mAbs最新文献

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.

Antibodies recognize antigens via complementary and structurally dependent mechanisms. Therefore, inclusion of antibody inputs is crucial for accurate epitope prediction. Given the limited availability of antibody-antigen complex structures, any epitope prediction model will require minimal yet sufficient antibody inputs to ensure precise epitope identification. To address this need, we introduce Epi4Ab, an antibody-specific epitope prediction model that focuses on identifying unique in-contact antigen residues for a given antibody. Epi4Ab requires minimal antibody inputs, specifically VH/VL families and complementarity-determining region sequences.

Here, we describe a new VHH library for therapeutic discovery which optimizes humanness, stability, affinity, diversity, developability, and facile purification using protein A in the absence of an Fc domain. Four therapeutic humanized VHHs were used as scaffolds, into which we inserted human HCDR1s, HCDR2s and HCDR3s. The HCDR1 and HCDR2 sequences were derived from human VH3 family next-generation sequencing datasets informatically purged of sequence liabilities, synthesized as array-based oligonucleotides, cloned as single CDR libraries into each of the parental scaffolds and filtered for protein A binding by yeast display to ensure correct folding and display. After filtering, the CDR1 and CDR2 libraries were combined with amplified human HCDR3 from human CD19⁺ IgM⁺ B cells. This library was further improved by eliminating long consecutive stretches of tyrosines in CDR3 and enriching for CDR1-2 diversity with elevated tolerance to high temperatures. A broad diversity of high affinity (100 pM-10 nM), developable binders was directly isolated, with developability evaluated for most assays using the isolated VHHs, rather than fused to Fc, which is customary. This represents the first systematic developability assessment of isolated VHH molecules.

Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens. These approaches, however, often focus on the antibody complementarity-determining region (CDR) whilst ignoring the immunoglobulin framework (FW), which provides structural rigidity and support for the flexible CDR loops. Here we present an integrated computational-experimental workflow, combining static structure analyses, molecular dynamics simulations and in vitro physicochemical and functional assays to generate rational designs of FW mutations for modulating antibody stability and activity. We first showed that recent antibody-specific language models lacked insights in FW mutagenesis, in comparison to approaches that use antibody structure information. Using the widely used breast cancer therapeutic trastuzumab as a use case, we designed stabilizing mutants which were distal to the CDR and preserved the antibody's functionality to engage its cognate antigen (HER2) and induce antibody-dependent cellular cytotoxicity. Interestingly, guided by local backbone motions predicted using molecular dynamics simulations, we designed a FW mutation on the trastuzumab light chain that retained antigen-binding effects, but lost Fab-mediated and Fc-mediated effector functions. This highlighted the effects of FW on immunological functions engendered in distal areas of the antibody, and the importance of considering attributes other than binding affinity when assessing antibody function. Our approach incorporates interdomain dynamics and distal effects between FW and the Fc domains, expands the scope of antibody engineering beyond the CDR, and underscores the importance of a holistic perspective that considers the entire antibody structure in optimizing antibody stability, developability and function.

Nanobodies (Nbs) are antigen-binding fragments derived from unique heavy-chain-only antibodies. In recent years, the development of Nbs has progressed rapidly due to their therapeutic potential. Here we present a comprehensive patent landscape of Nb technologies, focusing on uncovering innovation trends, identifying novel drug candidates, and analyzing opportunities and challenges for research, development, and commercialization. Using B-cell maturation antigen (BCMA) as an example drug target, we summarize the features, physicochemical properties, modification sites, and epitope-binding tendencies of patented sequences of Nb drugs, highlighting the importance of structural-level patent protection, and offering a theoretical foundation for Nb design and experimental validation. Through patent landscape and patent sequence analysis, our study provides valuable insights for Nb drug development and supports decision-making in patent strategy.