ABSTRACT Driven by the substantial limitations of first generation 4–1BB agonists urelumab and utomilumab, the field has shifted toward engineering next-generation molecules with improved therapeutic windows. This review provides a comprehensive analysis of this evolution, detailing how key molecular design strategies are used to restrict 4–1BB activation to the tumor microenvironment. We summarize available clinical data, highlighting that 4–1BB bispecific antibodies exhibit superior antitumor efficacy and more favorable safety profiles compared with their monospecific predecessors. Furthermore, we discuss strong rationale for combination strategies, emphasizing how 4–1BB signaling provides the crucial costimulatory signal necessary to sustain durable anti-tumor responses. In summary, this review elucidates the scientific basis of antibody engineering aimed at improving safety and tumor-selective activation of 4–1BB agonists and outlines future directions for optimizing their clinical application in cancer immunotherapy.

ABSTRACT Nanobodies have emerged as promising tools for many biotechnological applications due to their small size, high stability and remarkable binding specificity. Next-Generation Sequencing (NGS) enables deep profiling of large nanobody libraries and panning campaigns; however, the scale and diversity of nanobody NGS datasets presents a significant bioinformatic challenge. To this end, we have developed alpseq, an optimized, open-source software pipeline designed specifically for the efficient and accurate processing of NGS data from nanobody libraries and panning campaigns. alpseq is also paired with a PCR-free sequencing library preparation protocol to allow researchers to easily generate their own data while avoiding biases. The alpseq software pipeline is composed of two parts: a pre-processing module written in Nextflow efficiently handles raw nanobody reads in a single line of code. These results are then fed into the analysis module, which contains a comprehensive suite of functions for quality control, diversity analysis, identification of enriched sequences and clustering. alpseq also creates a user-friendly interactive report which empowers scientists to explore their data without the need for extensive bioinformatic experience. Sophisticated panning campaign designs are supported, such as replicates and comparisons between different pans to find cross-binding leads. alpseq thus generates insights into the nanobody selection process and delivers a list of lead candidates for further experimental validation and downstream applications. alspeq is available at https://github.com/kzeglinski/alpseq.

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 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.

ABSTRACTChinese 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.

ABSTRACT Over the past two decades, bi- and multispecific antibodies have emerged as a rapidly advancing class of therapeutic biologics, transforming oncology and immunotherapy. By simultaneously binding two or more distinct antigens or epitopes, these molecules achieve mechanisms of action beyond those of conventional monoclonal antibodies, including immune cell redirection, dual pathway modulation, and enhanced tissue selectivity. Bispecific and multispecific antibodies exhibit considerable structural diversity, encompassing a wide range of molecular architectures covering a steady growing ‘zoo’ of formats. The therapeutic success and diversity of molecules and formats is reflected in the 2021 revision of the international nonproprietary name system, which introduced the suffix – mig to denote multispecific immunoglobulins. In this review, we provide an overview of multispecific antibodies in clinical development, focusing on format, molecular design, and clinical status. In total, data for 501 multispecific antibodies were compiled and analyzed, identifying 112 different formats. Overall, this analysis highlights the rapid growth, enormous format diversity, and translational potential of multispecific antibodies. It underscores their emerging role as versatile therapeutics not only in oncology, but also in non-cancer indications, reflecting a field that continues to evolve rapidly in response to both scientific innovation and clinical needs.

ABSTRACT 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.

ABSTRACT On-target, off-tumor toxicities remain a major barrier for T-cell engagers in solid tumors. We present EVATM, a closed-loop design platform integrating high-throughput functional assays with multi-objective Bayesian optimization to explore combinatorial T-cell engager (TCE) spaces. In a HER2×CD3 case study, iterative design–build–test–learn cycles traversed 44,160 designs defined by valency, topology, affinity and spacing. Compared with a Sobol baseline, EVA achieved 14-fold enrichment of potent, tumor-selective candidates. Multiple architectures reached sub-10 pM potency on HER2-high cells, near-complete efficacy, and ≥ 10,000-fold selectivity over HER2-low models, consistent with avidity gating. EVA™ recovered diverse high-performing topologies and generalized to a second target, supporting density-gated avidity as a design principle and providing an operational template for rapid, data-efficient optimization.

ABSTRACT Protein glycosylation at asparagine typically occurs at a consensus motif. However, recent studies have reported instances of N-glycosylation at non-consensus sites, though the mechanisms and implications of these atypical modifications remain unclear. In this study, we identified novel non-consensus N-glycosylation motifs with low glycosylation occupancy in the Fab region of human antibodies. We developed a computational workflow to predict the interaction between non-consensus peptides and the eukaryotic oligosaccharyltransferase (OST) complex. This model was validated through site-directed mutagenesis around the asparagine residue and glycosylation quantification via mass spectrometry. Our results show that glycan occupancy at non-consensus sites can be modulated by mutations that influence OST binding affinity. Pharmacological inhibition of OST activity reduced non-consensus and consensus glycosylation in both Fab and Fc regions. Additionally, we identified new non-consensus glycosylation sites in natural human antibodies, revealing the sequence preferences governing these modifications. These findings provide mechanistic insights into OST sequence specificity and establish a computational and analytical framework for assessing atypical N-glycosylation, aiding glycan profile control in therapeutic antibody development.

ABSTRACT Antibodies that bind in a pH-dependent manner to their antigens show promise for enhanced neutralization potency and blocking capacity against extracellular targets. However, because the mechanisms governing pH-dependent antigen binding remain poorly understood, engineering approaches are often limited to incorporating histidine residues in the antibody complementarity-determining regions. Here, we use a panel of human monoclonal antibodies with neutralizing activity to long-chain α-neurotoxins (LNTxs) to investigate pH-dependent antigen binding. The antibodies vary in their light chains but have conserved histidine residues in their variable domains, allowing us to explore how other residues may affect pH dependence. Comparative structural and molecular dynamics studies between two antibodies with and without pH-dependent antigen-binding properties reveal that both antibodies neutralize LNTxs by mimicking LNTx-receptor interactions through their heavy chains. We hypothesize that part of the pH-dependency can be controlled by the light chain through modulation of water access to residues at the heavy-light-chain interface. We show that pH-dependent antigen-binding properties can be introduced into monoclonal antibodies through the substitution of selected residues at the heavy-light-chain interface. Specifically, we replaced tyrosine residues in the light chain with small polar and apolar amino acid residues in a structurally related anti-LNTx antibody with limited inherent pH-dependent antigen-binding properties, and found that these smaller substitutions enhanced pH-dependence more effectively than histidine substitutions alone. Our findings suggest a strategy for engineering pH-dependent antigen binding in antibodies that goes beyond the exclusive use of histidine doping.