Skip to content
2000
image of Deciphering Pharmacological Targets of Plumbagin in Cisplatin-resistant Ovarian Cancer Model using in vitro and in silico Approaches

Abstract

Introduction

Ovarian cancer (OC) is a malignancy of the female reproductive system for which cisplatin chemotherapy is one of the first-line treatments. Despite the initial response to chemotherapy, such patients eventually develop resistance, which poses a major obstacle to treatment, along with potential side effects. Phytochemicals function as chemosensitizers, offering novel therapies in OC patients by targeting drug resistance, and are perceived to be less toxic. Plumbagin has emerged as an anticancer compound, with some findings suggesting its anti-ovarian cancer activity. However, there is no study on the potential of plumbagin to target cisplatin resistance in non-high-grade OC. The current study aimed to determine the antitumor activity of plumbagin for cisplatin resistance in OC cells , and to identify its potential molecular target for therapeutic benefit using studies.

Methods

Plumbagin was used for cytotoxic effects on cisplatin-resistant (A2780-CR) and sensitive (A2780-CS) isogenic cell lines using a crystal violet cell viability assay. The binding of plumbagin to the nine selected molecular targets was estimated by molecular docking and their binding energies were compared. The stabilities of the selected docked complexes were confirmed by molecular dynamics simulation (MDS) and molecular mechanics generalized born surface area (MM-GBSA) calculations, and conclusions were drawn to predict the inhibition potential of plumbagin to its best targets.

Results

Plumbagin demonstrated the potential to kill A2780-CR cells, and, expectedly, the cell death effect on A2780-CS ovarian cancer cells demonstrated its anti-tumor activity . It was found to be non-effective in killing normal non-tumorigenic RPE cells, even at higher doses. Docking analysis suggested that it potentially inhibits through various pharmacological targets with high affinity for binding to Chk1 (PDB ID=1ia8) and Aurora Kinase (PDB ID=5ORL). Molecular dynamic simulation data revealed strong and stable protein-ligand complex formation, which was measured in terms of root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (Rg). On the other hand, the MM-GBSA study revealed that the binding free energy of the CT1019-1ia8 complex (-84.26 ± 2.99 Kcal/mol) and CT1019-5ORL (-67.04 ± 2.63 Kcal/mol) was better when compared to other complexes.

Discussion

Plumbagin showed anti-ovarian cancer benefits in cisplatin-resistant ovarian cells, and the potential pharmacological targets identified were Chk1 and Aurora kinase.

Conclusion

Our study offers promising insights into plumbagin, particularly in combating cisplatin-resistance OC. However, further and mechanistic studies are required to validate plumbagin's potential as a therapeutic candidate for OC treatment.

Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128385767250808102022
2025-08-27
2025-10-18
Loading full text...

Full text loading...

