mAbs最新文献

The viscosity of monoclonal antibody solutions is critical in their biopharmaceutical application, as it directly influences the ease of subcutaneous injection. Although many descriptors have been developed to enable the in silico prediction of viscosity, they are typically based on electrostatic properties while neglecting hydrophobicity, or rely on AI-based approaches with limited generalizability, both rendering the models inadequate. Moreover, the scarcity of high-quality experimental datasets further limits the use of machine learning algorithms, necessitating interpretable analysis of protein-protein interactions. In this work, we combine computational modeling with experimental viscosity measurements for a set of monoclonal antibodies. We introduce an algorithm for surface patch analysis capable of quantifying the characteristics of hydrophobic patches. By calculating physically meaningful interaction energies, we can discern between the propensity for high and low viscosity due to the hydrophobic effect. Furthermore, by analyzing antibodies with problematic hydrophobic patches, we introduce a theory explaining their solubilization. This method is adaptable to any protein format and can be generalized for early in silico screening of viscosity in protein-based biopharmaceutical solutions.

Immunogenicity prediction is widely used in the developability assessment of antibodies, and many marketed and clinical-stage therapeutics have a predicted T-cell epitope in the second complementary-determining region of their light chain (CDR2L). To investigate such CDR2Ls in more detail, we identified an antibody with a CDR2L for which a patient had developed treatment-emergent (TE) anti-drug antibodies (ADAs) in a clinical setting. With this, we establish the importance of predicted T-cell epitopes in CDR2L. In the course of deleting the T-cell epitope, we decided to aim for a solution that can be applied broadly to facilitate larger high-throughput discovery campaigns. For this purpose, we have developed a double-mutation scheme that targets AHo67 (Kabat51) and AHo68 (Kabat52) in the CDR2L. This 67G-68G mutation scheme was applied to all light chain sequences of a tri-specific single-chain diabody fused to a single-chain variable fragment (scMATCH3™) antibody for which TE ADAs had been observed. Analyses of patient sera showed that introduction of 67 G-68 G in CDR2L in combination with our previously described T101S-T146K (Kabat: T87S-T110K) framework mutations led to a scMATCH3 antibody with significantly reduced levels of both preexisting and TE ADA reactivities. For a diverse collection of single-chain variable fragments, application of the 67 G-68 G mutation scheme was experimentally seen to not substantially affect the functional or biophysical properties of the molecules, suggesting that this mutation scheme may be applicable to the improvement of therapeutic safety of antibodies of many types, with CDR2L-associated immunogenicity.

Precise inhibition of autoreactivity without concomitant induction of general immunosuppression is an overarching goal that remains elusive for the treatment of autoimmune diseases. PD-1 is preferentially expressed on activated T cells that drive autoimmunity. These PD-1⁺ T cells could serve as a target for therapeutic intervention. Here, we report the discovery of a unique PD-1 agonist antibody, GenSci120, that exhibited potent and selective T-cell inhibition in vitro and T-cell depletion activity both in vitro and in vivo. Target engagement by GenSci120 directly promoted SHP2 recruitment into the PD-1 signaling pathway but also enhanced the binding of PD-1 to its natural ligands and augmented PD-L1-induced PD-1 signaling. Moreover, GenSci120 exhibited robust efficacy in several animal models of human autoimmune disease. Thus, GenSci120, by selectively depleting PD-1⁺ T cells and by directly (via PD-1 binding and SHP2 recruitment) or indirectly (via enhancing PD-1 and ligand interaction) stimulating PD-1 signaling, has the capability to restore immune balance in autoimmunity. In a first-in-human study in healthy adults (NCT06827457), GenSci120 demonstrated favorable safety/tolerability and pharmacokinetic profiles as well as robust pharmacodynamic effect. Together, these findings suggest the potential of GenSci120 as an innovative precision medicine for treating autoimmune diseases and support further evaluation of this investigational new drug in future clinical trials.

Chinese hamster ovary (CHO) cells remain the dominant platform for therapeutic antibody and biopharmaceutical production, yet productivity bottlenecks persist, particularly for complex molecules. To identify overarching trends in host cell optimization, a systematic review and quantitative cross-study analysis of 164 publications (2011-2024) reporting CHO cell engineering strategies with effects on titer or specific productivity was conducted. Data from 466 engineered targets were extracted and analyzed by strategy, pathway, and production context. The field - driven largely by antibody production - has evolved from simple overexpression toward CRISPR-mediated knockouts, while combinatorial approaches, and engineering of nuclear, epigenetic, and apoptotic/proliferative targets achieved the greatest gains. Despite technological advances, reported improvement folds remained stable, highlighting the need for pathway-informed, multi-target engineering. Future progress in predictive modeling of engineering strategies will depend on standardized models and structured datasets. This review provides a data-driven framework for rational CHO design to support next-generation biotherapeutic production.

