Elucidating the multi-target therapeutic mechanism of Kangtai decoction in lung cancer through integrated network pharmacology and molecular docking
BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide, with a high incidence and limited effective treatments, the main focus being on symptom management and tumor control. Kangtai decoction (KTD), a traditional Chinese medicine, has shown promising results in alleviating lung cancer symptoms and improving quality of life in China, though its precise mechanism of action remains unclear. OBJECTIVE: This study employed a network pharmacology-based approach, complemented by molecular docking, to identify the bioactive components, key targets, and potential pharmacological mechanisms of KTD against lung cancer. MATERIALS AND METHODS: Bioactive components and drug targets of KTD, along with targets related to lung cancer, were collected from public databases. Bioinformatics analyses, including protein-protein interaction (PPI), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were conducted to identify core targets, bioactive components, and signaling pathways. Molecular docking was then performed to predict the binding between active compounds and core targets. RESULTS: A total of 145 effective components of KTD for treating lung cancer were screened, along with 113 common targets between KTD and lung cancer. Bioinformatics analysis and molecular docking indicated that in the treatment of lung cancer with KTD, neocryptotanshinone II was the most significant bioactive compound, IL1B was the most pivotal target, and the IL-17 signaling pathway was the most critical pathway. CONCLUSION: KTD is effective in the treatment of lung cancer, possibly because of the binding of its active components to key therapeutic targets involved in lung cancer.
Authors
- Chaoli Feng
- Jing Niu
- Yang Hui
- Yang Yang
Publication Details
- Journal
- International Journal of Clinical Pharmacology and Therapeutics
- Published
- 2026-10-12
- DOI
- https://doi.org/10.5414/cp205003
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00