Mitigation of reversible self-association and viscosity in a human IgG1 monoclonal antibody by rational, structure-guided Fv engineering

Elucidation of the Reversible Self-Association Interface of a Diabody-Interleukin Fusion Protein Using Hydrogen-Exchange Mass Spectrometry and In Silico Modeling

Reversible self-association (RSA) of therapeutic proteins presents major challenges in the development of high-concentration formulations, especially those intended for subcutaneous administration. Understanding self-association mechanisms is therefore critical to the design and selection of candidates with acceptable developability to advance to clinical trials. The combination of experiments and in silico modeling presents a powerful tool to elucidate the interface of self-association. RSA of monoclonal antibodies has been studied extensively under different solution conditions and have been shown to involve interactions for both the antigen-binding fragment and the crystallizable fragment. Novel modalities such as bispecific antibodies, antigen-binding fragments, single-chain-variable fragments, and diabodies constitute a fast-growing class of antibody-based therapeutics that have unique physiochemical properties compared to monoclonal antibodies. In this study, the RSA interface of a diabody-interleukin 22 fusion protein (FP-1) was studied using hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) in combination with in silico modeling. Taken together, the results show that a complex solution behavior underlies the self-association of FP-1 and that the interface thereof can be attributed to a specific segment in the variable light chain of the diabody. These findings also demonstrate that the combination of HDX-MS with in silico modeling is a powerful tool to guide the design and candidate selection of novel biotherapeutic modalities.

Sequence-Based Viscosity Prediction for Rapid Antibody Engineering

Through machine learning, identifying correlations between amino acid sequences of antibodies and their observed characteristics, we developed an internal viscosity prediction model to empower the rapid engineering of therapeutic antibody candidates. For a highly viscous anti-IL-13 monoclonal antibody, we used a structure-based rational design strategy to generate a list of variants that were hypothesized to mitigate viscosity. Our viscosity prediction tool was then used as a screen to cull virtually engineered variants with a probability of high viscosity while advancing those with a probability of low viscosity to production and testing. By combining the rational design engineering strategy with the in silico viscosity prediction screening step, we were able to efficiently improve the highly viscous anti-IL-13 candidate, successfully decreasing the viscosity at 150 mg/mL from 34 cP to 13 cP in a panel of 16 variants.

A bioengineered anti-VEGF protein with high affinity and high concentration for intravitreal treatment of wet age-related macular degeneration

Intravitreal (IVT) injection of anti-vascular endothelial growth factor (anti-VEGF) has greatly improved the treatment of many retinal disorders, including wet age-related macular degeneration (wAMD), which is the third leading cause of blindness. However, frequent injections can be difficult for patients and may lead to various risks such as elevated intraocular pressure, infection, and retinal detachment. To address this issue, researchers have found that IVT injection of anti-VEGF proteins at their maximally viable concentration and dose can be an effective strategy. However, the intrinsic protein structure can limit the maximum concentration due to stability and solution viscosity. To overcome this challenge, we developed a novel anti-VEGF protein called nanoFc by fusing anti-VEGF nanobodies with a crystallizable fragment (Fc). NanoFc has demonstrated high binding affinity to VEGF165 through multivalency and potent bioactivity in various bioassays. Furthermore, nanoFc maintains satisfactory chemical and physical stability at 4°C over 1 month and is easily injectable at concentrations up to 200 mg/mL due to its unique architecture that yields a smaller shape factor. The design of nanoFc offers a bioengineering strategy to ensure both strong anti-VEGF binding affinity and high protein concentration, with the goal of reducing the frequency of IV injections.

Modulation of the high concentration viscosity of IgG1 antibodies using clinically validated Fc mutations

