The Role of Amino Acid Sequence in the Self-Association of Therapeutic Monoclonal Antibodies: Insights from Coarse-Grained Modeling

A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles

The formulation of high-concentration monoclonal antibody (mAb) solutions in low dose volumes for autoinjector devices poses challenges in manufacturability and patient administration due to elevated solution viscosity. Often many therapeutically potent mAbs are discovered, but their commercial development is stalled by unfavourable developability challenges. In this work, we present a systematic experimental framework for the computational screening of molecular descriptors to guide the design of 24 mutants with modified viscosity profiles accompanied by experimental evaluation. Our experimental observations using a model anti-IL8 mAb and eight engineered mutant variants reveal that viscosity reduction is influenced by the location of hydrophobic interactions, while targeting positively charged patches significantly increases viscosity in comparison to wild-type anti-IL-8 mAb. We conclude that most predicted in silico physicochemical properties exhibit poor correlation with measured experimental parameters for antibodies with suboptimal developability characteristics, emphasizing the need for comprehensive case-by-case evaluation of mAbs. This framework combining molecular design and triage via computational predictions with experimental evaluation aids the agile and rational design of mAbs with tailored solution viscosities, ensuring improved manufacturability and patient convenience in self-administration scenarios.

Drug product Formulation and Fill/Finish Manufacturing Process Considerations for AAV-Based Genomic Medicines

Adeno-associated viruses (AAVs) have become the delivery medium of choice for a variety of genomic medicine applications i.e., gene therapy, gene editing/regulation, and ex-vivo cell therapy. AAVs are protein-DNA complexes which have unique stability characteristics that are susceptible to various stress exposure conditions commonly seen in the drug product (DP) life cycle. This review takes a comprehensive look at AAV DP formulation and process development considerations that could impact critical quality attributes (CQAs) during manufacturing, packaging, shipping, and clinical use. Additional aspects related to AAV development reviewed herein are: (1) Different AAV serotypes with unique protein sequences and charge characteristics potentially leading to discrete stability profiles; (2) Manufacturing process challenges and optimization efforts to improve yield, recovery and purity especially during early development activities; and (3) Defining and identifying CQAs with analytical methods which are constantly evolving and present unique characterization challenges for AAV-based products.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Minimal Coarse-Grained Model for Immunoglobulin G: Diffusion and Binding under Crowding

Immunoglobulin G (IgG) is the most common type of antibody found in blood and extracellular fluids and plays an essential role in our immune response. However, studies of the dynamics and reaction kinetics of IgG-antigen binding under physiological crowding conditions are scarce. Herein, we develop a coarse-grained model of IgG consisting of only six beads that we find minimal for a coarse representation of IgG's shape and a decent reproduction of its flexibility and diffusion properties measured experimentally. Using this model in Brownian dynamics simulations, we find that macromolecular crowding affects only slightly the IgG's flexibility, as described by the distribution of angles between the IgG's arms and stem. Our simulations indicate that, contrary to expectations, crowders slow down the translational diffusion of an IgG less strongly than they do for a smaller Ficoll 70, which we relate to the IgG's conformational size changes induced by crowding. We also find that crowders affect the binding kinetics by decreasing the rate of the first binding step and enhancing the second binding step.

Effects of flexibility in coarse-grained models for bovine serum albumin and immunoglobulin G

We construct a coarse-grained, structure-based, low-resolution, 6-bead flexible model of bovine serum albumin (BSA, PDB: 4F5S), which is a popular example of a globular protein in biophysical research. The model is obtained via direct Boltzmann inversion using all-atom simulations of a single molecule, and its particular form is selected from a large pool of 6-bead coarse-grained models using two suitable metrics that quantify the agreement in the distribution of collective coordinates between all-atom and coarse-grained Brownian dynamics simulations of solutions in the dilute limit. For immunoglobulin G (IgG), a similar structure-based 12-bead model has been introduced in the literature [Chaudhri et al., J. Phys. Chem. B 116, 8045 (2012)] and is employed here to compare findings for the compact BSA molecule and the more anisotropic IgG molecule. We define several modified coarse-grained models of BSA and IgG, which differ in their internal constraints and thus account for a variation of flexibility. We study denser solutions of the coarse-grained models with purely repulsive molecules (achievable by suitable salt conditions) and address the effect of packing and flexibility on dynamic and static behavior. Translational and rotational self-diffusivity is enhanced for more elastic models. Finally, we discuss a number of effective sphere sizes for the BSA molecule, which can be defined from its static and dynamic properties. Here, it is found that the effective sphere diameters lie between 4.9 and 6.1 nm, corresponding to a relative spread of about ±10% around a mean of 5.5 nm.

