ABSTRACT Enhancing antibody-dependent cellular cytotoxicity (ADCC) via N-glycan afucosylation of Asn-297 is a validated strategy to improve the clinical efficacy of therapeutic antibodies. However, the impact of distinct glycoengineering approaches on the function of antibodies has not been systematically elucidated. Here, we experimentally compared two type II anti-CD20 antibodies, Obinutuzumab (Gazyva®) and Obinutuzumab beta (MIL62, Bejescin®), which share identical amino acid sequences but exhibit divergent glycosylation profiles. MIL62 was engineered with complete core afucosylation (fucose < 0.1%) lacking bisecting N-acetylglucosamine (GlcNAc) via knockout of the GDP-fucose transporter (GFT) in Chinese hamster ovary (CHO) cells used to produce the antibody. Conversely, Gazyva was produced in CHO cells that overexpress β-1,4-N-acetylglucosaminyltransferase III (GnT-III) and α-mannosidase II (α-ManII), resulting in ~50% fucose content and >80% bisecting GlcNAc occupancy. These distinct glycoengineering strategies led to disparate functional outcomes: afucosylated MIL62 showed improved FcγRIIIA binding and ADCC potency, while Gazyva with bisecting GlcNAc modifications exhibited higher glycoform heterogeneity and reduced thermal stability. Both antibodies displayed comparable FcRn binding, mannosylation, sialylation, and murine pharmacodynamics, mediating complete depletion of B cells in blood, lymph nodes, and spleen. Upon antigen rechallenge, MIL62 suppressed specific antibody titers, indicating memory B-cell eradication and profound potential to prevent autoimmune relapse. This study demonstrates that distinct glycoengineering strategies fundamentally reshape the antibody glycan profile beyond merely reducing fucose. These structural differences not only fine-tune ADCC potency, but also impact antibody stability. Particularly, complete afucosylation of MIL62 led to optimized ADCC potency and effectively suppressed pathogenic B cells repopulation in a delta-like ligand 3 (DLL3) re-challenge model, providing a critical framework for designing next-generation antibodies with superior therapeutic efficacy.

ABSTRACT The Antibodies to Watch article series provides annual updates on commercial late-stage clinical development, regulatory review, and marketing approvals of antibody therapeutics. Since the first article was published in 2010, the late-stage pipeline has grown from 26 antibody therapeutics to over 200, while during the same time numerous molecules in late-stage studies either transitioned to regulatory review and were approved or were terminated. In this installment of the series, we recap first marketing approvals granted to 19 antibody therapeutics in 2025, discuss 26 molecules currently in regulatory review, including the bispecific antibody-drug conjugate izalontamab brengitecan, and predict which molecules of the 209 currently in the commercial late-stage pipeline might transition to regulatory review by the end of 2026. Most antibody therapeutics in the latter category are for non-cancer indications (16/21, 76%) and have a conventional format (13/21, 62%), but the category also includes numerous antibody-oligo or -drug conjugates, such as delpacibart etedesiran, delpacibart zotadirsen, zeleciment rostudirsen, sonesitatug vedotin, trastuzumab pamirtecan, and ifinatamab deruxtecan, as well as the bispecific petosemtamab. As antibody therapeutics development is a global enterprise, we also discuss trends in annual first approvals granted to antibody therapeutics in any country since 2010, stratified by the antibody’s country of origin, documenting the notable increases in the total number of first approvals and those approved first in China. Finally, to benchmark the time typically required for clinical development and regulatory review, we calculated this period for recently approved antibody therapeutic products stratified by their therapeutic area, mechanism of action, format, and country of origin. Our data show that the development and approval period were typically ~6 years, but on average this period was shorter for China-originated products.

