mAbs最新文献

Monoclonal antibodies are well established as promising treatment options for a broad range of patients with severe diseases. In some cases, the formation of anti-drug antibodies (ADA) may limit their clinical use and potentially affect safety and efficacy for patients. Despite extensive research, some factors contributing to the immunogenicity of therapeutic antibodies remain poorly understood. In particular, the immunogenicity potential associated with multivalent antibody formats targeting oligomeric protein antigens has thus far received insufficient attention. Large, target-related immune complexes (TRICs) may be formed that can trigger Fc-mediated downstream effects and have the potential to contribute to the development of an ADA response. Here, we present experimental evidence highlighting the roles of epitope, paratope, and binding geometry in defining the composition and size distribution of TRICs formed by IL-17A, a homodimeric cytokine, with four clinical anti-IL-17 antibodies, secukinumab (Cosentyx^Ⓡ), ixekizumab (Taltz^Ⓡ), bimekizumab (Bimzelx^Ⓡ) and CJM112. Widely different ADA incidence rates have been reported for these antibodies. We found that all four antibodies formed closed-chain TRICs, each comprising two or more IgG molecules connected by an equivalent number of IL-17A homodimers. Secukinumab, the antibody with the lowest ADA incidence rate, uniquely exhibited primarily 2 + 2 closed-chain complexes. In contrast, CJM112 and bimekizumab showed higher amounts of 3 + 3 and 4 + 4 complexes. Additionally, CJM112, and to a greater extent, bimekizumab and ixekizumab, formed very high molecular weight TRICs. Our findings underscore the importance of conducting in-depth biophysical analyses of TRICs formed by therapeutic antibody candidates targeting multivalent protein antigens, to develop safer and more efficacious treatments.

Since the approval of OKT3® in 1986, monoclonal antibodies (mAbs) have become a cornerstone of modern therapeutics. However, their complex physicochemical properties pose challenges, particularly for high-concentration formulation and subcutaneous administration. Excipient selection is crucial for maintaining mAb stability and efficacy, yet existing studies often lack systematic, cross-source analyses. This study integrates data from marketed products and patents to investigate formulation trends and excipient preferences. Data were retrieved from the Drugs@FDA databases, CAS Formulations Database and Derwent Innovation as of December 31, 2024. Extracted information included target, indication, dosage form, route of administration, and formulation composition. The associations between formulation-related factors (e.g., antibody concentration, route of administration) and excipient selection were evaluated using proportion tests. A total of 6,119 patent records and 108 marketed mAb products (covering 388 patented and 141 marketed formulations) were analyzed. Proportion tests revealed significant associations between antibody concentration and the use of histidine (marketed p = 0.0017) and citric acid (marketed p = 0.0047). The route of administration also influenced excipient choice, notably for hyaluronidase (marketed p = 0.0167; patent p = 0.0056). In addition, lyophilized formulations accounted for a relatively small fraction of both marketed (14.18%) and patented (14.69%) products, with sucrose emerging as the predominant lyoprotectant. This study analyzed excipient usage in marketed and patent formulations by time, concentration, and administration route. High-concentration products more frequently included histidine, arginine, and hyaluronidase, while low-concentration ones used citric/phosphoric acid, trehalose, and NaCl. Intravenous formulations commonly used phosphate/citric buffers, while histidine, arginine, hyaluronidase, and methionine buffers were favored for subcutaneous administration. Lyophilized formulations consistently contained sucrose as the main excipient to mitigate freeze-drying stresses. Additionally, surfactants were essential across formulations to prevent surface-induced aggregation. Patent data could provide early indications of emerging formulation strategies, though further validation is needed to confirm their predictive value.

Antibody-drug conjugates (ADCs) rely on antibody-mediated internalization to deliver cytotoxic payloads into tumor cells. Therefore, quantitative assessment of antibody internalization is essential for ADC development, particularly during early antibody screening stages. However, conventional internalization assays, whether direct or indirect, often face challenges such as low throughput, reduced sensitivity, and limited target specificity due to spatial hindrance. Here, we introduce a versatile 3C peptide conjugate platform that utilizes the high-affinity binding of IgG by the C1-C3 domains of streptococcal protein G. This platform includes 3C-toxin for cytotoxicity-based internalization detection and 3C-pHAb for pH-sensitive fluorescent tracking. By simply incubating these reagents with antibodies, effective labeling is achieved without complex modifications, enabling sensitive and high-throughput evaluation of internalization. We validated the platform across multiple tumor-associated targets, including HER2, CDH6, LIV-1, LYPD3, and GPC3, demonstrating a strong correlation between 3C-based assays and the cytotoxic efficacy of corresponding ADCs. Notably, 3C-toxin showed superior target promiscuity compared to traditional DT3C methods, expanding applicability to a broader range of antigens. This platform provides a scalable solution for antibody internalization analysis, positioned to accelerate ADC discovery by providing reliable early-stage screening metrics.