High concentration formulation developability approaches and considerations

The growing need for biologics to be administered subcutaneously and ocularly, coupled with certain indications requiring high doses, has resulted in an increase in drug substance (DS) and drug product (DP) protein concentrations. With this increase, more emphasis must be placed on identifying critical physico-chemical liabilities during drug development, including protein aggregation, precipitation, opalescence, particle formation, and high viscosity. Depending on the molecule, liabilities, and administration route, different formulation strategies can be used to overcome these challenges. However, due to the high material requirements, identifying optimal conditions can be slow, costly, and often prevent therapeutics from moving rapidly into the clinic/market. In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.

Utilizing cross-product prior knowledge to rapidly de-risk chemical liabilities in therapeutic antibody candidates

There is considerable pressure in the pharmaceutical industry to advance better molecules faster. One pervasive concern for protein-based therapeutics is the presence of potential chemical liabilities. We have developed a simple methodology for rapidly de-risking specific chemical concerns in antibody-based molecules using prior knowledge of each individual liability at a specific position in the molecule’s sequence. Our methodology hinges on the development of sequence-aligned chemical liability databases of molecules from different stages of commercialization and on sequence-aligned experimental data from prior molecules that have been developed at Amgen. This approach goes beyond the standard practice of simply flagging all instances of each motif that fall in a CDR. Instead, we de-risk motifs that are common at a specific site in commercial mAb-based molecules (and therefore did not previously pose an insurmountable barrier to commercialization) and motifs at specific sites for which we have prior experimental data indicating acceptably low levels of modification. We have used this approach successfully to identify candidates in a discovery phase program with exclusively very low risk potential chemical liabilities. Identifying these candidates in the discovery phase allowed us to bypass protein engineering and accelerate the program’s timeline by 6 months.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Antibodies with Weakly Basic Isoelectric Points Minimize Trade-offs between Formulation and Physiological Colloidal Properties

The widespread interest in antibody therapeutics has led to much focus on identifying antibody candidates with favorable developability properties. In particular, there is broad interest in identifying antibody candidates with highly repulsive self-interactions in standard formulations (e.g., low ionic strength buffers at pH 5-6) for high solubility and low viscosity. Likewise, there is also broad interest in identifying antibody candidates with low levels of non-specific interactions in physiological solution conditions (PBS, pH 7.4) to promote favorable pharmacokinetic properties. To what extent antibodies that possess both highly repulsive self-interactions in standard formulations and weak non-specific interactions in physiological solution conditions can be systematically identified remains unclear and is a potential impediment to successful therapeutic drug development. Here, we evaluate these two properties for 42 IgG1 variants based on the variable fragments (Fvs) from four clinical-stage antibodies and complementarity-determining regions from 10 clinical-stage antibodies. Interestingly, we find that antibodies with the strongest repulsive self-interactions in a standard formulation (pH 6 and 10 mM histidine) display the strongest non-specific interactions in physiological solution conditions. Conversely, antibodies with the weakest non-specific interactions under physiological conditions display the least repulsive self-interactions in standard formulations. This behavior can be largely explained by the antibody isoelectric point, as highly basic antibodies that are highly positively charged under standard formulation conditions (pH 5-6) promote repulsive self-interactions that mediate high colloidal stability but also mediate strong non-specific interactions with negatively charged biomolecules at physiological pH and vice versa for antibodies with negatively charged Fv regions. Therefore, IgG1s with weakly basic isoelectric points between 8 and 8.5 and Fv isoelectric points between 7.5 and 9 typically display the best combinations of strong repulsive self-interactions and weak non-specific interactions. We expect that these findings will improve the identification and engineering of antibody candidates with drug-like biophysical properties.

New Model to Predict the Concentration-Dependent Viscosity of Monoclonal Antibody Solutions

Concentrated monoclonal antibody solutions exhibit high solution viscosity, which is experimentally measured to be ∼1-2 orders of magnitude higher than the viscosity of water. However, physical processes responsible for the high antibody viscosity are not fully understood. We show that fluid occlusion due to the trapped solvent molecules within the boundaries formed by the aggregated antibodies is responsible for the elevated solution viscosity. We develop a theory to predict the viscosity of monoclonal antibodies based on the geometry of the antibody molecule and the aggregate morphology. We validate our theory with experiments and highlight useful insights obtained from the viscosity equation which could help in controlling the drug viscosity at the molecular design stage itself.

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.

