Native ion mobility-mass spectrometry reveals the binding mechanisms of anti-amyloid therapeutic antibodies

One of the most important attributes of anti-amyloid antibodies is their selective binding to oligomeric and amyloid aggregates. However, current methods of examining the binding specificities of anti-amyloid β (Aβ) antibodies have limited ability to differentiate between complexes that form between antibodies and monomeric or oligomeric Aβ species during the dynamic Aβ aggregation process. Here, we present a high-resolution native ion-mobility mass spectrometry (nIM-MS) method to investigate complexes formed between a variety of Aβ oligomers and three Aβ-specific IgGs, namely two antibodies with relatively high conformational specificity (aducanumab and A34) and one antibody with low conformational specificity (crenezumab). We found that crenezumab primarily binds Aβ monomers, while aducanumab preferentially binds Aβ monomers and dimers and A34 preferentially binds Aβ dimers, trimers, and tetrameters. Through collision induced unfolding (CIU) analysis, our data indicate that antibody stability is increased upon Aβ binding and, surprisingly, this stabilization involves the Fc region. Together, we conclude that nIM-MS and CIU enable the identification of Aβ antibody binding stoichiometries and provide important details regarding antibody binding mechanisms.

Meet the Authors: Harkamal S. Jhajj and Peter M. Tessier

In an interview with Dr. Mishtu Dey, Editor-in-chief of Cell Chemical Biology, two authors of the Technology article entitled "Facile generation of biepitopic antibodies with intrinsic agonism for activating tumor necrosis factor receptors"-Dr. Peter M. Tessier and Dr. Harkamal S. Jhajj-share more about their paper and their lives as scientists.

Facile generation of biepitopic antibodies with intrinsic agonism for activating tumor necrosis factor receptors

Agonist antibodies are being pursued for therapeutic applications ranging from neurodegenerative diseases to cancer. For the tumor necrosis factor (TNF) receptor superfamily, higher-order clustering of three or more receptors is key to their activation, which can be achieved using antibodies that recognize two unique epitopes. However, the generation of biepitopic (i.e., biparatopic) antibodies typically requires animal immunization and is laborious and unpredictable. Here, we report a simple method for identifying biepitopic antibodies that potently activate TNF receptors without the need for additional animal immunization. Our approach uses existing, receptor-specific IgGs, which lack intrinsic agonist activity, to block their corresponding epitopes, then selects single-chain antibodies that bind accessible epitopes. The selected antibodies are fused to the light chains of IgGs to generate human tetravalent antibodies. We highlight the broad utility of this approach by converting several clinical-stage antibodies against OX40 and CD137 (4-1BB) into biepitopic antibodies with potent agonist activity.

Bispecific antibody shuttles targeting CD98hc mediate efficient and long-lived brain delivery of IgGs

The inability of antibodies to penetrate the blood-brain barrier (BBB) is a key limitation to their use in diverse applications. One promising strategy is to deliver IgGs using a bispecific BBB shuttle, which involves fusing an IgG to a second affinity ligand that engages a cerebrovascular endothelial target and facilitates transport across the BBB. Nearly all prior efforts have focused on shuttles that target transferrin receptor (TfR-1) despite inherent delivery and safety challenges. Here, we report bispecific antibody shuttles that engage CD98hc, the heavy chain of the large neutral amino acid transporter (LAT1), and efficiently transport IgGs into the brain. Notably, CD98hc shuttles lead to much longer-lived brain retention of IgGs than TfR-1 shuttles while enabling more specific targeting due to limited CD98hc engagement in the brain parenchyma, which we demonstrate for IgGs that either agonize a neuronal receptor (TrkB) or target other endogenous cell-surface proteins on neurons and astrocytes.

Computational optimization of antibody humanness and stability by systematic energy-based ranking

Conventional methods for humanizing animal-derived antibodies involve grafting their complementarity-determining regions onto homologous human framework regions. However, this process can substantially lower antibody stability and antigen-binding affinity, and requires iterative mutational fine-tuning to recover the original antibody properties. Here we report a computational method for the systematic grafting of animal complementarity-determining regions onto thousands of human frameworks. The method, which we named CUMAb (for computational human antibody design; available at http://CUMAb.weizmann.ac.il ), starts from an experimental or model antibody structure and uses Rosetta atomistic simulations to select designs by energy and structural integrity. CUMAb-designed humanized versions of five antibodies exhibited similar affinities to those of the parental animal antibodies, with some designs showing marked improvement in stability. We also show that (1) non-homologous frameworks are often preferred to highest-homology frameworks, and (2) several CUMAb designs that differ by dozens of mutations and that use different human frameworks are functionally equivalent.

