mAbs最新文献

{"title":"Structure-based calculation of excipient effects on the viscosity of concentrated antibody solutions.","authors":"John C Shelley, Qing Chai, Lina Wu, Shaghayegh Vafaei, Mee Y Shelley, Eric Feyfant, Jiangyan Feng, Mahlet A Woldeyes, Volodymyr Babin, Jonathan D Jou","doi":"10.1080/19420862.2026.2645310","DOIUrl":"10.1080/19420862.2026.2645310","url":null,"abstract":"<p><p>Computational prediction of the viscosity of therapeutic monoclonal antibodies (mAbs) at high concentration is highly desirable in the early discovery and development phases where the material needed for experimental determination is typically limited. Here, we present a unique coarse-grained (CG) simulation method that enables residue-level simulation of full-length antibodies with an elastic network, under simulated shearing force, to <i>de novo</i> predict viscosities of solutions of two distinct mAbs (an IgG1 and an IgG4), in the absence and presence of six excipients. Our results suggest the method can properly distinguish the viscosity profile of the two model mAbs, and directionally forecast viscosity change in response to added excipients. Furthermore, this CG modeling approach provides detailed protein-protein interaction mapping down to residue-level contacts, including contact lifetimes and nature of interactions, illuminating microscopic insights into the underlying molecular interactions. It serves as a valuable tool for viscosity prediction, mechanistic insights, and mitigation strategies.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2645310"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13007400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147493923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NAStructuralDB : structural database to facilitate computational studies of molecular modeling and recognition of proteins with special focus on antibody-antigen interactions.","authors":"Dawid Chomicz, Paweł Dudzic, Sonia Wrobel, Tomasz Gawlowski, Samuel Demharter, Roberto Spreafico, Hervé Minoux, Andrew Phillips, Konrad Krawczyk","doi":"10.1080/19420862.2026.2630438","DOIUrl":"10.1080/19420862.2026.2630438","url":null,"abstract":"<p><p>Studying the interactions between antibodies and antigens is fundamental to the development of novel therapeutic biologics. Predictions of such interactions start with data collection. Though there exist reliable resources to identify antibody structures in the Protein Data Bank (PDB), such data still requires substantial processing to be usable in predictive tasks. Redundancy in sequences needs to be removed to avoid data leakages between train, test, and validation sets. Descriptors such as surface accessibility, secondary structure, and antibody region information need to be additionally annotated. Information on inter- and intra-molecular contacts, which is crucial to studying paratope/epitope information, needs to be collected. The specialized immunoglobulin format of Nanobodies® requires a separate dataset mirroring that of antibodies, given that their structure contains only a single VHH chain. Because antibody-antigen structures account for a small amount of all protein-protein contacts, having a molecular contact reference from other proteins is also desired. To address these issues, we introduce NAStructuralDB (https://naturalantibody.com/na-structural/), a dataset of processed structures of antibodies, Nanobodies®, proteins, and their complexes with molecular contact information and associated annotations. We use the opportunity of having collected the contact data to provide a reference of binding propensities of different residues across distinct contact types.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2630438"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147378094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing human FcRn mouse models to improve pharmacokinetic evaluation of antibody drug candidates.","authors":"Gregory Christianson, Zachary M Howard, Samantha Kenney, Emily Lowell, Nijaguna Bethur, Derry Roopenian, Elena Gonzalo-Gil","doi":"10.1080/19420862.2026.2649990","DOIUrl":"10.1080/19420862.2026.2649990","url":null,"abstract":"<p><p>The use of animal models that can reliably predict drug performance in human patients is critical to antibody therapeutic development. Along with assessing toxicity and efficacy, determining the pharmacokinetic (PK) properties of therapeutics in Tg32 and Tg276 mice is essential to preclinical characterization. While Tg32 mice have been well established as indispensable in their ability to model the PK properties of antibody therapeutics, their intact immunity leaves them capable of mounting anti-drug antibody responses that interfere with PK interpretation. Here, we demonstrate the negative impact anti-drug responses can have on PK parameters derived from Tg32 mice, and provide strong evidence to support the use of immunodeficient Tg32 SCID mice as an equivalent means to model human PK. In addition, we investigate one possible cause for reduced FcRn function observed in Tg276 mice when compared to Tg32 mice in spite of evidence that their FcRn protein levels are actually higher. We also introduce NSG Tg32 mice and our attempts to block off-target binding of human IgGs to their high-affinity Fc gamma receptors, which failed to recover FcRn function similar to that observed from Tg32 mouse controls, dramatically limiting their utility for PK analysis. Taken together, our results provide a comparison of these preclinical animal models, so they can be used to improve human PK predictions of antibody therapeutic candidates in development.