mAbs最新文献

The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These building blocks play an important role in the overall quality of the molecule and can influence many important aspects from potency and specificity to stability and half-life. Single-domain antibodies, particularly camelid-derived variable heavy domain of heavy chain (VHH) antibodies, are becoming an increasingly popular choice for bispecific construction due to their single-domain modularity, favorable biophysical properties, and potential to work in multiple antibody formats. Here, we review the use of VHH domains as building blocks in the construction of multispecific antibodies and the challenges in creating optimized molecules. In addition to exploring traditional approaches to VHH development, we review the integration of machine learning techniques at various stages of the process. Specifically, the utilization of machine learning for structural prediction, lead identification, lead optimization, and humanization of VHH antibodies.

Glycosylation affects the safety and efficacy of therapeutic proteins and is often considered a critical quality attribute (CQA). Therefore, it is important to identify and quantify glycans during drug development. Glycosylation is a highly complex post-translational modification (PTM) due to its structural heterogeneity, i.e. glycosylation site occupancy, glycan compositions, modifications, and isomers. Current analytical tools compromise either structural resolution or site specificity. Hydrophilic interaction liquid chromatography-fluorescence-mass spectrometry (HILIC-FLR-MS) is the gold standard for structural analysis of released glycans, but lacks information on site specificity and occupation. However, HILIC-FLR-MS often uses salt in the solvent, which impairs analysis robustness and sensitivity. Site-specific glycosylation analysis via glycopeptides, upon proteolytic digestion, is commonly performed by reversed-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS), but provides only compositional and limited structural glycan information. In this study, we introduce a salt-free, glycopeptide-based HILIC-tandem mass spectrometry (HILIC-MS/MS) method that provides glycan identification, glycan isomer separation and site-specific information simultaneously. Moreover, HILIC-MS/MS demonstrated comparable relative quantification results as released glycan HILIC-FLR-MS. Further, our new method improves the retention of hydrophilic peptides, allowing simultaneous analysis of important CQAs such as deamidation in antibodies. The developed method offers a valuable tool to streamline the site-specific glycosylation analysis of glycoproteins, which is particularly important for the expanding landscape of novel therapeutic formats in the biopharmaceutical industry.

Glycosylation plays a crucial role in determining the quality and efficacy of therapeutic antibodies. This necessitates a thorough analysis and monitoring process to ensure consistent product quality during manufacturing. In this study, we introduce a custom-designed lectin microarray featuring nine distinct lectins: rPhoSL, rOTH3, RCA120, rMan2, MAL_I, rPSL1a, PHAE, rMOA, and PHAL. These lectins have been specifically tailored to selectively bind to common N-glycan epitopes found in therapeutic IgG antibodies. By utilizing intact glycoprotein samples, our nine-lectin microarray provides a high-throughput platform for rapid glycan profiling, enabling comparative analysis of glycosylation patterns. Our results demonstrate the practical utility of this microarray in assessing glycosylation across various manufacturing batches or between biosimilar and innovator products. This capacity empowers informed decision-making in the development and production of therapeutic antibodies.

Natural killer (NK) cells emerged as a promising effector population that can be harnessed for anti-tumor therapy. In this work, we constructed NK cell engagers (NKCEs) based on NKp30-targeting single domain antibodies (sdAbs) that redirect the cytotoxic potential of NK cells toward epidermal growth factor receptor (EGFR)-expressing tumor cells. We investigated the impact of crucial parameters such as sdAb location, binding valencies, the targeted epitope on NKp30, and the overall antibody architecture on the redirection capacity. Our study exploited two NKp30-specific sdAbs, one of which binds a similar epitope on NKp30 as its natural ligand B7-H6, while the other sdAb addresses a non-competing epitope. For EGFR-positive tumor targeting, humanized antigen-binding domains of therapeutic antibody cetuximab were used. We demonstrate that NKCEs bivalently targeting EGFR and bivalently engaging NKp30 are superior to monovalent NKCEs in promoting NK cell-mediated tumor cell lysis and that the architecture of the NKCE can substantially influence killing capacities depending on the NKp30-targeting sdAb utilized. While having a pronounced impact on NK cell killing efficacy, the capabilities of triggering antibody-dependent cellular phagocytosis or complement-dependent cytotoxicity were not significantly affected comparing the bivalent IgG-like NKCEs with cetuximab. However, the fusion of sdAbs can have a slight impact on the NK cell release of immunomodulatory cytokines, as well as on the pharmacokinetic profile of the NKCE due to unfavorable spatial orientation within the molecule architecture. Ultimately, our findings reveal novel insights for the engineering of potent NKCEs triggering the NKp30 axis.

