mAbs最新文献

The 'Antibodies to Watch' article series provides an annual summary of commercially sponsored monoclonal antibody therapeutics currently in late-stage clinical development, regulatory review, and those recently granted a first approval in any country. In this installment, we discuss key details for 16 antibody therapeutics granted a first approval in 2023, as of November 17 (lecanemab (Leqembi), rozanolixizumab (RYSTIGGO), pozelimab (VEOPOZ), mirikizumab (Omvoh), talquetamab (Talvey), elranatamab (Elrexfio), epcoritamab (EPKINLY), glofitamab (COLUMVI), retifanlimab (Zynyz), concizumab (Alhemo), lebrikizumab (EBGLYSS), tafolecimab (SINTBILO), narlumosbart (Jinlitai), zuberitamab (Enrexib), adebrelimab (Arelili), and divozilimab (Ivlizi)). We briefly review 26 product candidates for which marketing applications are under consideration in at least one country or region, and 23 investigational antibody therapeutics that are forecast to enter regulatory review by the end of 2024 based on company disclosures. These nearly 50 product candidates include numerous innovative bispecific antibodies, such as odronextamab, ivonescimab, linvoseltamab, zenocutuzumab, and erfonrilimab, and antibody-drug conjugates, such as trastuzumab botidotin, patritumab deruxtecan, datopotamab deruxtecan, and MRG002, as well as a mixture of two immunocytokines (bifikafusp alfa and onfekafusp alfa). We also discuss clinical phase transition and overall approval success rates for antibody therapeutics, which are crucial to the biopharmaceutical industry because these rates inform decisions about resource allocation. Our analyses indicate that these molecules have approval success rates in the range of 14-32%, with higher rates associated with antibodies developed for non-cancer indications. Overall, our data suggest that antibody therapeutic development efforts by the biopharmaceutical industry are robust and increasingly successful.

Engineered antibody formats, such as antibody fragments and bispecifics, have the potential to offer improved therapeutic efficacy compared to traditional full-length monoclonal antibodies (mAbs). However, the translation of these non-natural molecules into successful therapeutics can be hampered by developability challenges. Here, we systematically analyzed 64 different antibody constructs targeting Tumor Necrosis Factor (TNF) which cover 8 distinct molecular format families, encompassing full-length antibodies, various types of single chain variable fragments, and bispecifics. We measured 15 biophysical properties related to activity, manufacturing, and stability, scoring variants with a flag-based risk approach and a recent in silico developability profiler. Our comparative assessment revealed that overall developability is higher for the natural full-length antibody format. Bispecific antibodies, antibodies with scFv fragments at the C-terminus of the light chain, and single-chain Fv antibody fragments (scFvs) have intermediate developability properties, while more complicated formats, such as scFv- scFv, bispecific mAbs with one Fab exchanged with a scFv, and diabody formats are collectively more challenging. In particular, our study highlights the propensity for fragmentation and aggregation, both in bulk and at interfaces, for many current engineered formats.

Biparatopic antibodies (bpAbs) bind distinct, non-overlapping epitopes on an antigen. This unique binding mode enables new mechanisms of action beyond monospecific and bispecific antibodies (bsAbs) that can make bpAbs effective therapeutics for various indications, including oncology and infectious diseases. Biparatopic binding can lead to superior affinity and specificity, promote antagonism, lock target conformation, and result in higher-order target clustering. Such antibody-target complexes can elicit strong agonism, increase immune effector function, or result in rapid target downregulation and lysosomal trafficking. These are not only attractive properties for therapeutic antibodies but are increasingly being explored for other modalities such as antibody-drug conjugates, T-cell engagers and chimeric antigen receptors. Recent advances in bpAb engineering have enabled the construction of ever more sophisticated formats that are starting to show promise in the clinic.

Bispecific antibodies (bsAbs) are a class of antibodies that can mediate novel mechanisms of action compared to monospecific monoclonal antibodies (mAbs). Since the discovery of mAbs and their adoption as therapeutic agents in the 1980s and 1990s, the development of bsAbs has held substantial appeal. Nevertheless, only three bsAbs (catumaxomab, blinatumomab, emicizumab) were approved through the end of 2020. However, since then, 11 bsAbs received regulatory agency approvals, of which nine (amivantamab, tebentafusp, mosunetuzumab, cadonilimab, teclistamab, glofitamab, epcoritamab, talquetamab, elranatamab) were approved for the treatment of cancer and two (faricimab, ozoralizumab) in non-oncology indications. Notably, of the 13 currently approved bsAbs, two, emicizumab and faricimab, have achieved blockbuster status, showing the promise of this novel class of therapeutics. In the 2020s, the approval of additional bsAbs can be expected in hematological malignancies, solid tumors and non-oncology indications, establishing bsAbs as essential part of the therapeutic armamentarium.

