP: Nanoparticle; SEC: Size exclusion chromatography; SMAC: Stand-up monolayer chromatography; WT: Wild type

Probing interactions of therapeutic antibodies with serum via second virial coefficient measurements

Antibody-based therapeutics are the fastest-growing drug class on the market, used to treat aggressive forms of cancer, chronic autoimmune conditions, and numerous other disease states. Although the specificity, affinity, and versatility of therapeutic antibodies can provide an advantage over traditional small-molecule drugs, their development and optimization can be much more challenging and time-consuming. This is, in part, because the ideal formulation buffer systems used for in vitro characterization inadequately reflect the crowded biological environments (serum, endosomal lumen, etc.) that these drugs experience once administered to a patient. Such environments can perturb the binding of antibodies to their antigens and receptors, as well as homo- and hetero-aggregation, thereby altering therapeutic effect and disposition in ways that are incompletely understood. Although excluded volume effects are classically thought to favor binding, weak interactions with co-solutes in crowded conditions can inhibit binding. The second virial coefficient (B₂) parameter quantifies such weak interactions and can be determined by a variety of techniques in dilute solution, but analogous methods in complex biological fluids are not well established. Here, we demonstrate that fluorescence correlation spectroscopy is able to measure diffusive B₂-values directly in undiluted serum. Apparent second virial coefficient (B_2,app) measurements of antibodies in serum reveal that changes in the balance between attractive and repulsive interactions can dramatically impact global nonideality. Furthermore, our findings suggest that the approach of isolating specific components and completing independent cross-term virial coefficient measurements may not be an effective approach to characterizing nonideality in serum. The approach presented here could enrich our understanding of the effects of biological environments on proteins in general and advance the development of therapeutic antibodies and other protein-based therapeutics.

Measuring aggregates, self-association, and weak interactions in concentrated therapeutic antibody solutions

Monoclonal antibodies are a class of biotherapeutics used for an increasing variety of disorders, including cancer, autoimmune, neurodegenerative, and viral diseases. Besides their antigen specificity, therapeutic use also mandates control of their solution interactions and colloidal properties in order to achieve a stable, efficacious, non-immunogenic, and low viscosity antibody solution at concentrations in the range of 50–150 mg/mL. This requires characterization of their reversible self-association, aggregation, and weak attractive and repulsive interactions governing macromolecular distance distributions in solution. Simultaneous measurement of these properties, however, has been hampered by solution nonideality. Based on a recently introduced sedimentation velocity method for measuring macromolecular size distributions in a mean-field approximation for hydrodynamic interactions, we demonstrate simultaneous measurement of polydispersity and weak and strong solution interactions in a panel of antibodies with concentrations up to 45 mg/mL. By allowing approximately an order of magnitude higher concentrations than previously possible in sedimentation velocity size distribution analysis, this approach can substantially improve efficiency and sensitivity for characterizing polydispersity and interactions of therapeutic antibodies at or close to formulation conditions.

Ultradilute Measurements of Self-Association for the Identification of Antibodies with Favorable High-Concentration Solution Properties

There is significant interest in formulating antibody therapeutics as concentrated liquid solutions, but early identification of developable antibodies with optimal manufacturability, stability, and delivery attributes remains challenging. Traditional methods of identifying developable mAbs with low self-association in common antibody formulations require relatively concentrated protein solutions (>1 mg/mL), and this single challenge has frustrated early-stage and large-scale identification of antibody candidates with drug-like colloidal properties. Here, we describe charge-stabilized self-interaction nanoparticle spectroscopy (CS-SINS), an affinity-capture nanoparticle assay that measures colloidal self-interactions at ultradilute antibody concentrations (0.01 mg/mL), and is predictive of antibody developability issues of high viscosity and opalescence that manifest at four orders of magnitude higher concentrations (>100 mg/mL). CS-SINS enables large-scale, high-throughput selection of developable antibodies during early discovery.

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.

