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Research Article

Computational Analysis of Plumbagin in Prostate Cancer Using Network Pharmacology and Molecular Dynamics Simulations

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September 11th, 2026

 ,  ,  ,  ,  ,  ,  , 
1Department of Clinical Laboratory, DongGuan SongShan Lake Tungwah Hospital, 2Dongguan Key Laboratory of Medical Molecular Biology, 3Department of Hematology, DongGuan Tungwah Hospital, 4Department of Emergency, DongGuan Tungwah Hospital, 5School of Medical Technology, Guangdong Medical University, 6Department of Clinical Laboratory, DongGuan Tungwah Hospital

* These authors contributed equally

In This Article

Summary

This study employed network pharmacology and molecular dynamics simulations to investigate the molecular mechanisms and pathways of plumbagin in the treatment of prostate cancer. The results demonstrate that plumbagin may stably bind to AKT1, ESR1, BCL2, EGFR, TNF, MAPK3, HSP90AA1, SRC, and PPARG.

Abstract

Prostate cancer is a major contributor to cancer-related mortality among men. Because many patients are diagnosed only after the disease has progressed to a locally advanced or metastatic stage, curative treatment is often no longer possible. In the present study, we applied an integrated computational approach combining network pharmacology, molecular docking, and molecular dynamics simulations to explore the potential molecular mechanisms underlying the therapeutic effects of plumbagin in prostate cancer. Potential therapeutic targets were identified through integrated database analysis, followed by protein-protein interaction network construction, functional enrichment analysis, molecular docking, and molecular dynamics simulations to evaluate the stability of protein-ligand interactions. Our computational analyses identified that plumbagin may form stable interactions with multiple hub targets, including AKT serine/threonine kinase 1 (AKT1), estrogen receptor 1 (ESR1), BCL2 apoptosis regulator (BCL2), epidermal growth factor receptor (EGFR), tumor necrosis factor (TNF), mitogen-activated protein kinase 3 (MAPK3), heat shock protein 90 alpha family class A member 1 (HSP90AA1), SRC proto-oncogene, non-receptor tyrosine kinase (SRC), and peroxisome proliferator activated receptor gamma (PPARG), suggesting its potential to modulate key pathways involved in prostate cancer progression. These in silico findings provide new insights into the possible molecular mechanisms of plumbagin and offer a rationale for future experimental validation. However, further in vitro and in vivo studies are warranted to confirm its functional activity and therapeutic efficacy.

Introduction

Prostate cancer is heterogeneous, with clinical manifestations ranging from asymptomatic screen-detected lesions that may never progress to aggressive malignancies, and it is a leading cause of morbidity and mortality worldwide1,2. Globally, the number of new cases of prostate cancer is projected to double from 1.4 million in 2020 to 2.9 million in 2040, while the annual number of deaths is expected to increase from 375,000 in 2020 to approximately 700,000 in 20403. Initially diagnosed as an androgen-dependent malignancy, prostate cancer is treatable with androgen deprivation therapy. Nevertheless, despite an effective initial response, the disease inevitably progresses to an androgen-independent form. Patients with hormone-refractory prostate cancer are at a significantly increased risk of developing bone metastases, leading to clinically significant skeletal lesions4,5,6,7. Furthermore, while early-stage prostate cancer can be cured with surgery or radiotherapy, many patients already present with locally advanced or metastatic disease at the time of diagnosis, for which no curative treatment currently exists8,9. Therefore, there is an urgent need to develop effective and highly selective agents for the prevention and/or treatment of prostate cancer metastasis.

A variety of natural extracts, such as lycopene, soy products, green tea, pomegranate phenolics, apigenin, as well as vitamins D and E, have been shown to effectively prevent the development of prostate cancer10,11,12,13. Plumbagin (PLB), a naturally occurring naphthoquinone compound widely found in nature and a major component of Plumbago zeylanica, possesses anti-infective14, anti-inflammatory15, anti-atherosclerotic16, and anti-tumor17,18 properties. Studies have demonstrated that PLB exerts anti-tumor effects in various cancer cell types, including breast cancer, non-small cell lung cancer, liver cancer, pancreatic cancer, colorectal cancer, ovarian cancer, prostate cancer, glioma, and retinoblastoma19,20,21. In vitro experiments have shown that PLB inhibits the proliferation of prostate cancer cells22,23. PLB also regulates microprotein expression, which in turn affects multiple cellular behaviors in prostate cancer cells, including cell cycle control, apoptosis, autophagy, and epithelial-to-mesenchymal transition24,25. In addition, PLB delays the growth of androgen-independent cancer cells in subcutaneous xenograft mouse models22,26.