ABSTRACT The analytical comparability of biologic products and their biosimilars, including higher-order structure (HOS) assessment, ensures product quality and is required for regulatory approval. In this study, nuclear magnetic resonance (NMR) spectroscopy was used to evaluate the HOS of Humira (adalimumab) and its biosimilars under normal and photo-stressed conditions. Under normal conditions, 1D and 2D NMR spectra showed strong structural similarity among all products. However, photo-stressed samples exhibited distinct structural differences, including increased methionine oxidation, and localized conformational changes, most notably in the reference product. These changes correlated with findings from size-exclusion chromatography, capillary isoelectric focusing, and mass spectrometry (MS), which revealed size and charge heterogeneity, as well as site-specific methionine oxidation in the heavy chains. The differences in photostability were found to be influenced by container closure systems (CCSs) and formulations. In contrast, circular dichroism spectral analysis showed minimal variation in secondary structures among stressed and unstressed samples. These results underscore the utility of NMR as a sensitive tool for comparative structural analysis of monoclonal antibodies and their biosimilars, particularly under stress conditions, and highlight the impact of formulation and CCS on product stability.

ABSTRACT Antibody phage display, a biotechnological method for selecting fully human antibodies from diverse libraries, is firmly established in antibody drug discovery due to its speed and flexibility. In this study, we introduce Pioneer, one of the largest synthetic human antibody libraries developed to date, with approximately 2.2 × 1011 functional members. Pioneer utilizes SpyDisplay, a phage display selection system that relies on SpyTag-SpyCatcher protein ligation technology and enables rapid selection of high-affinity antibodies. The library’s antibodies are designed for favorable biophysical properties and developability and they can be rapidly labeled or converted into various formats for screening and functional assays. We validated the performance of Pioneer in a series of selection campaigns against diverse antigens. For antibodies against four targets, TIGIT, IL-6RA, C5aR and CXCR4, we performed in-depth functional and developability characterization. For all four targets, including the two challenging G-protein coupled receptors, we demonstrated that antibodies with parameters comparable to late-stage clinical candidates can be selected directly from the library. Our results highlight the effectiveness of the Pioneer library combined with SpyDisplay in generating therapeutic lead candidates with excellent affinity, specificity, and developability, offering a robust platform for rapid antibody discovery and development.

ABSTRACT Since the approval of the first antibody drug in 1986, a total of 162 antibodies have been approved for a wide range of therapeutic areas, including cancer, autoimmune, infectious, or cardiovascular diseases. Despite advances in biotechnology that accelerated the development of antibody drugs, the drug discovery process for this modality remains lengthy and costly, requiring multiple rounds of optimizations before a drug candidate can progress to preclinical and clinical trials. This multi-optimization problem involves increasing the affinity of the antibody to the target antigen while refining additional biophysical properties that are essential to drug development such as solubility, thermostability or aggregation propensity. Additionally, antibodies that resemble natural human antibodies are particularly desirable, as they are likely to offer improved profiles in terms of safety, efficacy, and reduced immunogenicity, further supporting their therapeutic potential. In this article, we explore the use of energy-based generative models to optimize a candidate monoclonal antibody. We identify tradeoffs when optimizing for multiple properties, focusing on solubility, humanness and affinity and use the generative model we develop to generate candidate antibodies that lie on optimal Pareto fronts with respect to these properties.

ABSTRACT With monoclonal antibodies becoming one of the largest classes of biopharmaceuticals, it is important to have curated data to train computational models that can accelerate their design. Despite the massive amount of mutagenesis data generated on antibody–antigen interactions, only a few small, well-curated datasets are available. This paper introduces AbAgym, a manually curated repository comprising approximately 324k mutations in antibody–antigen complexes, including approximately 10% of interface mutations, whose effects on antibody–antigen binding have been experimentally quantified through deep mutational scanning (DMS) experiments. We collected and curated 68 DMS datasets from the literature together with the three-dimensional structure of each antibody–antigen complex. We benchmarked the performance of established force field methods as well as recent machine learning models that predict the change in binding affinity upon mutation. The former achieved modest performance, whereas the latter performed only marginally better than random. Finally, our analysis of hotspot residues responsible for immune evasion highlights the importance of accounting for biological complexities, such as conformational changes or oligomeric states that influence antibody–antigen binding, which are often overlooked. Abagym is freely available for academic use at https://github.com/3BioCompBio/Abagym.

