Search

[Display omitted] • Based on our previous work, site-specific ADCs targeting TROP2 were synthesized via IgG glycoengineering or affinity-directed traceless conjugation. These site-specific conjugation methods of modifying IgG Fc fragment were unique. • With improved homogeneity, hydrophilicity & stability, the one-step-made gsADC 3b showed superior in vitro & in vivo anti-tumor efficacy. • The reduced in vivo efficacy was observed in two-step site-specific gsADCs. One way to retain in vivo anti-tumor activity is by balancing the cyclooctyne-functionalized linker fragment with the hydrophilicity fragment mPEG 24 (gsADC 5b vs. gsADC 5d). The approval of Trodelvy® validates TROP2 as a druggable but challenging target for antibody-drug conjugates (ADCs) to treat metastatic triple-negative breast cancer (mTNBC). Here, based on the TROP2-targeted antibody sacituzumab, we designed and developed several site-specific ADC candidates, which employ MMAE (monomethyl auristatin E) as the toxin, via IgG glycoengineering or affinity-directed traceless conjugation. Systematic evaluation of these site-specific ADCs in homogeneity, hydrophilicity, stability, and antitumor efficiency was conducted. The results indicate that the site-specific ADCs gsADC 3b made from one-step glycoengineering exhibit good aggregation stability and in vivo efficacy, providing a new format of ADCs that target TROP2. [ABSTRACT FROM AUTHOR]

In recent years, multivariate time-series classification (MTSC) has attracted considerable attention owing to the advancement of sensing technology. Existing deep-learning-based MTSC techniques, which mostly rely on convolutional or recurrent neural networks, focus primarily on the temporal dependency of a single time series. Based on this, complex pairwise dependencies among multivariate variables can be better described using advanced graph methods, where each variable is regarded as a node in the graph, and their dependencies are regarded as edges. Furthermore, current spatial–temporal modeling (e.g., graph classification) methodologies based on graph neural networks (GNNs) are inherently flat and cannot hierarchically aggregate node information. To address these limitations, we propose a novel graph-pooling-based framework, MTPool, to obtain an expressive global representation of MTS. We first convert MTS slices into graphs using the interactions of variables via a graph structure learning module and obtain the spatial–temporal graph node features via a temporal convolutional module. To obtain global graph-level representation, we design an "encoder-decoder"-based variational graph pooling module to create adaptive centroids for cluster assignments. Then, we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node. Finally, a differentiable classifier uses this coarsened representation to obtain the final predicted class. Experiments on ten benchmark datasets showed that MTPool outperforms state-of-the-art strategies in the MTSC task. [ABSTRACT FROM AUTHOR]