High-Throughput Screening for Colloidal Stability of Peptide Formulations Using Dynamic and Static Light Scattering

Selection of an appropriate formulation to stabilize therapeutic proteins against aggregation is one of the most challenging tasks in early-stage drug product development. The amount of aggregates is more difficult to quantify in the case of peptides due to their small molecular size. Here, we investigated the suitability of diffusion self-interaction parameters (k_D) and osmotic second virial coefficients (B₂₂) for high-throughput (HT) screening of peptide formulations regarding their aggregation risk. These parameters were compared to the effect of thermal stress on colloidal stability. The formulation matrix comprised six buffering systems at two selected pH values, four tonicity agents, and a common preservative. The results revealed that electrostatic interactions are the main driver to control colloidal stability. Preferred formulations consisted of acetate and succinate buffer at pH 4.5 combined with glycerol or mannitol and optional m-cresol. k_D proved to be a suitable surrogate for B₂₂ as an indicator of high colloidal stability in the case of peptides as was previously described for globular proteins and antibodies. Formulation assessment solely based on k_D obtained by HT methods offers important insights into the optimization of colloidal stability during the early development of peptide-based liquid formulations and can be performed with a limited amount of peptide (∼360 mg).

Effect of Buffer on Protein Stability in Aqueous Solutions: A Simple Protein Aggregation Model

Liquid-liquid phase separation (LLPS) of proteins has recently been associated with the onset of numerous diseases. Despite several studies in this area of protein aggregation, buffer-specific effects always seem to be overlooked. In this study we investigated the influence of buffers on the phase stability of hen egg-white lysozyme (HEWL) and its respective protein-protein interactions by measuring the cloud point temperature, second virial coefficient, and interaction diffusion coefficient of several HEWL-buffer solutions (MOPS, phosphate, HEPES, cacodylate) at pH 7.0. The results indicate that the buffer molecules, depending on their hydration, adsorb on the protein surface, and modulate their electrostatic stability. The obtained information was used to extend the recently developed coarse-grained protein model to incorporate buffer-specific effects. Treated by Wertheim's perturbation theory the model qualitatively correctly predicted the experimentally observed phase separation of all investigated HEWL-buffer solutions, and further allowed us to predict the phase stability of protein formulations even in experimentally unattainable conditions. Since the theory can be straightforwardly extended to include multiple components it presents a useful tool to study protein aggregation in crowded cell-like systems.

Genetic modification to design a stable yeast-expressed recombinant SARS-CoV-2 receptor binding domain as a COVID-19 vaccine candidate

BACKGROUND

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has now spread worldwide to infect over 110 million people, with approximately 2.5 million reported deaths. A safe and effective vaccine remains urgently needed.

METHOD

We constructed three variants of the recombinant receptor-binding domain (RBD) of the SARS-CoV-2 spike (S) protein (residues 331-549) in yeast as follows: (1) a "wild type" RBD (RBD219-WT), (2) a deglycosylated form (RBD219-N1) by deleting the first N-glycosylation site, and (3) a combined deglycosylated and cysteine-mutagenized form (C538A-mutated variant (RBD219-N1C1)). We compared the expression yields, biophysical characteristics, and functionality of the proteins produced from these constructs.

RESULTS AND CONCLUSIONS

These three recombinant RBDs showed similar secondary and tertiary structure thermal stability and had the same affinity to their receptor, angiotensin-converting enzyme 2 (ACE-2), suggesting that the selected deletion or mutations did not cause any significant structural changes or alteration of function. However, RBD219-N1C1 had a higher fermentation yield, was easier to purify, was not hyperglycosylated, and had a lower tendency to form oligomers, and thus was selected for further vaccine development and evaluation.

GENERAL SIGNIFICANCE

By genetic modification, we were able to design a better-controlled and more stable vaccine candidate, which is an essential and important criterion for any process and manufacturing of biologics or drugs for human use.

Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction

Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein-protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab'2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab'2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development.