Although numerous studies have shown that PLB inhibits the proliferation and invasion of prostate cancer cells, there is currently no systematic investigation of the targets and pathways by which PLB acts on these cells. Using network pharmacology, molecular docking, and molecular dynamics simulations, this study sought to elucidate the potential targets and pathways underlying PLB's therapeutic effects in prostate cancer.

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Protocol

Prediction and screening of potential targets of PLB

The chemical structure information of PLB was obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Molecular targets of PLB were predicted based on ligand-based two-dimensional and three-dimensional similarity using the SwissTarget (https://swisstargetprediction.ch/index.php), SEA (https://sea.bkslab.org/), TargetNet (http://targetnet.scbdd.com/calcnet/index/), PharmMapper (https://www.lilab-ecust.cn/pharmmapper/index.html), Comparative Toxicogenomics Database (CTD, https://ctdbase.org/), Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://www.tcmsp-e.com/index.php), and HERB (http://herb.ac.cn/) databases. To ensure data reliability and reproducibility, database‑specific filtering thresholds were applied as follows: TCMSP retained targets with oral bioavailability (OB) ≥ 30% and drug‑likeness (DL) ≥ 0.18; SwissTargetPrediction kept entries with a prediction probability ≥ 0.5; PharmMapper selected targets with a normalized fit score ≥ 4.0; SEA included only targets with an E‑value < 0.001 as significantly enriched; TargetNet retained targets with a prediction probability > 0.5; CTD included only targets with curated evidence (inferred from chemical–gene interactions) and an interaction score > 0.3; and HERB retained targets with a literature‑supported score ≥ 0.4. All retrieved protein identifiers from the above databases were standardized to official human HGNC gene symbols using the UniProt database (https://www.uniprot.org/), with species restricted to Homo sapiens. After removing duplicate entries across all sources, a total of 500 unique PLB‑related targets were obtained for subsequent analysis.

Retrieval of prostate cancer-associated genes

Disease-related genes for prostate cancer were obtained from GeneCards (https://www.GeneCardss.org/), DrugBank (https://go.drugbank.com/), CTD, and HERB. Database-specific inclusion criteria were applied to ensure data reliability. GeneCardss retained genes with a relevance score ≥ 0.5, as this threshold captures genes with moderate-to-strong evidence linking them to the disease query. DrugBank included only entries with experimental evidence (e.g., FDA-approved or investigational drugs for prostate cancer) and excluded computationally predicted or theoretical interactions. CTD retained only records with evidence levels classified as "marker" or "mechanism" based on curated chemical-gene-disease interactions. HERB included targets with a literature-supported confidence score ≥ 0.4 to ensure sufficient experimental or text-mining evidence. All gene symbols were unified to the HGNC nomenclature via the UniProt database (https://www.uniprot.org/), with species restricted to Homo sapiens, and duplicates were removed, yielding 1,199 unique prostate cancer–related targets for subsequent analysis.

Overlapping target identification and conflict annotation correction

The 500 PLB targets and 1,199 prostate cancer targets were intersected using standardized HGNC symbols in Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html), yielding 151 overlapping candidate targets. To resolve cross‑database annotation conflicts, a three‑step correction pipeline was implemented: (i) gene alias inconsistencies were unified via the UniProt ID mapping tool; (ii) paralogous redundant genes were excluded using CD‑HIT with a sequence similarity threshold > 0.4; and (iii) contradictory functional annotations were retained only if supported by at least two independent databases, while unique conflicting descriptions from a single source were discarded.

Network construction and hub identification

A protein-protein interaction (PPI) network was constructed by submitting the overlapping targets to the STRING database (version 12.0, https://cn.string-db.org/), with the search restricted to Homo sapiens. Only interactions with a combined confidence score ≥ 0.700 were retained to ensure high reliability, and all nodes lacking any connections were excluded from the network. The constructed PPI network was visualized and analyzed topologically in Cytoscape (version 3.10.2), after which hub genes were determined using the cytoHubba plugin, employing MCC (Maximal Clique Centrality) as the primary ranking algorithm and Degree as an auxiliary cross‑validation metric.

Functional enrichment analysis

Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the differentially expressed genes were performed using the Bioinformatics Online Analysis Platform (https://www.bioinformatics.com.cn, last accessed on May 4, 2026). The platform employs Fisher's exact test to calculate raw P‑values, and the Benjamini-Hochberg (BH) method was used for multiple-testing correction to control the false discovery rate (FDR). GO terms (biological process, cellular component, and molecular function) and KEGG pathways with an FDR < 0.05 were considered significantly enriched.

