Transfer learning algorithm assisted in the discovery of novel gp130 inhibitors and their application in colorectal cancer treatment

Abstract

The gp130 protein is the common receptor for IL-6 family cytokines, and its aberrant activation significantly promotes tumor progression. However, research on inhibitors targeting gp130 remains limited. Here, we employed an artificial intelligence (AI)-assisted drug design strategy and identified evodiamine as a candidate gp130-targeting scaffold. Based on this, we developed a series of novel indolopyridine-based small molecules. The representative compound 8a effectively downregulates the JAK2/STAT3 signaling pathway by directly targeting the gp130 protein (K_D = 2.17 μmol·L −1), disrupting the binding of STAT3 to DNA, and thereby inducing apoptosis. In HT-29 xenograft tumor models, 8a demonstrated 56.20% tumor growth inhibition (20 mg·kg −1). In summary, the compound 8a can be used as a gp130-targeted compound for the treatment of colorectal cancer. Furthermore, the method applied in the research can provide a feasible case for the development of drugs targeting related pathways.

Acknowledgments

This work was funded by the National Natural Science Foundation of China (Grant No. 82473791) and the Basic Research Program of Jiangsu Province (Grant No. BK20250102). We are also grateful to Yuxuan Hu for his insightful feedback and helpful suggestions for this study.

Ethical statements

All animal experiments were conducted according to the principles and procedures approved by the Institutional Animal Care and Use Committee (IACUC) of China Pharmaceutical University (License Number: SYXK [SU] 2023–0019, ethics approval number: 2024–05–086).

Author contributions

The authors confirm their contributions to the work as follows: study conception and design: Guan J, Xiao L, Liao Y, Richardson DR, Hong J, Yu W; data collection: Guan J, Xiao L, Wang J, Cai G; analysis and interpretation of results: Guan J, Xiao L, Wang J; draft manuscript preparation: Guan J, Xiao L, Wang J, Cai G, Liao Y, Richardson DR. All authors reviewed the results and approved the final version of the manuscript.

Data availability

The data supporting the findings of this study includes a combination of publicly available datasets, literature-curated compound activity data, commercial compound libraries, and model-generated results produced during the current study. The STAT3 inhibitor dataset was obtained from the PubChem Bioassay database (AID 862, https://pubchem.ncbi.nlm.nih.gov/bioassay/862). The candidate compound library was derived from the SelleckChem Natural Products Database (SelleckChem Natural Products Database, www.selleckchem.com/screening/natural-product-library.html). The curated gp130 inhibitor dataset, including molecular structures (SMILES) and binary activity labels, as well as the AI-predicted scores and ranking results for screened compounds, are provided in the Supplementary Materials 1 and 3. All other data generated or analyzed during this study is included in this published article and its supplementary information files.

Conflict of interest

The authors declare that they have no conflicts of interest.

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