The self-association of therapeutic antibodies can result in elevated viscosity and create problems in manufacturing and formulation, as well as limit delivery by subcutaneous injection. The high concentration viscosity of some antibodies has been reduced by variable domain mutations or by the addition of formulation excipients. In contrast, the impact of Fc mutations on antibody viscosity has been minimally explored. Here, we studied the effect of a panel of common and clinically validated Fc mutations on the viscosity of two closely related humanized IgG1, κ antibodies, omalizumab (anti-IgE) and trastuzumab (anti-HER2). Data presented here suggest that both Fab-Fab and Fab-Fc interactions contribute to the high viscosity of omalizumab, in a four-contact model of self-association. Most strikingly, the high viscosity of omalizumab (176 cP) was reduced 10.7- and 2.2-fold by Fc modifications for half-life extension (M252Y:S254T:T256E) and aglycosylation (N297G), respectively. Related single mutations (S254T and T256E) each reduced the viscosity of omalizumab by ~6-fold. An alternative half-life extension Fc mutant (M428L:N434S) had the opposite effect in increasing the viscosity of omalizumab by 1.5-fold. The low viscosity of trastuzumab (8.6 cP) was unchanged or increased by ≤ 2-fold by the different Fc variants. Molecular dynamics simulations provided mechanistic insight into the impact of Fc mutations in modulating electrostatic and hydrophobic surface properties as well as conformational stability of the Fc. This study demonstrates that high viscosity of some IgG1 antibodies can be mitigated by Fc mutations, and thereby offers an additional tool to help design future antibody therapeutics potentially suitable for subcutaneous delivery.

Reduction of monoclonal antibody viscosity using interpretable machine learning

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.

Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG1 antibody

Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤ 2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2-10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.

Comparative Thermodynamics of the Reversible Self-Association of Therapeutic mAbs Reveal Opposing Roles for Linked Proton- and Ion-Binding Events

PURPOSE

Reversible self-association (RSA) has long been a concern in therapeutic monoclonal antibody (mAb) development. Because RSA typically occurs at high mAb concentrations, accurate assessment of the underlying interaction parameters requires explicitly addressing hydrodynamic and thermodynamic nonideality. We previously examined the thermodynamics of RSA for two mAbs, C and E, in phosphate buffered saline (PBS). Here we continue to explore the mechanistic aspects of RSA by examining the thermodynamics of both mAbs under reduced pH and salt conditions.

METHODS

Dynamic light scattering and sedimentation velocity (SV) studies were conducted for both mAbs at multiple protein concentrations and temperatures, with the SV data analyzed via global fitting to determine best-fit models, interaction energetics, and nonideality contributions.

RESULTS

We find that mAb C self-associates isodesmically irrespective of temperature, and that association is enthalpically driven but entropically penalized. Conversely, mAb E self-associates cooperatively and via a monomer-dimer-tetramer-hexamer reaction pathway. Moreover, all mAb E reactions are entropically driven and enthalpically modest or minimal.

CONCLUSIONS

The thermodynamics for mAb C self-association are classically seen as originating from van der Waals interactions and hydrogen bonding. However, relative to the energetics we determined in PBS, self-association must also be linked to proton release and/or ion uptake events. For mAb E, the thermodynamics implicate electrostatic interactions. Furthermore, self-association is instead linked to proton uptake and/or ion release, and primarily by tetramers and hexamers. Finally, although the origins of mAb E cooperativity remain unclear, ring formation remains a possibility whereas linear polymerization reactions can be eliminated.

Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation

Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep learning methods have not yet been explored due to the relative scarcity of relevant training data. In this work, we overcome this limitation using a biophysically meaningful representation that enables us to develop generalizable models even under limited training data. We present, PfAbNet-viscosity, a 3D convolutional neural network architecture, to predict high-concentration viscosity of therapeutic antibodies. We show that with the electrostatic potential surface of the antibody variable region as the only input to the network, the models trained on as few as couple dozen datapoints can generalize with high accuracy. Our feature attribution analysis shows that PfAbNet-viscosity has learned key biophysical drivers of viscosity. The applicability of our approach to other biological systems is discussed.

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.

Spray-Dried Monoclonal Antibody Suspension for High-Concentration and Low-Viscosity Subcutaneous Injection

Administration of highly concentrated monoclonal antibodies (mAbs) through injection is often not possible as the viscosity can be readily above 50 mPa·s when the concentration exceeds 150 mg/mL. Besides, highly concentrated mAb solutions always exhibit increased aggregation propensity and lower stability, which raise the difficulty for the successful development of highly concentrated mAb formulations. We hereby explored the possibility of suspension as another formulation form for high-concentration proteins to reduce viscosity and maintain stability. Specifically, we demonstrated that spray drying can serve as a process to prepare particles for suspension. Particles prepared from formulations with different mAb/trehalose mass ratios displayed good physical stability and antibody binding affinity, as indicated by circular dichroism, fluorescence spectroscopy, and surface plasmon resonance (SPR)-based bioassay analyses. During spray drying, a surface tension-dominated enrichment of mAb on the particle surface was observed, but this did not show a significant negative impact on mAb stability. Spray-dried particles were subsequently suspended into benzyl benzoate, and the resulting suspension showed good stability and a lower viscosity when compared to its counterpart solution. Furthermore, mAbs recovered from the suspension maintained their conformational structure. Our study demonstrated that the suspension displayed low viscosity and good physical stability, so it may offer novel opportunities for the preparation of highly concentrated protein formulations.