Glycosylation plays a crucial role in determining the quality and efficacy of therapeutic antibodies. This necessitates a thorough analysis and monitoring process to ensure consistent product quality during manufacturing. In this study, we introduce a custom-designed lectin microarray featuring nine distinct lectins: rPhoSL, rOTH3, RCA120, rMan2, MAL_I, rPSL1a, PHAE, rMOA, and PHAL. These lectins have been specifically tailored to selectively bind to common N-glycan epitopes found in therapeutic IgG antibodies. By utilizing intact glycoprotein samples, our nine-lectin microarray provides a high-throughput platform for rapid glycan profiling, enabling comparative analysis of glycosylation patterns. Our results demonstrate the practical utility of this microarray in assessing glycosylation across various manufacturing batches or between biosimilar and innovator products. This capacity empowers informed decision-making in the development and production of therapeutic antibodies.

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.

Precise measurement of the binding activity changes of therapeutic antibodies is important to determine the potential critical quality attributes (CQAs) in developability assessment at the early stage of antibody development. Here, we report a surface plasmon resonance (SPR)-based relative binding activity method, which incorporates both binding affinity and binding response and allows us to determine relative binding activity of antibodies with high accuracy and precision. We applied the SPR-based relative binding activity method in multiple forced degradation studies of antibody developability assessment. The current developability assessment strategy provided comprehensive, precise characterization of antibody binding activity in the stability studies, enabling us to perform correlation analysis and establish the structure-function relationship between relative binding activity and quality attributes. The impact of a given quality attribute on binding activity could be confidently determined without isolating antibody variants. We identified several potential CQAs, including Asp isomerization, Asn deamidation, and fragmentation. Some potential CQAs affected binding affinity of antibody and resulted in a reduction of binding activity. Certain potential CQAs impaired antibody binding to antigen and led to a loss of binding activity. A few potential CQAs could influence both binding affinity and binding response and cause a substantial decrease in antibody binding activity. Specifically, we identified low abundance Asn33 deamidation in the light chain complementarity-determining region as a potential CQA, in which all the stressed antibody samples showed Asn33 deamidation abundances ranging from 4.2% to 27.5% and a mild binding affinity change from 1.76 nM to 2.16 nM.

Therapeutic monoclonal antibody (mAb) development and the processes for manufacturing drug substance have evolved since the first approval of the mAb in 1986. As the past is often the prologue to the future, the history of these technologies has been classified here into three eras, leading to speculation about what the next era may hold with regard to development and manufacturing strategies, as well as the potential impacts to patients. The substantial increase in production culture titers and bioreactor production volumes and the availability of large-scale contract manufacturing facilities could translate into improved global access for these therapies and an expansion of indications for therapeutic antibodies.

Antibody-mediated delivery of immunogenic viral CD8⁺ T-cell epitopes to redirect virus-specific T cells toward cancer cells is a promising new therapeutic avenue to increase the immunogenicity of tumors. Multiple strategies for viral epitope delivery have been shown to be effective. So far, most of these have relied on a free C-terminus of the immunogenic epitope for extracellular delivery. Here, we demonstrate that antibody-epitope conjugates (AECs) with genetically fused epitopes to the N-terminus of the antibody can also sensitize tumors for attack by virus-specific CD8⁺ T cells. AECs carrying epitopes genetically fused at the N-terminus of the light chains of cetuximab and trastuzumab demonstrate an even more efficient delivery of the T-cell epitopes compared to AECs with the epitope fused to the C-terminus of the heavy chain. We demonstrate that this increased efficiency is not caused by the shift in location of the cleavage site from the N- to the C-terminus, but by its increased proximity to the cell surface. We hypothesize that this facilitates more efficient epitope delivery. These findings not only provide additional insights into the mechanism of action of AECs but also broaden the possibilities for genetically fused AECs as an avenue for the redirection of multiple virus-specific T cells toward tumors.

Antibodies are one of the most important reagents used in biomedical and fundamental research, used to identify, and quantify proteins, contribute to knowledge of disease mechanisms, and validate drug targets. Yet many antibodies used in research do not recognize their intended target, or recognize additional molecules, compromising the integrity of research findings and leading to waste of resources, lack of reproducibility, failure of research projects, and delays in drug development. Researchers frequently use antibodies without confirming that they perform as intended in their application of interest. Here we argue that the determinants of end-user antibody choice and use are critical, and under-addressed, behavioral drivers of this problem. This interacts with the batch-to-batch variability of these biological reagents, and the paucity of available characterization data for most antibodies, making it more difficult for researchers to choose high quality reagents and perform necessary validation experiments. The open-science company YCharOS works with major antibody manufacturers and knockout cell line producers to characterize antibodies, identifying high-performing renewable antibodies for many targets in neuroscience. This shows the progress that can be made by stakeholders working together. However, their work so far applies to only a tiny fraction of available antibodies. Where characterization data exists, end-users need help to find and use it appropriately. While progress has been made in the context of technical solutions and antibody characterization, we argue that initiatives to make best practice behaviors by researchers more feasible, easy, and rewarding are needed. Global cooperation and coordination between multiple partners and stakeholders will be crucial to address the technical, policy, behavioral, and open data sharing challenges. We offer potential solutions by describing our Only Good Antibodies initiative, a community of researchers and partner organizations working toward the necessary change. We conclude with an open invitation for stakeholders, including researchers, to join our cause.