Evidence map›Paper›PMID 42661803›Full record

ArticleFrontiers in immunology2026

Artemisinin ameliorates rheumatoid arthritis through modulation of the IL-17/PI3K/AKT signaling pathway: integrated network pharmacology, bioinformatics, and experimental validation.

Zongyuan Zhou, Le Wang, Yongzuo Li, Yue Yang, Lingyu Li, Xing Qin, Thomas Efferth, Xiaohua Lu, Zhe Qiang

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zongyuan ZhouAntibiotic Innovation and Resistance Control Key Laboratory of Sichuan Province, National Base for International Science and Technology Cooperation, School of Pharmacy, Chengdu University, Chengdu, China.
Le WangAntibiotic Innovation and Resistance Control Key Laboratory of Sichuan Province, National Base for International Science and Technology Cooperation, School of Pharmacy, Chengdu University, Chengdu, China.
Yongzuo LiAntibiotic Innovation and Resistance Control Key Laboratory of Sichuan Province, National Base for International Science and Technology Cooperation, School of Pharmacy, Chengdu University, Chengdu, China.
Yue YangAffiliated Hospital of Chengdu University, Chengdu University, Chengdu, China.
Lingyu LiCenter for Natural Products Research, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China.
Xing QinAntibiotic Innovation and Resistance Control Key Laboratory of Sichuan Province, National Base for International Science and Technology Cooperation, School of Pharmacy, Chengdu University, Chengdu, China.
Thomas EfferthDepartment of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University Mainz, Mainz, Germany.
Xiaohua LuDepartment of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University Mainz, Mainz, Germany.
Zhe QiangSichuan-Chongqing Joint Key Laboratory of Innovation of New Drugs of Traditional Chinese Medicine, Chongqing Academy of Chinese Materia Medica, Chongqing University of Chinese Medicine, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Artemisinin regulates the immune system and is applied in RA treatment. Nevertheless, the effectiveness and potential mechanisms of artemisinin in the treatment of RA remain unclear, especially in fibroblast-like synoviocytes (FLSs). This study aimed to investigate the mechanism of artemisinin in the treatment of rheumatoid arthritis (RA) through an integrated network pharmacology and bioinformatics approach, supported by experimental validation both in MH7A cells and collagen-induced arthritis (CIA) mice. Methods: SwissTargetPrediction database, TCMSP, TargetNet, SuperPred, and PharmMapper were used to identify the targets of artemisinin. RA-related differentially expressed genes (DEGs) were collected by integrating three GEO datasets. DEGs related to artemisinin were utilized to create protein-protein interaction networks and visualize them with STRING and Cytoscape. GO and KEGG functional enrichment analyses were executed. Molecular docking and molecular dynamics (MD) simulations were carried out to analyze core targets using the AutoDock Vina and Desmond software. Furthermore, CIA mouse and MH7A cells were used to validate the results obtained from network pharmacology. Results: There were 73 target genes intersecting between the artemisinin targets and the DEGs. PPI network analysis and its topology indicated that AR, EGFR, JAK2, and PTGS2 had the greatest centrality. GO and KEGG enrichment analyses suggested that the candidate targets were significantly associated with the PI3K/AKT signaling pathway. Molecular docking and an in vivo study were used for further investigation. Artemisinin interacted with the cavities of AR, JAK2, EGFR, and PTGS2 with binding energies of -8.65, -8.61, -7.48, and -8.48 kcal/mol, respectively. MD simulations showed that the AR-artemisinin complex maintained persistent interactions and stable energy profiles over 20 ns. The CIA model and MH7A cell studies confirmed that artemisinin exposure inhibited the proliferation and production of IL-1β, IL-6, and IL-8 triggered by IL-17. Furthermore, artemisinin attenuated IL-17-induced AKT phosphorylation levels. Conclusion: These findings suggest that artemisinin may ameliorate RA partly by suppressing IL-17-induced inflammatory activation and PI3K/AKT signaling. AR, EGFR, JAK2, and PTGS2 were prioritized as candidate hub targets.

Indexed as

ArtemisininsArthritis, ExperimentalArthritis, RheumatoidInterleukin-17Phosphatidylinositol 3-KinasesProto-Oncogene Proteins c-aktSignal TransductionAnimalsCell LineComputational BiologyHumansMiceMolecular Docking SimulationNetwork PharmacologyProtein Interaction MapsartemisininArtemisininsInterleukin-17Phosphatidylinositol 3-KinasesProto-Oncogene Proteins c-aktbioinformaticsIL-17molecular dockingnetwork pharmacologyPI3K/AKTrheumatoid arthritis

Identifiers

PMID42661803
PMCPMC13518343

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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.