mAbs最新文献

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.

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.