mAbs最新文献

{"title":"Deep learning assessment of nativeness and pairing likelihood for antibody and nanobody design with AbNatiV2.","authors":"Aubin Ramon, Niccolò Frassetto, Haowen Zhao, Xing Xu, Matthew Greenig, Shimobi Onuoha, Pietro Sormanni","doi":"10.1080/19420862.2026.2646361","DOIUrl":"10.1080/19420862.2026.2646361","url":null,"abstract":"<p><p>Most immune-system created antibodies balance good binding and stability with low toxicity and self-reactivity. Quantifying the nativeness of a candidate sequence - its likelihood of belonging to natural immune repertoires - has thus emerged as a valuable strategy for hit selection from synthetic libraries, optimization and humanization, and for guiding de novo design toward developable candidates. We previously introduced AbNatiV, a transformer-based VQ-VAE for nativeness assessment, which proved effective across multiple nanobody engineering tasks. However, AbNatiV1 operated on unpaired sequences, limiting applicability to conventional VH-VL antibodies. Moreover, its performance on nanobody nativeness was constrained by the limited number and diversity of nanobody repertoires available at the time. Here, we sequenced new camelid repertoires, curated additional recent datasets, and present AbNatiV2: an enhanced architecture comprising various models each trained on ≥20 million sequences. AbNatiV2 improves nanobody nativeness classification across held-out and diverse test sets, and more robustly detects nativeness changes upon CDR grafting. We also introduce p-AbNatiV2, a cross-attention model fine-tuned on 3.7 million paired human sequences. p-AbNatiV2 provides residue- and sequence-level humanness for VH/VL pairs and learns pairing-likelihood via noise-contrastive training. On held-out tests, it assigns the native pair a higher score in 74% of cases, substantially outperforming recent pairing models. Together, AbNatiV2 and p-AbNatiV2 extend nativeness assessment and engineering to both nanobodies and conventional antibodies, supporting design decisions at single-residue, Fv-sequence, and paired-domain levels. AbNatiV2 is available as downloadable software and webserver.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2646361"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147633834","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":"Discovery and optimization of marstacimab, a human monoclonal antibody targeting tissue factor pathway inhibitor for the treatment of hemophilia A and B.","authors":"Macy Jin, Sunita Hett, Reema Jasuja, Swapnil Rakhe, Jatin Narula, Amy Tam, James R Apgar, Zong Sean Juo, Lidia Mosyak, Matthew Holsti, Alison Joyce, Susan Hurst, Robert Webster, Laura Lin, Mark Stahl, Debra D Pittman, Laird Bloom","doi":"10.1080/19420862.2026.2685362","DOIUrl":"10.1080/19420862.2026.2685362","url":null,"abstract":"<p><p>We report the discovery and optimization of marstacimab, a novel monoclonal antibody targeting tissue factor pathway inhibitor (TFPI), for the treatment of hemophilia A and B. Hybridoma and phage display approaches identified antibodies that blocked the TFPI: coagulation factor Xa (FXa) interaction. Antibodies bound TFPI with nanomolar affinity to multiple epitopes, including one that covered the entire FXa binding surface and others that partially overlapped the interface from different sides of the TFPI-K2 domain. Several antibodies reduced bleeding in a hemophilia A mouse injury model for up to 96 h following a single dose. Rabbit pharmacokinetic studies indicated that antibodies with ≤~1 nM TFPI-binding affinity were cleared rapidly from circulation, whereas TFPI-23 (5.72 nM) had a longer plasma residence time. Pharmacokinetic-pharmacodynamic modeling indicated that this intermediate affinity allowed circulating concentrations of antibodies such as TFPI-23 to maintain concentrations required for 50% residual activity; in contrast, higher-affinity antibodies quickly decreased to concentrations that would not effectively neutralize TFPI. The end-to-end process leading to final candidate selection incorporated rigorous assessment of biophysical properties, including thermal stability, aggregation propensity, viscosity, predicted immunogenicity, stable cell line productivity, and nonspecific binding. An optimized derivative of TFPI-23, TFPI-106 (PF-06741086, known as marstacimab), had the functional and biophysical properties with other characteristics suitable for clinical development and was selected as the final candidate. TFPI-106 elicited enhanced hemostasis in hemophilic plasma, shortened clotting time, and enhanced thrombin generation velocity. Marstacimab is now approved for patients with hemophilia A or B with or without inhibitors.