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6 recordsLinked to original sources
PubMed2026

Illicium verum polysaccharide targets fimbriae and flagella to disrupt biofilm and inhibit multidrug-resistant Escherichia coli proliferation.

The widespread dissemination of multidrug-resistant (MDR) E. coli has led to a decrease in the efficacy of antibiotics, posing severe challenges to clinical anti-infective therapy. Owing to their safety, multitarget activities, and low risk of inducing drug resistance, plant polysaccharides represent a promising alternative strategy. In this study, an acidic polysaccharide (IVP-3) was isolated and purified from the medicinal and edible plant Illicium verum, and it was found to inhibit MDR E. coli colonization by disrupting its biofilm. The Mw of IVP-3 was determined to be 35.566 kDa. Its backbone consists of →4)-α-D-GalpA-6-OMe-(1→, →4)-α-D-GalpA-(1→, →4)-β-D-Galp-(1→, and →3,4)-α-D-GalpA-(1 → residues, whereas the branched chain is composed of α-L-Araf-(1 → 5)-α-L-Araf-(1 → attached to the O-5 position of →2,5)-α-L-Araf-(1→, which is further linked to the O-3 position of the backbone. Mechanistically, IVP-3 disrupts the structure of fimbriae and flagella, inhibits bacterial motility, effectively prevents initial biofilm adhesion, and eradicates preformed mature biofilms. Additionally, IVP-3 damages cell membrane integrity, disrupts the proton motive force, and induces energy metabolism disorder, efflux pump inhibition, and oxidative stress, ultimately leading to bacterial lysis. This study provides a theoretical basis for the development of natural antibacterial agents targeting MDR E. coli biofilms and for the high-value utilization of Illicium verum.

Biofilms↗
PubMed2026

Multitargeted comparative evaluation suggests 2-Aoeobenoxmide shows favourable in silico binding compared to Tucatinib against ERα, HER2, AKT1, EGFR, and PIK3CA in breast cancer.

Breast cancer is a leading cause of cancer-related morbidity and mortality globally, with the WHO reporting approximately 2.3 million new cases and 685,000 deaths annually. Drug resistance in breast cancer complicates treatment, with mutations in critical proteins contributing to therapy failure. Key oncogenic proteins involved in breast cancer progression-namely ERα (a ligand-activated nuclear receptor; PDB: 1A52) and the kinase domains of HER2 (PDB ID: 3PP0), AKT1 (PDB ID: 4EJN), EGFR (PDB ID: 4I23) and PIK3CA (PDB ID: 7R9V)-are pivotal in tumour progression and resistance mechanisms. Targeting these proteins using multitargeted therapeutic strategies may overcome resistance by disrupting key signalling pathways involved in cell proliferation, survival, and metastasis. Such combinatorial approaches promise to improve treatment efficacy and patient outcomes in cases of resistant breast cancer. In this study, we performed multitarget docking on prepared and validated protein structures against the ZINC natural compound library using HTVS, SP, and XP, with pose validation using MM-GBSA. We identified 2-Aoeobenoxmide (2-[1-(2-amino-2-oxo-ethoxy)-6-oxo-benzo[c]chromen-3-yl]oxyacetamide, ZINC134008) with docking and MM-GBSA scores ranging from -8.162 to -10.327 kcal/mol and from -47.18 to -57.62 kcal/mol, respectively, and compared the results with the FDA-approved drug Tucatinib, which exhibited lower binding affinity scores. We further evaluated pharmacokinetic properties using QikProp and electronic properties using DFT (Jaguar) and compared the descriptors of 2-Aoeobenoxmide with those of Tucatinib and with accepted reference ranges. We also performed the WaterMap for 5 nanoseconds (ns), computed various energies, interactions and hydration sites, and the comparison suggests that 2-Aoeobenoxmide shows more favourable hydration-site displacement and binding interactions than Tucatinib. Additionally, a 100 ns MD Simulation has resulted in far less deviation, fluctuations, and intermolecular interactions than Tucatinib, suggesting stable protein-ligand interactions, while the binding free energy and total complex energy computed across 0-1000 frames of the MD trajectories indicate that 2-Aoeobenoxmide is a promising in silico candidate. Importantly, because the entire study is computational, the findings should be interpreted as in silico hypotheses, and experimental validation through in vitro and in vivo assays is warranted before any clinical translation is considered.

Humans↗
PubMed2026

Spectral-Proteomic Integration Analysis (SPIA) Deciphers Molecular Trajectories of Breast Cancer and Enables Multitarget Therapeutic Assessment.