Monoclonal Antibody Aggregation near Silicone Oil-Water Interfaces

In this work, we study the hydrodynamic behavior of monoclonal antibodies in the presence of silicone oil-water interfaces. We model the antibody molecules using a coarse-grained 24-bead model, where two beads are used to represent each antibody domain. We consider the spatial variation of the antibody polarity in our model as each bead represents a set of hydrophilic or hydrophobic amino acids. We use the dissipative particle dynamics scheme to represent the coarse-grained force field which governs the motion of the beads. In addition, interprotein interactions are modeled using an electrostatic force field. The model parameters are determined by comparing the structure factor against experimental structure factor data ranging from a low concentration regime (10 mg/mL) to a high concentration regime (150 mg/mL). Next, we conduct simulations for a suspension of antibody molecules in the presence of silicone oil-water interfaces. Protein loss from the bulk solution is noticed as the molecules adsorb at the interface. We observe dynamic cluster formation in the solution bulk and at the interface, as the antibody molecules self-associate along their trajectories. We quantify the aggregation using a density clustering algorithm and investigate the effect of the antibody concentration on the diffusivity of the antibody solution, aggregation propensity, and protein loss from the bulk. Our study shows that numerical simulations can be an important tool for understanding the molecular mechanisms driving protein aggregation near hydrophobic interfaces.

Modeling and mitigation of high-concentration antibody viscosity through structure-based computer-aided protein design

For an antibody to be a successful therapeutic many competing factors require optimization, including binding affinity, biophysical characteristics, and immunogenicity risk. Additional constraints may arise from the need to formulate antibodies at high concentrations (>150 mg/ml) to enable subcutaneous dosing with reasonable volume (ideally <1.0 mL). Unfortunately, antibodies at high concentrations may exhibit high viscosities that place impractical constraints (such as multiple injections or large needle diameters) on delivery and impede efficient manufacturing. Here we describe the optimization of an anti-PDGF-BB antibody to reduce viscosity, enabling an increase in the formulated concentration from 80 mg/ml to greater than 160 mg/ml, while maintaining the binding affinity. We performed two rounds of structure guided rational design to optimize the surface electrostatic properties. Analysis of this set demonstrated that a net-positive charge change, and disruption of negative charge patches were associated with decreased viscosity, but the effect was greatly dependent on the local surface environment. Our work here provides a comprehensive study exploring a wide sampling of charge-changes in the Fv and CDR regions along with targeting multiple negative charge patches. In total, we generated viscosity measurements for 40 unique antibody variants with full sequence information which provides a significantly larger and more complete dataset than has previously been reported.

Multiscale Coarse-Grained Approach to Investigate Self-Association of Antibodies

Self-association of therapeutic monoclonal antibodies (mabs) are thought to modulate the undesirably high viscosity observed in their concentrated solutions. Computational prediction of such a self-association behavior is advantageous early during mab drug candidate selection when material availability is limited. Here, we present a coarse-grained (CG) simulation method that enables microsecond molecular dynamics simulations of full-length antibodies at high concentrations. The proposed approach differs from others in two ways: first, charges are assigned to CG beads in an effort to reproduce molecular multipole moments and charge asymmetry of full-length antibodies instead of only localized charges. This leads to great improvements in the agreement between CG and all-atom electrostatic fields. Second, the distinctive hydrophobic character of each antibody is incorporated through empirical adjustments to the short-range van der Waals terms dictated by cosolvent all-atom molecular dynamics simulations of antibody variable regions. CG simulations performed on a set of 15 different mabs reveal that diffusion coefficients in crowded environments are markedly impacted by intermolecular interactions. Diffusion coefficients computed from the simulations are in correlation with experimentally measured observables, including viscosities at a high concentration. Further, we show that the evaluation of electrostatic and hydrophobic characters of the mabs is useful in predicting the nonuniform effect of salt on the viscosity of mab solutions. This CG modeling approach is particularly applicable as a material-free screening tool for selecting antibody candidates with desirable viscosity properties.

Coarse-Grained Molecular Dynamics Simulations for Understanding the Impact of Short-Range Anisotropic Attractions on Structure and Viscosity of Concentrated Monoclonal Antibody Solutions

Understanding protein-protein interactions in concentrated therapeutic monoclonal antibody (mAb) solutions is desirable for improved drug discovery, processing, and administration. Here, we deduce both the net protein charge and the magnitude and geometry of short-ranged, anisotropic attractions of a mAb across multiple concentrations and cosolute conditions by comparing structure factors S(q) obtained from small-angle X-ray scattering experiments with those from molecular dynamics (MD) simulations. The simulations, which utilize coarse-grained 12-bead models exhibiting a uniform van der Waals attraction, uniform electrostatic repulsion, and short-range attractions between specific beads, are versatile enough to fit S(q) of a wide range of protein concentrations and ionic strength with the same charge on each bead and a single anisotropic short-range attraction strength. Cluster size distributions (CSDs) obtained from best fit simulations reveal that the experimental structure is consistent with small reversible oligomers in even low viscosity systems and help quantify the impact of these clusters on viscosity. The ability to systematically use experimental S(q) data together with MD simulations to discriminate between different possible protein-protein interactions, as well as to predict viscosities from protein CSDs, is beneficial for designing mAbs and developing formulation strategies that avoid high viscosities and aggregation at high concentration.