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

Purpose

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

Methods

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

Result

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

Conclusion

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

Supplementary Information

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

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Deciphering the high viscosity of a therapeutic monoclonal antibody in high concentration formulations by microdialysis-hydrogen/deuterium exchange mass spectrometry

High concentration formulations of therapeutic monoclonal antibodies (mAbs) are highly desired for subcutaneous injection. However, high concentration formulations can exhibit unusual molecular behaviors, such as high viscosity or aggregation, that present challenges for manufacturing and administration. To understand the molecular mechanism of the high viscosity exhibited by high concentration protein formulations, we analyzed a human IgG4 (mAb1) at high protein concentrations using sedimentation velocity analytical ultracentrifugation (SV-AUC), X-ray crystallography, hydrogen/deuterium exchange mass spectrometry (HDX-MS), and protein surface patches analysis. Particularly, we developed a microdialysis HDX-MS method to determine intermolecular interactions at different protein concentrations. SV-AUC revealed that mAb1 displayed a propensity for self-association of Fab-Fab, Fab-Fc, and Fc-Fc. mAb1 crystal structure and HDX-MS results demonstrated self-association between complementarity-determining regions (CDRs) and Fc through electrostatic interactions. HDX-MS also indicated Fab-Fab interactions through hydrophobic surface patches constructed by mAb1 CDRs. Our multi-method approach, including fast screening of SV-AUC as well as interface analysis by X-ray crystallography and HDX-MS, helped to elucidate the high viscosity of mAb1 at high concentrations as induced by self-associations of Fab-Fc and Fab-Fab.

High-throughput profiling of antibody self-association in multiple formulation conditions by PEG stabilized self-interaction nanoparticle spectroscopy

Affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) is an assay developed to monitor the propensity of antibody self-association, hence assessing its colloidal stability. It has been widely used by pharmaceutical companies to screen antibodies at the early discovery stages, aiming to flag potential issues with high concentration formulation. However, the original assay format is not suitable for certain formulation conditions, in particular histidine buffer. In addition, the previous data extrapolation method is suboptimal and cumbersome for processing large amounts of data (100s of molecules) in a high-throughput fashion. To address these limitations, we developed an assay workflow with two major improvements: 1) use of a stabilizing reagent to enable screening of a broader range of formulation conditions beyond phosphate-buffered saline, pH 7.4; and 2) inclusion of a novel algorithm and robust data processing schema that empowers streamlined data analysis. The optimized assay format expands the screening applicability to a wider range of formulation conditions critical for downstream development. Such capability is enhanced by a custom data management workflow for optimal data extraction, analysis, and automation. Our protocol and the R/Shiny application for analysis are publicly available and open-source to benefit the broader scientific community.

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.

Reduction of therapeutic antibody self-association using yeast-display selections and machine learning

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; V_H: variable heavy; V_L: variable light.

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.

Caffeine as a Viscosity Reducer for Highly Concentrated Monoclonal Antibody Solutions

Many monoclonal antibody (mAb) solutions exhibit high viscosity at elevated concentrations, which prevents manufacturing and injecting of concentrated mAb drug products at the small volumes needed for subcutaneous (SC) administration. Addition of excipients that interrupt intermolecular interactions is a common approach to reduce viscosity of high concentration mAb formulations. However, in some cases widely used excipients can fail to lower viscosity. Here, using infliximab and ipilimumab as model proteins, we show that caffeine effectively lowers the viscosity of both mAb formulations, whereas other common viscosity-reducing excipients, sodium chloride and arginine, do not. Furthermore, stability studies under accelerated conditions show that caffeine has no impact on stability of lyophilized infliximab or liquid ipilimumab formulations. In addition, presence of caffeine in the formulations does not affect in vitro bioactivities of infliximab or ipilimumab. Results from this study suggest that caffeine could be a useful viscosity reducing agent that complements other traditional excipients and provides viscosity reduction to a wider range of mAb drug products.