References

  1. Köberle B. Schoch S. Platinum complexes in colorectal cancer and other solid tumors. Cancers 2021 13 9 2073 10.3390/cancers13092073 33922989
    [Google Scholar]
  2. Kim J. Kim S. Park S.Y. Molecular subtypes and tumor microenvironment characteristics of small-cell lung cancer associated with platinum-resistance. Cancers 2023 15 14 3568 10.3390/cancers15143568 37509231
    [Google Scholar]
  3. Havasi A. Cainap S.S. Havasi A.T. Cainap C. Ovarian Cancer—Insights into platinum resistance and overcoming it. Medicina (Kaunas) 2023 59 3 544 10.3390/medicina59030544 36984544
    [Google Scholar]
  4. Torre L.A. Trabert B. DeSantis C.E. Ovarian cancer statistics, 2018. CA Cancer J. Clin. 2018 68 4 284 296 10.3322/caac.21456 29809280
    [Google Scholar]
  5. Mantia-Smaldone G.M. Edwards R.P. Vlad A.M. Targeted treatment of recurrent platinum-resistant ovarian cancer: Current and emerging therapies. Cancer Manag. Res. 2011 3 25 38 21734812
    [Google Scholar]
  6. Roy A. Plumbagin: A potential anti-cancer compound. Mini Rev. Med. Chem. 2021 21 6 731 737 10.2174/18755607MTEx2NTM02 33200707
    [Google Scholar]
  7. Ahmad I. Tabrez S. Exploring natural resources: Plumbagin as a potent anticancer agent. S. Afr. J. Bot. 2024 174 167 179 10.1016/j.sajb.2024.08.037
    [Google Scholar]
  8. Ahmad I Ahmad S Samad MA Synergistic inhibition of colon cancer cell proliferation via p53, Bax, and Bcl-2 modulation by curcumin and plumbagin combination. ACS Omega 2025 10 18 acsomega.5c01258 10.1021/acsomega.5c01258 40385152
    [Google Scholar]
  9. Pan S.T. Ye F.F. Huang G. Qiu J.X. Plumbagin enhances the anticancer effects of PF chemotherapy via downregulation of the PI3K/AKT/mTOR/p70S6K pathway in human tongue squamous cell carcinoma. J. Oncol. 2023 2023 1 16 10.1155/2023/8306514 36814557
    [Google Scholar]
  10. Rehan M. Sheikh I.A. Suhail M. Tabrez S. Shakil S. Computational exploration of a diverse flavonoid library for targeted allosteric inhibition of AKT1 in cancer therapy. Anticancer Res. 2025 45 2 593 604 10.21873/anticanres.17446 39890175
    [Google Scholar]
  11. Sandur S.K. Pandey M.K. Sung B. Aggarwal B.B. 5-hydroxy-2-methyl-1,4-naphthoquinone, a vitamin K3 analogue, suppresses STAT3 activation pathway through induction of protein tyrosine phosphatase, SHP-1: potential role in chemosensitization. Mol. Cancer Res. 2010 8 1 107 118 10.1158/1541‑7786.MCR‑09‑0257 20068065
    [Google Scholar]
  12. Feoktistova M. Geserick P. Leverkus M. Crystal violet assay for determining viability of cultured cells. Cold Spring Harb. Protoc. 2016 2016 4 10.1101/pdb.prot087379
    [Google Scholar]
  13. Nastasă C. Tamaian R. Oniga O. Tiperciuc B. 5-Arylidene (chromenyl-methylene)-thiazolidinediones: Potential new agents against mutant oncoproteins K-Ras, N-Ras and B-Raf in colorectal cancer and melanoma. Medicina (Kaunas) 2019 55 4 85 10.3390/medicina55040085 30935124
    [Google Scholar]
  14. Trott O. Olson A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  15. Agu P.C. Afiukwa C.A. Orji O.U. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci. Rep. 2023 13 1 13398 10.1038/s41598‑023‑40160‑2 37592012
    [Google Scholar]
  16. Jabir N.R. Rehman M.T. Alsolami K. Concatenation of molecular docking and molecular simulation of BACE-1, γ-secretase targeted ligands: In pursuit of Alzheimer’s treatment. Ann. Med. 2021 53 1 2332 2344 10.1080/07853890.2021.2009124 34889159
    [Google Scholar]
  17. Aina O.S. Rofiu M.O. Oloba-Whenu O.A. Olasupo I.A. Adams L.A. Familoni O.B. Drug design and in-silico study of 2-alkoxylatedquinoline-3-carbaldehyde compounds: Inhibitors of Mycobacterium tuberculosis. Sci. Am. 2024 23 e01985
    [Google Scholar]
  18. Jabir N.R. Rehman M.T. AlAjmi M.F. Ahmed B.A. Tabrez S. Prioritization of bioactive compounds envisaging yohimbine as a multi targeted anticancer agent: insight from molecular docking and molecular dynamics simulation. J. Biomol. Struct. Dyn. 2023 41 20 10463 10477 10.1080/07391102.2022.2158137 36533328
