mAbs最新文献

FcγRI (CD64) is the only Fcγ receptor capable of high-affinity binding to monomeric IgG and found on monocytes, macrophages, eosinophils, neutrophils, and dendritic cells. FcγRI contains three C2-type immunoglobulin (Ig) extracellular domains (EC1-3), while all other Fcγ receptors contain only two EC domains. For detection, several FcγRI-specific antibodies have been described. The most frequently used commercial antibody is clone 10.1, which is proposed to bind the membrane proximal domain EC3. Other anti-FcγRI antibodies include 197, m22/H22 and C09, but their exact binding domains are unknown. A clear overview of binding affinities and functional properties for all these antibodies is lacking. We identified the binding characteristics and functional properties of five anti-human FcγRI antibodies via flow cytometry, LigandTracer and luminol-based chemiluminescence assays. Subsequently we verified their domain specificity using chimeric FcγRI receptors in which EC1-EC3 were swapped with their murine counterparts. Surprisingly, all anti-FcγRI antibodies bind to EC1 of FcγRI, while swapping of EC3 had no effect on binding. Affinity measurements showed similar affinities amongst all antibodies, despite varying association and dissociation rates, except for clone 10.1, which has a > 100-fold lower affinity. These findings strengthen the notion that EC1 is critical for receptor folding, structural integrity, and high-affinity IgG recognition, reinforcing its importance in FcγRI and its potential implications for targeted therapies. By redefining the binding domain of anti-FcγRI antibodies, this study provides a more accurate framework for utilizing FcγRI as a biomarker and therapeutic target in immunotherapy and diagnostics.

Antibodies are versatile therapeutic molecules that use combinatorial sequence diversity to cover a vast fitness landscape. Designing optimal antibody sequences, however, remains a major challenge. Recent advances in deep learning provide opportunities to address this challenge by learning sequence-function relationships to accurately predict fitness landscapes. These models enable efficient in silico prescreening and optimization of antibody candidates. By focusing experimental efforts on the most promising candidates guided by deep learning predictions, antibodies with optimal properties can be designed more quickly and effectively. Here we present AlphaBind, a domain-specific model that uses protein language model embeddings and pre-training on millions of quantitative laboratory measurements of antibody-antigen binding strength to achieve state-of-the-art performance for guided affinity optimization of parental antibodies. We demonstrate that an AlphaBind-powered antibody optimization pipeline can deliver candidates with substantially improved binding affinity across four parental antibodies (some of which were already affinity-matured) and using two different types of training data. The resulting candidates, which include up to 11 mutations from parental sequence, yield a sequence diversity that allows optimization of other biophysical characteristics, all while using only a single round of data generation for each parental antibody. AlphaBind weights and code are publicly available at: https://github.com/A-Alpha-Bio/alphabind.

The discovery and development of multispecific antibodies present unique challenges in optimizing their physicochemical properties to enhance developability and manufacturability. Common developability challenges include increased risk of aggregation, high viscosity, poor solubility, low expression yields, complex purification requirements, greater propensity for fragmentation, immunogenicity, or pharmacokinetics. In this study, we systematically investigate the solution behavior of engineered bispecific IgG1-VHH constructs derived from a parental NKp30 ×EGFR natural killer cell engager (NKCE) molecule, focusing on colloidal stability, hydrophobicity, thermal stability, pH sensitivity, and viscosity. By combining in silico predictions and experimental evaluations, we engineered variants with altered isoelectric points (pIs) of the Fab and VHH domains and characterized them across a broad, formulation-relevant pH range (pH 4.5-8.0). Our findings indicate that aligning slightly basic pI profiles (approximately 7.5-9.0) across variable domains within bispecific antibodies can effectively mitigate charge asymmetries in standard acidic formulations that may lead to unfavorable solution behavior. Importantly, rational design and early-stage experimental validation yielded optimized variants exhibiting significantly improved colloidal stability and viscosity compared to the starting molecule. This systematic study, the first of its kind for bispecific antibodies, highlights the value of integrating domain-level in silico assessments early in antibody design, facilitating efficient optimization toward improved solution behavior of multispecific biotherapeutics.

