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Deep Learning-Driven Co-Assembly of Naturally Sourced Compound Nanoparticles for Potentiated Cancer Immunotherapy

文献类型：期刊论文

作者Shan, Yiming1,2,3; Zhang, Zimei4; Zhou, Huiling2,3,5; Hou, Bo2,3; Chen, Fangmin1,2,3; Pan, Jiaxing2,3; Ren, Siyuan2,3; Yu, Miaomiao6; Xu, Zhiai5; Zheng, Mingyue1,4
刊名ADVANCED FUNCTIONAL MATERIALS
出版日期2025-09-29
页码14
关键词cancer immunotherapy deep learning drug compatibility excipient-free nanodrug nature-sourced compound
ISSN号1616-301X
DOI10.1002/adfm.202519567
英文摘要Co-assembly of excipient-free nanoparticles has emerged as a promising drug delivery platform due to their high drug-loading capacity, ease of preparation, and ability to achieve combination therapeutic effects. However, the absence of systematic design strategies has hindered their broader application. In this study, a deep learning platform, Gramord, is developed to rationally design the excipient-free anti-tumor nanoparticles of nature-sourced compounds. A comprehensive database of excipient-free nanoparticles is first built and used to train Gramord for predicting self-assembly compatibility. By screening 1800 naturally-derived small molecules and their derivatives, the compound pairs capable of forming excipient-free nanoparticles are identified. Leveraging the advantage of oridonin (Ori) for inducing apoptosis of tumor cells and cepharanthine (Cep) for eliciting immunogenic cell death of tumor cells, the Ori-Cep pair for preparing the self-assemble nanoparticles (namely OCN) is subsequently selected. Using a mouse model of CT26 colorectal tumor, it is demonstrated that the systemically administrated OCN specifically accumulate at the tumor sites, and regress tumor growth by inducing anti-tumor immunogenicity and recruiting tumor-infiltrating cytotoxic T lymphocytes. This study highlights the application of artificial intelligence in designing excipient-free nanomedicine, offering a scalable and cost-effective approach to expanded therapeutic options.
WOS关键词IMMUNOGENIC CELL-DEATH ; ACCUMULATION ; RESISTANCE ; TUMOR
资助项目Shanghai Institute of Materia Medica
WOS研究方向Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
语种英语
WOS记录号WOS:001582875800001
出版者WILEY-V C H VERLAG GMBH
源URL[http://119.78.100.183/handle/2S10ELR8/321564]  
专题国家级研究中心_原创新药研究全国重点实验室
通讯作者Zheng, Mingyue; Yu, Haijun
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Chem Biol, Shanghai 201203, Peoples R China
3.Chinese Acad Sci, Shanghai Inst Mat Med, Ctr Pharmaceut, Shanghai 201203, Peoples R China
4.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China
5.East China Normal Univ, Sch Chem & Mol Engn, Shanghai 200241, Peoples R China
6.Shanghai Miano Nord Biotechnol Co Ltd, Shanghai 200540, Peoples R China
推荐引用方式
GB/T 7714		 Shan, Yiming,Zhang, Zimei,Zhou, Huiling,et al. Deep Learning-Driven Co-Assembly of Naturally Sourced Compound Nanoparticles for Potentiated Cancer Immunotherapy[J]. ADVANCED FUNCTIONAL MATERIALS,2025:14.
APA Shan, Yiming.,Zhang, Zimei.,Zhou, Huiling.,Hou, Bo.,Chen, Fangmin.,...&Yu, Haijun.(2025).Deep Learning-Driven Co-Assembly of Naturally Sourced Compound Nanoparticles for Potentiated Cancer Immunotherapy.ADVANCED FUNCTIONAL MATERIALS,14.
MLA Shan, Yiming,et al."Deep Learning-Driven Co-Assembly of Naturally Sourced Compound Nanoparticles for Potentiated Cancer Immunotherapy".ADVANCED FUNCTIONAL MATERIALS (2025):14.

入库方式： OAI收割

来源：上海药物研究所

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