    [Google Scholar]
  19. Jabir N.R. Shakil S. Tabrez S. Khan M.S. Rehman M.T. Ahmed B.A. In silico screening of glycogen synthase kinase-3β targeted ligands against acetylcholinesterase and its probable relevance to Alzheimer’s disease. J. Biomol. Struct. Dyn. 2021 39 14 5083 5092 10.1080/07391102.2020.1784796 32588759
    [Google Scholar]
  20. Suresh Kumar G. Manivannan R. Nivetha B. In silico identification of flavonoids from Corriandrum sativum seeds against coronavirus COVID-19 Main protease. J. Drug Deliv. Ther. 2021 11 2 145 152 10.22270/jddt.v11i2.4610
    [Google Scholar]
  21. Iqbal D. Rehman M.T. Bin Dukhyil A. High-throughput screening and molecular dynamics simulation of natural product-like compounds against Alzheimer’s disease through multitarget approach. Pharmaceuticals 2021 14 9 937 10.3390/ph14090937 34577637
    [Google Scholar]
  22. Rizvi S.M.D. Shaikh S. Naaz D. Kinetics and molecular docking study of an anti-diabetic drug glimepiride as acetylcholinesterase inhibitor: Implication for Alzheimer’s disease-diabetes dual therapy. Neurochem. Res. 2016 41 6 1475 1482 10.1007/s11064‑016‑1859‑3 26886763
    [Google Scholar]
  23. Shaker B. Yu M.S. Lee J. Lee Y. Jung C. Na D. User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation. J. Microbiol. 2020 58 3 235 244 10.1007/s12275‑020‑9563‑z 32108318
    [Google Scholar]
  24. Shaw D.E. Maragakis P. Lindorff-Larsen K. Atomic-level characterization of the structural dynamics of proteins. Science 2010 330 6002 341 346 10.1126/science.1187409 20947758
    [Google Scholar]
  25. Bowers K.J. Chow D.E. Xu H. Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters. SC ’06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing Tampa, FL, USA, 11-17 November 2006, pp. 43-43 10.1109/SC.2006.54
    [Google Scholar]
  26. Mukerjee N. Das A. Maitra S. Dynamics of natural product Lupenone as a potential fusion inhibitor against the spike complex of novel Semliki Forest Virus. PLoS One 2022 17 2 e0263853 10.1371/journal.pone.0263853 35213606
    [Google Scholar]
  27. Chakrobarty S. Garai S. Ghosh A. Mukerjee N. Das D. Bioactive plantaricins as potent anti-cancer drug candidates: Double docking, molecular dynamics simulation and in vitro cytotoxicity analysis. J. Biomol. Struct. Dyn. 2023 41 23 13605 13615 10.1080/07391102.2023.2177732 36775653
    [Google Scholar]
  28. Chow E. Rendleman C. Bowers K. Desmond performance on a cluster of multicore processors. 2008 DoD HPCMP Users Group Conference. Seattle, WA, USA, July 14-17, 2008, pp. 305-310 10.1109/DoD.HPCMP.UGC.2008.29
    [Google Scholar]
  29. Shivakumar D. Williams J. Wu Y. Damm W. Shelley J. Sherman W. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J. Chem. Theory Comput. 2010 6 5 1509 1519 10.1021/ct900587b 26615687
    [Google Scholar]
  30. Jorgensen W.L. Chandrasekhar J. Madura J.D. Impey R.W. Klein M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983 79 2 926 935 10.1063/1.445869
    [Google Scholar]
  31. Martyna G.J. Tobias D.J. Klein M.L. Constant pressure molecular dynamics algorithms. J. Chem. Phys. 1994 101 5 4177 4189 10.1063/1.467468
    [Google Scholar]
  32. Martyna G.J. Klein M.L. Tuckerman M. Nosé–Hoover chains: The canonical ensemble via continuous dynamics. J. Chem. Phys. 1992 97 4 2635 2643 10.1063/1.463940
    [Google Scholar]
  33. Toukmaji A.Y. Board J.A. Ewald summation techniques in perspective: A survey. Comput. Phys. Commun. 1996 95 2-3 73 92 10.1016/0010‑4655(96)00016‑1
    [Google Scholar]
  34. Akash S. Baeza J. Mahmood S. Development of a new drug candidate for the inhibition of Lassa virus glycoprotein and nucleoprotein by modification of evodiamine as promising therapeutic agents. Front. Microbiol. 2023 14 1206872 10.3389/fmicb.2023.1206872 37497547