The multi-attribute method (MAM) by liquid chromatography-mass spectrometry peptide mapping has the potential to replace multiple conventional HPLC- and capillary electrophoresis-based purity/impurity assays for release and stability testing of protein biopharmaceuticals such as monoclonal antibodies. Prerequisite is the availability of the new peak detection (NPD) functionality to reliably detect new, absent, and changed peptide species that may impair the quality, safety, and efficacy of the drug. Here, we describe the development, qualification, and application of a highly efficient and robust NPD workflow within the Genedata Expressionist® software. The detection thresholds have been rationally designed, and the NPD workflow has been successfully validated according to ICH Q2 guidelines. Individual case studies, including stability testing of drug product and detection of unknown impurities in drug substance, highlight the workflows' ability to reliably recognize relevant peptide species below 1% relative abundance without reporting any false positive peaks. The application of this NPD workflow signifies a substantial leap forward in the use of MAM as a quality control tool, as it allows identification of true positive peaks at adequate sensitivity in the absence of false positive peaks.

The receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein, responsible for engaging the hACE2 receptor, is the principal target of neutralizing antibodies (NAb). To better understand how viral evolution undermines NAb protection, we present a comprehensive, topology-based classification derived from 544 NAbs and 60 nanobody-RBD complex structures. Five major NAb classes, each subdivided into two subclasses, were defined by binding zone, angle of approach, hACE2 competition, and hotspot usage. A systematic mapping of NAb-antigen contacts revealed 91 recurrent hotspot residues, some of which remain fully conserved across all Omicron variants. Leveraging > 2,300 experimentally dissociation constants spanning the Wuhan strain and Omicron lineages, we conducted a comparative affinity analysis across subclasses. NAbs in classes 1-3, which overlap the receptor-binding site, show progressive loss of affinity against Omicron, with many failing to bind recent subvariants due to emergent steric clashes and limited affinity maturation against the ancestral Wuhan RBD. Nonetheless, cases of Abs exhibiting resilience to viral drift have been documented. In contrast, classes 4 and 5 maintain high affinity regardless of their initial affinity for parental strains. Contemporary in-silico epitope predictors captured only ~40% of experimentally defined hotspots, highlighting the need for structure-guided approaches. By introducing a refined topological segmentation of the RBD grounded in previously described but unsystematized regions, our classification captures a broad diversity of NAb binding modes and provides an integrative structural framework that harmonizes prior classification schemes, its relationship with circulating variants, and highlights conserved epitope features relevant to broad-spectrum vaccine and therapeutic NAb design.

Charge heterogeneity in monoclonal antibodies (mAbs), caused by post-translational modifications, remains a substantial obstacle to ensuring consistent, stable, and effective therapeutics. Conventional optimization techniques, such as one-factor-at-a-time and design of experiments, often fail to capture the complex, nonlinear interactions between culture parameters (e.g. pH, temperature, duration) and medium components (e.g. glucose, metal ions, amino acids). This review highlights machine learning (ML) as a powerful approach for modeling these relationships and forecasting charge variant profiles in CHO cell-based mAb process development. We summarize supervised learning and regression methods used to link process conditions with charge heterogeneity and present case studies showing ML's role in reducing acidic and basic variants. We also discuss challenges related to data quality, model interpretability, scalability, and regulatory compliance. Finally, we propose a roadmap for adaptive, ML-driven optimization strategies for bioprocess development, aligned with Quality-by-Design principles.

T cell bispecific antibodies (TCBs) are a promising new class of therapeutics for relapsed/refractory multiple myeloma. A frequently observed, yet incompletely understood effect of this treatment is the transient reduction of circulating T cell counts, also known as T cell margination (TCM). After administration of the GPRC5D-targeting TCB forimtamig (RG6234), TCM occurred in patients and correlated with cytokine release and soluble B cell maturation antigen decrease. We demonstrate that TCM is accurately represented in the humanized NSG mouse model and occurs at a lower threshold of target expression than systemic cytokine release. Application of whole-mouse tissue clearing and 3D imaging revealed that T cells accumulate in the bone marrow after treatment. We hypothesize that low amounts of targets are sufficient to rapidly redirect T cells upon TCB engagement. Therefore, we propose TCM as a beneficial, highly sensitive and early effect of forimtamig that leads T cells to likely sites of bone marrow tumor lesions.

