mAbs最新文献

{"title":"Tuning antibody stability and function by rational designs of framework mutations.","authors":"Joseph C F Ng, Alicia Chenoweth, Maria Laura De Sciscio, Melanie Grandits, Anthony Cheung, Tooki Chu, Alexandra McCraw, Jitesh Chauhan, Yi Liu, Dongjun Guo, Semil Patel, Alice Kosmider, Daniela Iancu, Sophia N Karagiannis, Franca Fraternali","doi":"10.1080/19420862.2025.2532117","DOIUrl":"10.1080/19420862.2025.2532117","url":null,"abstract":"<p><p>Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens. These approaches, however, often focus on the antibody complementarity-determining region (CDR) whilst ignoring the immunoglobulin framework (FW), which provides structural rigidity and support for the flexible CDR loops. Here we present an integrated computational-experimental workflow, combining static structure analyses, molecular dynamics simulations and <i>in vitro</i> physicochemical and functional assays to generate rational designs of FW mutations for modulating antibody stability and activity. We first showed that recent antibody-specific language models lacked insights in FW mutagenesis, in comparison to approaches that use antibody structure information. Using the widely used breast cancer therapeutic trastuzumab as a use case, we designed stabilizing mutants which were distal to the CDR and preserved the antibody's functionality to engage its cognate antigen (HER2) and induce antibody-dependent cellular cytotoxicity. Interestingly, guided by local backbone motions predicted using molecular dynamics simulations, we designed a FW mutation on the trastuzumab light chain that retained antigen-binding effects, but lost Fab-mediated and Fc-mediated effector functions. This highlighted the effects of FW on immunological functions engendered in distal areas of the antibody, and the importance of considering attributes other than binding affinity when assessing antibody function. Our approach incorporates interdomain dynamics and distal effects between FW and the Fc domains, expands the scope of antibody engineering beyond the CDR, and underscores the importance of a holistic perspective that considers the entire antibody structure in optimizing antibody stability, developability and function.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2532117"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12269682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144626606","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":"Mechanistic and predictive formulation development for viscosity mitigation of high-concentration biotherapeutics.","authors":"Matthew A Cruz, Marco Blanco, Iriny Ekladious","doi":"10.1080/19420862.2025.2550757","DOIUrl":"10.1080/19420862.2025.2550757","url":null,"abstract":"<p><p>Proteins are an important class of therapeutics for combatting a wide variety of diseases. The increasing demand for convenient, patient-centric treatment options has propelled the development of subcutaneously delivered protein therapies and increased the interest in novel formulations and delivery methods. However, subcutaneous delivery of protein therapeutics remains a challenge due to the high protein concentrations ( >100 mg/mL) required to circumvent lower bioavailability and the smaller injection volumes required to enable the use of mature and cost-effective devices, such as standard prefilled syringes and autoinjectors. At high concentrations, protein solutions exhibit elevated viscosity, which poses injectability and manufacturing challenges. Here, we review the state of the art in experimental and computationally predictive formulation development approaches for viscosity mitigation of high-concentration protein solution therapeutics, and we suggest new directions for expanding the utility of these approaches beyond traditional monoclonal antibodies. Innovative approaches should leverage and combine advances in both experimental and computational methods, including machine learning and artificial intelligence, to rapidly identify formulation compositions for viscosity reduction, and subsequently facilitate the development of patient-centric biotherapeutics.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2550757"},"PeriodicalIF":7.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145064869","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":"Exploring the nanobody patent landscape: a focus on BCMA sequences and structural analysis.","authors":"Jiaqi Xu, Yan Wang, Ni Yuan, Guang Hu, Yuanjia Hu","doi":"10.1080/19420862.2025.2560893","DOIUrl":"10.1080/19420862.2025.2560893","url":null,"abstract":"<p><p>Nanobodies (Nbs) are antigen-binding fragments derived from unique heavy-chain-only antibodies. In recent years, the development of Nbs has progressed rapidly due to their therapeutic potential. Here we present a comprehensive patent landscape of Nb technologies, focusing on uncovering innovation trends, identifying novel drug candidates, and analyzing opportunities and challenges for research, development, and commercialization. Using B-cell maturation antigen (BCMA) as an example drug target, we summarize the features, physicochemical properties, modification sites, and epitope-binding tendencies of patented sequences of Nb drugs, highlighting the importance of structural-level patent protection, and offering a theoretical foundation for Nb design and experimental validation. Through patent landscape and patent sequence analysis, our study provides valuable insights for Nb drug development and supports decision-making in patent strategy.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2560893"},"PeriodicalIF":7.