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.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

Facile generation of biepitopic antibodies with intrinsic agonism for activating receptors in the tumor necrosis factor superfamily

Agonist antibodies that activate cellular receptors are being pursued for therapeutic applications ranging from neurodegenerative diseases to cancer. For the tumor necrosis factor (TNF) receptor superfamily, higher-order clustering of three or more receptors is key to their potent activation. This can be achieved using antibodies that recognize two unique epitopes on the same receptor and mediate receptor superclustering. However, identifying compatible pairs of antibodies to generate biepitopic antibodies (also known as biparatopic antibodies) for activating TNF receptors typically requires animal immunization and is a laborious and unpredictable process. Here, we report a simple method for systematically identifying biepitopic antibodies that potently activate TNF receptors without the need for additional animal immunization. Our approach uses off-the-shelf, receptor-specific IgG antibodies, which lack intrinsic (Fc-gamma receptor-independent) agonist activity, to first block their corresponding epitopes. Next, we perform selections for single-chain antibodies from human nonimmune libraries that bind accessible epitopes on the same ectodomains using yeast surface display and fluorescence-activated cell sorting. The selected single-chain antibodies are finally fused to the light chains of IgGs to generate human tetravalent antibodies that engage two different receptor epitopes and mediate potent receptor activation. We highlight the broad utility of this approach by converting several existing clinical-stage antibodies against TNF receptors, including ivuxolimab and pogalizumab against OX40 and utomilumab against CD137, into biepitopic antibodies with highly potent agonist activity. We expect that this widely accessible methodology can be used to systematically generate biepitopic antibodies for activating other receptors in the TNF receptor superfamily and many other receptors whose activation is dependent on strong receptor clustering.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

MOTIVATION

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

RESULTS

Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

AVAILABILITY AND IMPLEMENTATION

All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.

Simplifying complex antibody engineering using machine learning

Machine learning is transforming antibody engineering by enabling the generation of drug-like monoclonal antibodies with unprecedented efficiency. Unsupervised algorithms trained on massive and diverse protein sequence datasets facilitate the prediction of panels of antibody variants with native-like intrinsic properties (e.g., high stability), greatly reducing the amount of subsequent experimentation needed to identify specific candidates that also possess desired extrinsic properties (e.g., high affinity). Additionally, supervised algorithms, which are trained on deep sequencing datasets obtained after enrichment of in vitro antibody libraries for one or more specific extrinsic properties, enable the prediction of antibody variants with desired combinations of extrinsic properties without the need for additional screening. Here we review recent advances using both machine learning approaches and how they are impacting the field of antibody engineering as well as key outstanding challenges and opportunities for these paradigm-changing methods.

Quantitative flow cytometric selection of tau conformational nanobodies specific for pathological aggregates

Single-domain antibodies, also known as nanobodies, are broadly important for studying the structure and conformational states of several classes of proteins, including membrane proteins, enzymes, and amyloidogenic proteins. Conformational nanobodies specific for aggregated conformations of amyloidogenic proteins are particularly needed to better target and study aggregates associated with a growing class of associated diseases, especially neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. However, there are few reported nanobodies with both conformational and sequence specificity for amyloid aggregates, especially for large and complex proteins such as the tau protein associated with Alzheimer's disease, due to difficulties in selecting nanobodies that bind to complex aggregated proteins. Here, we report the selection of conformational nanobodies that selectively recognize aggregated (fibrillar) tau relative to soluble (monomeric) tau. Notably, we demonstrate that these nanobodies can be directly isolated from immune libraries using quantitative flow cytometric sorting of yeast-displayed libraries against tau aggregates conjugated to quantum dots, and this process eliminates the need for secondary nanobody screening. The isolated nanobodies demonstrate conformational specificity for tau aggregates in brain samples from both a transgenic mouse model and human tauopathies. We expect that our facile approach will be broadly useful for isolating conformational nanobodies against diverse amyloid aggregates and other complex antigens.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Motivation

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

Results

Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

Availability

All deep sequencing datasets and code to do the analyses presented within are available via GitHub.

Contact

Peter Tessier, ptessier@umich.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Flow cytometric isolation of drug-like conformational antibodies specific for amyloid fibrils

Antibodies that recognize specific protein conformational states are broadly important for research, diagnostic and therapeutic applications, yet they are difficult to generate in a predictable and systematic manner using either immunization or in vitro antibody display methods. This problem is particularly severe for conformational antibodies that recognize insoluble antigens such as amyloid fibrils associated with many neurodegenerative disorders. Here we report a quantitative fluorescence-activated cell sorting (FACS) method for directly selecting high-quality conformational antibodies against different types of insoluble (amyloid fibril) antigens using a single, off-the-shelf human library. Our approach uses quantum dots functionalized with antibodies to capture insoluble antigens, and the resulting quantum dot conjugates are used in a similar manner as conventional soluble antigens for multi-parameter FACS selections. Notably, we find that this approach is robust for isolating high-quality conformational antibodies against tau and α-synuclein fibrils from the same human library with combinations of high affinity, high conformational specificity and, in some cases, low off-target binding that rival or exceed those of clinical-stage antibodies specific for tau (zagotenemab) and α-synuclein (cinpanemab). This approach is expected to enable conformational antibody selection and engineering against diverse types of protein aggregates and other insoluble antigens (e.g., membrane proteins) that are compatible with presentation on the surface of antibody-functionalized quantum dots.