Microwell Plate-Based Dynamic Light Scattering as a High-Throughput Characterization Tool in Biopharmaceutical Development

High-throughput light scattering instruments are widely used in screening of biopharmaceutical formulations and can be easily incorporated into processes by utilizing multi-well plate formats. High-throughput plate readers are helpful tools to assess the aggregation tendency and colloidal stability of biological drug candidates based on the diffusion self-interaction parameter (k_D). However, plate readers evoke issues about the precision and variability of determined data. In this article, we report about the statistical evaluation of intra- and inter-plate variability (384-well plates) for the k_D analysis of protein and peptide solutions. ANOVA revealed no significant differences between the runs. In conclusion, the reliability and precision of k_D was dependent on the plate position of the sample replicates and k_D value. Positive k_D values (57.0 mL/g, coefficients of variation (CV) 8.9%) showed a lower variability compared to negative k_D values (−14.8 mL/g, CV 13.4%). The variability of k_D was not reduced using more data points (120 vs. 30). A k_D analysis exclusively based on center wells showed a lower CV (<2%) compared to edge wells (5–12%) or a combination of edge and center wells (2–5%). We present plate designs for k_D analysis within the early formulation development, screening up to 20 formulations consuming less than 50 mg of active pharmaceutical ingredient (API).

Washing with alkaline solutions in protein A purification improves physicochemical properties of monoclonal antibodies

Protein A affinity chromatography has been widely used for both laboratory scale purification and commercial manufacturing of monoclonal antibodies and Fc-fusion proteins. Protein A purification is specific and efficient. However, there still remain several issues to be addressed, such as incomplete clearance of impurities including host cell proteins, DNA, aggregates, etc. In addition, the effects of wash buffers in protein A purification on the physicochemical characteristics of antibodies have yet to be fully understood. Here we found a new purification protocol for monoclonal antibodies that can improve physicochemical properties of monoclonal antibodies simply by inserting an additional wash step with a basic buffer after the capture step to the conventional protein A purification. The effects of the alkaline wash on monoclonal antibodies were investigated in terms of physicochemical characteristics, yields, and impurity clearance. The simple insertion of an alkaline wash step resulted in protection of antibodies from irreversible aggregation, reduction in free thiols and impurities, an improvement in colloidal and storage stability, and enhanced yields. This new procedure is widely applicable to protein A affinity chromatography of monoclonal antibodies.

Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

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.

Assessing the Structures and Interactions of γD-Crystallin Deamidation Variants

Cataracts involve the deposition of the crystallin proteins in the vertebrate eye lens, causing opacification and blindness. They are associated with either genetic mutation or protein damage that accumulates over the lifetime of the organism. Deamidation of Asn residues in several different crystallins has been observed and is frequently invoked as a cause of cataract. Here, we investigated the properties of Asp variants, deamidation products of γD-crystallin, by solution NMR, X-ray crystallography, and other biophysical techniques. No substantive structural or stability changes were noted for all seven Asn to Asp γD-crystallins. Importantly, no changes in diffusion interaction behavior could be detected. Our combined experimental results demonstrate that introduction of single Asp residues on the surface of γD-crystallin by deamidation is unlikely to be the driver of cataract formation in the eye lens.

Analysis of nonideality: insights from high concentration simulations of sedimentation velocity data

The Aviv fluorescence detection system (Aviv-FDS) has allowed the performance of sedimentation velocity experiments on therapeutic antibodies in highly concentrated environments like formulation buffers and serum. Methods were implemented in the software package SEDANAL for the analysis of nonideal, weakly associating AUC data acquired on therapeutic antibodies and proteins (Wright et al. Eur Biophys J 47:709–722, 2018, Anal Biochem 550:72–83, 2018). This involved fitting both hydrodynamic, k_s, and thermodynamic, BM₁, nonideality where concentration dependence is expressed as s = s^o/(1 + k_sc) and D = D^o(1 + 2BM₁c)/(1 + k_sc) and s^o and D^o are values extrapolated to c = 0 (mg/ml). To gain insight into the consequences of these phenomenological parameters, we performed simulations with SEDANAL of a monoclonal antibody as a function of k_s (0–100 ml/g) and BM₁ (0–100 ml/g). This provides a visual understanding of the separate and joint impact of k_s and BM₁ on the shape of high-concentration sedimentation velocity boundaries and the challenge of their unique determination by finite element methods. In addition, mAbs undergo weak self- and hetero-association (Yang et al. Prot Sci 27:1334–1348, 2018) and thus we have simulated examples of nonideal weak association over a wide range of concentrations (1–120 mg/ml). Here we demonstrate these data are best analyzed by direct boundary global fitting to models that account for k_s, BM₁ and weak association. Because a typical clinical dose of mAb is 50–200 mg/ml, these results have relevance for biophysical understanding of concentrated therapeutic proteins.