Determination of Protein-Protein Interactions in a Mixture of Two Monoclonal Antibodies

The coformulation of monoclonal antibody (mAb) mixtures provides an attractive route to achieving therapeutic efficacy where the targeting of multiple epitopes is necessary. Controlling and predicting the behavior of such mixtures requires elucidating the molecular basis for the self- and cross-protein-protein interactions and how they depend on solution variables. While self-interactions are now beginning to be well understood, systematic studies of cross-interactions between mAbs in solution do not exist. Here, we have used static light scattering to measure the set of self- and cross-osmotic second virial coefficients in a solution containing a mixture of two mAbs, mAbA and mAbB, as a function of ionic strength and pH. mAbB exhibits strong association at a low ionic strength, which is attributed to an electrostatic attraction that is enhanced by the presence of a strong short-ranged attraction of nonelectrostatic origin. Under all solution conditions, the measured cross-interactions are intermediate self-interactions and follow similar patterns of behavior. There is a strong electrostatic attraction at higher pH values, reflecting the behavior of mAbB. Protein-protein interactions become more attractive with an increasing pH due to reducing the overall protein net charges, an effect that is attenuated with an increasing ionic strength due to the screening of electrostatic interactions. Under moderate ionic strength conditions, the reduced cross-virial coefficient, which reflects only the energetic contribution to protein-protein interactions, is given by a geometric average of the corresponding self-coefficients. We show the relationship can be rationalized using a patchy sphere model, where the interaction energy between sites i and j is given by the arithmetic mean of the i-i and j-j interactions. The geometric mean does not necessarily apply to all mAb mixtures and is expected to break down at a lower ionic strength due to the nonadditivity of electrostatic interactions.

Self-diffusion of a highly concentrated monoclonal antibody by fluorescence correlation spectroscopy: insight into protein-protein interactions and self-association

The dynamic behavior of monoclonal antibodies (mAbs) at high concentration provides insight into protein microstructure and protein-protein interactions (PPI) that influence solution viscosity and protein stability. At high concentration, interpretation of the collective-diffusion coefficient Dc, as determined by dynamic light scattering (DLS), is highly challenging given the complex hydrodynamics and PPI at close spacings. In contrast, self-diffusion of a tracer particle by Brownian motion is simpler to understand. Herein, we develop fluorescence correlation spectroscopy (FCS) for the measurement of the long-time self-diffusion of mAb2 over a wide range of concentrations and viscosities in multiple co-solute formulations with varying PPI. The normalized self-diffusion coefficient D0/Ds (equal to the microscopic relative viscosity ηeff/η0) was found to be smaller than η/η0. Smaller ratios of the microscopic to macroscopic viscosity (ηeff/η) are attributed to a combination of weaker PPI and less self-association. The interaction parameters extracted from fits of D0/Ds with a length scale dependent viscosity model agree with previous measurements of PPI by SLS and SAXS. Trends in the degree of self-association, estimated from ηeff/η with a microviscosity model, are consistent with oligomer sizes measured by SLS. Finally, measurements of collective diffusion and osmotic compressibility were combined with FCS data to demonstrate that the changes in self-diffusion between formulations are due primarily to changes in the protein-protein friction in these systems, and not to protein-solvent friction. Thus, FCS is a robust and accessible technique for measuring mAb self-diffusion, and, by extension, microviscosity, PPI and self-association that govern mAb solution dynamics.

Binding symmetry and surface flexibility mediate antibody self-association

Solution stability is an important factor in the optimization of engineered biotherapeutic candidates such as monoclonal antibodies because of its possible effects on manufacturability, pharmacology, efficacy and safety. A detailed atomic understanding of the mechanisms governing self-association of natively folded protein monomers is required to devise predictive tools to guide screening and re-engineering along the drug development pipeline. We investigated pairs of affinity-matured full-size antibodies and observed drastically different propensities to aggregate from variants differing by a single amino-acid. Biophysical testing showed that antigen-binding fragments (Fabs) from the aggregating antibodies also reversibly associated with equilibrium dissociation constants in the low-micromolar range. Crystal structures (PDB accession codes 6MXR, 6MXS, 6MY4, 6MY5) and bottom-up hydrogen-exchange mass spectrometry revealed that Fab self-association occurs in a symmetric mode that involves the antigen complementarity-determining regions. Subtle local conformational changes incurred upon point mutation of monomeric variants foster formation of complementary polar interactions and hydrophobic contacts to generate a dimeric Fab interface. Testing of popular in silico tools generally indicated low reliabilities for predicting the aggregation propensities observed. A structure-aggregation data set is provided here in order to stimulate further improvements of in silico tools for prediction of native aggregation. Incorporation of intermolecular docking, conformational flexibility, and short-range packing interactions may all be necessary features of the ideal algorithm.