Bispecific antibody shuttles targeting CD98hc mediate efficient and long-lived brain delivery of IgGs

The inability of antibodies and other biologics to penetrate the blood-brain barrier (BBB) is a key limitation to their use in diagnostic, imaging, and therapeutic applications. One promising strategy is to deliver IgGs using a bispecific BBB shuttle, which involves fusing an IgG with a second affinity ligand that engages a cerebrovascular endothelial target and facilitates transport across the BBB. Nearly all prior efforts have focused on the transferrin receptor (TfR-1) as the prototypical endothelial target despite inherent delivery and safety challenges. Here we report bispecific antibody shuttles that engage CD98hc (also known as 4F2 and SLC3A2), the heavy chain of the large neutral amino acid transporter (LAT1), and efficiently transport IgGs into the brain parenchyma. Notably, CD98hc shuttles lead to much longer-lived brain retention of IgGs than TfR-1 shuttles while enabling more specific brain targeting due to limited CD98hc engagement in the brain parenchyma. We demonstrate the broad utility of the CD98hc shuttles by reformatting three existing IgGs as CD98hc bispecific shuttles and delivering them to the mouse brain parenchyma that either agonize a neuronal receptor (TrkB) or target other endogenous antigens on specific types of brain cells (neurons and astrocytes).

Characterization of Pairs of Toxic and Nontoxic Misfolded Protein Oligomers Elucidates the Structural Determinants of Oligomer Toxicity in Protein Misfolding Diseases

ConspectusThe aberrant misfolding and aggregation of peptides and proteins into amyloid aggregates occurs in over 50 largely incurable protein misfolding diseases. These pathologies include Alzheimer's and Parkinson's diseases, which are global medical emergencies owing to their prevalence in increasingly aging populations worldwide. Although the presence of mature amyloid aggregates is a hallmark of such neurodegenerative diseases, misfolded protein oligomers are increasingly recognized as of central importance in the pathogenesis of many of these maladies. These oligomers are small, diffusible species that can form as intermediates in the amyloid fibril formation process or be released by mature fibrils after they are formed. They have been closely associated with the induction of neuronal dysfunction and cell death. It has proven rather challenging to study these oligomeric species because of their short lifetimes, low concentrations, extensive structural heterogeneity, and challenges associated with producing stable, homogeneous, and reproducible populations. Despite these difficulties, investigators have developed protocols to produce kinetically, chemically, or structurally stabilized homogeneous populations of protein misfolded oligomers from several amyloidogenic peptides and proteins at experimentally ameneable concentrations. Furthermore, procedures have been established to produce morphologically similar but structurally distinct oligomers from the same protein sequence that are either toxic or nontoxic to cells. These tools offer unique opportunities to identify and investigate the structural determinants of oligomer toxicity by a close comparative inspection of their structures and the mechanisms of action through which they cause cell dysfunction.This Account reviews multidisciplinary results, including from our own groups, obtained by combining chemistry, physics, biochemistry, cell biology, and animal models for pairs of toxic and nontoxic oligomers. We describe oligomers comprised of the amyloid-β peptide, which underlie Alzheimer's disease, and α-synuclein, which are associated with Parkinson's disease and other related neurodegenerative pathologies, collectively known as synucleinopathies. Furthermore, we also discuss oligomers formed by the 91-residue N-terminal domain of [NiFe]-hydrogenase maturation factor from E. coli, which we use as a model non-disease-related protein, and by an amyloid stretch of Sup35 prion protein from yeast. These oligomeric pairs have become highly useful experimental tools for studying the molecular determinants of toxicity characteristic of protein misfolding diseases. Key properties have been identified that differentiate toxic from nontoxic oligomers in their ability to induce cellular dysfunction. These characteristics include solvent-exposed hydrophobic regions, interactions with membranes, insertion into lipid bilayers, and disruption of plasma membrane integrity. By using these properties, it has been possible to rationalize in model systems the responses to pairs of toxic and nontoxic oligomers. Collectively, these studies provide guidance for the development of efficacious therapeutic strategies to target rationally the cytotoxicity of misfolded protein oligomers in neurodegenerative conditions.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

Unlocking the potential of agonist antibodies for treating cancer using antibody engineering

Agonist antibodies that target immune checkpoints, such as those in the tumor necrosis factor receptor (TNFR) superfamily, are an important class of emerging therapeutics due to their ability to regulate immune cell activity, especially for treating cancer. Despite their potential, to date, they have shown limited clinical utility and further antibody optimization is urgently needed to improve their therapeutic potential. Here, we discuss key antibody engineering approaches for improving the activity of antibody agonists by optimizing their valency, specificity for different receptors (e.g., bispecific antibodies) and epitopes (e.g., biepitopic or biparatopic antibodies), and Fc affinity for Fcγ receptors (FcγRs). These powerful approaches are being used to develop the next generation of cancer immunotherapeutics with improved efficacy and safety.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape

