mAbs最新文献

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.

Successful development of monoclonal antibodies (mAbs) for the treatment of central nervous system disorders has been challenging due to their minimal ability to cross the blood-brain barrier (BBB), resulting in poor brain exposure. Bispecific antibodies (bsAb) that bind to transmembrane protein expressed at the BBB, such as the transferrin receptor (TfR), have shown enhanced brain exposure in rodents and non-human primate (NHP) due to receptor-mediated transcytosis. However, it remains unclear how preclinical findings translate to humans. Moreover, optimal TfR binding affinity remains a subject of debate. Model-informed drug discovery and development is a powerful approach that has been successfully used to support research and development. The goal of this analysis was to expand a published brain minimal physiologically based pharmacokinetic (mPBPK) model to investigate the optimal TfR binding affinity for maximal brain delivery in NHP and to facilitate prediction of the PK of anti-TfR bsAbs in humans from NHP data. Literature data for plasma, cerebrospinal fluid (CSF), and brain exposure after administration of non-TfR mAbs and monovalent bsAbs with respect to TfR in NHP were used to develop the TfR mPBPK model. Clinical validation using human PK data from plasma and CSF for the monovalent anti-TfR bsAb trontinemab demonstrated good predictive performance without major model recalibration. The availability of the TfR mPBPK model is envisaged to provide better understanding of the relationship between TfR binding affinity, dose, and brain exposure, which would lead to more robust selection of lead candidates and efficacious dosing regimens.

Hybridomas, the first method for creating monoclonal antibodies (mAbs), were reported 50 years ago. This approach, which transformed biomedical research and laid the foundation for many of the current therapeutic, diagnostic, and research reagent applications of mAbs, is still used today, despite reported low fusion yields between short-lived B cells and immortal myeloma cells. To improve hybridoma production yields and accelerate development of new mAbs, we addressed two key limitations: 1) random pairing between myeloma cells and antibody-producing cells, and 2) low efficiency of the polyethylene-glycol-mediated fusion process. We first characterized and isolated antibody-secreting cells (ASCs) from the spleen of immunized mice before cell fusion to increase the probability of successive pairing between the most suitable cell fusion partners and favor the generation of functional hybridomas. Specifically, we developed an optimized workflow combining fluorescence-activated cell sorting with antibody secretion assays, using a panel of five cell-surface markers (CD3, TACI, CD138, MHC-II, and B220) to identify a distinct ASC subset with key characteristics. Such ASCs exhibited a plasmablast phenotype with high MHC-II expression and secreted high levels of antigen (Ag)-specific antibodies in immunized mice. We then implemented a cell electrofusion procedure adapted to low cell numbers (<10⁶ cells), in order to perform the targeted electrofusion of TACI^highCD138^high sorted ASCs. This targeted approach yielded viable hybridomas in 100% of seeded culture wells compared to only 40% for the electrofusion of unsorted cells. In particular, over 60% of hybridomas generated from TACI^highCD138^high sorted ASCs secreted Ag-specific mAbs, including IgGs with high Ag binding affinity (<10^-9 M). These results pave the way for a high-yield mAb production method via cell fusion, with the potential to streamline hybridoma generation and thereby expand access to mAbs.

T cell bispecific antibodies (TCBs) are an emerging class of cancer therapy that are typically designed for high binding affinity to CD3 and tumor antigen (TA). Using this approach, TCBs have demonstrated significant clinical efficacy, but they have also elicited cytokine release syndrome and off-target on-tumor toxicities. CD3 affinity-attenuation has recently been reported as an approach to maintain efficacy while reducing cytokine release, but the interdependence of CD3 affinity with other factors is often not systematically explored. For this purpose, we generated a series of TCBs comprising CD3 binders with varying affinities and TA binders with either high or low affinities, utilizing FOLR1 and CEACAM5 as tumor targets. The CD3 binders were classified into high, intermediate, low, and very low affine binders based on affinity measurements as well as functionality. Depending on the target, different combinations of binders showed the most advantageous profile of tumor-cell killing while coupled with lower cytokine secretion. For instance, within the FOLR1-TCBs series, CD3^intermed exhibited a favorable profile compared to CD3^high in vitro using cocultures and in vivo using humanized mice. For CEACAM5-TCBs, CD3^low, along with CD3^intermed, showed a favorable profile compared to CD3^high in both in vitro and in vivo settings. Additionally, CD3^low avoided on-target, off-tumor toxicity and reduced cytokine release in transgenic mice. Taken together, reducing cytokine release while maintaining adequate efficacy is possible through CD3 binder affinity attenuation, but optimizing cytokine release profiles by CD3 binder affinity-attenuation is dependent on additional parameters.