Manufacturing Challenges and Rational Formulation Development for AAV Viral Vectors

Adeno-associated virus (AAV) has emerged as a leading platform for gene delivery for treating various diseases due to its excellent safety profile and efficient transduction to various target tissues. However, the large-scale production and long-term storage of viral vectors is not efficient resulting in lower yields, moderate purity, and shorter shelf-life compared to recombinant protein therapeutics. This review provides a comprehensive analysis of upstream, downstream and formulation unit operation challenges encountered during AAV vector manufacturing, and discusses how desired product quality attributes can be maintained throughout product shelf-life by understanding the degradation mechanisms and formulation strategies. The mechanisms of various physical and chemical instabilities that the viral vector may encounter during its production and shelf-life because of various stressed conditions such as thermal, shear, freeze-thaw, and light exposure are highlighted. The role of buffer, pH, excipients, and impurities on the stability of viral vectors is also discussed. As such, the aim of this review is to outline the tools and a potential roadmap for improving the quality of AAV-based drug products by stressing the need for a mechanistic understanding of the involved processes.

Energetic Dissection of Mab-Specific Reversible Self-Association Reveals Unique Thermodynamic Signatures

PURPOSE

Reversible self-association (RSA) remains a challenge in the development of therapeutic monoclonal antibodies (mAbs). We recently analyzed the energetics of RSA for five IgG mAbs (designated as A-E) under matched conditions and using orthogonal methods. Here we examine the thermodynamics of RSA for two of the mAbs that showed the strongest evidence of RSA (mAbs C and E) to identify underlying mechanisms.

METHODS

Concentration-dependent dynamic light scattering and sedimentation velocity (SV) studies were carried out for each mAb over a range of temperatures. Because self-association was weak, the SV data were globally analyzed via direct boundary fitting to identify best-fit models, accurately determine interaction energetics, and account for the confounding effects of thermodynamic and hydrodynamic nonideality.

RESULTS

mAb C undergoes isodesmic self-association at all temperatures examined, with the energetics indicative of an enthalpically-driven reaction offset by a significant entropic penalty. By contrast, mAb E undergoes monomer-dimer self-association, with the reaction being entropically-driven and comprised of only a small enthalpic contribution.

CONCLUSIONS

Classical interpretations implicate van der Waals interactions and H-bond formation for mAb C RSA, and electrostatic interactions for mAb E. However, noting that RSA is likely coupled to additional equilibria, we also discuss the limitations of such interpretations.

A Multi-Method Approach to Assess the Self-Interaction Behavior of Infliximab

Attractive self-interaction processes in antibody formulations increase the risk of aggregation and extraordinarily elevated viscosity at high protein concentrations. These challenges affect manufacturing and application. This study aimed to understand the self-interaction process of Infliximab as a model system with pronounced attractive self-interaction. The association mechanism was studied by a multi-method approach comprising analytical ultracentrifugation, dynamic light scattering, small angle X-ray scattering, self-interaction bio-layer interferometry and hydrogen-deuterium exchange mass spectrometry. Based on our results, both Fab and Fc regions of Infliximab are involved in self-interaction. We hypothesize a mechanism based on electrostatic interactions of polar and charged residues within the identified areas of the heavy and the light chain of the mAb. The combination of fast and reliable screening methods and low throughput but high resolution methods can contribute to detailed characterization and deeper understanding of specific self-interaction processes.

Dissecting the molecular basis of high viscosity of monospecific and bispecific IgG antibodies