SARS-CoV-2 variants with enhanced transmissibility represent a serious threat to global health. Here we report machine learning models that can predict the impact of receptor-binding domain (RBD) mutations on receptor (ACE2) affinity, which is linked to infectivity, and escape from human serum antibodies, which is linked to viral neutralization. Importantly, the models predict many of the known impacts of RBD mutations in current and former Variants of Concern on receptor affinity and antibody escape as well as novel sets of mutations that strongly modulate both properties. Moreover, these models reveal key opposing impacts of RBD mutations on transmissibility, as many sets of RBD mutations predicted to increase antibody escape are also predicted to reduce receptor affinity and vice versa. These models, when used in concert, capture the complex impacts of SARS-CoV-2 mutations on properties linked to transmissibility and are expected to improve the development of next-generation vaccines and biotherapeutics.

Machine learning is a powerful predictive tool that is well suited for diverse infectious disease applications. In this study, we apply machine learning to comprehensively predict the impact of mutations in the SARS-CoV-2 receptor-binding domain on both receptor affinity, which mediates viral infectivity, and escape from human serum antibodies, which mediates virus neutralization. These methods identify key mutations in current and former SARS-CoV-2 Variants of Concern, and predict novel high-risk variants that may warrant further consideration for vaccine and therapeutic development. Moreover, these models provide a valuable framework for future investigations aimed at understanding and mitigating COVID-19, especially as continued viral evolution remains a key global health threat.

Facile isolation of high-affinity nanobodies from synthetic libraries using CDR-swapping mutagenesis

The generation of high-affinity nanobodies for diverse biomedical applications typically requires immunization or affinity maturation. Here, we report a simple protocol using complementarity-determining region (CDR)-swapping mutagenesis to isolate high-affinity nanobodies from common framework libraries. This approach involves shuffling the CDRs of low-affinity variants during the sorting of yeast-displayed libraries to directly isolate high-affinity nanobodies without the need for lead isolation and optimization. We expect this approach, which we demonstrate for SARS-CoV-2 neutralizing nanobodies, will simplify the generation of high-affinity nanobodies.

For complete details on the use and execution of this profile, please refer to Zupancic et al. (2021).

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Protocol enables direct isolation of high-affinity nanobodies from synthetic libraries

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Individual CDRs are amplified and recombined to obtain nanobodies with shuffled CDRs

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Libraries of CDR-shuffled nanobodies are rapidly sorted to obtain high-affinity clones

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Monovalent and bivalent nanobody affinities are determined using flow cytometry

Rapid and Quantitative In Vitro Evaluation of SARS-CoV-2 Neutralizing Antibodies and Nanobodies

Neutralizing monoclonal antibodies and nanobodies have shown promising results as potential therapeutic agents for COVID-19. Identifying such antibodies and nanobodies requires evaluating the neutralization activity of a large number of lead molecules via biological assays, such as the virus neutralization test (VNT). These assays are typically time-consuming and demanding on-lab facilities. Here, we present a rapid and quantitative assay that evaluates the neutralizing efficacy of an antibody or nanobody within 1.5 h, does not require BSL-2 facilities, and consumes only 8 μL of a low concentration (ng/mL) sample for each assay run. We tested the human angiotensin-converting enzyme 2 (ACE2) binding inhibition efficacy of seven antibodies and eight nanobodies and verified that the IC₅₀ values of our assay are comparable with those from SARS-CoV-2 pseudovirus neutralization tests. We also found that our assay could evaluate the neutralizing efficacy against three widespread SARS-CoV-2 variants. We observed increased affinity of these variants for ACE2, including the β and γ variants. Finally, we demonstrated that our assay enables the rapid identification of an immune-evasive mutation of the SARS-CoV-2 spike protein, utilizing a set of nanobodies with known binding epitopes.

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.

Agonist antibody discovery: Experimental, computational, and rational engineering approaches

Agonist antibodies that activate cellular signaling have emerged as promising therapeutics for treating myriad pathologies. Unfortunately, the discovery of rare antibodies with the desired agonist functions is a major bottleneck during drug development. Nevertheless, there has been important recent progress in discovering and optimizing agonist antibodies against a variety of therapeutic targets that are activated by diverse signaling mechanisms. Herein, we review emerging high-throughput experimental and computational methods for agonist antibody discovery as well as rational molecular engineering methods for optimizing their agonist activity.

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.