    [Google Scholar]
  35. Galluzzi L. Senovilla L. Vitale I. Molecular mechanisms of cisplatin resistance. Oncogene 2012 31 15 1869 1883 10.1038/onc.2011.384 21892204
    [Google Scholar]
  36. El-Awady E.S.E. Moustafa Y.M. Abo-Elmatty D.M. Radwan A. Cisplatin-induced cardiotoxicity: Mechanisms and cardioprotective strategies. Eur. J. Pharmacol. 2011 650 1 335 341 10.1016/j.ejphar.2010.09.085 21034734
    [Google Scholar]
  37. Rudzińska A. Juchaniuk P. Oberda J. Phytochemicals in cancer treatment and cancer prevention—review on epidemiological data and clinical trials. Nutrients 2023 15 8 1896 10.3390/nu15081896 37111115
    [Google Scholar]
  38. Woźniak M. Krajewski R. Makuch S. Agrawal S. Phytochemicals in gynecological cancer prevention. Int. J. Mol. Sci. 2021 22 3 1219 10.3390/ijms22031219 33530651
    [Google Scholar]
  39. Ahmad I. Ahmad S. Ahmad A. Zughaibi T.A. Alhosin M. Tabrez S. Curcumin, its derivatives, and their nanoformulations: Revolutionizing cancer treatment. Cell Biochem. Funct. 2024 42 1 e3911 10.1002/cbf.3911 38269517
    [Google Scholar]
  40. Suhail M. AlZahrani W.M. Shakil S. Analysis of some flavonoids for inhibitory mechanism against cancer target phosphatidylinositol 3-kinase (PI3K) using computational tool. Front. Pharmacol. 2023 14 1236173 10.3389/fphar.2023.1236173 37900167
    [Google Scholar]
  41. Barboza J.R. Pereira F.A.N. Vasconcelos C.C. de Sousa Ribeiro M.N. Lopes A.J.O. Molecular mechanisms of action and chemosensitization of tumor cells in ovarian cancer by phytochemicals: A narrative review on pre‐clinical and clinical studies. Phytother. Res. 2023 37 6 2484 2512 10.1002/ptr.7842 37098735
    [Google Scholar]
  42. Sun C-Y. Zhang Q-Y. Zheng G-J. Feng B. Phytochemicals: Current strategy to sensitize cancer cells to cisplatin. Biomed. Pharmacother. 2019 110 518 527 10.1016/j.biopha.2018.11.029
    [Google Scholar]
  43. Song M. Cui M. Liu K. Therapeutic strategies to overcome cisplatin resistance in ovarian cancer. Eur. J. Med. Chem. 2022 232 114205 10.1016/j.ejmech.2022.114205 35217497
    [Google Scholar]
  44. Srinivas G. Annab L.A. Gopinath G. Banerji A. Srinivas P. Antisense blocking of BRCA1 enhances sensitivity to plumbagin but not tamoxifen in BG‐1 ovarian cancer cells. Mol. Carcinog. 2004 39 1 15 25 10.1002/mc.10164 14694444
    [Google Scholar]
  45. Sinha S. Pal K. Elkhanany A. Plumbagin inhibits tumorigenesis and angiogenesis of ovarian cancer cells in vivo. J Int du Cancer 2013 132 1201 1221 10.1002/ijc.27724
    [Google Scholar]
  46. Liang K. Pan X. Chen Y. Huang S. Anti-ovarian cancer actions and pharmacological targets of plumbagin. Naunyn Schmiedebergs Arch. Pharmacol. 2023 396 6 1205 1210 10.1007/s00210‑023‑02393‑w 36692828
    [Google Scholar]
  47. Villodre E.S. Kipper F.C. Pereira M.B. Lenz G. Roles of OCT4 in tumorigenesis, cancer therapy resistance and prognosis. Cancer Treat. Rev. 2016 51 1 9 10.1016/j.ctrv.2016.10.003 27788386
    [Google Scholar]
  48. Lin T.C. Wang K.H. Chuang K.H. Kao A.P. Kuo T.C. Oct-4 induces cisplatin resistance and tumor stem cell-like properties in endometrial carcinoma cells. Taiwan. J. Obstet. Gynecol. 2023 62 1 16 21 10.1016/j.tjog.2022.08.014 36720532
    [Google Scholar]
  49. Abuzenadah A. Al-Sayes F. Alam S. Elucidating anti-angiogenic potential of Rauwolfia serpentina: VEGFR-2 targeting based molecular docking study. Evid. Based Complement. Alternat. Med. 2022 2 224666
    [Google Scholar]
  50. Jha V. Devkar S. Gharat K. Screening of phytochemicals as potential inhibitors of breast cancer using structure based multitargeted molecular docking analysis. Phytomedicine. Plus 2022 2 2 100227 10.1016/j.phyplu.2022.100227
    [Google Scholar]
  51. Mendie L.E. Hemalatha S. Molecular docking of phytochemicals targeting GFRs as therapeutic sites for cancer: An in silico study. Appl. Biochem. Biotechnol. 2022 194 1 215 231 10.1007/s12010‑021‑03791‑7 34988844
