mAbs最新文献

The repertoire of large-molecule treatments continues to expand, resulting in diverse discovery and development workflows. This diversity yields a proliferation of software solutions and procedures for molecule registration, material tracking, experiment planning, data analytics, quality control, data sharing, and decision-making. Contrasting with this manual, labor intensive, and error-prone approach, we introduce the concept of a transformative solution: an integrated platform that translates this complexity into a harmonized, open architecture encompassing all workflows and hardware systems, covering the discovery process up to developability assessment. The benefits and complexities of such a platform are evident in examples spanning different use cases and maturity levels, such as developing multi-specific antibodies and antibody-drug conjugates using shared workflows or incorporating artificial intelligence for predictive and generative tasks. This review outlines state-of-the-art concepts behind a digital platform for automating and streamlining the discovery of new large-molecule treatments.

Nipocalimab is a human immunoglobulin G (IgG)1 monoclonal antibody that binds to the neonatal Fc receptor (FcRn) with high specificity and high affinity at both neutral (extracellular) and acidic (intracellular) pH, resulting in the reduction of circulating IgG levels, including those of pathogenic IgG antibodies. Here, we present the molecular, cellular, and nonclinical characteristics of nipocalimab that support the reported clinical pharmacology and potential clinical application in IgG-driven, autoantibody- and alloantibody-mediated diseases. The crystal structure of the nipocalimab antigen binding fragment (Fab)/FcRn complex reveals its binding to a unique epitope on the IgG binding site of FcRn that supports the observed pH-independent high-binding affinity to FcRn. Cell-based and in vivo studies demonstrate concentration/dose- and time-dependent FcRn occupancy and IgG reduction. Nipocalimab selectively reduces circulating IgG levels without detectable effects on other adaptive and innate immune functions. In vitro experiments and in vivo studies in mice and cynomolgus monkeys generated data that align with observations from clinical studies of nipocalimab in IgG autoantibody- and alloantibody-mediated diseases.

Folate receptor alpha (FRα) has long been the focus of therapeutics development in oncology across several solid tumors, notably ovarian, lung, and subsets of breast cancers. Its multiple roles in cellular metabolism and carcinogenesis and tumor-specific overexpression relative to normal tissues render FRα an attractive target for biological therapies. Here we review the biological significance, expression distribution, and characteristics of FRα as a highly promising and now established therapy target. We discuss the ongoing development of FRα-targeting antibodies and antibody-drug conjugates (ADCs), the first of which has been approved for the treatment of ovarian cancer, providing the impetus for heightened research and therapy development. Novel insights into the tumor microenvironment, advances in antibody engineering to enhance immune-mediated effects, the emergence of ADCs, and several studies of anti-FRα agents combined with chemotherapy, targeted and immune therapy are offering new perspectives and treatment possibilities. Hence, we highlight key translational research and discuss several preclinical studies and clinical trials of interest, with an emphasis on agents and therapy combinations with potential to change future clinical practice.

T-cell engagers (TCEs) represent a powerful drug modality for redirecting a patient's own T cells to recognize and eradicate cancer cells. Although TCEs have been effective in treating hematological cancers, their broad application for solid tumors has been more challenging due to the absence of tumor-specific antigens. This often leads to on-target, off-tumor toxicities and a low therapeutic index (TI). Strategies for dual-antigen targeting of double-positive cancer cells over single-positive normal tissue may improve the TI of TCEs. In this study, we report the development and characterization of a conditional dual tumor-associated antigen (TAA)-targeting trispecific antibody (TriMab) TCE composed of a non-active anchoring arm (i.e. anti-TAA1), deficient in mediating an active immunological synapse, and an affinity-tuned active arm (i.e. anti-TAA2), paired with an anti-CD3 domain to drive AND-gated targeting and elimination of dual-TAA tumors while sparing single-TAA healthy cells. Using an anti-receptor tyrosine kinase-like orphan receptor 1 (ROR1) mAb as a proof-of-concept anchoring arm and an array of affinity-modulated variants of the anti-epidermal growth factor receptor (EGFR) GA201 mAb as active arms, we show in vitro conditional engagement and elimination of double-positive human NCI-H358 non-small cell lung cancer cells over single-positive, non-target NCI-H358.ROR1.KO cells by affinity-modulated TriMab TCEs. In vivo, the TriMab TCE exhibits selective targeting and eradication of ROR1/EGFR double-positive tumors in a mouse xenograft model. We further demonstrate the generality of the anchoring arm in TriMab using anti-HER2 mAbs targeting different binding epitopes and discuss the interplay of factors regulating immunological synapse formation. Lastly, we demonstrate that the TriMab modality exhibits a favorable developability profile and mAb-like pharmacokinetic properties in human neonatal Fc receptor transgenic mice. Overall, this work presents a generalizable approach to utilizing the TriMab modality by leveraging avidity effects and molecular geometry to achieve conditional AND-gated dual TAA-targeting with a significantly improved TI.

