ArticleFrontiers in medicine2022
Network Pharmacology-Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Luteolin fromPharmaceuticals (Basel, Switzerland) · 2026Article
- A six-gene prognostic signature for aflatoxin B1-associated hepatocellular carcinoma identified through integrated bioinformatics and network toxicology.Translational gastroenterology and hepatology · 2026Article
- Network Pharmacology-Based Exploration: Non-Targeted Metabolites ofBiologics : targets & therapy · 2026Article
- Rethinking network analysis in ethnopharmacology: a multi-omics and AI roadmap to overcome conceptual and methodological biases.Frontiers in pharmacology · 2026Article
- Revealing the Mechanism of Esculin in Treating Renal Cell Carcinoma Based on Network Pharmacology and Experimental Validation.Biomolecules · 2024Article
- The immunological mechanisms and therapeutic potential in drug-induced liver injury: lessons learned from acetaminophen hepatotoxicity.Cell & bioscience · 2022Review
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Authors and funding
7 authors.
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Abstract
Astragali Radix (AR) has been widely used in traditional Chinese medicine prescriptions for acute and chronic liver injury. However, little is known about the effects of AR on acetaminophen (APAP)-induced liver injury (ALI). In the current study, a network pharmacology-based approach was applied to characterize the action mechanism of AR on ALI. All compounds of AR were obtained from the corresponding databases, and active compounds were selected according to its oral bioavailability and drug-likeness index. The potential genes of AR were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) and PubChem, whereas the potential genes related to ALI were obtained from Online databases (GeneCards and Online Mendelian Inheritance in Man) and Gene Expression Omnibus profiles. The enriched processes, pathways, and target genes of the diseases were analyzed by referring to the Search Tool for the Retrieval of Interacting Genes/Proteins database. A network constructed through Cytoscape software was used to identify the target proteins that connected the compounds in AR with the differential genes of ALI. Subsequently, the potential underlying action mechanisms of AR on ALI predicted by the network pharmacology analyses were experimentally validated in APAP-induced liver injury in mice and HL7702 cells incubated with APAP. The compound-target network included 181 targets, whereas the potential genes related to ALI were 4,621. A total of 49 AR-ALI crossover proteins, corresponding to 49 genes, were filtered into a protein-protein interaction network complex and designated as the potential targets of AR on ALI. Among the genes, the three highest-scoring genes,
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