ArticleNaunyn-Schmiedeberg's archives of pharmacology2026
Exploration of network-based, machine learning, MD simulations, and MM/GBSA approaches revealed luteolin from Gynura procumbens as key inhibitor of MMP9 in NSCLC.
Article in Naunyn-Schmiedeberg's archives of pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The primary leading reason for cancer death is non-small-cell lung cancer (NSCLC) still up to date globally. Even with today's advanced technology, it has very limited successful treatment options and very low survival rates. The intricate molecular pathogenesis of NSCLC, involving the dysregulation of PI3K-AKT and EGFR biological pathways, requires novel multi-targeted approaches. A medicinal plant rich in phytochemicals, Gynura procumbens, has emerged as a potential candidate to be explored for NSCLC, named Gynura procumbens. This plant has anticancer, antioxidant, and anti-inflammatory properties. To distinguish and discover the computational potential of G. procumbens' therapeutic potential against NSCLC, this study combines traditional network pharmacology (NP) with machine learning and molecular dynamic (MD) simulations. Using drug-likeness and pharmacokinetic properties as screening criteria, 16 lead compounds were identified, including luteolin, vanillic acid, and methyl gallate, with robust safety profiles. A total of 263 overlapping genes were identified after overlap of NSCLC-G. procumbens-related targets. As revealed by functional enrichment analysis, these targets are involved in apoptosis, signal transduction, and PI3K-AKT signaling. Machine learning (ML) identified the top genes as CASP3, MMP9, and SRC. Luteolin was validated as a predicted multi-target agent by showing strong binding affinities (-7.2, -10.7, and -8.5 kcal/mol) with CASP3, MMP9, and SRC, with stable hydrogen bonds, respectively. MD simulations and MMGBSA validated the docking study results of luteolin with the key MMP9 target, and it was observed that RMSD, RMSF, and a pattern of hydrogen bonds make luteolin a promising agent for further experimental investigation against NSCLC. These findings demonstrate luteolin's ability to disrupt key NSCLC pathways, offering a promising avenue for combating tumor progression and drug resistance. Future experimental validation could establish its role in personalized cancer therapy and address the unmet clinical needs of NSCLC patients.
Indexed as
Identifiers
41854859What 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.