@article{APS10975,
author = {Fan Yang and Lin Jia and Hong-chao Zhou and Jing-nan Huang and Meng-yun Hou and Feng-ting Liu and Nayana Prabhu and Zhi-jie Li and Chuan-bin Yang and Chang Zou and Pär Nordlund and Ji-gang Wang and Ling-yun Dai},
title = {Deep learning enables the discovery of a novel cuproptosis- inducing molecule for the inhibition of hepatocellular carcinoma},
journal = {Acta Pharmacologica Sinica},
volume = {45},
number = {2},
year = {2024},
keywords = {},
abstract = {Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.},
issn = {1745-7254}, url = {http://www.chinaphar.com/article/view/10975}
}