Phenotypic screening has played an important role in discovering innovative small-molecule drugs and clinical candidates with unique molecular mechanisms of action.However,conducting cell-based high-throughput screeni...Phenotypic screening has played an important role in discovering innovative small-molecule drugs and clinical candidates with unique molecular mechanisms of action.However,conducting cell-based high-throughput screening from vast compound libraries is extremely time-consuming and expensive.Fortunately,deep learning has provided a new paradigm for identifying compounds with specific phenotypic properties.Herein,we developed a data-driven classification-generation cascade model to discover new chemotype antitumor drugs.Through wet-lab validation,WJ0976 and WJ0909 were identified as tetrahydrocarbazole derivatives and displayed potent broad-spectrum antitumor activity as well as growth inhibitory properties against multidrug-resistant cancer cells.Furthermore,the R-(−)-WJ0909(WJ0909B),demonstrated optimal antitumor efficacy in vitro and ex vivo patient-derived organoids(PDOs).Further investigations revealed that WJ0909B upregulates p53 expression and cause mitochondria-dependent endogenous apoptosis.Moreover,WJ0909B and the click-activated prodrug WJ0909B-TCO potently inhibited tumor growth in cell-derived xenograft models.This research highlights the significant potential of deep learning-guided approach to phenotypic drug discovery for anticancer drugs and the strategy of click-activated prodrug for targeted cancer therapy.展开更多
基金supported by CAMS Innovation Fund for Medical Sciences,China(No.2021-I2M-1-028 and No.2021-I2M-1-054,China)+6 种基金the National Natural Science Foundation of China,China(No.82303782,China)the China Postdoctoral Science Foundation,China(2024M763807,China)the 2024 China Industrial Technology Infrastructure Public Service Platform Project,China(GN2024-31-4700)The computing resources were supported by Biomedical High Performance Computing Platform,Chinese Academy of Medical Sciences,China.
文摘Phenotypic screening has played an important role in discovering innovative small-molecule drugs and clinical candidates with unique molecular mechanisms of action.However,conducting cell-based high-throughput screening from vast compound libraries is extremely time-consuming and expensive.Fortunately,deep learning has provided a new paradigm for identifying compounds with specific phenotypic properties.Herein,we developed a data-driven classification-generation cascade model to discover new chemotype antitumor drugs.Through wet-lab validation,WJ0976 and WJ0909 were identified as tetrahydrocarbazole derivatives and displayed potent broad-spectrum antitumor activity as well as growth inhibitory properties against multidrug-resistant cancer cells.Furthermore,the R-(−)-WJ0909(WJ0909B),demonstrated optimal antitumor efficacy in vitro and ex vivo patient-derived organoids(PDOs).Further investigations revealed that WJ0909B upregulates p53 expression and cause mitochondria-dependent endogenous apoptosis.Moreover,WJ0909B and the click-activated prodrug WJ0909B-TCO potently inhibited tumor growth in cell-derived xenograft models.This research highlights the significant potential of deep learning-guided approach to phenotypic drug discovery for anticancer drugs and the strategy of click-activated prodrug for targeted cancer therapy.