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2649990"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13020886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147513308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of protein language models for antibody developability prediction.","authors":"Samad Amini, Yimin Huang, Mark Julian, Christina Palmer, Simone Sciabola, Ye Wang","doi":"10.1080/19420862.2026.2647489","DOIUrl":"10.1080/19420862.2026.2647489","url":null,"abstract":"<p><p>Protein language models (PLMs) provide a powerful framework for learning sequence - property relationships in antibodies. However, their performance and reliability in real-world industrial antibody discovery pipelines remain underexplored. Here, we systematically evaluate several state-of-the-art PLMs using internal datasets comprising antibody sequences and developability assay measurements from 33 historical therapeutic programs. The assays span three critical developability dimensions: polyspecificity reagent (PSR), hydrophobic interaction chromatography (HIC), and affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS). Across all assays, domain-adaptive fine-tuning of PLMs on internal antibody sequence data consistently improves predictive performance relative to pretrained representations alone. In addition, we assess sequence likelihoods derived from pretrained PLMs as unsupervised indicators of developability risk and analyze their strengths and limitations across assay types. Together, these results demonstrate that PLMs can provide robust and complementary signals for antibody developability assessment, supporting their practical use in early-stage candidate optimization and selection.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2647489"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13007433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147486577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning predictions of IgG1 and IgG4 self-association and high-concentration solution properties.","authors":"Na-Young Kwon, Chloe N Brown, Hsin-Ting Chen, Steven R Cottle, Ronan M Kelly, Bryan E Jones, William F Weiss, Peter M Tessier","doi":"10.1080/19420862.2026.2663641","DOIUrl":"https://doi.org/10.1080/19420862.2026.2663641","url":null,"abstract":"<p><p>To meet the widespread demand for subcutaneous delivery of antibody therapeutics, candidates with low viscosity, high solubility, and/or low aggregation propensity in concentrated formulations must be identified. Moreover, early identification of candidates with low self-association increases the likelihood of success at later stages of the development process. Here, we experimentally profile the self-association behavior of a panel of clinical-stage antibodies as a function of pH, excipient content, and antibody isotype. We find that acidic formulations (pH 5) with proline (200 mM) are most effective at suppressing self-association for both IgG1 and IgG4 variants. Moreover, our self-association measurements are correlated with antibody viscosity measurements and inversely correlated with antibody recovery after their concentration using membrane filters. Notably, we developed interpretable machine learning-based classifier and regressor models for predicting IgG1 and IgG4 self-association and demonstrated that they identify antibodies with favorable high-concentration properties. These findings are expected to improve the antibody development process by facilitating the identification of drug-like molecules during their discovery and optimization.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2663641"},"PeriodicalIF":7.3,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction.","authors":"","doi":"10.1080/19420862.2026.2644040","DOIUrl":"10.1080/19420862.2026.2644040","url":null,"abstract":"","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2644040"},"PeriodicalIF":7.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12987510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147444112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of afucosylation strategy on antibody function: a comparative study of glycoengineered anti-CD20 antibodies Obinutuzumab and Obinutuzumab beta.","authors":"Qing Shuang, Mingze Sun, Guangzhong Lin, Lunfeng Zhang, Shuo Huang, Qunfang Shen, Lingling Jiang, Lijing Yang, Jicui Sun, Hong Chen, Feng Li, Jiangmei Li","doi":"10.1080/19420862.2026.2657099","DOIUrl":"10.1080/19420862.2026.2657099","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2657099"},"PeriodicalIF":7.