Molecular docking

Crystal structures of the selected target proteins, representing active conformations, were obtained from the RCSB Protein Data Bank (https://www.rcsb.org/). Protein preparation was performed in Discovery Studio, including removal of water molecules and heteroatoms, deletion of redundant chains, repair of missing residues, addition of hydrogen atoms at pH 7.4, assignment of Gasteiger charges, and energy minimization. The binding pocket was defined based on the co‑crystallized ligand coordinates, using a grid that encompassed all critical substrate‑binding residues. Molecular docking of PLB was carried out using the SwissDock web server (http://www.swissdock.ch/) via the Attracting Cavities module. Binding free energies were calculated to assess affinity, and the lowest‑energy conformation for each target was selected as the final docking pose. The results of the molecular docking simulations were visualized to validate them.

Molecular dynamics simulation

Molecular dynamics (MD) simulations were carried out using GROMACS 2022.2. The Amber14SB force field was used to describe the protein, with the system solvated in TIP3P water. Plumbagin parameters, including AM1-BCC partial charges and GAFF2 atom types, were generated using Antechamber, followed by topology conversion with ACPYPE and assignment of Joung-Cheatham ion parameters. Each protein-ligand complex was embedded in a truncated dodecahedral simulation box with a minimum protein-to-box distance of 1.2 nm, solvated with TIP3P water molecules, and neutralized by adding 0.15 M Na⁺/Cl⁻. After energy minimization using the steepest descent algorithm (Fmax < 1,000 kJ·mol⁻1·nm⁻1), the system underwent sequential NVT and NPT equilibration for 200 ps each at 298 K. Production simulations were then performed for 200 ns under NPT conditions with a 2 fs integration time step using the Verlet cutoff scheme. Long-range electrostatic interactions were calculated with the particle mesh Ewald (PME) method, while both electrostatic and van der Waals interactions employed a cutoff distance of 1.2 nm. The LINCS algorithm was used to constrain hydrogen-containing bonds. Temperature was maintained at 298 K using the Nosé-Hoover thermostat and pressure at 1 bar using the Parrinello-Rahman barostat. Coordinates were saved every 10 ps for subsequent analyses. Trajectory analysis and visualization were performed using GROMACS utilities, VMD, and PyMOL, whereas MM-PBSA binding free energy calculations were carried out with gmx_MMPBSA where appropriate.

Equilibration assessment of molecular dynamics trajectories and MM-PBSA free energy sampling protocol

For each protein–plumbagin complex, a 200 ns all-atom production molecular dynamics simulation was performed using GROMACS. The 100 ns time point was set as the equilibration boundary: the 0–100 ns segment was designated as the conformational relaxation phase, during which the protein backbone and ligand-binding pocket underwent continuous conformational adjustments; the 100–200 ns segment was identified as the thermodynamically stable plateau, as evidenced by the absence of unidirectional drift in RMSD, radius of gyration (Rg), residue-wise RMSF, buried SASA of the ligand, intermolecular hydrogen bonds, and ligand–receptor interaction energies, which exhibited only minor steady-state fluctuations. All quantitative kinetic parameters and MM/PBSA binding free energies were calculated exclusively from the 100–200 ns equilibrium phase, from which frames were uniformly extracted every 100 ps, yielding 1,000 equilibrium snapshots per system for gmx_MMPBSA input. The first 100 ns of relaxation trajectories were discarded to eliminate interference from conformational drift in free-energy calculations.

Prognostic analysis of target genes in prostate cancer

Prognostic analyses were conducted using prostate cancer datasets from The Cancer Genome Atlas (TCGA). RNA sequencing (STAR-counts) data and corresponding clinical information were obtained from the Genomic Data Commons portal (https://portal.gdc.cancer.gov). Gene expression counts were converted to transcripts per million (TPM) and normalized using log2(TPM + 1). Following the exclusion of samples with incomplete clinical information, 498 cases were included in the analysis. The median expression level of each gene was used to divide patients into high- and low-expression groups. Kaplan-Meier survival analysis with the log-rank test and univariate Cox proportional hazards regression were performed to evaluate overall survival (OS) for SRC and progression-free survival (PFS) for MAPK3, with hazard ratios (HRs) and 95% confidence intervals (CIs) reported. Predictive performance was further assessed by generating time-dependent receiver operating characteristic (ROC) curves at 1, 3, and 5 years. Statistical analyses were carried out in R version 4.0.3, with P < 0.05 considered statistically significant.

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Results

Prediction results of PLB and prostate cancer targets

The PubChem CID of PLB is 10205, with the IUPAC name: 5-hydroxy-2-methylnaphthalene-1,4-dione, SMILES: CC1=CC(=O)C2=C(C1=O)C=CC=C2O, InChIKey: VCMMXZQDRFWYSE-UHFFFAOYSA-N, InChI: InChI=1S/C11H8O3/c1-6-5-9(13)10-7(11(6)14)3-2-4-8(10)12/h2-5,12H,1H3, molecular weight: 188.18, molecular formula: C11H8O3, CAS number: 481-42-5. After removing duplicates, this study predicted 500 potential PLB targets using the SwissTarget, SEA, ...

