mAbs最新文献

Targeting checkpoint inhibitors is an effective therapy for treating cancer, with human programmed cell death protein 1 (hPD-1) being one of the most successful targets for developing antibody-based drugs. In this work, we isolated a panel of anti-PD-1 single-chain variable fragments with different binding and functional profiles from a fully synthetic human phage display library. Conversion of the best clone to hIgG1LALA and hIgG4PE formats, called UDIZ-007 and UDIZ-008, respectively, resulted in antibodies that effectively blocked the PD-1:PD-L1/L2 interaction and were highly selective as they did not cross-react with CD28 receptor family members. Doses of UDIZ-007 or UDIZ-008 at 10 mg/kg every 3 days for a total of six intraperitoneal administrations eradicated MC38-hPD-L1 colon tumors in B-hPD-1 transgenic mice for hPD-1 at day 17, with no relapse until the end of the study at day 56. Importantly, these antibodies bind hPD-1 in a unique region compared to the anti-PD-1 antibodies of known structure, which might have an impact on novel oncology indications when used as a standalone therapy or in combination with currently approved anti-PD-1 therapeutic antibodies. Therefore, UDIZ-007 and UDIZ-008 seem to be promising candidates for the development of antibody-based drugs targeting checkpoint inhibitors as a treatment for cancer.

To generate antibodies (Abs) against SARS-CoV-2 emerging variants, we integrated multiple tools and engineered molecules with excellent neutralizing breadth and potency. Initially, the screening of an immune library identified a nanobody (Nb), termed Nb4, specific to the receptor-binding domain (RBD) of the Omicron BA.1 variant. A Nb4-derived heavy chain antibody (hcAb4) recognized the spike (S) of the Wuhan, Beta, Delta, Omicron BA.1, and BA.5 SARS-CoV-2 variants. A high-resolution crystal structure of the Nb4 variable (VHH) domain in complex with the SARS-CoV-2 RBD (Wuhan) defined the Nb4 binding mode and interface. The Nb4 VHH domain grasped the RBD and covered most of its outer face, including the core and the receptor-binding motif (RBM), which was consistent with hcAb4 blocking RBD binding to the SARS-CoV-2 receptor. In mouse models, a humanized hcAb4 showed therapeutic potential and prevented the replication of SARS-CoV-2 BA.1 virus in the lungs of the animals. In vitro, hcAb4 neutralized Wuhan, Beta, Delta, Omicron BA.1, and BA.5 viral variants, as well as the BQ.1.1 subvariant, but showed poor neutralization against the Omicron XBB.1.5. Structure-based computation of the RBD-Nb4 interface identified three Nb4 residues with a reduced contribution to the interaction with the XBB.1.5 RBD. Site-saturation mutagenesis of these residues resulted in two hcAb4 mutants with enhanced XBB.1.5 S binding and virus neutralization, further improved by mutant Nb4 trimers. This research highlights an approach that combines library screening, Nb engineering, and structure-based computational predictions for the generation of SARS-CoV-2 Omicron-specific Abs and their adaptation to emerging variants.

The shift toward subcutaneous administration for biologic therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, a significant challenge associated with this transition is managing the viscosity of the administered solutions. High viscosity poses substantial development and manufacturability challenges, directly affecting patients by increasing injection time and causing pain at the injection site. Furthermore, high viscosity formulations can prolong residence time at the injection site, affecting absorption kinetics and potentially altering the intended pharmacological profile and therapeutic efficacy of the biologic candidate. Here, we report the application of a multimodal feature learning workflow for predicting the viscosity of antibodies in therapeutics discovery. It integrates multiple data sources including sequence, structural, physicochemical properties, as well as embeddings from a language model. This approach enables the model to learn from various underlying rules, such as physicochemical rules from molecular simulations and protein evolutionary patterns captured by large, pre-trained deep learning models. By comparing the effectiveness of this approach to other selected published viscosity prediction methods, this study provides insights into their intrinsic viscosity predictive potential and usability in early-stage therapeutics antibody development pipelines.