Isolating Anti-amyloid Antibodies from Yeast-Displayed Libraries

Conformational antibodies specific for amyloid-forming peptides and proteins are important for a range of biomedical applications, including detecting, inhibiting, and potentially treating protein aggregation disorders ranging from Alzheimer's to Parkinson's diseases. Generation of anti-amyloid antibodies is greatly complicated by the complex, heterogeneous and insoluble nature of amyloid antigens. Here we describe systematic methods for isolating and affinity maturing anti-amyloid antibodies using yeast surface display. Magnetic-activated cell sorting is used to sort single-chain antibody libraries positively for binding to amyloid antigens and negatively against the corresponding disaggregated antigens to remove antibodies that bind in a conformation-independent manner. Isolated lead antibody clones with conformational specificity are affinity matured via targeted CDR mutagenesis and magnetic-activated cell sorting.

Improving antibody drug development using bionanotechnology

Monoclonal antibodies are being used to treat a remarkable breadth of human disorders. Nevertheless, there are several key challenges at the earliest stages of antibody drug development that need to be addressed using simple and widely accessible methods, especially related to generating antibodies against membrane proteins and identifying antibody candidates with drug-like biophysical properties (high solubility and low viscosity). Here we highlight key bionanotechnologies for preparing functional and stable membrane proteins in diverse types of lipoparticles that are being used to improve antibody discovery and engineering efforts. We also highlight key bionanotechnologies for high-throughput and ultra-dilute screening of antibody biophysical properties during antibody discovery and optimization that are being used for identifying antibodies with superior combinations of in vitro (formulation) and in vivo (half-life) properties.

Discovery and characterization of high-affinity, potent SARS-CoV-2 neutralizing antibodies via single B cell screening

Monoclonal antibodies that target SARS-CoV-2 with high affinity are valuable for a wide range of biomedical applications involving novel coronavirus disease (COVID-19) diagnosis, treatment, and prophylactic intervention. Strategies for the rapid and reliable isolation of these antibodies, especially potent neutralizing antibodies, are critical toward improved COVID-19 response and informed future response to emergent infectious diseases. In this study, single B cell screening was used to interrogate antibody repertoires of immunized mice and isolate antigen-specific IgG1⁺ memory B cells. Using these methods, high-affinity, potent neutralizing antibodies were identified that target the receptor-binding domain of SARS-CoV-2. Further engineering of the identified molecules to increase valency resulted in enhanced neutralizing activity. Mechanistic investigation revealed that these antibodies compete with ACE2 for binding to the receptor-binding domain of SARS-CoV-2. These antibodies may warrant further development for urgent COVID-19 applications. Overall, these results highlight the potential of single B cell screening for the rapid and reliable identification of high-affinity, potent neutralizing antibodies for infectious disease applications.

Systematic Engineering of Optimized Autonomous Heavy-Chain Variable Domains

Autonomous heavy-chain variable (V_H) domains are the smallest functional antibody fragments, and they possess unique features, including small size and convex paratopes, which provide enhanced targeting of concave epitopes that are difficult to access with larger conventional antibodies. However, human V_H domains have evolved to fold and function with a light chain partner, and alone, they typically suffer from low stability and high aggregation propensity. Development of autonomous human V_H domains, in which aggregation propensity is reduced without compromising antigen recognition, has proven challenging. Here, we used an autonomous human V_H domain as a scaffold to construct phage-displayed synthetic libraries in which aspartate was systematically incorporated at different paratope positions. In selections, the library yielded many anti-EphA1 receptor V_H domains, which were characterized in detail. Structural analyses of a parental anti-EphA1 V_H domain and an improved variant provided insights into the effects of aspartate and other substitutions on preventing aggregation while retaining function. Our naïve libraries and in vitro selection procedures offer a systematic approach to generating highly functional autonomous human V_H domains that resist aggregation and could be used for basic research and biomedical applications.

A hybridoma-derived monoclonal antibody with high homology to the aberrant myeloma light chain

The identification of antibody variable regions in the heavy (V_H) and light (V_L) chains from hybridomas is necessary for the production of recombinant, sequence-defined monoclonal antibodies (mAbs) and antibody derivatives. This process has received renewed attention in light of recent reports of hybridomas having unintended specificities due to the production of non-antigen specific heavy and/or light chains for the intended antigen. Here we report a surprising finding and potential pitfall in variable domain sequencing of an anti-human CD63 hybridoma. We amplified multiple V_L genes from the hybridoma cDNA, including the well-known aberrant Sp2/0 myeloma V_K and a unique, full-length V_L. After finding that the unique V_L failed to yield a functional antibody, we discovered an additional full-length sequence with surprising similarity (~95% sequence identify) to the non-translated myeloma kappa chain but with a correction of its key frameshift mutation. Expression of the recombinant mAb confirmed that this highly homologous sequence is the antigen-specific light chain. Our results highlight the complexity of PCR-based cloning of antibody genes and strategies useful for identification of correct sequences.