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Discussion

Studies have shown that AKT1, ESR1, BCL2, EGFR, TNF, MAPK3, HSP90AA1, SRC, and PPARG play diverse roles in promoting cancer progression. The phosphatidylinositol 3-kinase (PI3K)/serine/threonine protein kinase (AKT)/mechanistic target of rapamycin (mTOR) signaling pathway is a crucial intracellular signal transduction pathway that regulates various pathophysiological processes, including cell growth, proliferation, apoptosis, angiogenesis, inflammatory responses, and chemotaxis43. AKT is an import...

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Disclosures

The authors have no conflicts of interest to declare.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Comparative Toxicogenomics DatabaseNational Institute of Environment Health Studies, National Institute of Neurological Disorders and Strokehttps://ctdbase.org/CTD is a robust, publicly available database that aims to advance understanding about how environmental exposures affect human health.
It provides manually curated information about chemical-gene/protein interactions, chemical-disease and gene-disease relationships. These data are integrated with functional and pathway data to aid in development of hypotheses about the mechanisms underlying environmentally influenced diseases.
CytoscapeInstitute for Systems Biology, ISB, Leroy Hoodhttps://cytoscape.org/Initially designed specifically for biomolecular interaction networks, the software has now evolved into a versatile platform for analyzing complex networks, widely applied in fields such as bioinformatics, systems medicine, social networks, and semantic networks
DAVID databaseNational Institutes of Health (NIH)https://davidbioinformatics.nih.gov/list.jspDAVID, short for Database for Annotation, Visualization and Integrated Discovery, is the most classic and widely used free online platform for gene set functional annotation and enrichment analysis
DrugBankUniversity of Alberta, Canadahttps://go.drugbank.com/DrugBank is the intelligence operating system for biopharma, delivering the most comprehensive, structured biomedical knowledge to help advance therapies from discovery to patient impact
GeneCardLifeMap Scienceshttps://www.genecards.org/GeneCards is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes. The knowledgebase automatically integrates gene-centric data from 193 web sources, including genomic, transcriptomic, proteomic, genetic, clinical and functional information.
gmx_MMPBSAColumbia University of Medellinhttps://valdes-tresanco-ms.github.io/gmx_MMPBSA/gmx_MMPBSA, an open-source free energy calculation tool developed by Valdés-Tresanco et al., was adopted to compute binding free energies based on MM/PBSA and MM/GBSA methodologies. Built upon the MMPBSA.py engine from AmberTools, it directly processes trajectory files generated by GROMACS without manual format conversion. Residue-wise energy decomposition, computational alanine scanning and entropy correction were performed to quantify binding contributions of key amino acids, and the built-in gmx_MMPBSA_ana module was used for statistical analysis and visualization of energy components.
GROMACSRoyal Institute of Technology (KTH), Uppsala Universitywww.gromacs.orgGROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles and is a community-driven project. Contributions are welcome in many forms, including improvements to documentation, patches to fix bugs, advice on the forums, bug reports that let us reproduce the issue, and new functionality
HERBBeijing University of Chinese Medicine, Beijing University of Chinese Medicine.http://herb.ac.cn/A high-throughput experiment- and reference-guided database of traditional Chinese medicine.
PharmMapperSchool of Pharmaceutical Science and Technology / School of Information Science and Engineering, East China University of Science and Technologyhttps://www.lilab-ecust.cn/pharmmapper/index.htmlPharmMapper Server is a freely accessed web-server designed to identify potential target candidates for the given probe small molecules (drugs, natural products, or other newly discovered compounds with binding targets unidentified) using pharmacophore mapping approach. Benefited from the highly efficient and robust mapping method, PharmMapper bears high throughput ability and can identify the potential target candidates from the database within a few hours.
PubChemNational Institutes of Health (NIH)https://pubchem.ncbi.nlm.nih.gov/PubChem is an open chemistry database at the National Institutes of Health (NIH)