Subcutaneous (SC) delivery of therapeutic antibodies can offer multiple benefits to patients and healthcare providers, including convenience, time savings, and cost reduction. To improve the SC injection experience, drug developers may seek a low injection volume (1-2 mL), which for some antibody drugs necessitates a high concentration solution (≥100 mg/mL) to meet dosage requirements. Several molecular-level challenges hinder the development of high concentration antibody drug products, including high viscosity caused by reversible self-association (RSA). Here, we take an enhanced rational design approach to reduce RSA via protein engineering. Using hydrogen-deuterium exchange mass spectrometry (HDX-MS), we identified potential self-interaction hotspots on the surface of an in-house IgG1 which has known viscosity issues at high concentration. Then, using in silico antibody modeling, we identified sites near the complementary-determining regions for targeting by rational mutagenesis, which included predicted patches of charge or hydrophobicity within or near peptides highlighted by HDX-MS. Screening of nearly 70 variants using dynamic light scattering (DLS) and affinity capture self-interaction nanospectroscopy (AC-SINS) at low concentration showed decreased self-interaction in many variants. Viscosity at 150 mg/mL was reduced by 70% for 13 variants, while two of these variants designed to reduce surface hydrophobicity were found to retain antigen binding compared to the parent antibody. DLS and AC-SINS measurements of self-association were found to correlate with viscosity at high concentration, reinforcing their utility as effective low-concentration screening tools for viscosity. This work demonstrates an enhanced rational mutagenesis strategy informed by the combination of HDX-MS for self-association and in silico predictions of surface properties. The resulting variants are a vast improvement on the parent antibody's viscosity issues and offer insight into the mechanism of self-association.

Bispecific T cell engager (TCE) therapies have demonstrated transformative clinical success in the treatment of hematological cancers, but the lack of antigens that are sufficiently selective for malignant cells has hampered the success of TCEs in the solid-tumor space. To overcome the on-target, off-tumor toxicities that result from the expression of even low levels of tumor-associated antigens in healthy tissues, we sought to identify a TCE target with highly tumor-restricted expression patterns. Here, we characterize cancer-testes antigen Preferentially Expressed Antigen in Melanoma (PRAME) as a highly selective tumor antigen and identify a proteasomal degradation peptide PRAME_425-433 (PRAME₄₂₅) presented in the context of major histocompatibility complex I (MHCI) as an attractive TCE target. We designed a TCR-mimic (TCRm) antibody screening cascade that prioritizes screening anti-PRAME pMHC binders in off-target T cell dependent cellular cytotoxicity assays in a potent TCE format, rather than relying solely on traditional pMHC binding assays, to determine specificity. Using this screening cascade, we discovered antibodies that selectively bind PRAME₄₂₅ pMHC without over-recognition of off-target peptides or MHCI via a TCR-like binding geometry. We further solved the first structure of an anti-PRAME₄₂₅ pMHC TCRm antibody in complex with PRAME₄₂₅/HLA-A *02:01 using cryo electron microscopy to confirm the TCRm antibody binds in a TCR-like binding geometry and specifically recognizes the PRAME₄₂₅ peptide. By formatting these novel TCRm antibodies into potent TCEs, we demonstrate PRAME₄₂₅ pMHC-specific killing of tumor cells, representing a new class of anti-PRAME pMHC biologics.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease characterized by scarring and tissue remodeling. Current treatments have limited efficacy and significant side effects. To address these limitations, we developed AD-214, an anti-CXCR4-Fc-fusion protein composed of an anti-CXCR4 i-body (AD-114) tethered at its C terminus to constant domains 2 and 3 of the Fc region of a mutated human IgG1 lacking effector function. AD-214 binds with high affinity and specificity to CXCR4, modulates intracellular signaling, and inhibits key fibrotic pathways. Using fibrosis models, we demonstrate that AD-214 treatment significantly reduces collagen deposition and lung remodeling and has a unique mode of action. In Phase 1 clinical trials, intravenous infusion of AD-214 led to high and sustained CXCR4 receptor occupancy (RO), but whether RO and efficacy are causally linked remained to be determined. Herein, we demonstrate that CXCR4 RO by AD-214 inhibits primary human leukocyte migration, a model fibrotic process, and that migration inhibition is achievable at concentrations of AD-214 present in the serum of healthy human volunteers administered AD-214. Taken together, these data provide proof of concept for AD-214 as a novel treatment strategy for IPF and suggest that clinically feasible dosing regimens may be efficacious.