Engineered Multivalent Nanobodies Potently and Broadly Neutralize SARS-CoV-2 Variants

The COVID-19 pandemic continues to be a severe threat to human health, especially due to current and emerging SARS-CoV-2 variants with potential to escape humoral immunity developed after vaccination or infection. The development of broadly neutralizing antibodies that engage evolutionarily conserved epitopes on coronavirus spike proteins represents a promising strategy to improve therapy and prophylaxis against SARS-CoV-2 and variants thereof. Herein, a facile multivalent engineering approach is employed to achieve large synergistic improvements in the neutralizing activity of a SARS-CoV-2 cross-reactive nanobody (VHH-72) initially generated against SARS-CoV. This synergy is epitope specific and is not observed for a second high-affinity nanobody against a non-conserved epitope in the receptor-binding domain. Importantly, a hexavalent VHH-72 nanobody retains binding to spike proteins from multiple highly transmissible SARS-CoV-2 variants (B.1.1.7 and B.1.351) and potently neutralizes them. Multivalent VHH-72 nanobodies also display drug-like biophysical properties, including high stability, high solubility, and low levels of non-specific binding. The unique neutralizing and biophysical properties of VHH-72 multivalent nanobodies make them attractive as therapeutics against SARS-CoV-2 variants.

Directed evolution of potent neutralizing nanobodies against SARS-CoV-2 using CDR-swapping mutagenesis

There is widespread interest in facile methods for generating potent neutralizing antibodies, nanobodies, and other affinity proteins against SARS-CoV-2 and related viruses to address current and future pandemics. While isolating antibodies from animals and humans are proven approaches, these methods are limited to the affinities, specificities, and functional activities of antibodies generated by the immune system. Here we report a surprisingly simple directed evolution method for generating nanobodies with high affinities and neutralization activities against SARS-CoV-2. We demonstrate that complementarity-determining region swapping between low-affinity lead nanobodies, which we discovered unintentionally but find is simple to implement systematically, results in matured nanobodies with unusually large increases in affinity. Importantly, the matured nanobodies potently neutralize both SARS-CoV-2 pseudovirus and live virus, and possess drug-like biophysical properties. We expect that our methods will improve in vitro nanobody discovery and accelerate the generation of potent neutralizing nanobodies against diverse coronaviruses.

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

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

Rational affinity maturation of anti-amyloid antibodies with high conformational and sequence specificity

The aggregation of amyloidogenic polypeptides is strongly linked to several neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. Conformational antibodies that selectively recognize protein aggregates are leading therapeutic agents for selectively neutralizing toxic aggregates, diagnostic and imaging agents for detecting disease, and biomedical reagents for elucidating disease mechanisms. Despite their importance, it is challenging to generate high-quality conformational antibodies in a systematic and site-specific manner due to the properties of protein aggregates (hydrophobic, multivalent, and heterogeneous) and limitations of immunization (uncontrolled antigen presentation and immunodominant epitopes). Toward addressing these challenges, we have developed a systematic directed evolution procedure for affinity maturing antibodies against Alzheimer's Aβ fibrils and selecting variants with strict conformational and sequence specificity. We first designed a library based on a lead conformational antibody by sampling combinations of amino acids in the antigen-binding site predicted to mediate high antibody specificity. Next, we displayed this library on the surface of yeast, sorted it against Aβ42 aggregates, and identified promising clones using deep sequencing. The resulting antibodies displayed similar or higher affinities than clinical-stage Aβ antibodies (aducanumab and crenezumab). Moreover, the affinity-matured antibodies retained high conformational specificity for Aβ aggregates, as observed for aducanumab and unlike crenezumab. Notably, the affinity-maturated antibodies displayed extremely low levels of nonspecific interactions, as observed for crenezumab and unlike aducanumab. We expect that our systematic methods for generating antibodies with unique combinations of desirable properties will improve the generation of high-quality conformational antibodies specific for diverse types of aggregated conformers.

Highly sensitive detection of antibody nonspecific interactions using flow cytometry

The rapidly evolving nature of antibody drug development has resulted in technologies that generate vast numbers (hundreds to thousands) of lead antibody candidates during early discovery. These candidates must be rapidly pared down to identify the most drug-like candidates for in-depth analysis of their safety and efficacy, which can only be performed on a limited number of antibodies due to time and resource requirements. One key biophysical property of successful antibody therapeutics is high specificity, defined as low levels of nonspecific binding or polyspecificity. Although there has been some progress in developing assays for detecting antibody polyspecificity, most of these assays are limited by poor sensitivity or assay formats that require proprietary antibody surface display methods, and some of these assays use complex and poorly defined polyspecificity reagents. Here we report the PolySpecificity Particle (PSP) assay, a sensitive flow cytometry assay for evaluating antibody nonspecific interactions that overcomes previous limitations and can be used for evaluating diverse types of IgGs, multispecific antibodies and Fc-fusion proteins. Our approach uses micron-sized magnetic beads coated with Protein A to capture antibodies at extremely dilute concentrations (<0.02 mg/mL). Flow cytometry analysis of polyspecificity reagent binding to these conjugates results in sensitive detection of differences in nonspecific interactions for clinical-stage antibodies. Our PSP assay strongly discriminates between antibodies with different levels of polyspecificity using previously reported polyspecificity reagents that are either well-defined proteins or highly complex protein mixtures. Moreover, we also find that a unique reagent, namely ovalbumin, results in the best assay sensitivity and specificity. Importantly, our assay is much more sensitive than standard assays such as ELISAs. We expect that our simple, sensitive, and high-throughput PSP assay will accelerate the development of safe and effective antibody therapeutics.