PyMOLSchrödingerhttps://pymol.orgPyMOL, initially developed by Warren Lyford DeLano and currently maintained by Schrödinger, Inc., is a cross-platform molecular visualization tool widely adopted in structural biology and computer-aided drug discovery. Protein-ligand complex structures downloaded from RCSB PDB were loaded into PyMOL to visualize binding conformations, hydrogen bonds and key interacting residues. Structural superimposition, surface rendering and high-resolution publication-quality molecular graphics were generated via built-in Python scripting interface.
RCSB Protein Data BankResearch Collaboratory for Structural Bioinformaticshttps://www.rcsb.org/RCSB PDB (Research Collaboratory for Structural Bioinformatics Protein Data Bank) serves as the US data center of the Worldwide Protein Data Bank (wwPDB) consortium, established and led by Helen M. Berman in 1998. It archives experimentally determined three-dimensional atomic structures of proteins, nucleic acids and their complexes resolved by X-ray crystallography, cryo-electron microscopy and NMR spectroscopy, and integrates millions of AI-predicted protein structure models from AlphaFold. The web portal supports multi-dimensional search, real-time 3D molecular visualization, batch coordinate file download, and cross-reference annotations linking structural data to gene function, disease and small-molecule ligands, widely utilized for target identification and molecular docking studies.
SEA Shoichet Laboratory in the Department of Pharmaceutical Chemistry at the University of California, San Francisco (UCSFhttps://sea.bkslab.org/the Similarity Ensemble Approach (SEA) relates proteins based on the set-wise chemical similarity among their ligands. It can be used to rapidly search large compound databases and to build cross-target similarity maps
STRING databaseGlobal Biodata Coalition and ELIXIR.https://cn.string-db.org/STRING is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations; they stem from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases.
Swiss Dock web server Molecular Modelling Group of the University of Lausanne and the SIB Swiss Institute of Bioinformaticshttps://www.swissdock.ch/SwissDock is a web service that predicts the molecular interactions likely to occur between a target protein and a small molecule.
SwissTarget Molecular Modeling Group of the SIB | Swiss Institute of Bioinformaticshttps://swisstargetprediction.ch/index.phpThis website allows you to estimate the most probable macromolecular targets of a small molecule, assumed as bioactive. The prediction is founded on a combination of 2D and 3D similarity with a library of 37 0'000 known actives on more than 3000 proteins from three different species.
TargetNetComputational Biology & Drug Design Grouphttp://targetnet.scbdd.com/calcnet/index/TargetNet is an open web server that could be used for netting or predicting the binding of multiple targets for any given molecule
Traditional Chinese Medicine Systems Pharmacology Database and Analysis PlatformZhejiang Jiwei Health Co., Ltdhttps://www.tcmsp-e.com/index.phpTCMSP is a unique systems pharmacology platform of Chinese herbal medicines that captures the relationships between drugs, targets and diseases. The database includes chemicals, targets and drug-target networks, and associated drug-target-disease networks, as well as pharmacokinetic properties for natural compounds involving oral bioavailability, drug-likeness,intestinal epithelial permeability, blood-brain-barrier,  aqueous solubility and etc. This breakthrough has sparked a new interest in the search of candidate drugs in various types of traditional Chinese herbs.
UniProt databaseEuropean Bioinformatics Institute (EMBL-EBI), the SIB Swiss Institute of Bioinformatics and the Protein Information Resource (PIR)https://www.uniprot.org/UniProt is the world’s leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information
Venny 2.1 online toolCentro Nacional de Biotecnología, (CNB-CSIC)https://bioinfogp.cnb.csic.es/tools/venny/index.htmlVenny consists on a single standard html file. Feel free to save it to your hard disk, open it with your favorite browser, and start drawing nice Venn's diagrams in seconds, even without internet connection.
VMDTheoretical and Computational Biophysics Research Group (TCBG) at Beckman Institute, University of Illinois at Urbana Champaign, USAhttps://www.ks.uiuc.edu/Research/vmd/VMD (Visual Molecular Dynamics) is a free cross-platform molecular visualization and trajectory analysis software developed by the Theoretical and Computational Biophysics Group led by Professor Klaus Schulten at the University of Illinois Urbana-Champaign. It supports standard PDB structures and MD trajectories generated by GROMACS, NAMD and Amber, with diverse molecular rendering styles and built-in Tcl scripting interface. Quantitative analyses including RMSD, hydrogen bond occupancy, SASA and ligand binding pocket geometry were performed to characterize dynamic protein-ligand interactions and conformational fluctuations.

