mAbs最新文献

Antibodies are extensively used in treating various diseases, with over 100 canonical monoclonal antibodies (mAbs) approved. Population pharmacokinetic (PK) models are typically developed for each individual mAb, despite their similarities in size, shape, and susceptibility to lysosomal degradation. However, sparse datasets with limited PK information pose challenges in deriving accurate parameter estimates. Here, we provide a comprehensive overview of 160 published models of 69 mAbs, administered either intravenously or subcutaneously, examining their structural, statistical, and covariate components. Median estimates for the base parameters are linear clearance (0.22 L/d), central volume (3.42 L), peripheral volume (2.68 L), intercompartmental clearance (0.54 L/d), absorption rate (0.25 L/d), and bioavailability (69%). Using these to simulate a 'generic' mAb results in plausible kinetics with a terminal half-life of 21 ds. We demonstrated that the median linear clearance was 26% lower in models that included nonlinear target-mediated kinetics, when compared to linear models (0.18 vs. 0.25 L/d). For chimeric mAbs median linear clearance was 50% higher compared to fully human and humanized mAbs. Variability in PK parameter estimates across models was comparable to the inter-individual variability, which have consistently shown to be large for mAbs PK (e.g. 55% vs. 43% for clearance and 25% vs. 30% for central volume, respectively). Our meta-analysis suggests that a priori parameter estimates derived from the large body of existing pharmacokinetic models for mAbs are representative for many mAbs and can facilitate the design of new and/or more complex pharmacokinetic models or assist in dose optimization models.

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.

Snakebite envenoming is a neglected tropical disease that afflicts millions of people globally, leading to substantial morbidity and mortality. Developing novel antivenoms, particularly recombinant antivenoms based on broadly neutralizing monoclonal antibodies, offers a promising strategy to address the challenge posed by venom variability. However, the extensive diversity of snake venom toxins across species and geographical regions makes this goal inherently complex. Consequently, there is a pressing need for robust discovery methodologies capable of identifying broadly neutralizing antibodies with high affinity and functional potency against a wide range of toxin families. In this study, we engineered a short-chain consensus (SCC) α-neurotoxin to serve as an antigen for a phage display - based antibody discovery campaign. The SCC was expressed using a yeast system, enabling the identification of seven variable domains of heavy-chain-only antibodies (V_HHs) from immune libraries. These V_HHs exhibited nanomolar-binding affinities and low dissociation rates across a panel of short-chain α-neurotoxins, which translated into in vitro neutralization, protecting the target receptor. The best two V_HHs also conferred protection against lethality in a rodent model. These results highlight the unexpected value of consensus toxins in antibody discovery and offer a viable route for developing recombinant antivenoms with broad-spectrum efficacy.

Immunoglobulin (Ig) A has attracted interest as a proposed therapeutic agent due to its ability to engage cell groups differently compared to an IgG scaffold and elicit tumor eradication. Further, its multimeric forms enable increased flexibility in the design of available paratopes. The latter is particularly advantageous for bi- and multispecific antibody formats, which are unparalleled in their enhanced selectivity and unique biological functions. We engineered bispecific heterodimeric IgA-based antibodies using the strand-exchanged engineered domain (SEED) technology, which relies on intertwined segments of IgA and IgG in the C_H3 domain, and applied mutagenesis to introduce two additional binding sites to enable the interaction of IgA-Fc with the myeloid cell-activating receptor CD89 (FcαR). These antibodies exhibited good biophysical properties and thermostability similar to the parental SEED molecule. Binding capacity to both antigens recognized by variable domains, epidermal growth factor receptor (EGFR) and receptor tyrosine kinase like orphan receptor 1 (ROR1), was not impaired, and in contrast to the original SEED-IgA, trispecific mutants could bind to CD89-expressing cells, mediate tumor cell-effector cell clustering, and induce neutrophil-mediated specific lysis of tumor cells. Trispecific design was applicable to both SEED-IgA1 and -IgA2 scaffolds. Interestingly, HEK-expressed mutants featured a CH2-linked N-glycan pattern more similar to wild-type IgA, with reduced core fucosylation in comparison with IgA-SEED. Collectively, the presented format combines the mobilization of CD89-positive effector cells with the flexibility of incorporating antigen specificities of choice into the variable domains, and thus is a promising basis for biochemically stable multispecific IgA with high therapeutic potential.

Acute myeloid leukemia (AML) is a heterogeneous malignancy with poor clinical outcome. Aberrant expression of CD7 in AML patients is linked to shorter overall survival and lack of response to standard of care therapy. CD33/CD7 co-expression on leukemic blasts occurs in approximately one-third of AML patients and is known to be absent in normal myeloid cells. We propose that CD33⁺CD7⁺ AML constitutes an aggressive subgroup characterized by poorer prognosis and enrichment in stem-cell associated gene signatures. To address the substantial unmet need in this patient cohort, we developed the antibody-drug conjugate BVX001, a CD33xCD7-targeted bispecific antibody-binding fragment linked to an auristatin payload. Importantly, BVX001 relies on simultaneous binding to CD33 and CD7 in cis through an 'AND-gated' design, for optimal delivery of its cytotoxic payload. Consequently, BVX001 did not affect healthy myeloid progenitors or T cells at concentrations at which its monospecific counterparts showed toxicity. BVX001 induced significant tumor regression in AML cell line and patient-derived xenografts and increased overall survival. Finally, BVX001 showed significant blast ablation and reduced leukemic stem cell frequency in AML patient samples with both high and low target co-expression. Together, our findings support BVX001 as a new and promising approach for the treatment of this aggressive CD33⁺CD7⁺ AML subtype, currently lacking targeted therapeutic options.