The emergence of anti-drug antibodies (ADAs) poses a major obstacle in the clinical development of therapeutic proteins (TPs) such as monoclonal antibodies and their derivatives. While standard multitiered ADA assays and neutralizing antibody assays offer valuable insights into the humoral immunogenicity risks of TPs, they are not sufficient to provide in-depth knowledge such as ADA epitope specificities. For complex multidomain biotherapeutics (MDBs), ADAs targeting individual domains can elicit distinct pharmacological effects. Therefore, it is crucial to implement straightforward and reliable methodologies to deconvolute ADA epitope profiles of MDBs. Herein, we report a case study using domain specificity analysis, linear peptide scanning and bioinformatic B cell epitope prediction to unveil the clinical ADA epitope landscape of TAK-186, a multidomain T cell engager that has been discontinued from clinical development. By applying this workflow, we observed strong domain specificity variability among patient samples. Furthermore, the data showed that many patients demonstrated evolved ADA epitope specificities throughout the course of the treatment. Several potential linear epitopes were identified subsequently through experimental and computational approaches. Overall, we presented in this study a practical strategy to elucidate and potentially mitigate the immunogenicity liabilities of complex biotherapeutics.

Antibodies are naturally evolved molecular recognition scaffolds that can bind a variety of surfaces. Their designability is crucial to the development of biologics, with computational methods holding promise in accelerating the delivery of medicines to the clinic. Modeling antibody-antigen recognition is prohibitively difficult, with data paucity being one of the biggest hurdles. Current affinity datasets comprise a small number of experimental measurements, which are often not standardized between molecules. Here, we address these issues by creating a dataset of seven antigens with two antibodies each, for which we introduce a heterogeneous set of mutations to the CDR-H3 measured by ELISA. Each of the parental complexes has a known crystal structure. We perform benchmarking of state-of-the-art affinity prediction algorithms to gauge their effectiveness. Current computational methods exhibit substantial limitations in accurately predicting the effects of single-point mutations. In contrast, the older empirical, physics-based method FoldX performs well in identifying mutants that retain binding. These findings highlight the need for more resources like the one presented here, i.e. large, molecularly diverse, and experimentally consistent datasets.

Antibody-based therapeutics have demonstrated remarkable therapeutic benefit, but their susceptibility to biotransformation and degradation in the body can affect their safety, efficacy, and pharmacokinetic/pharmacodynamic (PK/PD) profiles. In vitro stability assessments play a pivotal role in proactively identifying potential liabilities of antibody therapeutics prior to animal studies. Liquid chromatography-mass spectrometry (LC-MS)-based in vitro stability assays has been developed and adopted in the biopharmaceutical industry for the characterization of antibody-based therapeutics. However, these methodologies often overlook operational error and random variation during sample preparation and analysis, leading to inaccurate stability estimation. To address this limitation, we have developed an LC-MS-based in vitro serum stability assessment that incorporates two internal standards (ISs), National Institute of Standards and Technology monoclonal antibody (NISTmAb) and its crystallizable fragment (Fc), to improve assay performance. Our method involves three steps: incubation of antibody therapeutics along with an IS in biological matrices, affinity purification, and LC-MS analysis. The stability of 21 monoclonal or bispecific antibodies was assessed in serums of preclinical species using this method. Our results showed improved accuracy and precision of recovery calculations with the incorporation of ISs, enabling a more confident stability assessment even in the absence of biotransformation or aggregation. In vitro stability correlated with in vivo exposure, suggesting that this in vitro assay could serve as a routine screening tool to select and advance stable antibody therapeutic candidates for subsequent in vivo studies.

Developing therapeutic antibodies is a challenging endeavor, often requiring large-scale screening to produce initial binders, that still often require optimization for developability. We present a computational pipeline for the discovery and design of therapeutic antibody candidates, which incorporates physics- and AI-based methods for the generation, assessment, and validation of candidate antibodies with improved developability against diverse epitopes, via efficient few-shot experimental screens. We demonstrate that these orthogonal methods can lead to promising designs. We evaluated our approach by experimentally testing a small number of candidates against multiple SARS-CoV-2 variants in three different tasks: (i) traversing sequence landscapes of binders, we identify highly sequence dissimilar antibodies that retain binding to the Wuhan strain, (ii) rescuing binding from escape mutations, we show up to 54% of designs gain binding affinity to a new subvariant and (iii) improving developability characteristics of antibodies while retaining binding properties. These results together demonstrate an end-to-end antibody design pipeline with applicability across a wide range of antibody design tasks. We experimentally characterized binding against different antigen targets, developability profiles, and cryo-EM structures of designed antibodies. Our work demonstrates how combined AI and physics computational methods improve productivity and viability of antibody designs.