Clinical and preclinical studies have confirmed insulin-like growth factor-1 receptor (IGF-1R) antagonism can reduce the inflammation and proptosis occurring in thyroid eye disease (TED). We assessed the preclinical pharmacology, pharmacokinetics, and pharmacodynamics of veligrotug (formerly VRDN-001), an anti-IGF-1R antibody in clinical development for TED. Veligrotug exhibited high-affinity binding to human IGF-1R protein (K_D 0.55 nM) and IGF-1R endogenously expressed in HOCF cells (mean EC50 2.41 nM). Veligrotug did not bind to the insulin receptor in ELISA assays or inhibit insulin-mediated receptor phosphorylation in HepG2 cells. Binding epitope and antagonist properties were compared to teprotumumab (Tepezza®), a marketed anti-IGF-1R antibody. Mutational scan analysis demonstrated veligrotug and teprotumumab have overlapping but distinct binding epitopes. Veligrotug behaved as a full antagonist, providing near-complete inhibition of IGF-1 binding at ≥ 50 nM, in contrast to teprotumumab which plateaued at ~50% inhibition. Veligrotug provided near-complete inhibition of IGF-1R autophosphorylation and AKT phosphorylation, in contrast to partial inhibition by teprotumumab. Veligrotug pharmacokinetic parameters in cynomolgus monkeys were consistent with other human/humanized antibodies in monkeys: half-life was ~5-6 days, serum clearance was low (7.6‒12.9 mL/day/kg), and volume of distribution was low (64‒93 mL/kg). A robust pharmacodynamic response was observed after a single dose of veligrotug, with ~2.5-fold increases in IGF-1 levels that remained elevated throughout the dosing period for the 10 mg/kg and 50 mg/kg dose groups. Veligrotug's pharmacologic, pharmacokinetic, and pharmacodynamic characteristics make it a good candidate for clinical development. Indeed, efficacy data at week 15 from two Phase 3 pivotal studies of veligrotug, THRIVE and THRIVE-2, showed statistically significant improvements in TED symptoms based on primary and secondary outcomes.

On-target, off-tumor toxicities remain a major barrier for T-cell engagers in solid tumors. We present EVA^TM, a closed-loop design platform integrating high-throughput functional assays with multi-objective Bayesian optimization to explore combinatorial T-cell engager (TCE) spaces. In a HER2×CD3 case study, iterative design-build-test-learn cycles  traversed 44,160 designs defined by valency, topology, affinity and spacing. Compared with a Sobol baseline, EVA achieved 14-fold enrichment of potent, tumor-selective candidates. Multiple architectures reached sub-10 pM potency on HER2-high cells, near-complete efficacy, and $\ge$10,000-fold selectivity over HER2-low models, consistent with avidity gating. EVA™ recovered diverse high-performing topologies and generalized to a second target, supporting density-gated avidity as a design principle and providing an operational template for rapid, data-efficient optimization.

Next-generation antibodies include a growing number of bispecific and multispecific antibodies that are commonly used to redirect the immune system to fight cancer. Herein, we assessed the depth and breadth of epitope coverage as a proxy for functional diversity in human immune repertoires produced by two complementary in vivo platforms utilizing a common light chain, in a chicken (OmniClic^TM) or rat (OmniFlic®) host species. We adopted NKp46 as a model to target antigen due to its use in emerging natural killer (NK) immune engagers that are being explored clinically as potentially safer alternatives to traditional CD3-based T cell engagers. To probe the epitope diversity of our antibody repertoires, we performed a detailed high throughput epitope binning study using surface plasmon resonance and corroborated our binning assignments with epitope mapping data deduced from hydrogen deuterium exchange mass spectrometry. Our results revealed broad epitope coverage and nuanced diversity both within and across repertoires, with few epitopes shared, suggesting that the complementary use of OmniClic^TM and OmniFlic® produces more comprehensive coverage than either alone. Furthermore, our epitope binning assignments aligned with our complementarity-determining region-based sequence lineage assignments, enabling a direct comparison of sequence diversity across Clic and Flic repertoires despite their use of different scaffolds, a single functionally rearranged V(D)J scaffold versus multiple combinatorially assembled V(D)J scaffolds, respectively. The rich epitope diversity of both OmniClic^TM and OmniFlic® yielded multiple candidates for functional NK activators, as determined in an antibody-dependent cellular cytotoxicity assay, demonstrating their value as building blocks in constructing optimized immune engagers.