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2685362"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13271316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148218349","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":"Predicting antibody self-association with sequence-structure fusion models: the central role of CSI-BLI in early developability screening.","authors":"Shafayat Ahmed, Federico Devalle, Lauren Leisen, Tony Pham, Bismark Amofah, Amber Lee, Mark Hutchinson, Chacko Chakiath, Jen DiChiara, Sharfa Farzandh, Madi Kreitz, Alison Hinton, Neil Mody, Andrew Dippel, Gilad Kaplan, Maryam Pouryahya","doi":"10.1080/19420862.2026.2694124","DOIUrl":"10.1080/19420862.2026.2694124","url":null,"abstract":"<p><p>Antibody-based biologics are expanding rapidly, yet challenges in development from self-association, high viscosity, aggregation, and unfavorable clearance underscore the need for accurate in silico screening. Clone self-interaction biolayer interferometry (CSI-BLI) is a plate-based, low-material assay of weak, reversible self-association that serves as an early proxy for high-concentration viscosity and a complementary predictor of in vivo clearance. In a panel of 246 monoclonal antibodies, CSI-BLI moderately correlates with viscosity; further, in hFcRn Tg32 mice (41 antibodies), CSI-BLI strongly associates with clearance. Here, we present an end-to-end framework that distinguishes high versus low self-interacting clones (CSI-BLI class) by coupling a fine-tuned protein language model (ESM-2) with residue-aligned 3D context from AlphaFold-predicted structures encoded as residue graphs. Disentangled multi-stream attention fuses sequence content, chain-aware positional information, and structural signals to capture spatially proximate interactions that are distant in sequence. Edit-distance - controlled splits across 1499 IgGs and 841 VHHs assess generalization. The structure-aware model achieves the highest hold-out performance (VHH F1 = 0.76; IgG F1 = 0.57), while a sequence-only disentangled variant outperforms a standard protein language model baseline without structural inputs. Complementary biophysical feature-based models, built from AlphaFold structures and sequence/structure-derived physicochemical descriptors with cluster-aware selection, deliver robust, interpretable performance (VHH; F1 = 0.72; IgG F1 = 0.57), with Shapley value analyses highlighting charge/dipole, hydrophobicity, and aggregation-propensity drivers across complementarity-determining regions and Frameworks. This interaction-aware sequence-structure framework, supported by interpretable feature models, is extensible to other developability endpoints and broader protein classification tasks where joint modeling of language-derived representations and residue-level geometry is advantageous.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2694124"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13349016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148412496","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":"AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for machine learning models.","authors":"Marc Hoffstedt, Jannis Wowra, Hermann Wätzig, Knut Baumann","doi":"10.1080/19420862.2026.2644655","DOIUrl":"10.1080/19420862.2026.2644655","url":null,"abstract":"<p><p>Many complex models for antibody affinity prediction have been developed and successfully deployed. Recent results for T-cell receptor epitope prediction have shown, that even simple distance-based models can achieve a similar performance while requiring less parameters, being more easily interpretable and faster to compute. Encouraged by these results AbDist, a new distance-based model, was developed for antibody affinity prediction. It uses fragments around mutation sites to calculate distances between antibody sequences, demonstrating that a local environment alone suffices as an effective featurization. AbDist was used to perform classification and regression tasks on multiple disjunct public datasets. Its performance matches state-of-the-art machine-learning (ML) models. AbDist is interpretable, computationally efficient, and well suited for data-sparse, early-stage antibody engineering workflows, while sharing the limited out-of-distribution generalization common to current models. AbDist is available as an open-source, publicly accessible tool.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2644655"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13003853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147474409","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":"Quantifying molecular specificity in excipient-driven antibody solubilization.","authors":"Zexiang Han, Nadia A Erkamp, Rob Scrutton, Giuseppe Licari, Olga Predeina, Andreas Evers, Pietro Sormanni, Tuomas P J Knowles","doi":"10.1080/19420862.2026.2722444","DOIUrl":"10.1080/19420862.2026.2722444","url":null,"abstract":"<p><p>Excipients are widely used to suppress the self-association of therapeutic proteins, yet their mechanisms of action are not well understood and often assumed to be nonspecific. Here we show that excipient-mediated solubilization of therapeutic antibodies is markedly molecularly specific. Using a high-throughput combinatorial droplet microfluidic platform, we systematically quantify the effects of common pharmaceutical excipients across a diverse panel of monoclonal antibodies (mAbs). Although all studied excipients enhance solubility, their effects can vary significantly between antibodies, spanning dynamic ranges from approximately 7-fold to over 200-fold. Integrating experimental solubilization measurements with sequence- and structure-derived molecular descriptors, we identify interpretable physicochemical determinants underlying excipient responses; for example, the histidine effect is strongly dependent on mAb dipole moment. Our findings reveal trends that highlight the molecular specificity and complexity of antibody-excipient interactions, as well as the limitations of purely generic formulation rules. Overall, this study provides a quantitative framework for analyzing excipient effects across diverse antibodies and supports the development of predictive approaches for rational formulation design. The integration of high-throughput experimentation with molecular feature analysis offers a foundation for improving our understanding and prediction of antibody-specific formulation behavior.