mAbs最新文献

Targeting various combinations of tumor antigens and immune cell receptors is of increasing importance in antibody-based cancer immunotherapy. Here, we present a novel modular P329G-engager platform that enables rapid combination of primary tumor-targeting and secondary immune effector antibodies. The platform utilizes two antibodies, each selected from: 1) a set of tumor-targeting adaptor antibodies, bearing P329G mutations in the Fc region, and 2) a set of P329G-targeting (bispecific) cell engagers, including innate and T cell engagers, costimulators and immunocytokines. Specifically, upon defining a tumor-associated cell surface target, a primary adaptor - tumor antigen-binding IgG1 antibody with Fc-silencing P329G L234A L235A mutations - is administered. Subsequently, a secondary antibody recognizing the P329G mutation is chosen from a panel of effector cell engagers with different modes of action - ADCC-competent P329G-innate cell engagers (P329G-ICE), P329G-T cell bispecifics (P329G-TCB), P329G-costimulators (P329G-CD28/4-1BBL), or P329G-immunocytokine (P329G-IL2v). In vitro assays showed that all P329G-targeting modalities induce anti-tumoral and/or immunomodulatory activity when both components were combined. In vivo, tumor shrinkage and T cell infiltration were confirmed in tumor-bearing humanized mice treated with P329G-mutated CEACAM5 adaptor IgG and P329G-TCB. Individually, neither the adaptor nor the P329G-TCB induced efficacy, validating the requirement for primary and secondary antibody assembly for T cell-engaging activity. These results provided evidence for the in vivo assembly and subsequent pharmacological activity, and provide preclinical proof-of-concept for the P329G-engager platform as an efficacious tool in drug discovery. Ultimately, this modular approach may enable mix-and-match drug assembly as a novel therapeutic principle in immunotherapy.

Therapeutic antibodies have gained prominence in recent years due to their precision in targeting specific diseases. As these molecules become increasingly essential in modern medicine, comprehensive data tracking and analysis are critical for advancing research and ensuring successful clinical outcomes. YAbS, The Antibody Society's Antibody Therapeutics Database, serves as a vital resource for monitoring the development and clinical progress of therapeutic antibodies. The database catalogs detailed information on over 2,900 commercially sponsored investigational antibody candidates that have entered clinical study since 2000, as well as all approved antibody therapeutics. Data for the late-stage clinical pipeline and antibody therapeutics in regulatory review or approved (over 450 molecules) are openly accessible (https://db.antibodysociety.org). Antibody-related information includes molecular format, targeted antigen, current development status, indications studied, and the clinical development timeline of the antibodies, as well as the geographical region of company sponsors. Furthermore, the database supports in-depth industry trends analysis, facilitating the identification of innovative developments and the assessment of success rates within the field. This resource is continually updated and refined, providing invaluable insights to researchers, clinicians, and industry professionals engaged in antibody therapeutics development.

ISB 1442 is a bispecific biparatopic antibody in clinical development to treat hematological malignancies. It consists of two adjacent anti-CD38 arms targeting non-overlapping epitopes that preferentially drive binding to tumor cells and a low-affinity anti-CD47 arm to enable avidity-induced blocking of proximal CD47 receptors. We previously reported the pharmacology of ISB 1442, designed to reestablish synthetic immunity in CD38+ hematological malignancies. Here, we describe the discovery, optimization and characterization of the ISB 1442 antigen binding fragment (Fab) arms, their assembly to 2 + 1 format, and present the high-resolution co-crystal structures of the two anti-CD38 Fabs, in complex with CD38. This, with biophysical and functional assays, elucidated the underlying mechanism of action of ISB 1442. In solution phase, ISB 1442 forms a 2:2 complex with CD38 as determined by size-exclusion chromatography with multi-angle light scattering and electron microscopy. The predicted antibody-antigen stoichiometries at different CD38 surface densities were experimentally validated by surface plasmon resonance and cell binding assays. The specific design and structural features of ISB 1442 enable: 1) enhanced trans binding to adjacent CD38 molecules to increase Fc density at the cancer cell surface; 2) prevention of avid cis binding to monomeric CD38 to minimize blockade by soluble shed CD38; and 3) greater binding avidity, with a slower off-rate at high CD38 density, for increased specificity. The superior CD38 targeting of ISB 1442, at both high and low receptor densities, by its biparatopic design, will enhance proximal CD47 blockade and thus counteract a major tumor escape mechanism in multiple myeloma patients.