A single molecular descriptor to predict solution behavior of therapeutic antibodies

Despite the therapeutic success of monoclonal antibodies (mAbs), early identification of developable mAb drug candidates with optimal manufacturability, stability, and delivery attributes remains elusive. Poor solution behavior, which manifests as high solution viscosity or opalescence, profoundly affects the developability of mAb drugs. Using a diverse dataset of 59 mAbs, including 43 approved products, and an array of molecular descriptors spanning colloidal, conformational, charge-based, hydrodynamic, and hydrophobic properties, we show that poor solution behavior is prevalent (>30%) in mAbs and is singularly predicted (>90%) by the diffusion interaction parameter (k _D), a dilute-solution measure of colloidal self-interaction. No other descriptor, individually or in combination, was found to be as effective as k _D. We also show that well-behaved mAbs, a substantial subset of which bear high positive charge and pI, present no disadvantages with respect to pharmacokinetics in humans. Here, we provide a systematic framework with quantitative thresholds for selecting well-behaved therapeutic mAbs during drug discovery.

Relationship of PEG-induced precipitation with protein-protein interactions and aggregation rates of high concentration mAb formulations at 5 °C

Native protein-protein interactions can play an important role in determining the tendency of monoclonal antibodies (mAbs) to aggregate under storage conditions. In this context, phase separation of mAb solutions induced by the addition of neutral polymers such as poly(ethylene glycol) (PEG) represents a simple method to assess the tendency of proteins to self-associate in the native state. Here, we investigated their relationships between PEG-induced phase separation, protein-protein interactions and long-term aggregation rate of several formulations of four mAbs at 100 mg/mL and 5 °C over 12 weeks of storage. We observed that the location of the phase boundary correlated well with the osmotic second virial coefficient B₂₂ determined in absence of the polymer, indicating that for our solutions PEG primarily leads to depletion forces between protein molecules, which are additive to protein-protein interactions. However, limited correlation between aggregation rate at 5 °C and phase behavior was observed across different mAbs, pH values and ionic strengths, indicating that colloidal stability is not the only determinant of aggregation even at such low temperature and high protein concentration. Our results contribute to the growing realization that aggregation propensity in the context of antibody developability is a complex feature, which depends on a variety of biophysical properties rather than one single parameter.

Protein-protein interactions and water activity coefficients can be used to aid a first excipient choice in protein formulations

With respect to all biopharmaceuticals marketed to date, monoclonal antibodies represent the largest fraction with more than 48% market share (2012). However, the development of biopharmaceutical formulations is a challenging task, and time-consuming and cost-intensive high-throughput screenings are still state-of-the-art in formulation design. These screening techniques are almost exclusively based on heuristic decisions thus the benefit in terms of mechanistic understanding is often unclear. It requires novel, physical-sound methods to enhance/optimize future formulation development, ideally by understanding molecular interactions in these complex solutions. A suitable and evaluated measure-of-choice to characterize protein-protein interactions in aqueous protein solutions is the second osmotic virial coefficient B₂₂ which can be measured using static light scattering techniques. Furthermore B₂₂ can be modeled/predicted via the extended mxDLVO model for protein-protein interactions in the presence of single excipients and excipient-mixtures. Building up on this approach, giving an additional insight into water-water and water-excipient interactions, the thermodynamic equation-of-state ePC-SAFT is used to calculate water activity coefficients in the presence of excipient-mixtures. Immunoglobulin G (IgG) was chosen as a model protein to predict B₂₂-values for IgG in the presence of model excipient-mixtures (trehalose-NaCl, l-histidine-trehalose, l-histidine-NaCl). The combination of water activity coefficients and B₂₂ allows to quickly identify a first guess on suitable formulation conditions that then can be further evaluated with existing methods/knowledge.