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.

Directed evolution of conformation-specific antibodies for sensitive detection of polypeptide aggregates in therapeutic drug formulations

Biologics such as peptides and proteins possess a number of attractive attributes that make them particularly valuable as therapeutics, including their high activity, high specificity, and low toxicity. However, one of the key challenges associated with this class of drugs is their propensity to aggregate. Given the safety and immunogenicity concerns related to polypeptide aggregates, it is particularly important to sensitively detect aggregates in therapeutic drug formulations as part of the quality control process. Here, we report the development of conformation-specific antibodies that recognize polypeptide aggregates composed of a GLP-1 receptor agonist (liraglutide) and their integration into a sensitive immunoassay for detecting liraglutide amyloid fibrils. We sorted single-chain antibody libraries against liraglutide fibrils using yeast surface display and magnetic-activated cell sorting, and identified several antibodies with high conformational specificity. Interestingly, these antibodies cross-react with amyloid fibrils formed by several other polypeptides, revealing that they recognize molecular features common to different types of fibrils. Moreover, we find that our immunoassay using these antibodies is >50-fold more sensitive than the conventional method for detecting liraglutide aggregation (Thioflavin T fluorescence). We expect that our systematic approach for generating a sensitive, aggregate-specific immunoassay can be readily extended to other biologics to improve the quality and safety of formulated drug products.

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.

Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity

The ability of antibodies to recognize their target antigens with high specificity is fundamental to their natural function. Nevertheless, therapeutic antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that can lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and rapid antibody clearance. Here we report a method for predicting the overall specificity of antibodies in terms of their relative risk for displaying high levels of nonspecific or self-interactions at physiological conditions. We find that individual and combined sets of chemical rules that limit the maximum and minimum numbers of certain solvent-exposed amino acids in antibody variable regions are strong predictors of specificity for large panels of preclinical and clinical-stage antibodies. We also demonstrate how the chemical rules can be used to identify sites that mediate nonspecific interactions in suboptimal antibodies and guide the design of targeted sublibraries that yield variants with high antibody specificity. These findings can be readily used to improve the selection and engineering of antibodies with drug-like specificity.

Directed evolution methods for overcoming trade-offs between protein activity and stability

Engineered proteins are being widely developed and employed in applications ranging from enzyme catalysts to therapeutic antibodies. Directed evolution, an iterative experimental process composed of mutagenesis and library screening, is a powerful technique for enhancing existing protein activities and generating entirely new ones not observed in nature. However, the process of accumulating mutations for enhanced protein activity requires chemical and structural changes that are often destabilizing, and low protein stability is a significant barrier to achieving large enhancements in activity during multiple rounds of directed evolution. Here we highlight advances in understanding the origins of protein activity/stability trade-offs for two important classes of proteins (enzymes and antibodies) as well as innovative experimental and computational methods for overcoming such trade-offs. These advances hold great potential for improving the generation of highly active and stable proteins that are needed to address key challenges related to human health, energy and the environment.

An engineered human Fc domain that behaves like a pH-toggle switch for ultra-long circulation persistence

The pharmacokinetic properties of antibodies are largely dictated by the pH-dependent binding of the IgG fragment crystallizable (Fc) domain to the human neonatal Fc receptor (hFcRn). Engineered Fc domains that confer a longer circulation half-life by virtue of more favorable pH-dependent binding to hFcRn are of great therapeutic interest. Here we developed a pH Toggle switch Fc variant containing the L309D/Q311H/N434S (DHS) substitutions, which exhibits markedly improved pharmacokinetics relative to both native IgG1 and widely used half-life extension variants, both in conventional hFcRn transgenic mice and in new knock-in mouse strains. engineered specifically to recapitulate all the key processes relevant to human antibody persistence in circulation, namely: (i) physiological expression of hFcRn, (ii) the impact of hFcγRs on antibody clearance and (iii) the role of competing endogenous IgG. DHS-IgG retains intact effector functions, which are important for the clearance of target pathogenic cells and also has favorable developability.