NISTCHO is a Chinese hamster ovary (CHO) cell line expressing the same amino acid sequences as the heavy and light chains of the National Institute of Standards and Technology (NIST) monoclonal antibody [Reference Material (RM) 8671 NISTmAb]. NISTCHO was generated by MilliporeSigma to be developed by NIST as a RM to support biomanufacturing research and innovation, method development and qualification, and pre-competitive research collaboration. The RM cell line, denoted as RM 8675 NISTCHO, Clonal CHO-K1 Cell Line Producing cNISTmAb, is of interest to the biopharmaceutical and biomanufacturing industries, regulatory and government agencies, and academic institutions. In contrast to other NIST RMs, however, which are typically discrete and finite, the NISTCHO is a living RM that can be propagated, expanded, and used repeatedly to express the non-originator NISTmAb product, cNISTmAb. Therefore, a uniform naming convention should be adopted by the user community to best track the origins of materials (the cell line and products) used in studies derived from the RM 8675 NISTCHO. Here, we provide a naming convention for the derivatives of the RM 8675 NISTCHO and the cNISTmAb produced by these NISTCHO derivatives and recommend these naming conventions for adoption by the scientific community.

Multidrug resistance (MDR) hinders efficacious cancer chemotherapy. Overexpression of the P-glycoprotein (P-gp) efflux pump (EP) on cancer cells is a primary cause of MDR since it expels numerous anticancer drugs. Small molecule intracellular P-gp antagonists have been investigated clinically to redress MDR but have failed primarily due to adverse effects on P-gp in normal tissue. We used a new approach to counteract P-gp with bispecific antibodies (BsAbs) that simultaneously bound P-gp and CD47 in cis on MDR cells but not normal tissue. Affinities of the individual arms of the BsAbs were low enough to minimize normal tissue binding, but, when the two targets were co-located on MDR cancer cells, both arms of the BsAb engaged with effective avidity. Proof-of-concept was shown in three different MDR xenograft tumor models with a non-humanized chimeric BsAb (targeting P-gp and CD47) that potently restored tumor sensitivity to paclitaxel. Fully humanized variants were successfully developed and characterized. Significant anti-tumor efficacy was observed with the BsAbs both when combined with paclitaxel and as single agents in the absence of paclitaxel. Treatment of MDR cancers with BsAbs using this novel approach has several distinct advantages over prior efforts with small molecule antagonists, including 1) invoking a direct immune attack on the tumors, 2) multimodal mechanisms of action, 3) tumor-specific targeting (with reduced toxicity to normal tissue), and 4) broad applicability as single agents and compatibility with other therapeutics.

Fc-mediated effector functions are key for conferring potent antibody-mediated killing of cancer cells. However, it is difficult to achieve highly selective targeting of cancer cells while minimizing toxicity on healthy tissue because of the expression of most receptors, albeit at lower levels, on non-cancer cells. Previous attempts to increase the selectivity of antibody-mediated effector functions have sought to reduce binding affinity and/or increase avidity, which typically results in modest improvements in selectivity. To overcome this limitation, we report the use of mixtures of antibody variants that achieve high selectivity based on receptor level while maintaining high activity for cells with high receptor levels. We have studied mixtures of two variants of an anti-HER2 antibody (trastuzumab), one that is affinity-reduced and effector-competent and a second high-affinity variant that is effectorless. Notably, we observe that the high-affinity, effectorless antibody reduces effector function for cells with low receptor levels, including reduced antibody-dependent cellular cytotoxicity (ADCC) and phagocytosis (ADCP), while the high-avidity, effector-competent antibody mediates significant effector function for cells with high receptor levels. Moreover, replacing the effector-competent Fc region of the affinity-reduced antibody with high-affinity Fc domains that enhance effector function drives high activity while maintaining high selectivity for the antibody mixtures. These findings outline a general strategy for maximizing the therapeutic window by selectively targeting cancer cells based on receptor levels that could be applied to a wide range of applications involving antibody-mediated synapse formation, including antibody-drug conjugates and bispecific antibodies, such as T cell engagers.