Science and art of protein formulation development

Protein pharmaceuticals have become a significant class of marketed drug products and are expected to grow steadily over the next decade. Development of a commercial protein product is, however, a rather complex process. A critical step in this process is formulation development, enabling the final product configuration. A number of challenges still exist in the formulation development process. This review is intended to discuss these challenges, to illustrate the basic formulation development processes, and to compare the options and strategies in practical formulation development.

Predicting High-Concentration Interactions of Monoclonal Antibody Solutions: Comparison of Theoretical Approaches for Strongly Attractive Versus Repulsive Conditions

Nonspecific protein-protein interactions of a monoclonal antibody were quantified experimentally using light scattering from low to high protein concentrations (c₂) and compared with prior work for a different antibody that yielded qualitatively different behavior. The c₂ dependence of the excess Rayleigh ratio (R^ex) provided the osmotic second virial coefficient (B₂₂) at low c₂ and the static structure factor (S_q=0) at high c₂, as a function of solution pH, total ionic strength (TIS), and sucrose concentration. Net repulsive interactions were observed at pH 5, with weaker repulsions at higher TIS. Conversely, attractive electrostatic interactions were observed at pH 6.5, with weaker attractions at higher TIS. Refined coarse-grained models were used to fit model parameters using experimental B₂₂ versus TIS data. The parameters were used to predict high-c₂ R^ex values via Monte Carlo simulations and separately with Mayer-sampling calculations of higher-order virial coefficients. For both methods, predictions for repulsive to mildly attractive conditions were quantitatively accurate. However, only qualitatively accurate predictions were practical for strongly attractive conditions. An alternative, higher resolution model was used to show semiquantitatively and quantitatively accurate predictions of strong electrostatic attractions at low c₂ and low ionic strength.

Current Trends in Biotherapeutic Higher Order Structure Characterization by Irreversible Covalent Footprinting Mass Spectrometry

BACKGROUND

Biotherapeutics, particularly monoclonal antibodies (mAbs), are a maturing class of drugs capable of treating a wide range of diseases. Therapeutic function and solutionstability are linked to the proper three-dimensional organization of the primary sequence into Higher Order Structure (HOS) as well as the timescales of protein motions (dynamics). Methods that directly monitor protein HOS and dynamics are important for mapping therapeutically relevant protein-protein interactions and assessing properly folded structures. Irreversible covalent protein footprinting Mass Spectrometry (MS) tools, such as site-specific amino acid labeling and hydroxyl radical footprinting are analytical techniques capable of monitoring the side chain solvent accessibility influenced by tertiary and quaternary structure. Here we discuss the methodology, examples of biotherapeutic applications, and the future directions of irreversible covalent protein footprinting MS in biotherapeutic research and development.

CONCLUSION

Bottom-up mass spectrometry using irreversible labeling techniques provide valuable information for characterizing solution-phase protein structure. Examples range from epitope mapping and protein-ligand interactions, to probing challenging structures of membrane proteins. By paring these techniques with hydrogen-deuterium exchange, spectroscopic analysis, or static-phase structural data such as crystallography or electron microscopy, a comprehensive understanding of protein structure can be obtained.

Protein-Protein Interactions of Highly Concentrated Monoclonal Antibody Solutions via Static Light Scattering and Influence on the Viscosity

The ability to design and formulate mAbs to minimize attractive interactions at high concentrations is important for protein processing, stability, and administration, particularly in subcutaneous delivery, where high viscosities are often challenging. The strength of protein-protein interactions (PPIs) of an IgG1 and IgG4 monoclonal antibody (mAb) from low to high concentration was determined by static light scattering (SLS) and used to understand viscosity data. The PPI were tuned using NaCl and five organic ionic co-solutes. The PPI strength was quantified by the normalized structure factor S(0)/ S(0)_HS and Kirkwood-Buff integral G₂₂/ G_22,HS (HS = hard sphere) determined from the SLS data and also by fits with (1) a spherical Yukawa potential and (2) an interacting hard sphere (IHS) model, which describes attraction in terms of hypothetical oligomers. The IHS model was better able to capture the scattering behavior of the more strongly interacting systems (mAb and/or co-solute) than the spherical Yukawa potential. For each descriptor of PPI, linear correlations were obtained between the viscosity at high concentration (200 mg/mL) and the interaction strengths evaluated both at low (20 mg/mL) and high concentrations (200 mg/mL) for a given mAb. However, the only parameter that provided a correlation across both mAbs was the oligomer mass ratio ( m_oligomer/ m_{monomer+dimer}) from the IHS model, indicating the importance of self-association (in addition to the direct influence of the attractive PPI) on the viscosity.