We introduce Ibex, a pan-immunoglobulin structure prediction model for antibodies, nanobodies, and T-cell receptors. Unlike previous approaches, Ibex explicitly distinguishes between bound and unbound protein conformations by training on labeled apo and holo structural pairs, enabling accurate prediction of both states at inference time. Ibex achieves state-of-the-art accuracy, demonstrating superior out-of-distribution performance on a comprehensive benchmark of high-resolution antibody structures with a mean CDR H3 RMSD of 2.28 Å. Ibex combines this accuracy with significantly reduced computational requirements, providing a robust foundation for accelerating large molecule design and therapeutic development.

Growing knowledge around disease states has led to opportunities within research to make designer molecules with improved specificity and broader efficacy. These next-generation molecules frequently take advantage of multispecific targeting and controlled mechanisms of action by utilizing four unique peptide chains as seen in many bispecific or trispecific antibodies. However, with all the opportunities these multispecifics offer, their increased biological complexities come with increased challenges during expression and purification to produce high-quality material. Lower yields accompanied with a high degree of mispairing after the initial capture purification step are often limiting factors. Developing new methods for stable pool expression can offer a strong advantage for progressing these molecules through research toward development. Here, we implemented optimized stable cell pools using targeted dual selection (TDS), a novel approach that combines specified selective pressure with transposon-guided semi-targeted gene integration. By utilizing key analytical data obtained during early-stage high-throughput transient productions, we can predict improved vector configurations for the generation of optimized TDS stable pools. We demonstrate that this design can improve molecule quality at the initial capture purification step in two Y-shaped bispecific molecules and two cross-over dual variable trispecific molecules by achieving up to four-fold increase in protein of interest yields while maintaining product quality. Use of this strategy in research can enable simplified purification strategies as well as increased production yields required for successful and timely project progression.

The development of computational models addressing therapeutic antibodies faces significant challenges. Particularly, the prediction of binding affinity across a diverse set of measurements, due to the scarcity of data. A critical data element is the set of antibody-antigen interaction pairs associated with sequences. To address this issue, we developed the Antigen Specific Antibody Database (ASD, https://naturalantibody.com/agab/), a database aggregating antibody-antigen interaction data from multiple studies with standardized formatting and annotations. Our dataset compilation strategy resulted in data from 15 distinct sources, resulting in 1,097,946 unique antibody-antigen interactions (with 9575 unique antigens). The ASD captures diverse affinity measures and qualitative binding assessment, along with metadata including UniProt and PDB identifiers, target protein names, confidence levels, and experimental conditions such as type of measured affinity, source organism, and germline genes. Through this integration drive, we make available an ample resource of interaction data gathered from the public domain to act as a foundation for model development and further data generation.

In antibody development, a mutagenesis approach has been widely used to improve affinity, but such mutations often compromise biophysical properties. Here, we combined molecular evolution with machine learning to simultaneously improve affinity and expression level of camelid heavy-chain antibody variable domains (VHHs). Using phage display and deep sequencing, we selected five residues in an anti-SARS-CoV-2 VHH for affinity maturation. We constructed training data using experimentally measured expression levels and target affinities of 117 variants with randomized residues. Machine-learning-predicted top-rank variants showed improved expression level and affinity compared to variants in the training data. Several variants achieved 50-70-fold stronger affinities in the pico-molar range and 4-5-fold higher expression level than wild-type. Furthermore, one variant showed 9.5°C improvement in thermal stability. These results highlight the utility of machine learning-assisted molecular evolution as a strategy for multidimensional optimization of antibody properties.

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.

The Ginkgo Datapoints Antibody Developability (AbDev) Competition, a blinded benchmark for developability prediction characterized entirely on a single, industrial-scale experimental platform, was conducted from September 8 to November 18, 2025. We benchmarked predictors across five biophysical properties - hydrophobicity, thermostability, self-association, expression titer, and polyreactivity - using a public training set of 246 clinical antibodies and a blinded, held-out test set of 80 antibodies. We received submissions from 113 teams spanning 25 countries, 38 companies, and 39 universities. Winning submissions differed by assay. Top Spearman's ρ values on the test set reached 0.708 (hydrophobicity), 0.392 (thermostability), 0.356 (polyreactivity), 0.337 (self-association), and 0.310 (titer). Cross-validation scores from the public training set consistently exceeded held-out test performance, indicating overfitting and limited out-of-distribution generalization. Together, these results provide a standardized snapshot of current antibody developability modeling capabilities and highlight a key bottleneck: available datasets are too small and heterogeneous to support robust, assay-spanning prediction. Meaningful progress will require larger, standardized, and diverse experimental datasets - with harmonized protocols and rich metadata - to train and validate models that generalize reliably for future antibody discovery campaigns.