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147674411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid AI/ML-mechanistic framework enables intelligent optimization of commercial biopharmaceutical downstream processing.","authors":"Rong-Zhu Wang, Yu-Xin Liao, Teng-Long Wang, Xiao-Yang Jing, Guang Yang, Hao Wang, Feng Xiao, Yi Zong, Yan Yang, Ye Yuan, Xuan Xian, Shan-Jing Yao, Lei Jin, Dong-Qiang Lin, Yun Kenneth Kang","doi":"10.1080/19420862.2026.2644662","DOIUrl":"10.1080/19420862.2026.2644662","url":null,"abstract":"<p><p>Biopharmaceutical manufacturing requires continuous improvement to ensure robust, efficient, and high-quality processes, yet traditional experimental designs remain resource-demanding and insufficient to capture interactions of multiple parameters. Here, we introduce a hybrid framework integrating artificial intelligence (AI)/machine learning (ML) with mechanistic modeling to optimize anion-exchange chromatography and resolve the long-standing yield-purity trade-off in PEGylated protein purification. Three critical process parameters were first identified through correlation analysis between 30 input factors and critical quality attributes/process yield from 400+ commercial manufacturing lots, which were further refined using equilibrium dispersive and steric mass action models. Over 40,000 <i>in silico</i> optimization via the mechanistic model resolved the yield-purity trade-off, achieving a 12% increase in yield and 33% reduction in high-molecular-weight impurities. The optimized process conditions were verified across laboratory (<i>n</i> = 3), pilot (<i>n</i> = 3), and commercial (<i>n</i> = 18) runs, consistently demonstrating scalability and process robustness. This study highlights the power of combining data-driven machine learning with mechanistic modeling for process optimization, leading to an improved commercial process with substantial cost savings and paving the way for upcoming intelligent biomanufacturing.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2644662"},"PeriodicalIF":7.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13020864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147513336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structure-function analysis of empasiprubart, a calcium- and pH-dependent clinical phase complement C2 blocking antibody.","authors":"Laura Bracke, Heidi Gytz Olesen, Erwin Pannecoucke, Tim Delahaye, Emma K Persson, Christophe Blanchetot, Hans de Haard, C Erik Hack, Gregers Rom Andersen, Inge Van de Walle","doi":"10.1080/19420862.2026.2666430","DOIUrl":"10.1080/19420862.2026.2666430","url":null,"abstract":"<p><p>Empasiprubart (ARGX-117) is a humanized recycling antibody that prevents binding of C2 to C4b, blocking downstream classical and lectin pathways of complement activation. Empasiprubart binds to the CCP2 domain of complement component C2 in a calcium- and pH-dependent manner, leveraging physiological differences between blood and endosomal environments to facilitate the release and subsequent degradation of bound C2. The molecule incorporates Fc region mutations (H433K and N434F) that enhance its affinity for the neonatal Fc receptor (FcRn) under acidic endosomal conditions, thereby prolonging its <i>in vivo</i> half-life and supporting its recycling capacity. However, despite the earlier description of the complex structure, the molecular mechanism underlying these dependencies has remained elusive. Here, we further explored the crystal structure of the empasiprubart fragment antigen-binding (Fab) complexed to a C2 fragment, and provide a molecular rationale for its unique properties, while recognizing that not all contributing factors have been fully elucidated. Our observations indicate that the pH-dependent target release is rooted in a subtle intramolecular complementarity-determining region (CDR) destabilization, rather than direct modulation of the binding interface, and highlight the interplay between framework residues and CDRs. Collectively, our results not only lead to a better understanding of the mode of action of empasiprubart but also demonstrate the pivotal role of framework residues in the orchestration of antibody CDR function for non-trivial target binding.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2666430"},"PeriodicalIF":7.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic characterization of lysine glucuronidation in a bispecific antibody.","authors":"Michael R Reyda, Qinqin Ji, Maggie Huang, Izabela Sokolowska, Qingrong Yan, Joseph Mulholland, Jingjie Mo, Ping Hu","doi":"10.1080/19420862.2025.2612471","DOIUrl":"10.1080/19420862.2025.2612471","url":null,"abstract":"<p><p>This study presents a systematic characterization of lysine glucuronidation that was revealed during the charge variant characterization of a bispecific antibody (bsAb). Site-specific quantitation by Glu-C/Asp-N peptide mapping suggested that glucuronidation occurred randomly across surface lysine residues. To understand the impact of glucuronidation on the structure and function of the bsAb, stressed samples with up to 84% total glucuronidation were generated and analyzed by a comprehensive panel of analytical methods. The results suggested that glucuronidation caused an acidic isoelectric point (pI) shift in the charge profile. However, it does not affect the higher-order structure or bioactivities of the bsAb, including antibody-dependent cell-mediated cytotoxicity, antigen binding, or Fc receptor interaction. To support routine process monitoring, a fit-for-purpose subunit mass method was developed and qualified for quantitation of glucuronidation, offering a higher-throughput alternative to peptide mapping for assessing process consistency and product comparability.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2612471"},"PeriodicalIF":7.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12795262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145933908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}