Evidence map›Paper›PMID 41854859›Full record

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.

Mingwen Shao, Raghad Ayad Majeed, Najeeb Ullah Khan, Jing Lai

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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.

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5 · Who and what money

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4 authors.

Mingwen ShaoDepartment of Internal Medicine, Yangzhou Maternal and Child Health Care Hospital Affiliated to Yangzhou University, Yangzhou, 225000, China.
Raghad Ayad MajeedDepartment of Basic Medical Sciences, College of Dentistry, University of Kirkuk, Kirkuk, 36001, Iraq.
Najeeb Ullah KhanInstitute of Biotechnology and Genetic Engineering, The University of Agriculture Peshawar, Peshawar, 25130, Pakistan.
Jing LaiDepartment of Respiratory and Critical Care Medicine, Ankang Central Hospital, Ankang, 725000, China. laijing3355@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Antineoplastic Agents, PhytogenicAsteraceaeCarcinoma, Non-Small-Cell LungLung NeoplasmsLuteolinMatrix Metalloproteinase 9Matrix Metalloproteinase InhibitorsHumansMachine LearningMolecular Docking SimulationMolecular Dynamics SimulationNetwork PharmacologyAntineoplastic Agents, PhytogenicLuteolinMatrix Metalloproteinase 9Matrix Metalloproteinase InhibitorsMMP9 protein, humanGynura procumbensLuteolinMachine learningMMGBSAMolecular dynamics simulationsNon-small-cell lung cancer

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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.