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SUMMARYThe 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).

Conspectus: The 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.

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.

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 hours, does not require BSL-2 facilities, and consumes only 8 μL of 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 IC(50) 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 Beta and Gamma variants. Finally, we demonstrated that our assay enables rapid identification of an immune-evasive mutation of the SARS-CoV-2 spike protein utilizing a set of nanobodies with known binding epitopes.

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 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.

Humanization is an essential step in developing animal-derived antibodies into therapeutics, and approximately one third of approved antibodies have been humanized. Conventional humanization approaches graft the complementarity-determining regions (CDRs) of the animal antibody onto several homologous human frameworks. This process, however, often drastically lowers stability and antigen binding, demanding iterative mutational fine-tuning to recover the original antibody properties. Here, we present Computational hUMan AntiBody design (CUMAb), a web-accessible method that starts from an experimental or model antibody structure, systematically grafts the animal CDRs on thousands of human frameworks, and uses Rosetta atomistic simulations to rank the designs by energy and structural integrity (http://CUMAb.weizmann.ac.il). CUMAb designs of five independent antibodies exhibit similar affinities to the parental animal antibody, and some designs show marked improvement in stability. Surprisingly, nonhomologous frameworks are often preferred to the highest-homology ones, and several CUMAb designs that use different human frameworks and differ by dozens of mutations are functionally equivalent. Thus, CUMAb presents a general and streamlined approach to optimizing antibody stability and expressibility while increasing humanness.

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.

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 non-specific 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 non-specific and/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 residues 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 non-specific interactions in suboptimal antibodies and guide the design of targeted sub-libraries 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.

Monoclonal antibodies are attractive therapeutic agents because of their impressive biological activities and favorable biophysical properties. Nevertheless, antibodies are susceptible to various types of chemical modifications, and the impact of such modifications on antibody physical stability and aggregation remains understudied. Here, we report a systematic analysis of the impact of methionine oxidation, tryptophan oxidation, and asparagine deamidation on antibody conformational and colloidal stability, hydrophobicity, solubility, and aggregation. Interestingly, we find little correlation between the impact of these chemical modifications on antibody conformational stability and aggregation. Methionine oxidation leads to significant reductions in antibody conformational stability while having little impact on antibody aggregation except at extreme conditions (low pH and elevated temperature). Conversely, tryptophan oxidation and asparagine deamidation have little impact on antibody conformational stability while promoting aggregation at a wide range of solution conditions, and the aggregation mechanisms appear linked to unique types of reducible and nonreducible covalent crosslinks and, in some cases, to increased levels of attractive colloidal interactions. These findings highlight that even related types of chemical modifications can lead to dissimilar antibody aggregation mechanisms, and evaluating these findings for additional antibodies will be important for improving the systematic generation of antibodies with high chemical and physical stability.

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.

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.

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 (

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.

The identification of antibody variable regions in the heavy (VH) and light (VL) 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 VL genes from the hybridoma cDNA, including the well-known aberrant Sp2/0 myeloma VK and a unique, full-length VL. After finding that the unique VL 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.

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., This work reports a multivalent engineering strategy to synergistically increase the neutralization potency of nanobodies against SARS‐CoV‐2. An engineered hexavalent nanobody directed against an evolutionarily conserved epitope potently neutralizes SARS‐CoV‐2 and two key variants of concern (B.1.1.7 and B.1.351). This facile approach significantly improves the neutralization breadth and potency of promising nanobodies for potential use in therapeutic applications.

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.

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.

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.

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.

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.

The Third Modeling Workshop focusing on bioprocess modeling was held in Kenilworth, NJ in May 2019. A summary of these Workshop proceedings is captured in this manuscript. Modeling is an active area of research within the biotechnology community, and there is a critical need to assess the current state and opportunities for continued investment to realize the full potential of models, including resource and time savings. Beyond individual presentations and topics of novel interest, a substantial portion of the Workshop was devoted toward group discussions of current states and future directions in modeling fields. All scales of modeling, from biophysical models at the molecular level and up through large scale facility and plant modeling, were considered in these discussions and are summarized in the manuscript. Model life cycle management from model development to implementation and sustainment are also considered for different stages of clinical development and commercial production. The manuscript provides a comprehensive overview of bioprocess modeling while suggesting an ideal future state with standardized approaches aligned across the industry.

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.

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.

Autonomous heavy-chain variable (VH) 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 VH 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 VH domains, in which aggregation propensity is reduced without compromising antigen recognition, has proven challenging. Here, we used an autonomous human VH 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 VH domains, which were characterized in detail. Structural analyses of a parental anti-EphA1 VH domain and an improved variant provided insights into the effects of aspartate and other substitutions on preventing aggregation while retaining function. Our naive libraries and in vitro selection procedures offer a systematic approach to generating highly functional autonomous human VH domains that resist aggregation and could be used for basic research and biomedical applications.