Net charge of antibody complementarity-determining regions is a key predictor of specificity

Specificity is one of the most important and complex properties that is central to both natural antibody function and therapeutic antibody efficacy. However, it has proven extremely challenging to define robust guidelines for predicting antibody specificity. Here we evaluated the physicochemical determinants of antibody specificity for multiple panels of antibodies, including >100 clinical-stage antibodies. Surprisingly, we find that the theoretical net charge of the complementarity-determining regions (CDRs) is a strong predictor of antibody specificity. Antibodies with positively charged CDRs have a much higher risk of low specificity than antibodies with negatively charged CDRs. Moreover, the charge of the entire set of six CDRs is a much better predictor of antibody specificity than the charge of individual CDRs, variable domains (VH or VL) or the entire variable fragment (Fv). The best indicators of antibody specificity in terms of CDR amino acid composition are reduced levels of arginine and lysine and increased levels of aspartic and glutamic acid. Interestingly, clinical-stage antibodies with negatively charged CDRs also have a lower risk for poor biophysical properties in general, including a reduced risk for high levels of self-association. These findings provide powerful guidelines for predicting antibody specificity and for identifying safe and potent antibody therapeutics.

Nature-inspired design and evolution of anti-amyloid antibodies

Antibodies that recognize amyloidogenic aggregates with high conformational and sequence specificity are important for detecting and potentially treating a wide range of neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. However, these types of antibodies are challenging to generate because of the large size, hydrophobicity, and heterogeneity of protein aggregates. To address this challenge, we developed a method for generating antibodies specific for amyloid aggregates. First, we grafted amyloidogenic peptide segments from the target polypeptide [Alzheimer's amyloid-β (Aβ) peptide] into the complementarity-determining regions (CDRs) of a stable antibody scaffold. Next, we diversified the grafted and neighboring CDR sites using focused mutagenesis to sample each WT or grafted residue, as well as one to five of the most commonly occurring amino acids at each site in human antibodies. Finally, we displayed these antibody libraries on the surface of yeast cells and selected antibodies that strongly recognize Aβ-amyloid fibrils and only weakly recognize soluble Aβ. We found that this approach enables the generation of monovalent and bivalent antibodies with nanomolar affinity for Aβ fibrils. These antibodies display high conformational and sequence specificity as well as low levels of nonspecific binding and recognize a conformational epitope at the extreme N terminus of human Aβ. We expect that this systematic approach will be useful for generating antibodies with conformational and sequence specificity against a wide range of peptide and protein aggregates associated with neurodegenerative disorders.

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.

Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility

The widespread use of monoclonal antibodies for therapeutic applications has led to intense interest in optimizing several of their natural properties (affinity, specificity, stability, solubility and effector functions) as well as introducing non-natural activities (bispecificity and cytotoxicity mediated by conjugated drugs). A common challenge during antibody optimization is that improvements in one property (e.g., affinity) can lead to deficits in other properties (e.g., stability). Here we review recent advances in understanding trade-offs between different antibody properties, including affinity, specificity, stability and solubility. We also review new approaches for co-optimizing multiple antibody properties and discuss how these methods can be used to rapidly and systematically generate antibodies for a wide range of applications.

Biophysical and Sequence-Based Methods for Identifying Monovalent and Bivalent Antibodies with High Colloidal Stability

In vitro antibody discovery and/or affinity maturation are often performed using antibody fragments (Fabs), but most monovalent Fabs are reformatted as bivalent IgGs (monoclonal antibodies, mAbs) for therapeutic applications. One problem related to reformatting antibodies is that the bivalency of mAbs can lead to increased antibody self-association and poor biophysical properties (e.g., reduced antibody solubility and increased viscosity). Therefore, it is important to identify monovalent Fabs early in the discovery and/or optimization process that will display favorable biophysical properties when reformatted as bivalent mAbs. Here we demonstrate a facile approach for evaluating Fab self-association in a multivalent assay format that is capable of identifying antibodies with low self-association and favorable colloidal properties when reformatted as bivalent mAbs. Our approach (self-interaction nanoparticle spectroscopy, SINS) involves immobilizing Fabs on gold nanoparticles in a multivalent format (multiple Fabs per nanoparticle) and evaluating their self-association behavior via shifts in the plasmon wavelength or changes in the absorbance values. Importantly, we find that SINS measurements of Fab self-association are correlated with self-interaction measurements of bivalent mAbs and are useful for identifying antibodies with favorable biophysical properties. Moreover, the significant differences in the levels of self-association detected for Fabs and mAbs with similar frameworks can be largely explained by the physicochemical properties of the complementarity-determining regions (CDRs). Comparison of the properties of the CDRs in this study relative to those of approved therapeutic antibodies reveals several key factors (net charge, fraction of charged residues, and presence of self-interaction motifs) that strongly influence antibody self-association behavior. Increased positive charge in the CDRs was observed to correlate with increased risk of high self-association for the mAbs in this study and clinical-stage antibodies. We expect that these findings will be useful for improving the development of therapeutic antibodies that are well suited for high concentration applications.