Some antibodies exhibit elevated viscosity at high concentrations, making them poorly suited for therapeutic applications requiring administration by injection such as subcutaneous or ocular delivery. Here we studied an anti-IL-13/IL-17 bispecific IgG₄ antibody, which has anomalously high viscosity compared to its parent monospecific antibodies. The viscosity of the bispecific IgG₄ in solution was decreased by only ~30% in the presence of NaCl, suggesting electrostatic interactions are insufficient to fully explain the drivers of viscosity. Intriguingly, addition of arginine-HCl reduced the viscosity of the bispecific IgG₄ by ~50% to its parent IgG level. These data suggest that beyond electrostatics, additional types of interactions such as cation-π and/or π-π may contribute to high viscosity more significantly than previously understood. Molecular dynamics simulations of antibody fragments in the mixed solution of free arginine and explicit water were conducted to identify hotspots involved in self-interactions. Exposed surface aromatic amino acids displayed an increased number of contacts with arginine. Mutagenesis of the majority of aromatic residues pinpointed by molecular dynamics simulations effectively decreased the solution's viscosity when tested experimentally. This mutational method to reduce the viscosity of a bispecific antibody was extended to a monospecific anti-GCGR IgG₁ antibody with elevated viscosity. In all cases, point mutants were readily identified that both reduced viscosity and retained antigen-binding affinity. These studies demonstrate a new approach to mitigate high viscosity of some antibodies by mutagenesis of surface-exposed aromatic residues on complementarity-determining regions that may facilitate some clinical applications.

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.

Generation of mAb Variants with Less Attractive Self-Interaction but Preserved Target Binding by Well-Directed Mutation

Strongly attractive self-interaction of therapeutic protein candidates can impose challenges for manufacturing, filling, stability, and administration due to elevated viscosity or aggregation propensity. Suitable formulations can mitigate these issues to a certain extent. Understanding the self-interaction mechanism on a molecular basis and rational protein engineering provides a more fundamental approach, and it can save costs and efforts as well as alleviate risks at later stages of development. In this study, we used computational methods for the identification of aggregation-prone regions in a mAb and generated mutants based on these findings. We applied hydrogen-deuterium exchange mass spectrometry to identify distinct self-interaction hot spots. Ultimately, we generated mAb variants based on a combination of both approaches and identified mutants with low attractive self-interaction propensity, minimal off-target binding, and even improved target binding. Our data show that the introduction of arginine in spatial proximity to hydrophobic patches is highly beneficial on all these levels. For our mAb, variants that contain more than one aspartate residue flanking to the hydrophobic HCDR3 show decreased attractive self-interaction at unaffected off-target and target binding. The combined engineering strategy described here underlines the high potential of understanding self-interaction in the early stages of development to predict and reduce the risk of failure in subsequent development.

Mitigation of liquid-liquid phase separation of a monoclonal antibody by mutations of negative charges on the Fab surface

Some monoclonal antibodies undergo liquid-liquid phase separation owing to self-attractive associations involving electrostatic and other soft interactions, thereby rendering monoclonal antibodies unsuitable as therapeutics. To mitigate the phase separation, formulation optimization is often performed. However, this is sometimes unsuccessful because of the limited time for the development of therapeutic antibodies. Thus, protein mutations with appropriate design are required. In this report, we describe a case study involving the design of mutants of negatively charged surface residues to reduce liquid-liquid phase separation propensity. Physicochemical analysis of the resulting mutants demonstrated the mutual correlation between the sign of second virial coefficient B2, the Fab dipole moment, and the reduction of liquid-liquid phase separation propensity. Moreover, both the magnitude and direction of the dipole moment appeared to be essential for liquid-liquid phase separation propensity, where electrostatic interaction was the dominant mechanism. These findings could contribute to a better design of mutants with reduced liquid-liquid phase separation propensity and improved drug-like biophysical properties.

Toward Drug-Like Multispecific Antibodies by Design

The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.

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.

Identifying Key Residues That Drive Strong Electrostatic Attractions between Therapeutic Antibodies

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

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.

Framework Mutations of the 10-1074 bnAb Increase Conformational Stability, Manufacturability, and Stability While Preserving Full Neutralization Activity

The broadly neutralizing anti-HIV antibody, 10-1074, is a highly somatically hypermutated IgG1 being developed for prophylaxis in sub-Saharan Africa. A series of algorithms were applied to identify potentially destabilizing residues in the framework of the Fv region. Of 17 residues defined, a variant was identified encompassing 1 light and 3 heavy chain residues, with significantly increased conformational stability while maintaining full neutralization activity. Central to the stabilization was the replacement of the heavy chain residue T108 with R108 at the base of the CDR3 loop which allowed for the formation of a nascent salt bridge with heavy chain residue D137. Three additional mutations were necessary to confer increased conformational stability as evidenced by differential scanning fluorimetry and isothermal chemical unfolding. In addition, we observed increased stability during low pH incubation in which 40% of the parental monomer aggregated while the combinatorial variant showed no increase in aggregation. Incubation of the variant at 100 mg/mL for 6 weeks at 40°C showed a 9-fold decrease in subvisible particles ≥2 μm relative to the parental molecule. Stability-based designs have also translated to improved pharmacokinetics. Together, these data show that increasing conformational stability of the Fab can have profound effects on the manufacturability and long-term stability of a monoclonal antibody.