The field of antibody therapeutics is rapidly growing, with over 210 antibodies currently approved or in regulatory review and ~ 1,250 antibodies in clinical development. Antibodies are highly versatile molecules that, with strategic design of their antigen-binding domain (Fab) and the domain responsible for mediating effector functions (Fc), can be used in a wide range of therapeutic indications. Building on many years of progress, the biopharmaceutical industry is now advancing innovative research and development by exploring new targets and new formats and using antibody engineering to fine-tune functions tailored to specific disease requirements. In addition to considering the target and the disease context, however, the unique features of each therapeutic antibody trigger a diverse set of Fc-mediated effector functions. To avoid unexpected results on safety and efficacy outcomes during the later stages of the development process, it is crucial to measure the impact of antibody design on Fc-mediated effector function early in the antibody development process. Given the breadth of effector functions antibodies can deploy and the close interplay between the antibody Fab and Fc functional domains, it is important to conduct a comprehensive evaluation of Fc-mediated functions using an array of antigen-specific biophysical and cell-mediated functional assays. Here, we review antibody and Fc receptor properties that influence Fc effector functions and discuss their implications on development of safe and efficacious antibody therapeutics.

Developability studies provide essential data to identify monoclonal antibodies (mAbs) with optimal drug-like properties, which are indicative of a molecule's suitability for large-scale manufacturing, long-term storage, and ease of administration. Hydrophobicity is a critical molecular attribute that affects solubility, aggregation, and stability at high protein concentrations and is routinely assessed in these studies. Although traditional analytical hydrophobic interaction chromatography (aHIC) is considered the benchmark for measuring hydrophobicity, its application in early developability studies is limited because the process requires serial sample injections, which is time-intensive and impractical for the evaluation of hundreds of molecules. To overcome this limitation, we developed an alternative aHIC method that uses a plate-based assay format, enabling rapid screening of large sample sets. Compatible with automation platforms, this surrogate aHIC method demonstrates excellent accuracy in distinguishing between low- and high-risk molecules, proving to be an efficient tool for preliminary developability assessments. This innovative assay provides a robust, timesaving, and sample-efficient means of evaluating hydrophobicity that readily supports early phase biotherapeutic antibody discovery through selection of mAbs with favorable drug-like properties. Furthermore, the potential for adaptation of this method to various molecular formats suggests its broad applicability in biotherapeutic discovery.

The efficacy of therapeutic monoclonal antibodies (mAbs) often hinges on biodistribution to their site of action. However, traditional pharmacokinetic (PK) assessments - typically based on measuring plasma or total tissue concentrations - fail to reflect the interstitial concentrations that are most relevant for tissue targets. This study aimed to address this limitation by integrating experimentally determined vascular and interstitial volumes from tissues in SCID-beige mice with a comprehensive PK time-course and biodistribution analysis of four distinct anti-viral monoclonal antibodies (mAbs 1-4) with no endogenous mouse target. The biodistribution studies included 11 tissues, characterizing tissue and plasma concentrations over a 168-h time-course. Total and interstitial tissue concentrations were evaluated to better understand concentrations within the interstitial space compared to bulk tissue values. These data revealed significant tissue-specific partitioning, with fold-change analysis suggesting groupings correlating with capillary endothelium characteristics. A dynamic model was implemented for the estimation of antibody biodistribution coefficient (ABC) values at steady-state, partitioning ratio (PR) values at steady-state, and their associated equilibrium rate constants (t_1/2eq, t'_1/2eq) across 11 (ABC, t_1/2eq) and 7 tissues (PR, t'_1/2eq), respectively. Specifically, to understand non-binding, target-independent biodistribution, we combined data from mAbs 1, 2, and 3 to create a "typical mAb" (mAb 123) profile, from which these coefficients and ratios were derived. Analysis of mAb 4, a structurally similar IgG molecule with undesirable PK properties, enabled comparative insights into antibody distribution and kinetics. These studies provided a comprehensive dataset for understanding interstitial antibody PK, crucial for improving predictions of PK at the site-of-action and in vivo efficacy.