Evaluating the Effects of Hinge Flexibility on the Solution Structure of Antibodies at Concentrated Conditions

Employing 2 different coarse-grained models, we evaluated the effect of intramolecular domain-domain distances and hinge flexibility on the general solution structure of monoclonal antibodies (mAbs), within the context of protein-protein steric repulsion. These models explicitly account for the hinge region, and represent antibodies at either domain or subdomain levels (i.e., 4-bead and 7-bead representations, respectively). Additionally, different levels of mAb flexibility are also considered. When evaluating mAbs as rigid structures, analysis of small-angle scattering profiles showed that changes in the relative internal distances between Fc and Fab domains significantly alter the local arrangement of neighboring molecules, as well as the molecular packing of the concentrated mAb solutions. Likewise, enabling hinge flexibility in either of the mAb models led to qualitatively similar results, where flexibility increases the spatial molecular arrangement at elevated concentrations. This occurs because fluctuations in mAb quaternary structure are modulated by the close proximity between molecules at elevated concentrations (>50 mg mL^-1), yielding an increased molecular packing and osmotic compressibility. However, our results also showed that the mechanism behind this synergy between flexibility and packing strongly depends on both the level of structural detail and the number of degrees-of-freedom considered in the coarse-grained model.

Structure, heterogeneity and developability assessment of therapeutic antibodies

Increasing attention has been paid to developability assessment with the understanding that thorough evaluation of monoclonal antibody lead candidates at an early stage can avoid delays during late-stage development. The concept of developability is based on the knowledge gained from the successful development of approximately 80 marketed antibody and Fc-fusion protein drug products and from the lessons learned from many failed development programs over the last three decades. Here, we reviewed antibody quality attributes that are critical to development and traditional and state-of-the-art analytical methods to monitor those attributes. Based on our collective experiences, a practical workflow is proposed as a best practice for developability assessment including in silico evaluation, extended characterization and forced degradation using appropriate analytical methods that allow characterization with limited material consumption and fast turnaround time.

Biophysical characterization and molecular simulation of electrostatically driven self-association of a single-chain antibody

Colloidal protein-protein interactions (PPI) are often expected to impact key behaviors of proteins in solution, such as aggregation rates and mechanisms, aggregate structure, protein solubility, and solution viscosity. PPI of an anti-fluorescein single chain antibody variable fragment (scFv) were characterized experimentally at low to intermediate ionic strength using a combination of static light scattering and sedimentation equilibrium ultracentrifugation. Surprisingly, the results indicated that interactions were strongly net-attractive and electrostatics promoted self-association. Only repulsive interactions were expected based on prior work and calculations based a homology model of a related scFv crystal structure. However, the crystal structure lacks the charged, net-neutral linker sequence. PyRosetta was used to generate a set of scFv structures with different linker conformations, and coarse-grained Monte Carlo simulations were used to evaluate the effect of different linker configurations via second osmotic virial coefficient (B₂₂ ) simulations. The results show that the configuration of the linker has a significant effect on the calculated B₂₂ values, and can result in strong electrostatic attractions between oppositely charged residues on the protein surface. This is particularly relevant for development of non-natural antibody products, where charged linkers and other loop regions may be prevalent. The results also provide a preliminary computational framework to evaluate the effect of unstructured linkers on experimental protein-protein interaction parameters such as B₂₂ .

Structure and Relaxation in Solutions of Monoclonal Antibodies

Reversible self-association of therapeutic antibodies is a key factor in high protein solution viscosities. In the present work, a coarse-grained computational model accounting for electrostatic, dispersion, and long-ranged hydrodynamic interactions of two model monoclonal antibodies is applied to understand the nature of self-association, predicting the solution microstructure and resulting transport properties of the solution. For the proteins investigated, the structure factor across a range of solution conditions shows quantitative agreement with neutron-scattering experiments. We observe a homogeneous, dynamical association of the antibodies with no evidence of phase separation. Calculations of self-diffusivity and viscosity from coarse-grained dynamic simulations show the appropriate trends with concentration but, respectively, over- and under-predict the experimentally measured values. By adding constraints to the self-associated clusters that rigidify them under flow, prediction of the transport properties is significantly improved with respect to experimental measurements. We hypothesize that these rigidity constraints are associated with missing degrees of freedom in the coarse-grained model resulting from patchy and heterogeneous interactions among coarse-grained domains. These results demonstrate how structural anisotropy and anisotropy of interactions generated by features at the 2-5 nm length scale in antibodies are sufficient to recover the dynamics and rheological properties of these important macromolecular solutions.