Raman spectroscopy and mass spectrometry-based proteomics offer deeply complementary yet largely disconnected views of cancer biology: the former provides a label-free, real-time biochemical phenotype, while the latter delivers a quantitative inventory of specific protein effectors. Bridging this gap remains a fundamental challenge in analytical biomedicine. Here, we introduce Spectral-Proteomic Integration Analysis (SPIA)─a novel, data-driven integrative framework that systematically links Raman spectroscopic phenotypes with quantitative proteomic profiles through machine learning and statistical correlation. Using a DMBA-induced rat breast cancer model with and without Toremifene (TOR) intervention, SPIA dynamically maps tumor microenvironment remodeling, capturing progressive collagen deposition and lipid metabolic reprogramming. An SVM classifier trained on Raman spectra achieves exceptional diagnostic accuracy (AUC ≥ 99.0%) and successfully predicts TOR therapeutic response. Proteomic analysis identifies 1,350 differentially expressed proteins, with convergent machine learning feature selection (LASSO, Random Forest, XGBoost) pinpointing core regulators including Luc7l2, Nucb1, Cbx3, and Csnk2a1. Crucially, Spearman correlation analysis between key Raman bands and core DEPs reveals strong, statistically robust associations (median ρ ∼ 0.75 in the 1533-1669 cm-1 region), empirically validating SPIA's core integrative logic. Leveraging this multimodal map, we elucidate a multitarget mechanism for TOR involving concurrent suppression of collagen deposition and correction of aberrant lipid metabolism. SPIA establishes a powerful, generalizable paradigm for integrating phenotypic and molecular data, with broad implications for biomarker discovery, drug mechanism elucidation, and precision oncology.

Animals↗
PubMed2025

Mechanism of action of curculigoside ameliorating osteoporosis: an analysis based on network pharmacology and experimental validation.

OBJECTIVE: This study aimed to predict and verify the mechanism of curculigoside in treating osteoporosis using network pharmacology, molecular docking technology, and micro-CT technology. METHODS: Herb databases were searched to identify and screen potential targets of curculigoside. The GeneCards platform was utilized to mine osteoporosis-related targets. Cytoscape 3.6.0 software was employed to construct a compound-target-disease network. A protein-protein interaction (PPI) network for curculigoside in osteoporosis treatment was established, and core targets were screened. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and GO biological process analyses were performed using the Metascape database. Finally, molecular docking and micro-CT were used to validate core targets relevant to osteoporosis. RESULTS: A total of 166 potential curculigoside targets and 4,313 osteoporosis-related targets were identified, with 91 common targets. Ten key targets, including matrix metalloproteinase (MMP)3, MMP9, interleukin (IL)-6, and caspase-3, were screened. KEGG pathway enrichment analysis indicated involvement in 10 pathways, such as the Rap1 signaling pathway and tumor necrosis factor (TNF) signaling pathway. Molecular docking results demonstrated strong binding affinity between curculigoside and the core targets. Micro-CT analysis revealed that curculigoside not only improved BMD, BV/TV, BS/BV, and Tb.Th but also reduced Tb.Sp in osteoporotic bone. CONCLUSIONS: Curculigoside is likely to treat osteoporosis through targets such as MMP3, MMP9, IL-6, and caspase-3, acting on signaling pathways including Rap1 and TNF. These results indicate that curculigoside exhibits multitarget and multipathway characteristics in osteoporosis treatment, providing a theoretical basis for further clinical investigation.

Osteoporosis↗
PubMed2025

Donepezil and Memantine Derivatives for Dual-Function and Prodrug Applications in Alzheimer's Disease.

The treatment of Alzheimer's disease by acetylcholinesterase (AChE) and N-methyl-d-aspartate receptor (NMDAR) inhibitors is limited by the narrow therapeutic window and adverse side effects of the drugs. This study aims to increase the efficacy and limit the side effects of donepezil, an AChE inhibitor, and memantine, an NMDAR inhibitor, through the addition of amyloid-β (Aβ)-targeting fragments to create dual-function compounds. The incorporation of the amyloid-targeting fragments successfully produced compounds with affinity for Aβ fibrils, and that can stain amyloid plaques in the brains of 5xFAD mice. The donepezil-based compounds showed significant changes in AChE inhibition compared to donepezil due to the incorporation of the Aβ-targeting fragment and as confirmed by molecular docking studies. The memantine-derived compound showed good brain uptake in 5xFAD mice but lacked compatibility with NMDAR inhibition based on in vitro assays and molecular docking. Importantly, the memantine-derived compound acts as a prodrug in vivo, releasing memantine within a pharmacologically relevant time frame. Overall, these findings suggest that dual-function compounds may be useful as drug delivery agents that can be metabolized to release an active drug in areas of the brain rich in amyloid plaques and thus could lead to improved treatments for Alzheimer's disease.

Animals↗
PubMed2026

Elucidating the Mechanism of Xiaoqinglong Decoction in Chronic Urticaria Treatment: An Integrated Approach of Network Pharmacology, Bioinformatics Analysis, Molecular Docking, and Molecular Dynamics Simulations.

INTRODUCTION: Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. METHODS: The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. RESULTS: A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. DISCUSSION: This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. CONCLUSION: This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Molecular Docking Simulation↗