Monoclonal antibodies (mAbs) and fusion proteins for immune applications (FPIA) play a crucial role in treating autoimmune diseases and cancers by targeting cell-surface proteins and triggering multiple immune mechanisms. These functions are mediated by the crystallizable fragment (Fc) region of mAbs and fusion proteins, whose interaction with Fc gamma receptors (FcγRs) can be modulated through Fc amino acid (AA) engineering. To aid research in this area, we developed the IMGT/FcVariantsExplorer tool (https://www.imgt.org/fcvariantsexplorer/) to identify engineered AA changes or variants within the Fc region in mAb and fusion proteins sequences from IMGT/2Dstructure-DB, the AA sequence database of IMGT®, the international ImMunoGeneTics information system®. We used the IMGT® nomenclature of engineered Fc variants involved in antibody effector properties and formats, applying a standardized classification in five categories: 'Effector,' 'Half-life,' 'Physicochemical properties,' 'Structure,' and 'Hybrid.' We analyzed sequences from 1,107 mAbs and fusion proteins, identifying 483 entries with Fc AA changes, resulting in 211 unique Fc variants in the dataset. We also used web scraping to retrieve associated biological data from literature. All data have been integrated into IMGT/mAb-DB, with links to sequences in IMGT/2Dstructure-DB, enabling users to query Fc variants by their 'Category' or 'Effect.' This curated dataset reveals key trends in antibody engineering.

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.

First-generation cancer immunotherapies, such as high-dose interleukin-2 (IL-2), have demonstrated clinical efficacy, but are limited by significant systemic toxicities due to their broad expression of cytokine receptors. This has driven the iterative development of targeted cytokine delivery strategies. Early efforts focused on receptor-biased IL-2 variants designed to attenuate or abrogate IL-2 receptor α (IL-2 Rα/CD25) binding. Subsequently, the concept of "cis-targeting" has emerged as a strategy to deliver cytokines to specific immune cell populations, enhancing anti-tumor responses while mitigating systemic toxicity. This review highlights key common γ-chain cytokines (IL-2, IL-7, IL-15, and IL-21) as well as IL-12, providing an overview of their structures, receptors, as well as their distinct T cell functions. Furthermore, we specifically focus on the current landscape of engineered cytokine variants that facilitate targeted cytokine delivery in cis to specific T cells. By successfully restricting cytokine activity to specific T cell populations, cis-targeting approaches represent a promising strategy in the field, enabling efficient immunotherapies with improved tolerability and enhanced anti-tumor responses.

Antibodies must bind their targets with high affinity and specificity to achieve useful therapeutic activity. They must also possess suitable developability properties (e.g. thermostability, solubility, viscosity, polyreactivity) to ensure favorable manufacturing, formulation, and in vivo performance. Both binding and developability properties are inherent to a given antibody amino acid sequence. Identification or selection of antibodies possessing suitable-binding characteristics is now routine, and de novo computational design models, trained on extensive complementarity-determining region sequence and structural data, are rapidly improving. Developability properties, however, remain difficult to predict largely due to insufficient training data, with empirical testing being heavily used to avoid challenges in late-stage antibody development. To fill this gap, we built a high-throughput antibody developability assay platform designed to generate the large datasets needed to train improved machine learning (ML) models. We optimized and automated known developability assays, and developed a robust integrated data analytics pipeline. Here, we report data on 246 antibodies - representing 106 approved, 135 clinical-stage, and 5 preregistration/withdrawn molecules - across a panel of 10 developability assays, in a "tidy data" format suitable for AI/ML modeling. We used these data to develop an XGBoost ML model that better predicts similarity to approved antibodies compared to conventional use of developability warning thresholds. Additionally, we confirm that preliminary predictive models do improve with more training data. Our high-throughput PROPHET-Ab platform enables data generation at the scale needed to develop improved ML models to predict antibody developability.

Bispecific monoclonal antibodies (bsmAbs) are expected to provide targeted drug delivery that overcomes the dose-limiting toxicities often accompanying antibody-drug conjugates (ADC) in clinical practice. Much attention has been paid in the past to target selection, mAb affinities and the payload linker design, but challenges remain. Here, we demonstrate, by physiologically based pharmacokinetic (PBPK) in silico modeling and simulation, that the tissue-targeting accuracy of mono- and bispecific antibody therapeutics is substantially limited by normal physiological characteristics like organ volumes, blood flow rates, lymphatic circulation, and rates of extravasation. Only a small fraction of blood flows through solid tumor, where the diffusion-driven extravasation is relatively slow compared with many other organs. EGFR and HER2 are used as model antigens based on their experimentally measured tissue and tumor expression levels, but the approach is generic and can account for the cellular expression variation of targets. The model confirms experimental observations that only about 0.1-1% of the dosed mAb is likely to reach the tumor, while the rest ends up in healthy tissues due to target-mediated internalization and nonspecific uptake. The model suggests that the dual-positive tumor cell targeting specificity with bispecific antibodies is likely to be higher at lower drug concentrations and doses. However, this can be offset by elevated drug exposure in more accessible healthy tissues, primarily endothelium. The balance of exposure can be shifted toward tumor cells by using higher doses, albeit at the expense of more extensive target engagement elsewhere in the body, suggesting the need to adapt the toxicity of the payload if ADCs are considered. We suggest that PBPK modeling can guide and support biologics and bsmAb development, from target evaluation and drug optimization to therapeutic dose selection.

Therapeutic monoclonal antibodies (mAbs) are a successful class of biologic drugs that are frequently selected from phage display libraries and transgenic mice that produce fully human antibodies. However, binding affinity to the correct epitope is necessary, but not sufficient, for a mAb to have therapeutic potential. Sequence and structural features affect the developability of an antibody, which influences its ability to be produced at scale and enter trials, or can cause late-stage failures. Using data on paired human antibody sequences, we introduce a pipeline using a machine learning approach that exploits protein language models to identify antibodies which cluster with antibodies that have entered the clinic and are therefore expected to have developability features similar to clinically acceptable antibodies, and triage out those without these features. We propose this pipeline as a useful tool in candidate selection from large libraries, reducing the cost of exploration of the antibody space, and pursuing new therapeutics.