A conventional antibody can be converted into its catalytic counterparts by deleting Pro95 in the CDR-3 of human and mice antibody light chains, as previously reported. T99wt is a naturally occurring human antibody light chain that we transformed into its catalytic antibody using Pro95 deletion. In peptidase activity tests, T99wt exhibited a low catalytic activity against a synthetic peptide Arg-pNA and hardly cleaved amyloid-β peptide. In contrast, the engineered variant (T99-Pro95(-)) demonstrated significant catalytic activity, effectively cleaving both Arg-pNA substrate and amyloid-β peptides. In this study, the structural basis for the acquisition of enzymatic function through Pro95 deletion in the CDR-3 region of the light chain was elucidated using X-ray crystallography and molecular dynamics (MD) simulations. X-ray crystallography revealed that Pro95 deletion substantially reduces the distance between Asp1 and His93-key residues for catalytic activity - from 9.56 Å in T99wt to 3.84 Å in T99-Pro95(-). The observed decrease in distance indicates a strong interaction between Asp1(Oδ1) and His93(Nε2), contributing to the formation of an active site in T99-Pro95(-). MD simulations revealed that the entire structure exhibits slight fluctuations and adopts various configurations upon the removal of Pro95. In particular, when His residues in the catalytic region are fully deprotonated, Asp1, His93, and Ser27a transiently come into close proximity, enabling the formation of a functional catalytic triad. Catalytic antibodies can be made starting from just the amino acid sequence of a desired mAb, which may be available in databases such as OAS or IMGT. Therefore, our finding represents a significant technological advancement.

In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalizability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelt's potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver.

IgG-based anti-cancer therapies have achieved promising clinical outcomes, but, especially for patients with solid tumors, response rates vary. IgE antibodies promote distinct immune responses compared to IgG and have shown anti-tumoral pre-clinical activity and preliminary efficacy and safety profile in clinical testing. To improve potency further, we engineered a hybrid IgE-IgG1 antibody (IgEG), to combine the functions of both isotypes. Two IgEGs were generated with variable regions taken from trastuzumab (Tras IgEG) and from a novel anti-HER2 IgE (26 IgEG). Both IgEGs expressed well in mammalian cells and demonstrated IgE-like stability. IgEGs demonstrated both IgE and IgG1 functionality in vitro. A lack of type I hypersensitivity associated with IgEG incubation with human blood is suggestive of acceptable safety. In vivo, IgEGs exhibited distinct pharmacokinetic profiles and produced anti-tumoral efficacy comparable to IgE. These findings highlight the potential of IgEG as a new therapeutic modality in oncology.

Since the approval of the first antibody drug in 1986, a total of 162 antibodies have been approved for a wide range of therapeutic areas, including cancer, autoimmune, infectious, or cardiovascular diseases. Despite advances in biotechnology that accelerated the development of antibody drugs, the drug discovery process for this modality remains lengthy and costly, requiring multiple rounds of optimizations before a drug candidate can progress to preclinical and clinical trials. This multi-optimization problem involves increasing the affinity of the antibody to the target antigen while refining additional biophysical properties that are essential to drug development such as solubility, thermostability or aggregation propensity. Additionally, antibodies that resemble natural human antibodies are particularly desirable, as they are likely to offer improved profiles in terms of safety, efficacy, and reduced immunogenicity, further supporting their therapeutic potential. In this article, we explore the use of energy-based generative models to optimize a candidate monoclonal antibody. We identify tradeoffs when optimizing for multiple properties, focusing on solubility, humanness and affinity and use the generative model we develop to generate candidate antibodies that lie on optimal Pareto fronts with respect to these properties.