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.

T cell bispecific antibodies (TCBs) are a promising new class of therapeutics for relapsed/refractory multiple myeloma. A frequently observed, yet incompletely understood effect of this treatment is the transient reduction of circulating T cell counts, also known as T cell margination (TCM). After administration of the GPRC5D-targeting TCB forimtamig (RG6234), TCM occurred in patients and correlated with cytokine release and soluble B cell maturation antigen decrease. We demonstrate that TCM is accurately represented in the humanized NSG mouse model and occurs at a lower threshold of target expression than systemic cytokine release. Application of whole-mouse tissue clearing and 3D imaging revealed that T cells accumulate in the bone marrow after treatment. We hypothesize that low amounts of targets are sufficient to rapidly redirect T cells upon TCB engagement. Therefore, we propose TCM as a beneficial, highly sensitive and early effect of forimtamig that leads T cells to likely sites of bone marrow tumor lesions.

Hydrophobic interaction chromatography (HIC) is commonly used to determine the drug-to-antibody ratio (DAR) and drug load distribution of antibody-drug conjugates (ADCs). However, identifying various DAR species separated by HIC is challenging due to the traditional use of mobile phases that are incompatible with mass spectrometry (MS). Existing approaches used to couple HIC with MS often encounter issues, such as complex instrumentation, compromised separation efficiency, and reduced MS sensitivity. In this study, we introduce a 22-min online native HIC-MS method for the separation and characterization of different DAR species in ADCs, addressing these challenges. The key novelty of this method is the use of ammonium tartrate, a kosmotropic and thermally decomposable salt, as the salt of HIC mobile phase, ensuring both excellent HIC separation and MS compatibility. Additionally, an ultrashort size exclusion chromatography step is integrated for online sample cleaning, enhancing MS sensitivity. This platform native HIC-MS method offers a rapid, sensitive, and robust solution for comprehensive profiling of DAR species in ADCs with a simple and cost-effective instrumental setup.

Antibody repurposing is the process of changing a known antibody so that it binds to a mutated antigen. One of the findings to emerge from the Coronavirus Disease 2019 (COVID-19) pandemic was that it was possible to repurpose neutralizing antibodies for Severe Acute Respiratory Syndrome, a related disease, to work for COVID-19. Thus, antibody repurposing is a possible pathway to prepare for and respond to future pandemics, as well as personalizing cancer therapies. For antibodies to be successfully repurposed, it is necessary to know both how antigen mutations disrupt their binding and how they should be mutated to recover binding, with this work describing an analysis to address the first of these topics. Every possible antigen point mutation in the interface of 246 antibody-protein complexes were analyzed using the Rosetta molecular mechanics force field. The results highlight a number of features of how antigen mutations affect antibody binding, including the effects of mutating critical hotspot residues versus other positions, how many mutations are necessary to be likely to disrupt binding, the prevalence of indirect effects of mutations on binding, and the relative importance of changing attractive versus repulsive energies. These data are expected to be useful in guiding future antibody repurposing experiments.

In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purification development often conflicts with timeline pressures and material constraints, limiting the number of molecules and process conditions that can reasonably be assessed. Recently, high-throughput batch-binding screen data along with improved molecular descriptors have enabled development of robust quantitative structure-property relationship (QSPR) models that predict monoclonal antibody chromatographic binding behavior from the amino acid sequence. Here, we describe a QSPR strategy for in silico monoclonal antibody purification process fit assessment. Principal Component Analysis is applied to extract a one-dimensional basis for comparison of molecular chromatographic binding behavior from multi-dimensional high-throughput batch-binding screen data. Kernel Ridge Regression is used to predict the first principal component for new molecular sequences. This workflow is demonstrated with a set of 97 monoclonal antibodies for five chromatography resins in two salt types across a range of pH and salt concentrations. Model development benchmarks four descriptor sets from biophysical structural models and protein language models. The investigation illustrates the value QSPR models can provide to purification process fit assessment, and selection of resins and operating conditions from sequence alone.

The commercial development of antibody therapeutics is a global enterprise involving thousands of biopharmaceutical firms and supporting service organizations. To date, their combined efforts have resulted in over 200 marketed antibody therapeutics and a pipeline of nearly 1,400 investigational product candidates that are undergoing evaluation in clinical studies as treatments for a wide variety of diseases. Here, we discuss key events in antibody therapeutics development that occurred during 2024 and forecast key events related to the late-stage clinical pipeline that may occur in 2025. In particular, we report on 21 antibody therapeutics granted a first approval in at least one country or region during 2024, including bispecific antibodies tarlatamab (IMDELLTRA®), zanidatamab (Ziihera®), zenocutuzumab (BIZENGRI®), odronextamab (Ordspono®), ivonescimab (®), and antibody-drug conjugate (ADC) sacituzumab tirumotecan (®). We also discuss 30 investigational antibody therapeutics for which marketing applications were undergoing review by at least one regulatory agency, as of our last update on December 9, 2024, including ADCs datopotamab deruxtecan, telisotuzumab vedotin, patritumab deruxtecan, trastuzumab botidotin, becotatug vedotin, and trastuzumab rezetecan. Of 178 antibody therapeutics we include in the late-stage pipeline, we summarize key data for 18 for which marketing applications may be submitted by the end of 2025, such as bi- or multispecific antibodies denecimig, sonelokimab, erfonrilimab, and anbenitamab. Key trends in the development and approval of antibody formats such as bispecifics and ADCs, as well as clinical-phase transition and global approval success rates for these antibody formats, are reported.