Improved Dynamic Light Scattering using an adaptive and statistically driven time resolved treatment of correlation data

Dynamic Light Scattering (DLS) is a ubiquitous and non-invasive measurement for the characterization of nano- and micro-scale particles in dispersion. The sixth power relationship between scattered intensity and particle radius is simultaneously a primary advantage whilst rendering the technique sensitive to unwanted size fractions from unclean lab-ware, dust and aggregated & dynamically aggregating sample, for example. This can make sample preparation iterative, challenging and time consuming and often requires the use of data filtering methods that leave an inaccurate estimate of the steady state size fraction and may provide no knowledge to the user of the presence of the transient fractions. A revolutionary new approach to DLS measurement and data analysis is presented whereby the statistical variance of a series of individually analysed, extremely short sub-measurements is used to classify data as steady-state or transient. Crucially, all sub-measurements are reported, and no data are rejected, providing a precise and accurate measurement of both the steady state and transient size fractions. We demonstrate that this approach deals intrinsically and seamlessly with the transition from a stable dispersion to the partially- and fully-aggregated cases and results in an attendant improvement in DLS precision due to the shorter sub measurement length and the classification process used.

Light Scattering to Quantify Protein-Protein Interactions at High Protein Concentrations

Static and dynamic (laser) light scattering (SLS and DLS, respectively) can be used to measure the so-called weak or colloidal protein-protein interactions in solution from low to high protein concentrations (c₂). This chapter describes a methodology to measure protein-protein self-interactions using SLS and DLS, with illustrative examples for monoclonal antibody solutions from low to high protein concentrations (c₂ ~ 1-10² g/L).

IgG Charge: Practical and Biological Implications

Practically, IgG charge can contribute significantly to thermodynamic nonideality, and hence to solubility and viscosity. Biologically, IgG charge isomers exhibit differences in clearance and potency. It has been known since the 1930s that all immunoglobulins carry a weak negative charge in physiological solvents. However, there has been no systematic exploration of this fundamental property. Accurate charge measurements have been made using membrane confined electrophoresis in two solvents (pH 5.0 and pH 7.4) on a panel of twelve mAb IgGs, as well as their F(ab’)₂ and Fc fragments. The following observations were made at pH 5.0: (1) the measured charge differs from the calculated charge by ~40 for the intact IgGs, and by ~20 for the Fcs; (2) the intact IgG charge depends on both Fv and Fc sequences, but does not equal the sum of the F(ab)’₂ and Fc charge; (3) the Fc charge is consistent within a class. In phosphate buffered saline, pH 7.4: (1) the intact IgG charges ranged from 0 to −13; (2) the F(ab’)₂ fragments are nearly neutral for IgG1s and IgG2s, and about −5 for some of the IgG4s; (3) all Fc fragments are weakly anionic, with IgG1 < IgG2 < IgG4; (4) the charge on the intact IgGs does not equal the sum of the F(ab’)₂ and Fc charge. In no case is the calculated charge, based solely on H⁺ binding, remotely close to the measured charge. Some mAbs carried a charge in physiological salt that was outside the range observed for serum-purified human poly IgG. To best match physiological properties, a therapeutic mAb should have a measured charge that falls within the range observed for serum-derived human IgGs. A thermodynamically rigorous, concentration-dependent protein–protein interaction parameter is introduced. Based on readily measured properties, interaction curves may be generated to aid in the selection of proteins and solvent conditions. Example curves are provided.

A Reappraisal of Sedimentation Nonideality Coefficients for the Analysis of Weak Interactions of Therapeutic Proteins

The study of weak or colloidal interactions of therapeutic proteins in different formulations allows prediction and optimization of protein stability. Various biophysical techniques have been applied to determine the second osmotic virial coefficient B₂ as it reflects on the macromolecular distance distribution that governs solution behavior at high concentration. In the present work, we exploit a direct link predicted by hydrodynamic theory between B₂ and the nonideality of sedimentation, commonly measured in sedimentation velocity analytical ultracentrifugation through the nonideality coefficient of sedimentation, k_S. Using sedimentation equilibrium analytical ultracentrifugation for independent measurement of B₂, we have examined the dependence of k_S on B₂ for model proteins in different buffers. The data exhibit the expected linear relationship and highlight the impact of protein shape on the magnitude of the nonideality coefficient k_S. Recently, measurements of k_S have been considerably simplified allowing higher throughput and simultaneous polydispersity assessment at higher protein concentrations. Thus, sedimentation velocity may offer a useful approach to compare the impact of formulation conditions on weak interactions and simultaneously on higher-order structure of therapeutic proteins.

