mAbs最新文献

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.

Proteins are an important class of therapeutics for combatting a wide variety of diseases. The increasing demand for convenient, patient-centric treatment options has propelled the development of subcutaneously delivered protein therapies and increased the interest in novel formulations and delivery methods. However, subcutaneous delivery of protein therapeutics remains a challenge due to the high protein concentrations ( >100 mg/mL) required to circumvent lower bioavailability and the smaller injection volumes required to enable the use of mature and cost-effective devices, such as standard prefilled syringes and autoinjectors. At high concentrations, protein solutions exhibit elevated viscosity, which poses injectability and manufacturing challenges. Here, we review the state of the art in experimental and computationally predictive formulation development approaches for viscosity mitigation of high-concentration protein solution therapeutics, and we suggest new directions for expanding the utility of these approaches beyond traditional monoclonal antibodies. Innovative approaches should leverage and combine advances in both experimental and computational methods, including machine learning and artificial intelligence, to rapidly identify formulation compositions for viscosity reduction, and subsequently facilitate the development of patient-centric biotherapeutics.

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.

Therapeutic monoclonal antibodies (mAbs) can be functionally enhanced via Fc engineering. To determine whether pairs of mAbs with different Fc modifications can be combined for functional complementarity, we investigated the in vitro activity of two HIV-1 mAb libraries, each equipped with 60 engineered Fc variants. Our findings demonstrate that the impact of Fc engineering on Fc functionality is dependent on the specific Fab clone. Notably, combinations of Fc variants of the same Fab specificity exhibited limited enhancement in functional breadth compared to combinations involving two distinct Fabs. This suggests that the strategic selection of complementary Fc modifications can enhance both functional activity and breadth. Furthermore, while some combinations of Fc variants displayed additive functional effects, others were detrimental, suggesting that the functional outcome of Fc mutations is not easily predicted. Collectively, these results provide preliminary evidence supporting the potential of complementary Fc modifications in mAb combinations. Future studies will be essential to identify the optimal Fc modifications that maximize in vivo efficacy.

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline. The ability to evaluate developability properties early in the process of antibody therapeutic development could accelerate the timeline from discovery to clinic and save considerable resources. In silico predictive approaches, such as machine learning models, which map molecular features to predictions of developability properties could offer a cost-effective and high-throughput alternative to experiments for antibody developability assessment. We developed a computational framework, PROPERMAB (PROPERties of Monoclonal AntiBodies), for large-scale and efficient in silico prediction of developability properties for monoclonal antibodies, using custom molecular features and machine learning modeling. We demonstrate the power of PROPERMAB by using it to develop models to predict antibody hydrophobic interaction chromatography retention time and high-concentration viscosity. We further show that structure-derived features can be rapidly and accurately predicted directly from sequences by pre-training simple models for molecular features, thus providing the ability to scale these approaches to repertoire-scale sequence datasets.

Identification of an optimal single protein sequence at the discovery stage for preclinical and clinical development is critical to the rapid development and overall success of a biologic drug. High throughput developability assessments at the discovery stage are used to rank potent molecules by their biophysical properties, deprioritize suboptimal molecules, or trigger additional rounds of protein engineering. Due to the amount of data acquired for these molecules, manual analysis methods to rank molecules are error prone and time-consuming. Here, we present applications of hierarchical clustering analysis for data-driven lead selection of biologics and preformulation screening using high throughput developability data. Hierarchical clustering analysis was applied here for prioritization of three different antibody modalities, including format and chain pairing of bispecific antibodies, sequence-optimized monoclonal antibodies from affinity maturation, preformulation screening of bispecific scFv-Fab fusion molecules, and monoclonal antibodies from an immunization campaign. This high-throughput method for ranking molecules by their developability characteristics and preformulation properties can substantially simplify, streamline, and accelerate biologics discovery and early development.

Therapeutic monoclonal antibodies, which are almost exclusively IgG isotypes, show significant promise but are prone to poor solution stability, including aggregation and elevated solution viscosity at dose-relevant concentrations. Recombinant IgE antibodies are emerging cancer immunotherapies. The first-in-class MOv18 IgE, recognizing the cancer-associated antigen folate receptor-alpha (FRα), completed a Phase 1 clinical trial in patients with solid tumors, showing early signs of efficacy at a low dose. The inaugural process development and scaled manufacture of MOv18 IgE for clinical testing were undertaken with little baseline knowledge about the solution phase behavior of recombinant IgE at dose-relevant concentrations. We evaluated MOv18 IgE physical stability in response to environmental and formulation stresses encountered throughout shelf life. We analyzed changes in physical stability using multiple orthogonal analytical techniques, including particle tracking analysis, size exclusion chromatography, and multidetector flow field flow fractionation hyphenated with UV. We used dynamic and multiangle light scattering to profile aggregation status. Formulation at pH 6.5, selected for use in the Phase 1 trial, resulted in high monomeric purity and no submicron proteinaceous particulates. Formulation at pH 5.5 and 7.5 induced significant submicron and sub-visible particle formation. IgE formulation was resistant to aggregation in response to freeze-thaw stress, retaining high monomeric purity. Exposure to thermal stress at elevated temperatures resulted in loss of monomeric purity and aggregation. Agitation stress-induced submicron and subvisible aggregation, but monomeric purity was not significantly affected. MOv18 IgE retains monomeric purity in response to formulation and stress conditions, confirming stability. Our results offer crucial guidance for future IgE-based drug development.

Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only limited to a few dozen data points for training, a bottleneck for generalizability. In this study, we measured the viscosity of a panel of 229 monoclonal antibodies (mAbs) to develop predictive models for high concentration mAb screening. We developed DeepViscosity, consisting of 102 ensemble artificial neural network models to classify low-viscosity (≤20 cP) and high-viscosity (>20 cP) mAbs at 150 mg/mL, using 30 features from a sequence-based DeepSP model. Two independent test sets, comprising 16 and 38 mAbs with known experimental viscosity, were used to assess DeepViscosity's generalizability. The model exhibited an accuracy of 87.5% and 89.5% on both test sets, respectively, surpassing other predictive methods. DeepViscosity will facilitate early-stage antibody development to select low-viscosity antibodies for improved manufacturability and formulation properties, critical for subcutaneous drug delivery. The webserver-based application can be freely accessed via https://devpred.onrender.com/DeepViscosity.