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2722444"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13532009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148857476","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":"A comprehensive evaluation of the human Tg32 and Tg276 FcRn transgenic mouse and non-human primate models for predicting antibody pharmacokinetics in human.","authors":"Monika Gajewska, Michael Kiffe, Claudio Calonder, Volker Engelhardt, Carla Pou I Miralbell, Elena Gonzalo-Gil, Gregory Christianson, Nijaguna Bethur, Adriano Flora","doi":"10.1080/19420862.2026.2668888","DOIUrl":"10.1080/19420862.2026.2668888","url":null,"abstract":"<p><p>In this study, we present a comprehensive evaluation of two human FcRn transgenic mouse models, Tg276 and Tg32, demonstrating their ability to predict the pharmacokinetic (PK) parameters of IgG-type antibodies in human and monkey (non-human primate, NHP), including molecules with and without half-life extension. To assess the translational relevance of the humanized FcRn mouse models, we integrated a broad dataset comprising <i>in vivo</i> PK parameters in both animals and humans derived from the literature and in-house experiments. Using this dataset, we optimized scaling exponents, performed allometric scaling, and evaluated the predictive performance of the models. The optimized exponents fell within expected ranges, 0.7 to 0.95 for systemic and intercompartmental clearance, and around 1 for central and peripheral volumes of distribution. Our analysis reveals the need to apply distinct scaling exponents for half-life extended versus non-extended antibodies, as well as model-specific exponents. The results demonstrate that human PK predictions from Tg32 and Tg276 mice are comparable to those from the NHP. These models therefore offer a good alternative to the monkey, potentially reducing the need to conduct early <i>in vivo</i> PK studies in NHPs. We also report for the first time the use of an immunocompromised version of the Tg276 model to mitigate anti-drug antibody responses. Our findings show that the Tg276 hemizygous model exhibits a translational performance equal to, and in some cases superior to, that of the Tg32 homozygous strain and the NHP, specifically, in terms of predicted clearance. These insights support the Tg276 model as a valuable tool for early-stage antibody screening and lead optimization in preclinical PK evaluation.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2668888"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13166230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839720","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":"DyAb: sequence-based antibody design and property prediction in a low-data regime.","authors":"Joshua Yao-Yu Lin, Jennifer L Hofmann, Andrew Leaver-Fay, Wei-Ching Liang, Stefania Vasilaki, Edith Lee, Pedro O Pinheiro, Natasa Tagasovska, James R Kiefer, Yan Wu, Franziska Seeger, Richard Bonneau, Vladimir Gligorijevic, Andrew Watkins, Kyunghyun Cho, Nathan Frey","doi":"10.1080/19420862.2026.2717460","DOIUrl":"10.1080/19420862.2026.2717460","url":null,"abstract":"<p><p>Protein therapeutic design and property prediction are frequently hampered by data scarcity. Here we propose a model, DyAb, that addresses these issues by leveraging a pair-wise representation to predict differences in binding affinity, rather than absolute values. DyAb is built on top of a pre-trained protein language model and achieves a Spearman rank correlation of up to 0.85 on binding affinity prediction across monoclonal antibodies targeting three different antigens (EGFR, IL-6, and an internal target), given as few as 100 training data. We employ DyAb in two design contexts: as a ranking model to score combinations of known mutations, and combined with a genetic algorithm to generate new sequences. Our method consistently generates antibody variants with high binding rates, including designs that improve on the binding affinity of the lead molecule by more than ten-fold. DyAb represents a powerful tool for optimizing antibody binding affinity in low data regimes common in early-stage drug development.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2717460"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13531962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850955","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":"The double-edged nature of antibody bivalency: mathematical and experimental analysis of cell surface antigen occupancy and opsonization.","authors":"Luke Heirene, James Lodge, Marina Fedorova, Ross Gauntlett, Joanna Cordy, Zahra Rattray, Helen Byrne, Eamonn Gaffney, James W T Yates","doi":"10.1080/19420862.2026.2681843","DOIUrl":"10.1080/19420862.2026.2681843","url":null,"abstract":"<p><p>Monoclonal antibodies are bivalent molecules and are thus able to engage two antigens concurrently, a property termed avidity. The therapeutic efficacy of an antibody drug can be broadly defined as a consequence of either antigen occupancy or target cell opsonization. Therefore, depending on the intended mechanism of action, avid engagement of a therapeutic antibody may be desirable in order to attain high antigen occupancy and consequent antagonism. In some cases, such as target cell opsonization and Fc-mediated cytotoxicity, avidity may limit efficacy when antigens are occupied with