The Investigation of Protein Diffusion via H-Cell Microfluidics

In this study, we developed a microfluidics method, using a so-called H-cell microfluidics device, for the determination of protein diffusion coefficients at different concentrations, pHs, ionic strengths, and solvent viscosities. Protein transfer takes place in the H-cell channels between two laminarly flowing streams with each containing a different initial protein concentration. The protein diffusion coefficients are calculated based on the measured protein mass transfer, the channel dimensions, and the contact time between the two streams. The diffusion rates of lysozyme, cytochrome c, myoglobin, ovalbumin, bovine serum albumin, and etanercept were investigated. The accuracy of the presented methodology was demonstrated by comparing the measured diffusion coefficients with literature values measured under similar solvent conditions using other techniques. At low pH and ionic strength, the measured lysozyme diffusion coefficient increased with the protein concentration gradient, suggesting stronger and more frequent intermolecular interactions. At comparable concentration gradients, the measured lysozyme diffusion coefficient decreased drastically as a function of increasing ionic strength (from zero onwards) and increasing medium viscosity. Additionally, a particle tracing numerical simulation was performed to achieve a better understanding of the macromolecular displacement in the H-cell microchannels. It was found that particle transfer between the two channels tends to speed up at low ionic strength and high concentration gradient. This confirms the corresponding experimental observation of protein diffusion measured via the H-cell microfluidics.

Measuring Ultra-Weak Protein Self-Association by Non-ideal Sedimentation Velocity

Ultra-weak self-association can govern the macroscopic solution behavior of concentrated macromolecular solutions ranging from food products to pharmaceutical formulations and the cytosol. For example, it can promote dynamic assembly of multi-protein signaling complexes, lead to intracellular liquid–liquid phase transitions, and seed crystallization or pathological aggregates. Unfortunately, weak self-association is technically extremely difficult to study, as it requires very high protein concentrations where short intermolecular distances cause strongly correlated particle motion. Additionally, protein samples near their solubility limit in vitro frequently show some degree of polydispersity. Here we exploit the strong mass-dependent separation of assemblies in the centrifugal field to study ultra-weak binding, using a sedimentation velocity technique that allows us to determine particle size distributions while accounting for colloidal hydrodynamic interactions and thermodynamic non-ideality (Chaturvedi, S. K.; et al. Nat. Commun.2018, 9, 4415; DOI: 10.1038/s41467-018-06902-x). We show that this approach, applied to self-associating proteins, can reveal a time-average association state for rapidly reversible self-associations from which the free energy of binding can be derived. The method is label-free and allows studying mid-sized proteins at millimolar protein concentrations in a wide range of solution conditions. We examine the performance of this method with hen egg lysozyme as a model system, reproducing its well-known ionic-strength-dependent weak self-association. The application to chicken γS-crystallin reveals weak monomer–dimer self-association with K_D = 24 mM, corresponding to a standard free energy change of approximately −9 kJ/mol, which is a large contribution to the delicate balance of forces ensuring eye lens transparency.

In vitro and in silico assessment of the developability of a designed monoclonal antibody library

Despite major advances in antibody discovery technologies, the successful development of monoclonal antibodies (mAbs) into effective therapeutic and diagnostic agents can often be impeded by developability liabilities, such as poor expression, low solubility, high viscosity and aggregation. Therefore, strategies to predict at the early phases of antibody development the risk of late-stage failure of antibody candidates are highly valuable. In this work, we employ the in silico solubility predictor CamSol to design a library of 17 variants of a humanized mAb predicted to span a broad range of solubility values, and we examine their developability potential with a battery of commonly used in vitro and in silico assays. Our results demonstrate the ability of CamSol to rationally enhance mAb developability, and provide a quantitative comparison of in vitro developability measurements with each other and with more resource-intensive solubility measurements, as well as with in silico predictors that offer a potentially faster and cheaper alternative. We observed a strong correlation between predicted and experimentally determined solubility values, as well as with measurements obtained using a panel of in vitro developability assays that probe non-specific interactions. These results indicate that computational methods have the potential to reduce or eliminate the need of carrying out laborious in vitro quality controls for large numbers of lead candidates. Overall, our study provides support to the emerging view that the implementation of in silico tools in antibody discovery campaigns can ensure rapid and early selection of antibodies with optimal developability potential.