ArticleDrug design, development and therapy2024
Integrated Metabolomics and Network Pharmacology Study on the Mechanism of
Article in Drug design, development and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Exploring the Distinct Regulatory Actions and Molecular Pathways of Coptisine Through Network Pharmacology: Insights Into Its Influence on Platelet Activation and Thrombus Development.Food science & nutrition · 2026Article
- Article
- Investigating the Effect and Mechanism of 3-Methyladenine Against Diabetic Encephalopathy by Network Pharmacology, Molecular Docking, and Experimental Validation.Pharmaceuticals (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Methods: A thrombosis model was established by intraperitoneal injection of type I carrageenan in rats, and antithrombotic function was evaluated at different doses of RR. Metabolomics was used to identify the differential metabolites in the serum. Network pharmacology was then applied to identify the potential targets for the antithrombotic activity of the RR. An integrated network of metabolomics and network pharmacology was constructed using Cytoscape. Finally, key targets were verified using molecular docking. Results: RR at 5.4 g/kg significantly alleviated the thrombosis. Thirteen potentially significant metabolites were involved in the therapeutic effects of RR against thrombosis, most of which were regulated for recovery after RR treatment. An integrated analysis of metabolomics and network pharmacology showed that the antithrombosis effect of RR was closely associated with the regulation of PLA2G2A, PTGS1, ALOX5, and CYP2C9. Molecular docking showed high affinity between the key targets and components of RR. We speculated that the components of RR, such as catalpol, ferulic acid methyl ester, and methyl 4-hydroxycinnamate, might act on key proteins, including PLA2G2A, PTGS1, and ALOX5, to exert antithrombosis effects. Conclusion: This study confirmed the antithrombotic effect of high-dose RR, revealed the antithrombotic mechanism and potential material basis, and laid the foundation for the antithrombotic clinical application of RR. Furthermore, it provides a successful case reference for screening natural herbal components and exploring their potential pharmacological mechanisms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.