Studies in animal models are essential to expanding the scope of interventions evaluated for safety, immunogenicity, and efficacy in clinical trials. Ferrets (Mustela putorius furo) are a key small-animal model for examining acquisition, replication, transmission, and disease manifestation, with particular relevance in modeling diverse viruses that target the respiratory tract. However, despite use in studies of vaccine immunogenicity and protection, as well as passive antibody transfer, there is little data characterizing antibody and Fc receptor biology in this species. To address this gap, ferret Fcγ receptors (FcγRI, FcγRII, FcγRIII) were identified and recombinantly expressed and characterized for binding to recombinant ferret and human IgG. In general, ferret IgG bound each receptor with slightly higher affinity than the human IgG subclasses, which exhibited similar ferret receptor binding profiles as observed for human receptors (IgG1 and IgG3 > IgG4 > IgG2). N-linked glycosylation motifs on ferret receptors were typically occupied, and binding was dependent on IgG Fc glycosylation. While further insight into the expression patterns and activities of innate immune cells stimulated by IgG is still needed, these data define Fc - FcγR recognition patterns in ferrets to help support optimal clinical translation of passive and active immunization studies.

Monoclonal antibody (mAb) charge variant analysis, which ensures the quality of biopharmaceuticals, is primarily performed by either cation exchange chromatography (CEX), capillary isoelectric focusing (CIEF), or capillary zone electrophoresis (CZE). Though selectivity is different for all methods, mAb proteoforms can be quantified on the intact level with a low risk of artifacts. Identification and characterization can be performed by mass spectrometry (MS), but coupling is hampered by the use of nonvolatile constituents. Here, we present comparative data and a thorough review of CEX-MS, CIEF-MS, and CZE-MS for the analysis of charge variants on the intact level. The separation of the proteoforms is confirmed by online UV detection with subsequent online MS coupling. Each method was tested for generic use by measuring 10 mAbs with an isoelectric point (pI) of 7.3 to 8.7 without individually adjusting the method parameters for each mAb. The methods and the identified proteoforms are compared among each other as well as to a previously published CZE-MS method on the subunit level. The lysine variant was used to compare the resolution of the different separation methods and showed a high resolving power for all separation methods tested. The different method selectivities were revealed by the monoglycosylation, which was found in the peak of the main variant with CEX and CIEF, while it was separated from the main peak with CZE. In combination with a comprehensive literature review, our results provide a complete overview of the current state of the methods with information on the advantages and limitations of each methodology.

The correct pairing of cognate heavy and light chains is critical to the efficient manufacturing of IgG-like bispecific antibodies (bsAbs) from a single host cell. We present a general solution for the elimination of heavy chain (HC):light chain (LC) mispairs in bsAbs with $\kappa$ LCs via the use of two orthogonal constant domain (C_H1:C$\kappa$) interfaces comprising computationally designed amino acid substitutions. Substitutions were designed by Rosetta to introduce novel hydrogen bond (H-bond) networks at the C_H1:C$\kappa$ interface, followed by Rosetta energy calculations to identify designs with enhanced pairing specificity and interface stability. Our final design, featuring a total of 11 amino acid substitutions across two Fab constant regions, was tested on a set of six IgG-like bsAbs featuring a diverse set of unmodified human antibody variable domains. Purity assessments showed near-complete elimination of LC mispairs, including in cases with high baseline mispairing with wild-type constant domains. The engineered bsAbs broadly recapitulated the antigen-binding and biophysical developability properties of their monospecific counterparts and no adverse immunogenicity signal was identified by an in vitro assay. Fab crystal structures containing engineered constant domain interfaces revealed no major perturbations relative to the wild-type coordinates and validated the presence of the designed hydrogen bond interactions. Our work enables the facile assembly of independently discovered IgG-like bispecific antibodies in a single-cell host and demonstrates a streamlined and generalizable computational and experimental workflow for redesigning conserved protein:protein interfaces.