mAbs最新文献

The Observed Antibody Space provides the most abundant collection of annotated paired antibody variable domain sequences, thus offering a unique platform for the systematic investigation of the factors governing the pairing of antibody heavy and light chains. By examining a range of characteristics, including amino acid conservation, structural features, charge distribution, and interface residue identity, we challenge the prevailing assumption that pairing is random. Our findings indicate that specific physicochemical properties of single amino acid residues may influence the compatibility and affinity of heavy and light chain combinations. Further structural analyses based on antibody Fv fragments deposited in the Protein Data Bank (PDB) provide insights into the underlying structural features driving these pairing preferences, including a novel definition for the residues constituting the V_H-V_L interface, based on a collection of over 3500 structures. These results have significant implications for understanding antibody assembly and may guide the rational design of therapeutic antibodies with desired properties. Moreover, we provide a complete description and reference characterizing the various human germlines.

Co-formulated antibody cocktails are becoming an increasingly popular therapeutic class; however, they present analytical challenges over traditional single monoclonal antibody (mAb) formulations. One paramount concern is the formation of heteromeric species that have unknown impacts on safety and efficacy. Consequently, effective approaches for identifying and characterizing high-molecular weight (HMW) impurities are critical to the successful development of this therapeutic class. In this study, we used a multifaceted mass spectrometry approach to characterize a unique dimer species formed between two co-formulated mAbs under thermal stress, revealing an intriguing dimerization mechanism that is driven by complementarity-determining region clipping-induced domain swap. Size exclusion chromatography-mass spectrometry, complemented by post-column denaturation, was utilized at both intact and subunit levels to pinpoint the dimerization interface. Additionally, by probing the disulfide bond susceptibility changes via limited reduction and middle-down analysis, the structural changes of the involved domains were studied. These results highlight the critical role of sophisticated analytical methods in comprehending and addressing the complexities linked to co-formulated mAb cocktails.

Trial registration: ClinicalTrials.gov identifier, NCT05280717.

Protein glycosylation at asparagine typically occurs at a consensus motif. However, recent studies have reported instances of N-glycosylation at non-consensus sites, though the mechanisms and implications of these atypical modifications remain unclear. In this study, we identified novel non-consensus N-glycosylation motifs with low glycosylation occupancy in the Fab region of human antibodies. We developed a computational workflow to predict the interaction between non-consensus peptides and the eukaryotic oligosaccharyltransferase (OST) complex. This model was validated through site-directed mutagenesis around the asparagine residue and glycosylation quantification via mass spectrometry. Our results show that glycan occupancy at non-consensus sites can be modulated by mutations that influence OST binding affinity. Pharmacological inhibition of OST activity reduced non-consensus and consensus glycosylation in both Fab and Fc regions. Additionally, we identified new non-consensus glycosylation sites in natural human antibodies, revealing the sequence preferences governing these modifications. These findings provide mechanistic insights into OST sequence specificity and establish a computational and analytical framework for assessing atypical N-glycosylation, aiding glycan profile control in therapeutic antibody development.

Single-chain antibodies (scAbs), derived from camelid antibodies, have gained attention as therapeutic candidates due to their small size and perceived low immunogenicity, but recent studies have reported immune responses to several scAbs. To better understand their immunogenicity, we investigated the T-cell responses induced by VHH76, a VHH-Fc engineered to target the SARS-CoV-2 RBD, along with its humanized and germlined variants. The humanized variant contains six human substitutions, while the germlined variant was obtained by screening of a combinatorial library of the VHH76 sequences, comprising human and wild-type substitutions at 12 different positions. The germlined variant finally contains 16 human substitutions. All VHH76 variants triggered CD4 T-cell responses from healthy donors, with the germlined VHH76 showing significantly reduced T-cell stimulation. Two epitope regions were identified: one overlapping CDR3 and another spreading from CDR1 to CDR2. Additional human substitutions at the VHH-conserved positions in FR2 compromised the biological properties of the germlined VHH76 and did not seem to reduce clearly the risk of T-cell response. In conclusion, using a sensitive T-cell assay, we showed that T cells specific for VHH76 variants were detected in the blood of healthy donors and that the frequency of responding T cells diminished with germlining. While epitopes in CDR3 are linked to VHH76 specificity, modifying the conserved FR2 region presents challenges for reducing VHH76 immunogenicity. This study contributes to the understanding of VHH76 immunogenicity and offers insights into strategies to mitigate immune responses.

Progress in developing effective large-molecule therapies for neurological diseases is limited by exposure at the sites of action, beyond the blood-brain barrier (BBB). While transferrin receptor (TfR1)-mediated transport is gaining validation as a mechanism to deliver medicines of multiple modalities to the brain, there is much still to be learned about maximizing the potential of TfR1 targeting. We systematically vary for the first time affinity and valency of two anti‑TfR1 antibodies, which have distinct epitopes and pH sensitivities, to investigate their effects on cellular trafficking, biodistribution, safety, and pharmacokinetics. We establish how transcytosis and receptor degradation trend with affinity and connect these in-vitro functions to in-vivo behaviors in brain uptake and reticulocyte depletion. We identify unique anti-TfR1 antibody profiles for either short-term maximal or long-term sustained brain delivery and demonstrate the utility of these shuttles for different pharmacological applications. Our results show that bivalent anti-TfR1 antibodies can be equally effective in brain uptake as monovalent antibodies if engineered to have similar cell-surface TfR1 binding strength, with prolonged brain exposure and less severe adverse effects, but epitope, as well as affinity and valency, factors into selecting a shuttle with maximal performance. These results challenge the view that monovalent formats are inherently superior and instead establish that affinity, valency, and epitope can be tuned to select TfR1 shuttles optimized for different therapeutic needs.