References

  1. Bray F, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-63.
  2. Loeb S, et al. Overdiagnosis and overtreatment of prostate cancer. Eur Urol. 2014;65(6):1046-55.
  3. James ND, et al. The Lancet Commission on prostate cancer: planning for the surge in cases. Lancet. 2024;403(10437):1683-722.
  4. Bussard KM, Gay CV, Mastro AM. The bone microenvironment in metastasis: what is special about bone? Cancer Metastasis Rev. 2008;27(1):41-55.
  5. Keller ET, et al. New trends in the treatment of bone metastasis. J Cell Biochem. 2007;102(5):1095-102.
  6. Kingsley LA, Fournier PG, Chirgwin JM, Guise TA. Molecular biology of bone metastasis. Mol Cancer Ther. 2007;6(10):2609-17.
  7. Valdespino V, Tsagozis P, Pisa P. Current perspectives in the treatment of advanced prostate cancer. Med Oncol. 2007;24(3):273-86.
  8. Albertsen P. Predicting survival for men with clinically localized prostate cancer: what do we need in contemporary practice? Cancer. 2008;112(1):1-3.
  9. So A, Gleave M, Hurtado-Col A, Nelson C. Mechanisms of the development of androgen independence in prostate cancer. World J Urol. 2005;23(1):1-9.
  10. Gupta S. Prostate cancer chemoprevention: current status and future prospects. Toxicol Appl Pharmacol. 2007;224(3):369-76.
  11. Patel D, Shukla S, Gupta S. Apigenin and cancer chemoprevention: progress, potential, and promise. Int J Oncol. 2007;30(1):233-45.
  12. Kallifatidis G, Hoy JJ, Lokeshwar BL. Bioactive natural products for chemoprevention and treatment of castration-resistant prostate cancer. Semin Cancer Biol. 2016;40-41:160-9.
  13. Montuori E, et al. Marine natural products with activities against prostate cancer: recent discoveries. Int J Mol Sci. 2023;24(2):1435.
  14. de Paiva SR, Figueiredo MR, Aragão TV, Kaplan MAC. Antimicrobial activity in vitro of plumbagin isolated from Plumbago species. Mem Inst Oswaldo Cruz. 2003;98(7):959-61.
  15. Luo P, et al. Anti-inflammatory and analgesic effect of plumbagin through inhibition of nuclear factor-κB activation. J Pharmacol Exp Ther. 2010;335(3):735-42.
  16. Sharma I, Gusain D, Dixit VP. Hypolipidaemic and antiatherosclerotic effects of plumbagin in rabbits. Indian J Physiol Pharmacol. 1991;35(1):10-4.
  17. Awale S, et al. Targeting pancreatic cancer with novel plumbagin derivatives: design, synthesis, molecular mechanism, in vitro and in vivo evaluation. J Med Chem. 2023;66(12):8054-65.
  18. Zhang R, Jiang Q, Guo R, Guo K, Qiu J. Unveiling the power of plumbagin: revitalizing exhausted T cells to combat tongue cancer. Cancer Cell Int. 2025;25(1):271.
  19. Panichayupakaranant P, Ahmad MI. Plumbagin and its role in chronic diseases. Adv Exp Med Biol. 2016;929:229-46.
  20. Kuete V, et al. Cytotoxicity of plumbagin, rapanone, and 12 other naturally occurring quinones from Kenyan flora towards human carcinoma cells. BMC Pharmacol Toxicol. 2016;17(1):60.
  21. Gharbaran R, Shi C, Onwumere O, Redenti S. Plumbagin induces cytotoxicity via loss of mitochondrial membrane potential and caspase activation in metastatic retinoblastoma. Anticancer Res. 2021;41(10):4725-32.
  22. Aziz MH, Dreckschmidt NE, Verma AK. Plumbagin, a medicinal plant-derived naphthoquinone, is a novel inhibitor of the growth and invasion of hormone-refractory prostate cancer. Cancer Res. 2008;68(21):9024-32.
  23. Powolny AA, Singh SV. Plumbagin-induced apoptosis in human prostate cancer cells is associated with modulation of cellular redox status and generation of reactive oxygen species. Pharm Res. 2008;25(9):2171-80.
  24. Qiu JX, et al. Plumbagin elicits differential proteomic responses mainly involving cell cycle, apoptosis, autophagy, and epithelial-to-mesenchymal transition pathways in human prostate cancer PC-3 and DU145 cells. Drug Des Devel Ther. 2015;9:349-417.
  25. Wang F, et al. Plumbagin induces cell cycle arrest and autophagy and suppresses epithelial-to-mesenchymal transition involving the PI3K/Akt/mTOR-mediated pathway in human pancreatic cancer cells. Drug Des Devel Ther. 2015;9:537-60.
  26. Lai L, et al. Plumbagin inhibits tumour angiogenesis and tumour growth through the Ras signalling pathway following activation of the VEGF receptor-2. Br J Pharmacol. 2012;165(4b):1084-96.
  27. Wu WI, et al. Crystal structure of human AKT1 with an allosteric inhibitor reveals a new mode of kinase inhibition. PLoS One. 2010;5(9):e12913.