Biparatopic antibodies (bpAbs) bind distinct, non-overlapping epitopes on an antigen. This unique binding mode enables new mechanisms of action beyond monospecific and bispecific antibodies (bsAbs) that can make bpAbs effective therapeutics for various indications, including oncology and infectious diseases. Biparatopic binding can lead to superior affinity and specificity, promote antagonism, lock target conformation, and result in higher-order target clustering. Such antibody-target complexes can elicit strong agonism, increase immune effector function, or result in rapid target downregulation and lysosomal trafficking. These are not only attractive properties for therapeutic antibodies but are increasingly being explored for other modalities such as antibody-drug conjugates, T-cell engagers and chimeric antigen receptors. Recent advances in bpAb engineering have enabled the construction of ever more sophisticated formats that are starting to show promise in the clinic.

Bispecific antibodies (bsAbs) are a class of antibodies that can mediate novel mechanisms of action compared to monospecific monoclonal antibodies (mAbs). Since the discovery of mAbs and their adoption as therapeutic agents in the 1980s and 1990s, the development of bsAbs has held substantial appeal. Nevertheless, only three bsAbs (catumaxomab, blinatumomab, emicizumab) were approved through the end of 2020. However, since then, 11 bsAbs received regulatory agency approvals, of which nine (amivantamab, tebentafusp, mosunetuzumab, cadonilimab, teclistamab, glofitamab, epcoritamab, talquetamab, elranatamab) were approved for the treatment of cancer and two (faricimab, ozoralizumab) in non-oncology indications. Notably, of the 13 currently approved bsAbs, two, emicizumab and faricimab, have achieved blockbuster status, showing the promise of this novel class of therapeutics. In the 2020s, the approval of additional bsAbs can be expected in hematological malignancies, solid tumors and non-oncology indications, establishing bsAbs as essential part of the therapeutic armamentarium.

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.

The higher order structure (HOS) of monoclonal antibodies (mAbs) is an important quality attribute with strong contribution to clinically relevant biological functions and drug safety. Due to the multi-faceted nature of HOS, the synergy of multiple complementary analytical approaches can substantially improve the understanding, accuracy, and resolution of HOS characterization. In this study, we applied one- and two-dimensional (1D and 2D) nuclear magnetic resonance (NMR) spectroscopy coupled with chemometric analysis, as well as circular dichroism (CD), differential scanning calorimetry (DSC), and fluorescence spectroscopy as orthogonal methods, to characterize the impact of methionine (Met) oxidation on the HOS of an IgG1 mAb. We used a forced degradation method involving concentration-dependent oxidation by peracetic acid, in which Met oxidation is site-specifically quantified by liquid chromatography-mass spectrometry. Conventional biophysical techniques report nuanced results, in which CD detects no change to the secondary structure and little change in the tertiary structure. Yet, DSC measurements show the destabilization of Fab and Fc domains due to Met oxidation. More importantly, our study demonstrates that 1D and 2D NMR and chemometric analysis can provide semi-quantitative analysis of chemical modifications and resolve localized conformational changes with high sensitivity. Furthermore, we leveraged a novel ¹⁵N-Met labeling technique of the antibody to directly observe structural perturbations at the oxidation sites. The NMR methods described here to probe HOS changes are highly reliable and practical in biopharmaceutical characterization.

The 'Antibodies to Watch' article series provides an annual summary of commercially sponsored monoclonal antibody therapeutics currently in late-stage clinical development, regulatory review, and those recently granted a first approval in any country. In this installment, we discuss key details for 16 antibody therapeutics granted a first approval in 2023, as of November 17 (lecanemab (Leqembi), rozanolixizumab (RYSTIGGO), pozelimab (VEOPOZ), mirikizumab (Omvoh), talquetamab (Talvey), elranatamab (Elrexfio), epcoritamab (EPKINLY), glofitamab (COLUMVI), retifanlimab (Zynyz), concizumab (Alhemo), lebrikizumab (EBGLYSS), tafolecimab (SINTBILO), narlumosbart (Jinlitai), zuberitamab (Enrexib), adebrelimab (Arelili), and divozilimab (Ivlizi)). We briefly review 26 product candidates for which marketing applications are under consideration in at least one country or region, and 23 investigational antibody therapeutics that are forecast to enter regulatory review by the end of 2024 based on company disclosures. These nearly 50 product candidates include numerous innovative bispecific antibodies, such as odronextamab, ivonescimab, linvoseltamab, zenocutuzumab, and erfonrilimab, and antibody-drug conjugates, such as trastuzumab botidotin, patritumab deruxtecan, datopotamab deruxtecan, and MRG002, as well as a mixture of two immunocytokines (bifikafusp alfa and onfekafusp alfa). We also discuss clinical phase transition and overall approval success rates for antibody therapeutics, which are crucial to the biopharmaceutical industry because these rates inform decisions about resource allocation. Our analyses indicate that these molecules have approval success rates in the range of 14-32%, with higher rates associated with antibodies developed for non-cancer indications. Overall, our data suggest that antibody therapeutic development efforts by the biopharmaceutical industry are robust and increasingly successful.