Cluster Size and Quinary Structure Determine the Rheological Effects of Antibody Self-Association at High Concentrations

The question of how nonspecific reversible intermolecular protein interactions affect solution rheology at high concentrations is fundamentally rooted in the translation of nanometer-scale interactions into macroscopic properties. Well-defined solutions of purified monoclonal antibodies (mAbs) provide a useful system with which to investigate the manifold intricacies of weak protein interactions at high concentrations. Recently, characterization of self-associating IgG1 antibody (mAb2) solutions has established the direct role of protein clusters on concentrated mAb rheology. Expanding on our earlier work with three additional mAbs (mAb1, mAb3, and mAb4), the observed concentration-dependent static light scattering and rheological data present a substantially more complex relationship between protein interactions and solution viscosity at high concentrations. The four mAb systems exhibited divergent correlations between cluster formation (size) and concentrated solution viscosities dependent on mAb primary sequence and solution conditions. To address this challenge, well-established features of colloidal cluster phenomena could be applied as a framework for interpreting our observations. The initial stages of mAb cluster formation were investigated with small-angle X-ray scattering (SAXS) and ensemble-optimized fit methods, to uncover shifts in the dimer structure populations which are produced by changes in mAb interaction modes and association valence under the different solution conditions. Analysis of mAb average cluster number and effective hydrodynamic radii at high concentrations revealed cluster architectures can have a wide range of fractal dimensions. Collectively, the static light scattering, SAXS, and rheological characterization demonstrate that nonspecific and anisotropic attractive intermolecular interactions produce antibody clusters with different quinary structures to regulate the rheological properties of concentrated mAb solutions.

A New Mixed All-Atom/Coarse-Grained Model: Application to Melittin Aggregation in Aqueous Solution

We introduce a new mixed resolution, all-atom/coarse-grained approach (AACG), for modeling peptides in aqueous solution and apply it to characterizing the aggregation of melittin. All of the atoms in peptidic components are represented, while a single site is used for each water molecule. With the full flexibility of the peptide retained, our AACG method achieves speedups by a factor of 3-4 for CPU time reduction and another factor of roughly 7 for diffusion. An Ewald treatment permits the inclusion of long-range electrostatic interactions. These characteristics fit well with the requirements for studying peptide association and aggregation, where the system sizes and time scales require considerable computational resources with all-atom models. In particular, AACG is well suited for biologics since changes in peptide shape and long-range electrostatics may play an important role. The application of AACG to melittin, a 26-residue peptide with a well-known propensity to aggregate in solution, serves as an initial demonstration of this technology for studying peptide aggregation. We observed the formation of melittin aggregates during our simulations and characterized the time-evolution of aggregate size distribution, buried surface areas, and residue contacts. Key interactions including π-cation and π-stacking involving TRP19 were also examined. Our AACG simulations demonstrated a clear salt effect and a moderate temperature effect on aggregation and support the molten globule model of melittin aggregates. As a showcase, this work illustrates the useful role for AACG in investigations of peptide aggregation and its potential to guide formulation and design of biologics.

Molecular Dynamics Simulation of Trimer Self-Assembly Under Shear

The self-assembly of patchy trimer particles consisting of one attractive site and two repulsive sites is investigated with nonequilibrium molecular dynamics simulations in the presence of a velocity gradient, as would be produced by the application of a shear stress on the system. As shear is increased, globular-shaped micellar clusters increase in size and become more elongated. The globular clusters are also more stable at higher temperatures in the presence of shear than at equilibrium. These results help to increase our understanding of the effect of shear on self-assembly for a variety of applications.

Charge-mediated Fab-Fc interactions in an IgG1 antibody induce reversible self-association, cluster formation, and elevated viscosity

Concentration-dependent reversible self-association (RSA) of monoclonal antibodies (mAbs) poses a challenge to their pharmaceutical development as viable candidates for subcutaneous delivery. While the role of the antigen-binding fragment (Fab) in initiating RSA is well-established, little evidence supports the involvement of the crystallizable fragment (Fc). In this report, a variety of biophysical tools, including hydrogen exchange mass spectrometry, are used to elucidate the protein interface of such non-covalent protein-protein interactions. Using dynamic and static light scattering combined with viscosity measurements, we find that an IgG1 mAb (mAb-J) undergoes RSA primarily through electrostatic interactions and forms a monomer-dimer-tetramer equilibrium. We provide the first direct experimental mapping of the interface formed between the Fab and Fc domains of an antibody at high protein concentrations. Charge distribution heterogeneity between the positively charged interface spanning complementarity-determining regions CDR3H and CDR2L in the Fab and a negatively charged region in C_H3/Fc domain mediates the RSA of mAb-J. When arginine and NaCl are added, they disrupt RSA of mAb-J and decrease the solution viscosity. Fab-Fc domain interactions between mAb monomers may promote the formation of large transient antibody complexes that ultimately cause increases in solution viscosity. Our findings illustrate how limited specific arrangements of amino-acid residues can cause mAbs to undergo RSA at high protein concentrations and how conserved regions in the Fc portion of the antibody can also play an important role in initiating weak and transient protein-protein interactions.