  28. Bai L, et al. A potent and selective small-molecule degrader of STAT3 achieves complete tumor regression in vivo. Cancer Cell. 2019;36(5):498-511.e17.
  29. Fanning SW, et al. The SERM/SERD bazedoxifene disrupts ESR1 helix 12 to overcome acquired hormone resistance in breast cancer cells. Elife. 2018;7:e37161.
  30. Suraweera CD, Caria S, Järvå M, Hinds MG, Kvansakul M. A structural investigation of NRZ-mediated apoptosis regulation in zebrafish. Cell Death Dis. 2018;9(10):967.
  31. Boulanger MJ, Chow DC, Brevnova EE, Garcia KC. Hexameric structure and assembly of the interleukin-6/IL-6 α-receptor/gp130 complex. Science. 2003;300(5628):2101-4.
  32. Jia Y, et al. Overcoming EGFR(T790M) and EGFR(C797S) resistance with mutant-selective allosteric inhibitors. Nature. 2016;534(7605):129-32.
  33. Dos Santos Nascimento IJ, da Silva-Júnior EF. TNF-α inhibitors from natural compounds: an overview, CADD approaches, and their exploration as anti-inflammatory agents. Comb Chem High Throughput Screen. 2022;25(14):2317-40.
  34. Chaikuad A, et al. A unique inhibitor binding site in ERK1/2 is associated with slow binding kinetics. Nat Chem Biol. 2014;10(10):853-60.
  35. Brasca MG, et al. Discovery of NMS-E973 as a novel, selective, and potent inhibitor of heat shock protein 90. Bioorg Med Chem. 2013;21(22):7047-63.
  36. Gurbani D, et al. Structure and characterization of a covalent inhibitor of Src kinase. Front Mol Biosci. 2020;7:81.
  37. Gellrich L, et al. L-thyroxin and the nonclassical thyroid hormone TETRAC are potent activators of PPARγ. J Med Chem. 2020;63(13):6727-40.
  38. Weng Z, Shen X, Zheng J, Liang H, Liu Y. Structural basis of DEPTOR to recognize phosphatidic acid using its tandem DEP domains. J Mol Biol. 2021;433(13):166989.
  39. Huck JD, et al. Structures of Hsp90α and Hsp90β bound to a purine-scaffold inhibitor reveal an exploitable residue for drug selectivity. Proteins. 2019;87(10):869-77.
  40. Luo G, et al. Discovery of isonicotinamides as highly selective, brain-penetrable, and orally active glycogen synthase kinase-3 inhibitors. J Med Chem. 2016;59(3):1041-51.
  41. Orlando BJ, Malkowski MG. Crystal structure of rofecoxib bound to human cyclooxygenase-2. Acta Crystallogr F Struct Biol Commun. 2016;72(Pt 10):772-6.
  42. Nuti E, et al. Development of thioaryl-based matrix metalloproteinase-12 inhibitors with alternative zinc-binding groups: synthesis, potentiometric, NMR, and crystallographic studies. J Med Chem. 2018;61(10):4421-35.
  43. Li Q, Li Z, Luo T, Shi H. Targeting the PI3K/AKT/mTOR and RAF/MEK/ERK pathways for cancer therapy. Mol Biomed. 2022;3(1):47.
  44. Wang B, et al. Propofol protects against hydrogen peroxide-induced injury in cardiac H9c2 cells via Akt activation and Bcl-2 up-regulation. Biochem Biophys Res Commun. 2009;389(1):105-11.
  45. Majewska E, Szeliga M. AKT/GSK3β signaling in glioblastoma. Neurochem Res. 2017;42(3):918-24.
  46. Yang H, et al. mTOR kinase structure, mechanism, and regulation. Nature. 2013;497(7448):217-23.
  47. Fong CJ, Sherwood ER, Mendelsohn J, Lee C, Kozlowski JM. Epidermal growth factor receptor monoclonal antibody inhibits constitutive receptor phosphorylation, reduces autonomous growth, and sensitizes androgen-independent prostatic carcinoma cells to tumor necrosis factor α. Cancer Res. 1992;52(21):5887-92.
  48. Visakorpi T, Kallioniemi OP, Koivula T, Harvey J, Isola J. Expression of epidermal growth factor receptor and ERBB2 (HER-2/Neu) oncoprotein in prostatic carcinomas. Mod Pathol. 1992;5(6):643-8.
  49. Ibrahim GK, et al. Differential immunoreactivity of epidermal growth factor receptor in benign, dysplastic, and malignant prostatic tissues. J Urol. 1993;149(1):170-3.
  50. Davies P, Eaton CL. Binding of epidermal growth factor by human normal, hypertrophic, and carcinomatous prostate. Prostate. 1989;14(2):123-32.
  51. Bahar ME, Kim HJ, Kim DR. Targeting the RAS/RAF/MAPK pathway for cancer therapy: from mechanism to clinical studies. Signal Transduct Target Ther. 2023;8(1):455.
  52. Guo YJ, et al. ERK/MAPK signalling pathway and tumorigenesis. Exp Ther Med. 2020;19(3):1997-2007.
  53. Ullah R, Yin Q, Snell AH, Wan L. RAF-MEK-ERK pathway in cancer evolution and treatment. Semin Cancer Biol. 2022;85:123-54.