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12452430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081189","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":"Correction.","authors":"","doi":"10.1080/19420862.2025.2458393","DOIUrl":"10.1080/19420862.2025.2458393","url":null,"abstract":"","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2458393"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11784643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066706","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":"Combinatorial Fc modifications for complementary antibody functionality.","authors":"Yannic C Bartsch, Nicholas E Webb, Eleanor Burgess, Jaewon Kang, Douglas A Lauffenburger, Boris D Julg","doi":"10.1080/19420862.2025.2465391","DOIUrl":"10.1080/19420862.2025.2465391","url":null,"abstract":"<p><p>Therapeutic monoclonal antibodies (mAbs) can be functionally enhanced via Fc engineering. To determine whether pairs of mAbs with different Fc modifications can be combined for functional complementarity, we investigated the <i>in vitro</i> activity of two HIV-1 mAb libraries, each equipped with 60 engineered Fc variants. Our findings demonstrate that the impact of Fc engineering on Fc functionality is dependent on the specific Fab clone. Notably, combinations of Fc variants of the same Fab specificity exhibited limited enhancement in functional breadth compared to combinations involving two distinct Fabs. This suggests that the strategic selection of complementary Fc modifications can enhance both functional activity and breadth. Furthermore, while some combinations of Fc variants displayed additive functional effects, others were detrimental, suggesting that the functional outcome of Fc mutations is not easily predicted. Collectively, these results provide preliminary evidence supporting the potential of complementary Fc modifications in mAb combinations. Future studies will be essential to identify the optimal Fc modifications that maximize <i>in vivo</i> efficacy.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2465391"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11834420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414631","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":"PROPERMAB: an integrative framework for <i>in silico</i> prediction of antibody developability using machine learning.","authors":"Bian Li, Shukun Luo, Wenhua Wang, Jiahui Xu, Dingjiang Liu, Mohammed Shameem, John Mattila, Matthew C Franklin, Peter G Hawkins, Gurinder S Atwal","doi":"10.1080/19420862.2025.2474521","DOIUrl":"10.1080/19420862.2025.2474521","url":null,"abstract":"<p><p>Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline. The ability to evaluate developability properties early in the process of antibody therapeutic development could accelerate the timeline from discovery to clinic and save considerable resources. <i>In silico</i> predictive approaches, such as machine learning models, which map molecular features to predictions of developability properties could offer a cost-effective and high-throughput alternative to experiments for antibody developability assessment. We developed a computational framework, PROPERMAB (PROPERties of Monoclonal AntiBodies), for large-scale and efficient <i>in silico</i> prediction of developability properties for monoclonal antibodies, using custom molecular features and machine learning modeling. We demonstrate the power of PROPERMAB by using it to develop models to predict antibody hydrophobic interaction chromatography retention time and high-concentration viscosity. We further show that structure-derived features can be rapidly and accurately predicted directly from sequences by pre-training simple models for molecular features, thus providing the ability to scale these approaches to repertoire-scale sequence datasets.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2474521"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11901398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143557313","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":"Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening.","authors":"Kevin James Metcalf, Galen Wo, Jan Paulo Zaragoza, Fahimeh Raoufi, Jeanne Baker, Daoyang Chen, Mehabaw Derebe, Jason Hogan, Amy Hsu, Esther Kofman, David Leigh, Mandy Li, Dan Malashock, Cate Mann, Soha Motlagh, Jihea Park, Karthik Sathiyamoorthy, Madhura Shidhore, Yinyan Tang, Kevin Teng, Katharine Williams, Andrew Waight, Sultan Yilmaz, Fan Zhang, Huimin Zhong, Laurence Fayadat-Dilman, Marc Bailly","doi":"10.1080/19420862.2025.2502127","DOIUrl":"https://doi.org/10.1080/19420862.2025.2502127","url":null,"abstract":"<p><p>Identification of an optimal single protein sequence at the discovery stage for preclinical and clinical development is critical to the rapid development and overall success of a biologic drug. High throughput developability assessments at the discovery stage are used to rank potent molecules by their biophysical properties, deprioritize suboptimal molecules, or trigger additional rounds of protein engineering. Due to the amount of data acquired for these molecules, manual analysis methods to rank molecules are error prone and time-consuming. Here, we present applications of hierarchical clustering analysis for data-driven lead selection of biologics and preformulation screening using high throughput developability data. Hierarchical clustering analysis was applied here for prioritization of three different antibody modalities, including format and chain pairing of bispecific antibodies, sequence-optimized monoclonal antibodies from affinity maturation, preformulation screening of bispecific scFv-Fab fusion molecules, and monoclonal antibodies from an immunization campaign. This high-throughput method for ranking molecules by their developability characteristics and preformulation properties can substantially simplify, streamline, and accelerate biologics discovery and early development.