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., Graphical abstract, Zupancic et al. report potent neutralizing nanobodies against SARS-CoV-2. They demonstrate an approach that involves swapping the complementarity-determining regions of low-affinity clones to generate matured nanobodies with large increases in affinity and neutralization activity.

Antibodies commonly accumulate charged mutations in their complementarity-determining regions (CDRs) during affinity maturation to enhance electrostatic interactions. However, charged mutations can mediate non-specific interactions, and it is unclear to what extent CDRs can accumulate charged residues to increase antibody affinity without compromising specificity. This is especially concerning for positively charged CDR mutations that are linked to antibody polyspecificity. To better understand antibody affinity/specificity trade-offs, we have selected single-chain antibody fragments specific for the negatively charged and hydrophobic Alzheimer's amyloid β peptide using weak and stringent selections for antibody specificity. Antibody variants isolated using weak selections for specificity were enriched in arginine CDR mutations and displayed low specificity. Alanine-scanning mutagenesis revealed that the affinities of these antibodies were strongly dependent on their arginine mutations. Antibody variants isolated using stringent selections for specificity were also enriched in arginine CDR mutations, but these antibodies possessed significant improvements in specificity. Importantly, the affinities of the most specific antibodies were much less dependent on their arginine mutations, suggesting that over-reliance on arginine for affinity leads to reduced specificity. Structural modeling and molecular simulations reveal unique hydrophobic environments near the arginine CDR mutations. The more specific antibodies contained arginine mutations in the most hydrophobic portions of the CDRs, whereas the less specific antibodies contained arginine mutations in more hydrophilic regions. These findings demonstrate that arginine mutations in antibody CDRs display context-dependent impacts on specificity and that affinity/specificity trade-offs are governed by the relative contribution of arginine CDR residues to the overall antibody affinity.

Tau aggregates into paired helical filaments within neurons, a pathological hallmark of Alzheimer's disease. Heparin promotes tau aggregation and recently has been shown to be involved in the cellular uptake of tau aggregates. Although the tau-heparin interaction has been extensively studied, little is known about the glycan determinants of this interaction. Here, we used surface plasmon resonance (SPR) and NMR spectroscopy to characterize the interaction between two tau fragments, K18 and K19, and several polysaccharides, including heparin, heparin oligosaccharides, chemically modified heparin, and related glycans. Using a heparin-immobilized chip, SPR revealed that tau K18 and K19 bind heparin with a K-D of 0.2 and 70 mu M, respectively. In SPR competition experiments, N-desulfation and 2-O-desulfation had no effect on heparin binding to K18, whereas 6-O-desulfation severely reduced binding, suggesting a critical role for 6-O-sulfation in the tau-heparin interaction. The tau-heparin interaction became stronger with longer-chain heparin oligosaccharides. As expected for an electrostatics-driven interaction, a moderate amount of salt (0.3 M NaCl) abolished binding. NMR showed the largest chemical-shift perturbation (CSP) in R2 in tau K18, which was absent in K19, revealing differential binding sites in K18 and K19 to heparin. Dermatan sulfate binding produced minimal CSP, whereas dermatan disulfate, with the additional 6-O-sulfo group, induced much larger CSP. 2-O-desulfated heparin induced much larger CSP in K18 than 6-O-desulfated heparin. Our data demonstrate a crucial role for the 6-O-sulfo group in the tau-heparin interaction, which to our knowledge has not been reported before.

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., Lee et al. report an engineered IgG1 Fc domain that behaves like an hFcRn binding pH toggle switch. The authors show that this new half-life extension Fc domain confers improved pharmacokinetics in new humanized knock-in mouse strains that recapitulate the key processes for antibody persistence in circulation.

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.

Monoclonal antibodies must be both chemically and physically stable to be developed into safe and effective drugs. Although there has been considerable progress in separately understanding the molecular determinants of antibody chemical and physical stability, it remains poorly understood how defects in one property (e.g., chemical stability) impact the other property (e.g., physical stability). Here, we have investigated the impact of a common chemical modification (deamidation) on the physical stability of two monoclonal antibodies as a function of pH (from pH 3.8 to 7.4). Interestingly, we find that deamidation has significant, antibody-specific impacts on physical stability at low pH values that are common during antibody purification. Deamidation causes increases in self-association and/or aggregation at low pH (3.8), and a key contributor to this behavior appears to be deamidation-dependent increases in antibody hydrophobicity at low pH. Our findings highlight pH-dependent impacts of deamidation on antibody colloidal stability and aggregation, which are important to understand in order to improve the development and production of potent antibody therapeutics with high chemical and physical stabilities.

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.

