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.

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.

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.

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 ß 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.

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.

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.

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.

Homologous Quorum Sensing Regulatory Circuit: A Dual-Input Genetic Controller for Modulating Quorum Sensing-Mediated Protein Expression in E. coli.

We developed a hybrid synthetic circuit that co-opts the genetic regulation of the native bacterial quorum sensing autoinducer-2 and imposes an extra external controller for maintaining tightly controlled gene expression. This dual-input genetic controller was mathematically modeled and, by design, can be operated in three modes: a constitutive mode that enables consistent and high levels of expression; a tightly repressed mode in which there is very little background expression; and an inducible mode in which concentrations of two signals (arabinose and autoinducer-2) determine the net amplification of the gene(s)-of-interest. We demonstrate the utility of the circuit for the controlled expression of human granulocyte macrophage colony stimulating factor in an engineered probiotic E. coli. This dual-input genetic controller is the first homologous AI-2 quorum sensing circuit that has the ability to be operated in three different modes. We believe it has the potential for wide-ranging biotechnological applications due its versatile features.

HER3-Targeted Affibodies with Optimized Formats Reduce Ovarian Cancer Progression in a Mouse Xenograft Model.

Expression of the receptor tyrosine kinase HER3 is negatively correlated with survival in ovarian cancer, and HER3 overexpression is associated with cancer progression and therapeutic resistance. Thus, improvements in HER3-targeted therapy could lead to significant clinical impact for ovarian cancer patients. Previous work from our group established multivalency as a potential strategy to improve the therapeutic efficacy of HER3-targeted ligands, including affibodies. Others have established HER3 affibodies as viable and potentially superior alternatives to monoclonal antibodies for cancer therapy. Here, bivalent HER3 affibodies were engineered for optimized production, specificity, and function as evaluated in an ovarian cancer xenograft model. Enhanced inhibition of HER3-mediated signaling and increased HER3 downregulation associated with multivalency could be achieved with a simplified construct, potentially increasing translational potential. Additionally, functional effects of affibodies due to multivalency were found to be specific to HER3 targeting, suggesting a unique molecular mechanism. Further, HER3 affibodies demonstrated efficacy in ovarian cancer xenograft mouse models, both as single agents and in combination with carboplatin. Overall, these results reinforce the potential of HER3-targeted affibodies for cancer therapy and establish treatment of ovarian cancer as an application where multivalent HER3 ligands may be useful. Further, this work introduces the potential of HER3 affibodies to be utilized as part of clinically relevant combination therapies (e.g., with carboplatin).

CD44 Promotes Inflammation and Extracellular Matrix Production During Arteriovenous Fistula Maturation.

OBJECTIVE: Arteriovenous fistulae (AVF) remain the optimal conduit for hemodialysis access but continue to demonstrate poor patency and poor rates of maturation. We hypothesized that CD44, a widely expressed cellular adhesion molecule that serves as a major receptor for extracellular matrix components, promotes wall thickening and extracellular matrix deposition during AVF maturation. APPROACH AND RESULTS: AVF were created via needle puncture in wild-type C57BL/6J and CD44 knockout mice. CD44 mRNA and protein expression was increased in wild-type AVF. CD44 knockout mice showed no increase in AVF wall thickness (8.9 versus 26.8 µm; P=0.0114), collagen density, and hyaluronic acid density, but similar elastin density when compared with control AVF. CD44 knockout mice also showed no increase in vascular cell adhesion molecule-1 expression, intercellular adhesion molecule-1 expression, and monocyte chemoattractant protein-1 expression in the AVF compared with controls; there were also no increased M2 macrophage markers (transglutaminase-2: 81.5-fold, P=0.0015; interleukin-10: 7.6-fold, P=0.0450) in CD44 knockout mice. Delivery of monocyte chemoattractant protein-1 to CD44 knockout mice rescued the phenotype with thicker AVF walls (27.2 versus 14.7 µm; P=0.0306), increased collagen density (2.4-fold; P=0.0432), and increased number of M2 macrophages (2.1-fold; P=0.0335). CONCLUSIONS: CD44 promotes accumulation of M2 macrophages, extracellular matrix deposition, and wall thickening during AVF maturation. These data show the association of M2 macrophages with wall thickening during AVF maturation and suggest that enhancing CD44 activity may be a strategy to increase AVF maturation.

Engineered Multivalency Enhances Affibody-Based HER3 Inhibition and Downregulation in Cancer Cells.

The receptor tyrosine kinase HER3 has emerged as a therapeutic target in ovarian, prostate, breast, lung, and other cancers due to its ability to potently activate the PI3K/Akt pathway, especially via dimerization with HER2, as well as for its role in mediating drug resistance. Enhanced efficacy of HER3-targeted therapeutics would therefore benefit a wide range of patients. This study evaluated the potential of multivalent presentation, through protein engineering, to enhance the effectiveness of HER3-targeted affibodies as alternatives to monoclonal antibody therapeutics. Assessment of multivalent affibodies on a variety of cancer cell lines revealed their broad ability to improve inhibition of Neuregulin (NRG)-induced HER3 and Akt phosphorylation compared to monovalent analogues. Engineered multivalency also promoted enhanced cancer cell growth inhibition by affibodies as single agents and as part of combination therapy approaches. Mechanistic investigations revealed that engineered multivalency enhanced affibody-mediated HER3 downregulation in multiple cancer cell types. Overall, these results highlight the promise of engineered multivalency as a general strategy for enhanced efficacy of HER3-targeted therapeutics against a variety of cancers.

Oncogene Knockdown via Active Loading of Small RNAs into Extracellular Vesicles by Sonication.

Extracellular vesicles (EVs), including exosomes and microvesicles, have emerged as promising drug delivery vehicles for small RNAs (siRNA and miRNA) due to their natural role in intercellular RNA transport. However, the application of EVs for therapeutic RNA delivery may be limited by loading approaches that can induce cargo aggregation or degradation. Here, we report the use of sonication as a means to actively load functional small RNAs into EVs. Conditions under which EVs could be loaded with small RNAs with minimal detectable aggregation were identified, and EVs loaded with therapeutic siRNA via sonication were observed to be taken up by recipient cells and capable of target mRNA knockdown leading to reduced protein expression. This system was ultimately applied to reduce expression of HER2, an oncogenic receptor tyrosine kinase that critically mediates breast cancer development and progression, and could be extended to other therapeutic targets. These results define important parameters informing the application of sonication as a small RNA loading method for EVs and demonstrate the potential utility of this approach for versatile cancer therapy.

Emerging roles for extracellular vesicles in tissue engineering and regenerative medicine.

Extracellular vesicles (EVs)-comprising a heterogeneous population of cell-derived lipid vesicles including exosomes, microvesicles, and others-have recently emerged as both mediators of intercellular information transfer in numerous biological systems and vehicles for drug delivery. In both roles, EVs have immense potential to impact tissue engineering and regenerative medicine applications. For example, the therapeutic effects of several progenitor and stem cell-based therapies have been attributed primarily to EVs secreted by these cells, and EVs have been recently reported to play direct roles in injury-induced tissue regeneration processes in multiple physiological systems. In addition, EVs have been utilized for targeted drug delivery in regenerative applications and possess unique potential to be harnessed as patient-derived drug delivery vehicles for personalized medicine. This review discusses EVs in the context of tissue repair and regeneration, including their utilization as drug carriers and their crucial role in cell-based therapies. Furthermore, the article highlights the growing need for bioengineers to understand, consider, and ultimately design and specifically control the activity of EVs to maximize the efficacy of tissue engineering and regenerative therapies.