  54. Chambard JC, Lefloch R, Pouysségur J, Lenormand P. ERK implication in cell cycle regulation. Biochim Biophys Acta. 2007;1773(8):1299-310.
  55. Gesmundo I, et al. Proton pump inhibitors promote the growth of androgen-sensitive prostate cancer cells through ErbB2, ERK1/2, PI3K/Akt, GSK-3β signaling and inhibition of cellular prostatic acid phosphatase. Cancer Lett. 2019;449:252-62.
  56. Schopf FH, Biebl MM, Buchner J. The HSP90 chaperone machinery. Nat Rev Mol Cell Biol. 2017;18(6):345-60.
  57. García-Alonso S, et al. Structure of the RAF1-HSP90-CDC37 complex reveals the basis of RAF1 regulation. Mol Cell. 2022;82(18):3438-52.e8.
  58. Chiosis G, Digwal CS, Trepel JB, Neckers L. Structural and functional complexity of HSP90 in cellular homeostasis and disease. Nat Rev Mol Cell Biol. 2023;24(11):797-815.
  59. Li J, Buchner J. Structure, function, and regulation of the Hsp90 machinery. Biomed J. 2013;36(3):106-17.
  60. Youssef ME, et al. Role of ganetespib, an HSP90 inhibitor, in cancer therapy: from molecular mechanisms to clinical practice. Int J Mol Sci. 2023;24(5):5014.
  61. Solit DB, Scher HI, Rosen N. Hsp90 as a therapeutic target in prostate cancer. Semin Oncol. 2003;30(5):709-16.
  62. Hoter A, Rizk S, Naim HY. The multiple roles and therapeutic potential of molecular chaperones in prostate cancer. Cancers (Basel). 2019;11(8):1194.
  63. Moon SJ, et al. Bruceantin targets HSP90 to overcome resistance to hormone therapy in castration-resistant prostate cancer. Theranostics. 2021;11(2):958-73.
  64. Li J, et al. Androgen-targeted hsa_circ_0085121 encodes a novel protein and improves the development of prostate cancer through facilitating the activity of the PI3K/Akt/mTOR pathway and enhancing AR-V7 alternative splicing. Cell Death Dis. 2024;15(11):848.
  65. Taneja SS, et al. Toremifene: a promising therapy for the prevention of prostate cancer and complications of androgen deprivation therapy. Expert Opin Investig Drugs. 2006;15(3):293-305.
  66. Yang J, et al. Protective effect of Fuzi Lizhong decoction against non-alcoholic fatty liver disease via an anti-inflammatory response through regulation of p53 and PPARG signaling. Biol Pharm Bull. 2020;43(11):1626-33.
  67. Sui Q, et al. Ganoderic acid A: an in-depth review of pharmacological effects and molecular docking analysis. J Ethnopharmacol. 2025;349:119868.
  68. Zhang T, et al. PPARG is a potential target of Tanshinone IIA in prostate cancer treatment: a combination study of molecular docking and dynamic simulation based on transcriptomic bioinformatics. Eur J Med Res. 2023;28(1):487.
  69. Astakhova A, et al. Inhibitors of oxidative phosphorylation modulate astrocyte inflammatory responses through AMPK-dependent Ptgs2 mRNA stabilization. Cells. 2019;8(10):1185.
  70. Liu Y, Cai Y, He C, Chen M, Li H. Anticancer properties and pharmaceutical applications of plumbagin: a review. Am J Chin Med. 2017;45(3):423-41.
  71. Ahmad I, et al. Synergistic inhibition of colon cancer cell proliferation via p53, Bax, and Bcl-2 modulation by curcumin and plumbagin combination. ACS Omega. 2025;10(18):19045-60.
  72. Bello IJ, Oyebode OT, Olanlokun JO, Omodara TO, Olorunsogo OO. Plumbagin induces testicular damage via mitochondrial-dependent cell death. Chem Biol Interact. 2021;347:109582.
  73. Hafeez BB, et al. Plumbagin inhibits prostate carcinogenesis in intact and castrated PTEN knockout mice via targeting PKCε, Stat3, and epithelial-to-mesenchymal transition markers. Cancer Prev Res (Phila). 2015;8(5):375-86.
  74. Kumar MR, et al. Formulation of plumbagin-loaded long-circulating pegylated liposomes: in vivo evaluation in C57BL/6J mice bearing B16F1 melanoma. Drug Deliv. 2011;18(7):511-22.
  75. Mandala Rayabandla SK, et al. Preparation, in vitro characterization, pharmacokinetic, and pharmacodynamic evaluation of chitosan-based plumbagin microspheres in mice bearing B16F1 melanoma. Drug Deliv. 2010;17(3):103-13.
  76. Tiwari SB, Pai RM, Udupa N. Temperature-sensitive liposomes of plumbagin: characterization and in vivo evaluation in mice bearing melanoma B16F1. J Drug Target. 2002;10(8):585-91.
  77. Onoue S, Yamada S, Chan HK. Nanodrugs: pharmacokinetics and safety. Int J Nanomedicine. 2014;9:1025-37.

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