ABSTRACT First-generation cancer immunotherapies, such as high-dose interleukin-2 (IL-2), have demonstrated clinical efficacy, but are limited by significant systemic toxicities due to their broad expression of cytokine receptors. This has driven the iterative development of targeted cytokine delivery strategies. Early efforts focused on receptor-biased IL-2 variants designed to attenuate or abrogate IL-2 receptor α (IL-2 Rα/CD25) binding. Subsequently, the concept of “cis-targeting” has emerged as a strategy to deliver cytokines to specific immune cell populations, enhancing anti-tumor responses while mitigating systemic toxicity. This review highlights key common γ-chain cytokines (IL-2, IL-7, IL-15, and IL-21) as well as IL-12, providing an overview of their structures, receptors, as well as their distinct T cell functions. Furthermore, we specifically focus on the current landscape of engineered cytokine variants that facilitate targeted cytokine delivery in cis to specific T cells. By successfully restricting cytokine activity to specific T cell populations, cis-targeting approaches represent a promising strategy in the field, enabling efficient immunotherapies with improved tolerability and enhanced anti-tumor responses.

ABSTRACT Alpha-galactosylation, galactose-α-1,3-galactose (α-Gal), is always regarded as a critical quality attribute due to its potential to provoke immunogenic responses in patients. Consequently, monitoring alpha-galactosylation in therapeutic proteins is essential, but current analytical techniques fall short in terms of identification sensitivity and quantification accuracy. Specifically, the released glycan assay by hydrophilic interaction liquid chromatography-fluorescence-mass spectrometry, the gold standard for glycan separation/identification, faces challenges due to ambiguities with isomeric glycan structures. To address these challenges, we developed a comprehensive analytical method that enhances both the identification sensitivity and quantification accuracy for α-Gal. We developed an integrated workflow that combines an advanced mass spectrometry technique – parallel reaction monitoring triple-stage mass spectrometry – with exoglycosidase sample treatment. This approach generates structurally specific signature ions, enhances identification sensitivity, enables the separation of α-Gal from its isomers, and improves quantification accuracy. By employing this more sensitive analytical approach without ambiguity in assignment for common glycan structures found in monoclonal antibodies, the safety of therapeutic proteins can be better assured, effectively minimizing the risk of α-Gal-induced immunogenicity.

ABSTRACT Multispecific antibodies are exhibiting rapid growth in drug development due to their ability to engage multiple therapeutic targets and deliver unique functional properties inaccessible to conventional monoclonal antibodies. The development of these complex modalities, however, is limited by the large design space created by the numerous possible combinations associated with multiple binding moieties and molecular formats. Here, we present a high-throughput platform for the efficient production and screening of complex combinatorial multispecific antibody libraries using intein-mediated protein splicing. To validate the fidelity of this screening approach, we demonstrate that spliced bispecific molecules exhibit indistinguishable biochemical and functional properties compared to bispecific antibodies generated through conventional production processes. We further demonstrate that intein splicing can be combined with the DuetMab platform to create trispecific antibodies and perform dual-splicing reactions to generate bivalent bispecific and trispecific formats. This work establishes intein-based protein splicing as a powerful strategy for the efficient generation and screening of multispecific antibodies libraries, enabling the rapid identification of optimal binding configurations, combinations, and molecular formats that elicit the desired mechanism of action.

ABSTRACT The multi-attribute method (MAM) by liquid chromatography-mass spectrometry peptide mapping has the potential to replace multiple conventional HPLC- and capillary electrophoresis-based purity/impurity assays for release and stability testing of protein biopharmaceuticals such as monoclonal antibodies. Prerequisite is the availability of the new peak detection (NPD) functionality to reliably detect new, absent, and changed peptide species that may impair the quality, safety, and efficacy of the drug. Here, we describe the development, qualification, and application of a highly efficient and robust NPD workflow within the Genedata Expressionist® software. The detection thresholds have been rationally designed, and the NPD workflow has been successfully validated according to ICH Q2 guidelines. Individual case studies, including stability testing of drug product and detection of unknown impurities in drug substance, highlight the workflows’ ability to reliably recognize relevant peptide species below 1% relative abundance without reporting any false positive peaks. The application of this NPD workflow signifies a substantial leap forward in the use of MAM as a quality control tool, as it allows identification of true positive peaks at adequate sensitivity in the absence of false positive peaks.