Targeting checkpoint inhibitors is an effective therapy for treating cancer, with human programmed cell death protein 1 (hPD-1) being one of the most successful targets for developing antibody-based drugs. In this work, we isolated a panel of anti-PD-1 single-chain variable fragments with different binding and functional profiles from a fully synthetic human phage display library. Conversion of the best clone to hIgG1LALA and hIgG4PE formats, called UDIZ-007 and UDIZ-008, respectively, resulted in antibodies that effectively blocked the PD-1:PD-L1/L2 interaction and were highly selective as they did not cross-react with CD28 receptor family members. Doses of UDIZ-007 or UDIZ-008 at 10 mg/kg every 3 days for a total of six intraperitoneal administrations eradicated MC38-hPD-L1 colon tumors in B-hPD-1 transgenic mice for hPD-1 at day 17, with no relapse until the end of the study at day 56. Importantly, these antibodies bind hPD-1 in a unique region compared to the anti-PD-1 antibodies of known structure, which might have an impact on novel oncology indications when used as a standalone therapy or in combination with currently approved anti-PD-1 therapeutic antibodies. Therefore, UDIZ-007 and UDIZ-008 seem to be promising candidates for the development of antibody-based drugs targeting checkpoint inhibitors as a treatment for cancer.

To generate antibodies (Abs) against SARS-CoV-2 emerging variants, we integrated multiple tools and engineered molecules with excellent neutralizing breadth and potency. Initially, the screening of an immune library identified a nanobody (Nb), termed Nb4, specific to the receptor-binding domain (RBD) of the Omicron BA.1 variant. A Nb4-derived heavy chain antibody (hcAb4) recognized the spike (S) of the Wuhan, Beta, Delta, Omicron BA.1, and BA.5 SARS-CoV-2 variants. A high-resolution crystal structure of the Nb4 variable (VHH) domain in complex with the SARS-CoV-2 RBD (Wuhan) defined the Nb4 binding mode and interface. The Nb4 VHH domain grasped the RBD and covered most of its outer face, including the core and the receptor-binding motif (RBM), which was consistent with hcAb4 blocking RBD binding to the SARS-CoV-2 receptor. In mouse models, a humanized hcAb4 showed therapeutic potential and prevented the replication of SARS-CoV-2 BA.1 virus in the lungs of the animals. In vitro, hcAb4 neutralized Wuhan, Beta, Delta, Omicron BA.1, and BA.5 viral variants, as well as the BQ.1.1 subvariant, but showed poor neutralization against the Omicron XBB.1.5. Structure-based computation of the RBD-Nb4 interface identified three Nb4 residues with a reduced contribution to the interaction with the XBB.1.5 RBD. Site-saturation mutagenesis of these residues resulted in two hcAb4 mutants with enhanced XBB.1.5 S binding and virus neutralization, further improved by mutant Nb4 trimers. This research highlights an approach that combines library screening, Nb engineering, and structure-based computational predictions for the generation of SARS-CoV-2 Omicron-specific Abs and their adaptation to emerging variants.

The shift toward subcutaneous administration for biologic therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, a significant challenge associated with this transition is managing the viscosity of the administered solutions. High viscosity poses substantial development and manufacturability challenges, directly affecting patients by increasing injection time and causing pain at the injection site. Furthermore, high viscosity formulations can prolong residence time at the injection site, affecting absorption kinetics and potentially altering the intended pharmacological profile and therapeutic efficacy of the biologic candidate. Here, we report the application of a multimodal feature learning workflow for predicting the viscosity of antibodies in therapeutics discovery. It integrates multiple data sources including sequence, structural, physicochemical properties, as well as embeddings from a language model. This approach enables the model to learn from various underlying rules, such as physicochemical rules from molecular simulations and protein evolutionary patterns captured by large, pre-trained deep learning models. By comparing the effectiveness of this approach to other selected published viscosity prediction methods, this study provides insights into their intrinsic viscosity predictive potential and usability in early-stage therapeutics antibody development pipelines.

Subcutaneous (SC) delivery of therapeutic antibodies can offer multiple benefits to patients and healthcare providers, including convenience, time savings, and cost reduction. To improve the SC injection experience, drug developers may seek a low injection volume (1-2 mL), which for some antibody drugs necessitates a high concentration solution (≥100 mg/mL) to meet dosage requirements. Several molecular-level challenges hinder the development of high concentration antibody drug products, including high viscosity caused by reversible self-association (RSA). Here, we take an enhanced rational design approach to reduce RSA via protein engineering. Using hydrogen-deuterium exchange mass spectrometry (HDX-MS), we identified potential self-interaction hotspots on the surface of an in-house IgG1 which has known viscosity issues at high concentration. Then, using in silico antibody modeling, we identified sites near the complementary-determining regions for targeting by rational mutagenesis, which included predicted patches of charge or hydrophobicity within or near peptides highlighted by HDX-MS. Screening of nearly 70 variants using dynamic light scattering (DLS) and affinity capture self-interaction nanospectroscopy (AC-SINS) at low concentration showed decreased self-interaction in many variants. Viscosity at 150 mg/mL was reduced by 70% for 13 variants, while two of these variants designed to reduce surface hydrophobicity were found to retain antigen binding compared to the parent antibody. DLS and AC-SINS measurements of self-association were found to correlate with viscosity at high concentration, reinforcing their utility as effective low-concentration screening tools for viscosity. This work demonstrates an enhanced rational mutagenesis strategy informed by the combination of HDX-MS for self-association and in silico predictions of surface properties. The resulting variants are a vast improvement on the parent antibody's viscosity issues and offer insight into the mechanism of self-association.