| Autor: |
Yu Guo, Zheyuan Shen, Wenbin Zhao, Jialiang Lu, Yi Song, Liteng Shen, Yang Lu, Mingfei Wu, Qiuqiu Shi, Weihao Zhuang, Yueping Qiu, Jianpeng Sheng, Zhan Zhou, Luo Fang, Jinxin Che, Xiaowu Dong |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Advanced Science, Vol 11, Iss 13, Pp n/a-n/a (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2198-3844 |
| DOI: |
10.1002/advs.202306309 |
| Popis: |
Abstract Bystander‐killing payloads can significantly overcome the tumor heterogeneity issue and enhance the clinical potential of antibody‐drug conjugates (ADC), but the rational design and identification of effective bystander warheads constrain the broader implementation of this strategy. Here, graph attention networks (GAT) are constructed for a rational bystander killing scoring model and ADC construction workflow for the first time. To generate efficient bystander‐killing payloads, this model is utilized for score‐directed exatecan derivatives design. Among them, Ed9, the most potent payload with satisfactory permeability and bioactivity, is further used to construct ADC. Through linker optimization and conjugation, novel ADCs are constructed that perform excellent anti‐tumor efficacy and bystander‐killing effect in vivo and in vitro. The optimal conjugate T‐VEd9 exhibited therapeutic efficacy superior to DS‐8201 against heterogeneous tumors. These results demonstrate that the effective scoring approach can pave the way for the discovery of novel ADC with promising bystander payloads to combat tumor heterogeneity. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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