fewer antibodies per cell. In this study, we utilized a mathematical model of antibody-antigen binding to identify conditions under which avidity hinders or enhances therapeutic potential. We calibrated the model with <i>in vitro</i> assays exploring the binding of a bivalent and monovalent panel of anti-PD-1 antibodies with a range of affinities, against cell lines with a range of target densities. In calibrating the model to the <i>in vitro</i> binding data, we observed an affinity-dependent discrepancy between experimentally observed and model-predicted cell binding that we hypothesize arises due to unavoidable assay limitations. The calibrated model was then reused to correct for the assay bias and to generate refined estimates for on-cell antibody binding. The predictions generated by this model for the influence of avidity on cell surface receptor engagement with therapeutic antibodies may guide strategies for their structural engineering and dosing.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2681843"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13240941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148150213","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":"Mannose receptor contributes to preferential oligomannosidic glycoform clearance in a murine pharmacokinetics model.","authors":"David Falck, Steinar Gijze, Anna M Wasynczuk, Sebastian Malik, Aline Tridon, Martin Lechmann, Manfred Wuhrer, Dietmar Reusch","doi":"10.1080/19420862.2026.2652119","DOIUrl":"10.1080/19420862.2026.2652119","url":null,"abstract":"<p><p>The pharmacokinetic profile of therapeutic antibodies, which affects the dosing frequency, efficacy, and cost of the products, is influenced by several molecular features, including N- glycosylation. While the accelerated clearance of antibodies carrying non-complex type glycans is well established, the mechanism behind this phenomenon is poorly understood. Though there have been indirect indications before, we provide the first direct evidence for the involvement of the mannose receptor in the clearance of therapeutic antibodies. Wild-type (WT) mice showed accelerated clearance of an oligomannose enriched antibody, and this effect was reduced in mannose receptor (MRC1) knockout (KO) mice. Subsequent glycoform-resolved analysis by mass spectrometry showed in WT an accelerated clearance of oligomannosidic compared to complex glycoforms. This accelerated clearance was also observed in MRC1 KO mice, but to a lesser extent. Interestingly, the glycoform distribution of total circulating mouse antibodies, which do not contain oligomannosidic glycoforms, was not affected by MRC1 KO. Hence, we show that the mannose receptor is involved in, but not exclusively responsible for, preferential clearance of oligomannosidic glycoforms of antibodies. This points toward a role for additional, glycan-targeting receptors for therapeutic antibody clearance. In addition, we observed a preferential clearance of complex afucosylated glycoforms, unique to the mouse model, which was MRC1-independent.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2652119"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147581653","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":"Rapid and selective characterization of antibody-drug conjugates in complex sample matrices by native affinity liquid chromatography-mass spectrometry.","authors":"Dan Bach Kristensen, Nanna Sofie Eskesen, Clara Coll-Satue, Alexandre Nicolas, Jan Kirkeby Simonsen, Lykke Rasmussen, Trine Meiborg Sloth, Martin Ørgaard, Elizabeta Madzharova, Simon Krabbe, Katrine Zinck Leth, Pernille Foged Jensen, Alain Beck","doi":"10.1080/19420862.2026.2618314","DOIUrl":"10.1080/19420862.2026.2618314","url":null,"abstract":"<p><p>Antibody-drug conjugates (ADCs) and other biopharmaceuticals require robust analytical methods to assess biotransformation in biological matrices. Current approaches often require off-line enrichment and extensive chromatographic separation, limiting throughput and complicating data processing. We developed a native affinity liquid chromatography-mass spectrometry (aLC-MS) method using POROS CaptureSelect FcXL columns combined with optimized solvents and MS parameters for direct analysis (1D aLC-MS) of ADCs and other antibody-derived formats in complex sample matrices, such as serum. The method was evaluated using stability studies and concentration series in mouse serum. Direct analysis enabled accurate determination of drug-antibody ratio (DAR), drug-load distribution (DLD) and relative drug abundance across samples without chromatographic peak integration. Stability studies revealed distinct ADC biotransformation profiles in serum versus PBS, including maleimide hydrolysis and disulfide exchange at under-conjugated cysteine sites. The aLC-MS method achieved excellent linearity (R² = 0.99) over 125-2000 µg/mL in serum and demonstrated sensitivity to 31.25 µg/mL. This rapid, selective aLC-MS method enables high-throughput monitoring of ADC quality attributes in complex matrices with minimal sample preparation, supporting biopharmaceutical product development and bioanalysis applications. The method is exclusively based on MS results, which makes data processing and reporting fast and easy to automate.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2618314"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12818810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994360","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}