mAbs最新文献

Successful development of monoclonal antibodies (mAbs) for the treatment of central nervous system disorders has been challenging due to their minimal ability to cross the blood-brain barrier (BBB), resulting in poor brain exposure. Bispecific antibodies (bsAb) that bind to transmembrane protein expressed at the BBB, such as the transferrin receptor (TfR), have shown enhanced brain exposure in rodents and non-human primate (NHP) due to receptor-mediated transcytosis. However, it remains unclear how preclinical findings translate to humans. Moreover, optimal TfR binding affinity remains a subject of debate. Model-informed drug discovery and development is a powerful approach that has been successfully used to support research and development. The goal of this analysis was to expand a published brain minimal physiologically based pharmacokinetic (mPBPK) model to investigate the optimal TfR binding affinity for maximal brain delivery in NHP and to facilitate prediction of the PK of anti-TfR bsAbs in humans from NHP data. Literature data for plasma, cerebrospinal fluid (CSF), and brain exposure after administration of non-TfR mAbs and monovalent bsAbs with respect to TfR in NHP were used to develop the TfR mPBPK model. Clinical validation using human PK data from plasma and CSF for the monovalent anti-TfR bsAb trontinemab demonstrated good predictive performance without major model recalibration. The availability of the TfR mPBPK model is envisaged to provide better understanding of the relationship between TfR binding affinity, dose, and brain exposure, which would lead to more robust selection of lead candidates and efficacious dosing regimens.

Hybridomas, the first method for creating monoclonal antibodies (mAbs), were reported 50 years ago. This approach, which transformed biomedical research and laid the foundation for many of the current therapeutic, diagnostic, and research reagent applications of mAbs, is still used today, despite reported low fusion yields between short-lived B cells and immortal myeloma cells. To improve hybridoma production yields and accelerate development of new mAbs, we addressed two key limitations: 1) random pairing between myeloma cells and antibody-producing cells, and 2) low efficiency of the polyethylene-glycol-mediated fusion process. We first characterized and isolated antibody-secreting cells (ASCs) from the spleen of immunized mice before cell fusion to increase the probability of successive pairing between the most suitable cell fusion partners and favor the generation of functional hybridomas. Specifically, we developed an optimized workflow combining fluorescence-activated cell sorting with antibody secretion assays, using a panel of five cell-surface markers (CD3, TACI, CD138, MHC-II, and B220) to identify a distinct ASC subset with key characteristics. Such ASCs exhibited a plasmablast phenotype with high MHC-II expression and secreted high levels of antigen (Ag)-specific antibodies in immunized mice. We then implemented a cell electrofusion procedure adapted to low cell numbers (<10⁶ cells), in order to perform the targeted electrofusion of TACI^highCD138^high sorted ASCs. This targeted approach yielded viable hybridomas in 100% of seeded culture wells compared to only 40% for the electrofusion of unsorted cells. In particular, over 60% of hybridomas generated from TACI^highCD138^high sorted ASCs secreted Ag-specific mAbs, including IgGs with high Ag binding affinity (<10^-9 M). These results pave the way for a high-yield mAb production method via cell fusion, with the potential to streamline hybridoma generation and thereby expand access to mAbs.

T cell bispecific antibodies (TCBs) are an emerging class of cancer therapy that are typically designed for high binding affinity to CD3 and tumor antigen (TA). Using this approach, TCBs have demonstrated significant clinical efficacy, but they have also elicited cytokine release syndrome and off-target on-tumor toxicities. CD3 affinity-attenuation has recently been reported as an approach to maintain efficacy while reducing cytokine release, but the interdependence of CD3 affinity with other factors is often not systematically explored. For this purpose, we generated a series of TCBs comprising CD3 binders with varying affinities and TA binders with either high or low affinities, utilizing FOLR1 and CEACAM5 as tumor targets. The CD3 binders were classified into high, intermediate, low, and very low affine binders based on affinity measurements as well as functionality. Depending on the target, different combinations of binders showed the most advantageous profile of tumor-cell killing while coupled with lower cytokine secretion. For instance, within the FOLR1-TCBs series, CD3^intermed exhibited a favorable profile compared to CD3^high in vitro using cocultures and in vivo using humanized mice. For CEACAM5-TCBs, CD3^low, along with CD3^intermed, showed a favorable profile compared to CD3^high in both in vitro and in vivo settings. Additionally, CD3^low avoided on-target, off-tumor toxicity and reduced cytokine release in transgenic mice. Taken together, reducing cytokine release while maintaining adequate efficacy is possible through CD3 binder affinity attenuation, but optimizing cytokine release profiles by CD3 binder affinity-attenuation is dependent on additional parameters.

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.