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.

Process optimization and protein engineering mitigated manufacturing challenges of a monoclonal antibody with liquid-liquid phase separation issue by disrupting inter-molecule electrostatic interactions

We report a case study in which liquid-liquid phase separation (LLPS) negatively impacted the downstream manufacturability of a therapeutic mAb. Process parameter optimization partially mitigated the LLPS, but limitations remained for large-scale manufacturing. Electrostatic interaction driven self-associations and the resulting formation of high-order complexes are established critical properties that led to LLPS. Through chain swapping substitutions with a well-behaved antibody and subsequent study of their solution behaviors, we found the self-association interactions between the light chains (L_Cs) of this mAb are responsible for the LLPS behavior. With the aid of in silico homology modeling and charged-patch analysis, seven charged residues in the L_C complementarity-determining regions (CDRs) were selected for mutagenesis, then evaluated for self-association and LLPS properties. Two charged residues in the light chain (K30 and D50) were identified as the most significant to the LLPS behaviors and to the antigen-binding affinity. Four adjacent charged residues in the light chain (E49, K52, R53, and R92) also contributed to self-association, and thus to LLPS. Molecular engineering substitution of these charged residues with a neutral or oppositely-charged residue disrupted the electrostatic interactions. A double-mutation in CDR2 and CDR3 resulted in a variant that retained antigen-binding affinity and eliminated LLPS. This study demonstrates the critical nature of surface charged resides on LLPS, and highlights the applied power of in silico protein design when applied to improving physiochemical characteristics of therapeutic antibodies. Our study indicates that in silico design and effective protein engineering may be useful in the development of mAbs that encounter similar LLPS issues.

Selecting and engineering monoclonal antibodies with drug-like specificity

Despite the recent explosion in the use of monoclonal antibodies (mAbs) as drugs, it remains a significant challenge to generate antibodies with a combination of physicochemical properties that are optimal for therapeutic applications. We argue that one of the most important and underappreciated drug-like antibody properties is high specificity - defined here as low levels of antibody non-specific and self-interactions - which is linked to low off-target binding and slow antibody clearance in vivo and high solubility and low viscosity in vitro. Here, we review the latest advances in characterizing antibody specificity and elucidating its molecular determinants as well as using these findings to improve the selection and engineering of antibodies with extremely high, drug-like specificity.

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.

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.

Predicting B cell receptor substitution profiles using public repertoire data

B cells develop high affinity receptors during the course of affinity maturation, a cyclic process of mutation and selection. At the end of affinity maturation, a number of cells sharing the same ancestor (i.e. in the same “clonal family”) are released from the germinal center; their amino acid frequency profile reflects the allowed and disallowed substitutions at each position. These clonal-family-specific frequency profiles, called “substitution profiles”, are useful for studying the course of affinity maturation as well as for antibody engineering purposes. However, most often only a single sequence is recovered from each clonal family in a sequencing experiment, making it impossible to construct a clonal-family-specific substitution profile. Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, we present the method “Substitution Profiles Using Related Families” (SPURF), a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence. Using this framework, we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction. We fit this model on a large public dataset and validate the robustness of our approach on two external datasets. Furthermore, we provide a command-line tool in an open-source software package (https://github.com/krdav/SPURF) implementing these ideas and providing easy prediction using our pre-fit models.

Antibody engineering can be greatly informed by knowledge about the underlying affinity maturation process. As such this can be probed by sequencing, but unfortunately, in practice often only one member of the clonal family is sequenced, making it difficult to determine a set of possible amino acid mutations that would retain the original antibody antigen binding affinity. We overcome this data sparsity by developing a statistical learning approach that leverages vast information about amino acid preferences available in public immune system repertoire data. We use a penalized regression approach to devise a flexible statistical model that integrates multiple sources of information into a coherent prediction framework and validate our prediction algorithm using subsampling and held out data.