Parallel temperature-dependent microrheological measurements in a microfluidic chip

Microfluidic stickers are used as a sample environment to measure the microrheology of monoclonal antibody (mAb) protein solutions. A Peltier-based microscope stage is implemented and validated, and is capable of controlling the sample temperature over the range 0.9-40 °C. The design accounts for heat transfer to and from the objective, controls the sample environment humidity to mitigate condensation, and provides adequate damping to reduce vibration from the cooling system. A concentrated sucrose solution is used as a standard sample to provide an in situ temperature measurement by the Stokes-Einstein-Sutherland relation. By combining microfluidic stickers and microrheology, 72 temperature-concentration viscosity measurements of mAb solutions can be made in 1 day, a significant increase in throughput over conventional rheometry.

Hydrogen exchange mass spectrometry reveals protein interfaces and distant dynamic coupling effects during the reversible self-association of an IgG1 monoclonal antibody

There is a need for new analytical approaches to better characterize the nature of the concentration-dependent, reversible self-association (RSA) of monoclonal antibodies (mAbs) directly, and with high resolution, when these proteins are formulated as highly concentrated solutions. In the work reported here, hydrogen exchange mass spectrometry (HX-MS) was used to define the concentration-dependent RSA interface, and to characterize the effects of association on the backbone dynamics of an IgG1 mAb (mAb-C). Dynamic light scattering, chemical cross-linking, and solution viscosity measurements were used to determine conditions that caused the RSA of mAb-C. A novel HX-MS experimental approach was then applied to directly monitor differences in local flexibility of mAb-C due to RSA at different protein concentrations in deuterated buffers. First, a stable formulation containing lyoprotectants that permitted freeze-drying of mAb-C at both 5 and 60 mg/mL was identified. Upon reconstitution with RSA-promoting deuterated solutions, the low vs. high protein concentration samples displayed different levels of solution viscosity (i.e., approx. 1 to 75 mPa.s). The reconstituted mAb-C samples were then analyzed by HX-MS. Two specific sequences covering complementarity-determining regions CDR2H and CDR2L (in the variable heavy and light chains, respectively) showed significant protection against deuterium uptake (i.e., decreased hydrogen exchange). These results define the major protein-protein interfaces associated with the concentration-dependent RSA of mAb-C. Surprisingly, certain peptide segments in the V_H domain, the constant domain (C_H2), and the hinge region (C_H1-C_H2 interface) concomitantly showed significant increases in local flexibility at high vs. low protein concentrations. These results indicate the presence of longer-range, distant dynamic coupling effects within mAb-C occurring upon RSA.

Probing Shear Thinning Behaviors of IgG Molecules at the Air-Water Interface via Rheological Methods

Shear thinning behavior, often observed in shear viscosity tests of IgG therapeutic molecules, could lead to significant disparities in the projections for the viscosity profile of a molecule. Despite its importance, molecular determinants of sheer thinning in protein suspensions are largely unknown. To better understand the factors influencing sheer thinning, viscosity profiles of IgG1 and IgG2 molecules were monitored over a wide range of bulk concentrations (0.007-70 mg/mL). The degree of shear-thinning of 70 and 0.007 mg/mL samples was minimal in comparison to the 0.7 mg/mL solution for both IgG molecules. These observations suggest that bulk concentration alone does not determine the degree of sheer thinning, and additional factors play a role. Additional data reveals, within a threshold range of concentrations, that a strong correlation exists between the degree of shear thinning and the surface area to volume (SA:V) ratio of an IgG sample exposed to the interface. The influence of the interface, however, diminishes when the bulk concentration falls outside this concentration window. Also revealed by interfacial oscillatory rheological testing, both IgG molecules showed solid-like behavior (G'i) at the air-water interface at 0.7 mg/mL, whereas liquid-like behavior (G″i) was dominant at 0.007 and 70 mg/mL concentrations. These observations imply that the lack of solid-like behavior was due to the absence of a network structure. Likewise the addition of polysorbate 20 (PS20) to the 0.7 mg/mL solutions decreased the degree of shear thinning by disrupting the network structure at the interface. Taken together, the results presented here suggest that, although shear thinning behavior is a manifestation of an interfacial, rather than a bulk, phenomenon, the extent of it depends on how susceptible the surface molecules are to the air-water interface, where the surface molecular structures are influenced by the bulk properties.

Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development

Effective translation of breakthrough discoveries into innovative products in the clinic requires proactive mitigation or elimination of several drug development challenges. These challenges can vary depending upon the type of drug molecule. In the case of therapeutic antibody candidates, a commonly encountered challenge is high viscosity of the concentrated antibody solutions. Concentration-dependent viscosity behaviors of mAbs and other biologic entities may depend on pairwise and higher-order intermolecular interactions, non-native aggregation, and concentration-dependent fluctuations of various antibody regions. This article reviews our current understanding of molecular origins of viscosity behaviors of antibody solutions. We discuss general strategies and guidelines to select low viscosity candidates or optimize lead candidates for lower viscosity at early drug discovery stages. Moreover, strategies for formulation optimization and excipient design are also presented for candidates already in advanced product development stages. Potential future directions for research in this field are also explored.