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2502127"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12068344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144017288","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":"Assessment of biophysical properties of the first-in-class anti-cancer IgE antibody drug MOv18 IgE demonstrates monomeric purity and stability.","authors":"Paul Considine, Panida Punnabhum, Callum G Davidson, Georgina B Armstrong, Michaela Kreiner, Heather J Bax, Jitesh Chauhan, James Spicer, Debra H Josephs, Sophia N Karagiannis, Gavin Halbert, Zahra Rattray","doi":"10.1080/19420862.2025.2512211","DOIUrl":"10.1080/19420862.2025.2512211","url":null,"abstract":"<p><p>Therapeutic monoclonal antibodies, which are almost exclusively IgG isotypes, show significant promise but are prone to poor solution stability, including aggregation and elevated solution viscosity at dose-relevant concentrations. Recombinant IgE antibodies are emerging cancer immunotherapies. The first-in-class MOv18 IgE, recognizing the cancer-associated antigen folate receptor-alpha (FRα), completed a Phase 1 clinical trial in patients with solid tumors, showing early signs of efficacy at a low dose. The inaugural process development and scaled manufacture of MOv18 IgE for clinical testing were undertaken with little baseline knowledge about the solution phase behavior of recombinant IgE at dose-relevant concentrations. We evaluated MOv18 IgE physical stability in response to environmental and formulation stresses encountered throughout shelf life. We analyzed changes in physical stability using multiple orthogonal analytical techniques, including particle tracking analysis, size exclusion chromatography, and multidetector flow field flow fractionation hyphenated with UV. We used dynamic and multiangle light scattering to profile aggregation status. Formulation at pH 6.5, selected for use in the Phase 1 trial, resulted in high monomeric purity and no submicron proteinaceous particulates. Formulation at pH 5.5 and 7.5 induced significant submicron and sub-visible particle formation. IgE formulation was resistant to aggregation in response to freeze-thaw stress, retaining high monomeric purity. Exposure to thermal stress at elevated temperatures resulted in loss of monomeric purity and aggregation. Agitation stress-induced submicron and subvisible aggregation, but monomeric purity was not significantly affected. MOv18 IgE retains monomeric purity in response to formulation and stress conditions, confirming stability. Our results offer crucial guidance for future IgE-based drug development.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2512211"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12123954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144158794","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":"Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning.","authors":"Lateefat A Kalejaye, Jia-Min Chu, I-En Wu, Bismark Amofah, Amber Lee, Mark Hutchinson, Chacko Chakiath, Andrew Dippel, Gilad Kaplan, Melissa Damschroder, Valentin Stanev, Maryam Pouryahya, Mehdi Boroumand, Jenna Caldwell, Alison Hinton, Madison Kreitz, Mitali Shah, Austin Gallegos, Neil Mody, Pin-Kuang Lai","doi":"10.1080/19420862.2025.2483944","DOIUrl":"10.1080/19420862.2025.2483944","url":null,"abstract":"<p><p>Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only limited to a few dozen data points for training, a bottleneck for generalizability. In this study, we measured the viscosity of a panel of 229 monoclonal antibodies (mAbs) to develop predictive models for high concentration mAb screening. We developed DeepViscosity, consisting of 102 ensemble artificial neural network models to classify low-viscosity (≤20 cP) and high-viscosity (>20 cP) mAbs at 150 mg/mL, using 30 features from a sequence-based DeepSP model. Two independent test sets, comprising 16 and 38 mAbs with known experimental viscosity, were used to assess DeepViscosity's generalizability. The model exhibited an accuracy of 87.5% and 89.5% on both test sets, respectively, surpassing other predictive methods. DeepViscosity will facilitate early-stage antibody development to select low-viscosity antibodies for improved manufacturability and formulation properties, critical for subcutaneous drug delivery. The webserver-based application can be freely accessed via https://devpred.onrender.com/DeepViscosity.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2483944"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12128653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764268","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":"Does one model fit all mAbs? An evaluation of population pharmacokinetic models.","authors":"Stefan P H van den Berg, Philine E A Adolfsen, Thomas P C Dorlo, Theo Rispens","doi":"10.1080/19420862.2025.2512217","DOIUrl":"10.1080/19420862.2025.2512217","url":null,"abstract":"<p><p>Antibodies are extensively used in treating various diseases, with over 100 canonical monoclonal antibodies (mAbs) approved. Population pharmacokinetic (PK) models are typically developed for each individual mAb, despite their similarities in size, shape, and susceptibility to lysosomal degradation. However, sparse datasets with limited PK information pose challenges in deriving accurate parameter estimates. Here, we provide a comprehensive overview of 160 published models of 69 mAbs, administered either intravenously or subcutaneously, examining their structural, statistical, and covariate components. Median estimates for the base parameters are linear clearance (0.22 L/d), central volume (3.42 L), peripheral volume (2.68 L), intercompartmental clearance (0.54 L/d), absorption rate (0.25 L/d), and bioavailability (69%). Using these to simulate a 'generic' mAb results in plausible kinetics with a terminal half-life of 21 ds. We demonstrated that the median linear clearance was 26% lower in models that included nonlinear target-mediated kinetics, when compared to linear models (0.18 vs. 0.25 L/d). For chimeric mAbs median linear clearance was 50% higher compared to fully human and humanized mAbs. Variability in PK parameter estimates across models was comparable to the inter-individual variability, which have consistently shown to be large for mAbs PK (e.g. 55% vs. 43% for clearance and 25% vs. 30% for central volume, respectively). Our meta-analysis suggests that a priori parameter estimates derived from the large body of existing pharmacokinetic models for mAbs are representative for many mAbs and can facilitate the design of new and/or more complex pharmacokinetic models or assist in dose optimization models.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"17 1","pages":"2512217"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12128664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187321","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}