MS-based conformation analysis of recombinant proteins in design, optimization and development of biopharmaceuticals

Mass spectrometry (MS)-based methods for analyzing protein higher order structures have gained increasing application in the field of biopharmaceutical development. The predominant methods used in this area include native MS, hydrogen deuterium exchange-MS, covalent labeling, cross-linking and limited proteolysis. These MS-based methods will be briefly described in this article, followed by a discussion on how these methods contribute at different stages of discovery and development of protein therapeutics.

Determination of Interaction Parameters for Reversibly Self-Associating Antibodies: A Comparative Analysis

Monoclonal antibodies (mAbs) represent a major class of biotherapeutics and are the fastest growing category of biologic drugs on the market. However, mAb development and formulation are often impeded by reversible self-association (RSA), defined as the dynamic exchange of monomers with native-state oligomers. Here, we present a comparative analysis of the self-association properties for 5 IgG mAbs, under matched conditions and using orthogonal methods. Concentration-dependent dynamic light scattering and sedimentation velocity studies revealed that the majority of mAbs examined exhibited weak to moderate RSA. However, because these studies were carried out at mAb concentrations in the mg/mL range, we also observed significant nonideality. Noting that nonideality frequently masks RSA and vice versa, we conducted direct boundary fitting of the sedimentation velocity data to determine stoichiometric binding models, interaction affinities, and nonideality terms for each mAb. These analyses revealed equilibrium constants from micromolar to millimolar and stoichiometric models from monomer-dimer to isodesmic. Moreover, even for those mAbs described by identical models, we observed distinct kinetics of self-association. The accuracy of the models and their corresponding equilibrium constants were addressed using sedimentation equilibrium and simulations. Overall, these results serve as the starting point for the comparative dissection of RSA mechanisms in therapeutic mAbs.

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.

Factors affecting the physical stability (aggregation) of peptide therapeutics

The number of biological therapeutic agents in the clinic and development pipeline has increased dramatically over the last decade and the number will undoubtedly continue to increase in the coming years. Despite this fact, there are considerable challenges in the development, production and formulation of such biologics particularly with respect to their physical stabilities. There are many cases where self-association to form either amorphous aggregates or highly structured fibrillar species limits their use. Here, we review the numerous factors that influence the physical stability of peptides including both intrinsic and external factors, wherever possible illustrating these with examples that are of therapeutic interest. The effects of sequence, concentration, pH, net charge, excipients, chemical degradation and modification, surfaces and interfaces, and impurities are all discussed. In addition, the effects of physical parameters such as pressure, temperature, agitation and lyophilization are described. We provide an overview of the structures of aggregates formed, as well as our current knowledge of the mechanisms for their formation.

Charge Shielding Prevents Aggregation of Supercharged GFP Variants at High Protein Concentration

Understanding protein stability is central to combatting protein aggregation diseases and developing new protein therapeutics. At the high concentrations often present in biological systems, purified proteins can exhibit undesirable high solution viscosities and poor solubilities mediated by short-range electrostatic and hydrophobic protein-protein interactions. The interplay between protein amino acid sequence, protein structure, and solvent conditions to minimize protein-protein interactions is key to designing well-behaved pharmaceutical proteins. However, theoretical approaches have yet to yield a general framework to address these problems. Here, we analyzed the high concentration behavior of superfolder GFP (sfGFP) and two supercharged sfGFP variants engineered to have formal charges of -18 or +15. Under low cosolute conditions, sfGFP and the -18 variant formed a gel or phase separated at ∼10 mg/mL. Under conditions that screen surface charges, including formulations with high histidine or high NaCl concentrations, all three variants attained concentrations up to 250 mg/mL with moderate viscosities. Moreover, all three variants exhibited very similar viscosity-concentration profiles over this range. This effect was not mimicked by high sugar concentrations that exert excluded-volume effects without shielding charge. Collectively, these data demonstrate that charge shielding neutralizes not only long-range electrostatic interactions but also, surprisingly, short-range electrostatic effects due to surface charge anisotropy. This work shows that supercharged sfGFP behavior under high ionic strength is largely determined by particle geometry, a conclusion that is supported by colloid models and may be applicable to pharmaceutically relevant proteins.