mAbs最新文献

The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These building blocks play an important role in the overall quality of the molecule and can influence many important aspects from potency and specificity to stability and half-life. Single-domain antibodies, particularly camelid-derived variable heavy domain of heavy chain (VHH) antibodies, are becoming an increasingly popular choice for bispecific construction due to their single-domain modularity, favorable biophysical properties, and potential to work in multiple antibody formats. Here, we review the use of VHH domains as building blocks in the construction of multispecific antibodies and the challenges in creating optimized molecules. In addition to exploring traditional approaches to VHH development, we review the integration of machine learning techniques at various stages of the process. Specifically, the utilization of machine learning for structural prediction, lead identification, lead optimization, and humanization of VHH antibodies.

Natural killer (NK) cells are key players in human innate immunity. Cell engager antibody formats that recruit and activate NK cells more effectively have emerged as a promising immunotherapy approach to target cancer cells through more effective antibody-dependent cell-mediated cytotoxicity (ADCC). Monoclonal antibody drugs with ADCC activity have shown clinical benefit and improved outcomes for patients with certain types of cancer. CD16a, a Fc gamma III receptor, is the major component that is responsible for the ADCC activity of NK cells. Screening AvantGen's yeast displayed human antibody libraries led to the isolation of 2 antibody clones, #1A2 and #2-2A2, that selectively recognize both isoforms (F and V) of CD16a on primary NK cells with high affinity, yet minimally (#1A2) or do not (#2-2A2) cross-react with both allelotypes of CD16b (NA1 and NA2) expressed by neutrophils. Epitope mapping studies revealed that they bind to an epitope dependent on residue Y158 of CD16a, since mutation of Y158 to the corresponding CD16b residue H158 completely abolishes binding to CD16a. When formatted as bispecific antibodies targeting CD16a and a tumor-associated antigen (TAA, e.g. CD19), they exhibit specific binding to NK cells and induce potent NK cell activation upon encountering tumor cells, resulting in effective tumor cell killing. Notably, these bispecific antibody engagers stimulate NK cell cytokine release during co-culture with target cells, resulting in target cell cytotoxicity. These anti-CD16a antibody clones are promising candidates for combination with any TAA of interest, offering the potential for novel NK cell engager-based cancer therapeutics that are minimally affected by the high concentrations of human IgG in the circulation.

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.

The higher order structure (HOS) of monoclonal antibodies (mAbs) is an important quality attribute with strong contribution to clinically relevant biological functions and drug safety. Due to the multi-faceted nature of HOS, the synergy of multiple complementary analytical approaches can substantially improve the understanding, accuracy, and resolution of HOS characterization. In this study, we applied one- and two-dimensional (1D and 2D) nuclear magnetic resonance (NMR) spectroscopy coupled with chemometric analysis, as well as circular dichroism (CD), differential scanning calorimetry (DSC), and fluorescence spectroscopy as orthogonal methods, to characterize the impact of methionine (Met) oxidation on the HOS of an IgG1 mAb. We used a forced degradation method involving concentration-dependent oxidation by peracetic acid, in which Met oxidation is site-specifically quantified by liquid chromatography-mass spectrometry. Conventional biophysical techniques report nuanced results, in which CD detects no change to the secondary structure and little change in the tertiary structure. Yet, DSC measurements show the destabilization of Fab and Fc domains due to Met oxidation. More importantly, our study demonstrates that 1D and 2D NMR and chemometric analysis can provide semi-quantitative analysis of chemical modifications and resolve localized conformational changes with high sensitivity. Furthermore, we leveraged a novel ¹⁵N-Met labeling technique of the antibody to directly observe structural perturbations at the oxidation sites. The NMR methods described here to probe HOS changes are highly reliable and practical in biopharmaceutical characterization.

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.

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.

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.

Currently approved human epidermal growth factor receptor 2 (HER2)-targeted antibody therapies are largely derived from trastuzumab, including trastuzumab-chemotherapy combinations, fixed-dose trastuzumab-pertuzumab combinations, and trastuzumab antibody-drug conjugates. To expand the options, bispecific antibodies, which may better utilize the benefits of combination therapy, are being developed. Among them, biparatopic antibodies (bpAbs) have shown improved efficacy compared to monoclonal antibody (mAb) combinations in HER2-positive patients. BpAbs bind two independent epitopes on the same antigen, which allows fine-tuning of mechanisms of action, including enhancement of on-target specificity and induction of strong antigen clustering due to the unique binding mode. To fully utilize the potential of bpAbs for anti-HER2 drug development, it is crucial to consider formats that offer stability and high-yield production, along with a functional balance between the two epitopes. In this study, we rationally designed a bpAb, KJ015, that shares a common light chain with two Fab arms and exhibits functionally balanced high affinity for two HER2 non-overlapping epitopes. KJ015 demonstrated high-expression titers over 7 g/L and stable physicochemical properties at elevated concentrations, facilitating subcutaneous administration with hyaluronidase. Moreover, KJ015 maintained comparable antibody-dependent cytotoxicity, phagocytosis, and complement-dependent cytotoxicity with trastuzumab plus pertuzumab. It exhibited enhanced synergy when administered subcutaneously with hyaluronidase and anti-PD-1 mAb in a mouse tumor model, suggesting promising clinical prospects for this combination.