Mechanism of the Proprietary Chinese Medicine “JiuLiWan” to Treat Ulcerative Colitis Revealed by Network Pharmacology, Molecular Docking, and Experimental Verification <i>In Vitro</i>
Bibliographic record
Abstract
Abstract JiuLiWan (JLW), as a classic traditional Chinese medicine formula, has been clinically used against ulcerative colitis (UC). However, the exact mechanism of its therapeutic effect remains unclear. This study aims to explore and validate the main components and pharmacological mechanism of JLW in the treatment of UC through network pharmacology, molecular docking, and cell experiments. Network pharmacology analyses indicated a total of 107 main components and 286 core targets of JLW against UC. Pathway enrichment analysis demonstrated the involvement of PI3K-AKT, MAPK, Ras, Rap1, TNF, T cell receptor, HIF-1, C-type lectin receptor, VEGF, and Th17 cell differentiation signal pathways in the efficacy of the formula. The molecular docking results indicated that the prominent components (ailanthone (AIL), butylidenephthalide, honokiol, dehydrocostuslactone, ganoderic acid A, atractylenolide I, neokurarinol, glycyrrhetinic acid, palmatine, tangeretin, and bruceine A) could bind to core targets AKT1, P53, STAT3, c-JUN, and ERK1. Subsequently, AIL was used as a representative compound to conduct cell experiments to verify its role and mechanism in anti-inflammation and immunomodulation. Interestingly, AIL could switch Jurkat T cells into a quiescence state without activating the inflammatory and immune status. However, AIL could significantly decrease the levels of interleukin-2 (IL-2) and interferon-gamma (IFN-γ), as well as the expression of surface activation markers CD69 and CD25, in PMA/ionomycin-activated Jurkat T cells by suppressing the RAF/ERK/STAT3 signaling pathway and increasing the phosphorylation of p53. This study combines network pharmacology prediction with experimental verification in vitro to demonstrate the mechanism of JLW in treating UC and provides an effective, safe, and inexpensive strategy for UC treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".