Sensitive detection of protein aggregates is important for evaluating the quality of biopharmaceuticals and detecting misfolded proteins in several neurodegenerative diseases. However, it is challenging to detect extremely low concentrations (10 ppm) of aggregated protein in the presence of high concentrations of soluble protein. Glucagon, a peptide hormone used in the treatment of extreme hypoglycemia, is aggregation-prone and forms amyloid fibrils. Detection of glucagon fibrils using conformation-specific antibodies is an attractive approach for identifying such aggregates during process and formulation development. Therefore, we have used yeast surface display and magnetic-activated cell sorting to sort single-chain antibody libraries to identify antibody variants with high conformational specificity for glucagon fibrils. Notably, we find several high-affinity antibodies that display excellent selectivity for glucagon fibrils, and we have integrated these antibodies into a sensitive immunoassay. Surprisingly, the sensitivity of our assay-which involves direct (nonantibody mediated) glucagon immobilization in microtiter plates-can be significantly enhanced by pretreating the microtiter plates with various types of globular proteins before glucagon immobilization. Moreover, increased total concentrations of glucagon peptide also significantly improve the sensitivity of our assay, which appears to be due to the strong seeding activity of immobilized fibrils at high glucagon concentrations. Our final assay is highly sensitive (fibril detection limit of ~0.5-1 ppm) and is20 times more sensitive than detection using a conventional, amyloid-specific fluorescent dye (Thioflavin T). We expect that this type of sensitive immunoassay can be readily integrated into the drug development process to improve the generation of safe and potent peptide therapeutics.

Background The prevalence of high fat diets (HFD), diet-induced obesity (DIO) and Type 2 diabetes continues to increase, associated with cognitive impairment in both humans and rodent models. Mechanisms transducing these impairments remain largely unknown: one possibility is that a common mechanism may be involved in the cognitive impairment seen in obese and/or diabetic states and in dementia, specifically Alzheimer's disease (AD). DIO is well established as a risk factor for development of AD. Oligomeric amyloid-β (Aβ) is neurotoxic, and we showed that intrahippocampal oligomeric Aβ produces cognitive and metabolic dysfunction similar to that seen in DIO or diabetes. Moreover, animal models of DIO show elevated brain Aβ, a hallmark of AD, suggesting that this may be one source of cognitive impairment in both conditions. Methods Intrahippocampal administration of a novel anti-Aβ domain antibody for aggregated Aβ, or a control domain antibody, to control or HFD-induced DIO rats. Spatial learning measured in a conditioned contextual fear (CCF) task after domain antibody treatment; postmortem, hippocampal NMDAR and AMPAR were measured. Results DIO caused impairment in CCF, and this impairment was eliminated by intrahippocampal administration of the active domain antibody. Measurement of hippocampal proteins suggests that DIO causes dysregulation of hippocampal AMPA receptors, which is also reversed by acute domain antibody administration. Conclusions Our findings support the concept that oligomeric Aβ within the hippocampus of DIO animals may not only be a risk factor for development of AD but may also cause cognitive impairment before the development of dementia. General Significance and Interest Our work integrates the engineering of domain antibodies with conformational- and sequence-specificity for oligomeric amyloid beta with a clinically relevant model of diet-induced obesity in order to demonstrate not only the pervasive effects of obesity on several aspects of brain biochemistry and behavior, but also the bioengineering of a successful treatment against the long-term detrimental effects of a pre-diabetic state on the brain. We show for the first time that cognitive impairment linked to obesity and/or insulin resistance may be due to early accumulation of oligomeric beta-amyloid in the brain, and hence may represent a pre-Alzheimer's state.

Successful development of monoclonal antibodies (mAbs) for therapeutic applications requires identification of mAbs with favorable biophysical properties (high solubility and low viscosity) in addition to potent bioactivities. Nevertheless, mAbs can also display complex, nonconventional biophysical properties that impede their development such as formation of soluble aggregates and subvisible particles as well as nonspecific interactions with various types of surfaces such as nonadsorptive chromatography columns. Here we have investigated the potential of using antibody self-interaction measurements obtained via affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) at dilute concentrations (0.01 mg/mL) for ranking a panel of 12 mAbs in terms of their expected biophysical properties at higher concentrations (1-30 mg/mL). Several mAb properties (solubility, % monomer, size-exclusion elution time and % recovery) displayed modest correlation with each other, as some mAbs with deficiencies in one or more properties (e.g., solubility) failed to show deficiencies in other properties (e.g., % monomer). The ranking of mAbs in terms of their level of self-association was correlated with their solubility ranking. However, the correlation was even stronger between the average ranking of the four biophysical properties and the AC-SINS measurements. This finding suggests that weak self-interactions detected via AC-SINS can manifest themselves in different ways and lead to complex biophysical properties. Our findings highlight the potential for using high-throughput self-interaction measurements to improve the identification of mAbs that possess a collection of excellent biophysical properties without the need for cumbersome analysis of each individual property during early candidate selection.