    [Google Scholar]
  52. Majeed A. Hussain W. Yasmin F. Akhtar A. Rasool N. Virtual screening of phytochemicals by targeting HR1 domain of SARS‐CoV‐2 S Protein: Molecular docking, molecular dynamics simulations, and DFT studies. BioMed Res. Int. 2021 2021 1 6661191 10.1155/2021/6661191 34095308
    [Google Scholar]
  53. Arthur D.E. Akoji J.N. Sahnoun R. A theoretical insight in interactions of some chemical compounds as mTOR inhibitors. Bull. Natl. Res. Cent. 2021 45 1 67 10.1186/s42269‑021‑00525‑x
    [Google Scholar]
  54. Durrant J.D. McCammon J.A. Molecular dynamics simulations and drug discovery. BMC Biol. 2011 9 1 71 10.1186/1741‑7007‑9‑71 22035460
    [Google Scholar]
  55. Hollingsworth S.A. Dror R.O. Molecular dynamics simulation for all. Neuron 2018 99 6 1129 1143 10.1016/j.neuron.2018.08.011 30236283
    [Google Scholar]
  56. Karplus M. McCammon J.A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 2002 9 9 646 652 10.1038/nsb0902‑646 12198485
    [Google Scholar]
  57. Langelier M.F. Planck J.L. Roy S. Pascal J.M. Structural basis for DNA damage-dependent poly(ADP-ribosyl)ation by human PARP-1. Science 2012 336 6082 728 732 10.1126/science.1216338 22582261
    [Google Scholar]
  58. Vogt P.K. Gymnopoulos M. Hart J.R. PI 3-kinase and cancer: Changing accents. Curr. Opin. Genet. Dev. 2009 19 1 12 17 10.1016/j.gde.2008.11.011 19185485
    [Google Scholar]
  59. Bulusu G. Desiraju G.R. Strong and weak hydrogen bonds in protein–ligand recognition. J. Indian Inst. Sci. 2020 100 1 31 41 10.1007/s41745‑019‑00141‑9
    [Google Scholar]
  60. Bultum L.E. Tolossa G.B. Lee D. Combining empirical knowledge, in silico molecular docking and ADMET profiling to identify therapeutic phytochemicals from Brucea antidysentrica for acute myeloid leukemia. PLoS One 2022 17 7 e0270050 10.1371/journal.pone.0270050 35895695
    [Google Scholar]
  61. Vaidyanathan R. Murugan Sreedevi S. Ravichandran K. Molecular docking approach on the binding stability of derivatives of phenolic acids (DPAs) with Human Serum Albumin (HSA): Hydrogen-bonding versus hydrophobic interactions or combined influences? JCIS Open 2023 12 100096 10.1016/j.jciso.2023.100096
    [Google Scholar]
  62. Tian C. Kasavajhala K. Belfon K.A.A. ff19SB: Amino-Acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J. Chem. Theory Comput. 2020 16 1 528 552 10.1021/acs.jctc.9b00591 31714766
    [Google Scholar]
  63. Wang Y. Zhou Y. Khan F.I. Molecular insights into structural dynamics and binding interactions of selected inhibitors targeting SARS-CoV-2 main protease. Int. J. Mol. Sci. 2024 25 24 13482 10.3390/ijms252413482 39769245
    [Google Scholar]
  64. Ribeiro J.V. Bernardi R.C. Rudack T. QwikMD — Integrative molecular dynamics toolkit for novices and experts. Sci. Rep. 2016 6 1 26536 10.1038/srep26536 27216779
    [Google Scholar]
  65. Burley S.K. Bhikadiya C. Bi C. RCSB protein data bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2021 49 D1 D437 D451 10.1093/nar/gkaa1038 33211854
    [Google Scholar]
  66. Kufareva I. Abagyan R. Methods of protein structure comparison. Methods Mol. Biol. 2011 857 231 257 10.1007/978‑1‑61779‑588‑6_10 22323224
    [Google Scholar]
  67. Sumi K. Tago K. Kasahara T. Funakoshi-Tago M. Aurora kinase A critically contributes to the resistance to anti-cancer drug cisplatin in JAK2 V617F mutant-induced transformed cells. FEBS Lett. 2011 585 12 1884 1890 10.1016/j.febslet.2011.04.068 21557940
    [Google Scholar]
  68. Wang L. Arras J. Katsha A. Cisplatin‐resistant cancer cells are sensitive to Aurora kinase A inhibition by alisertib. Mol. Oncol. 2017 11 8 981 995 10.1002/1878‑0261.12066 28417568
    [Google Scholar]
  69. Xu J. Yue C. Zhou W. Aurora-A contributes to cisplatin resistance and lymphatic metastasis in non-small cell lung cancer and predicts poor prognosis. J. Transl. Med. 2014 12 1 200 10.1186/1479‑5876‑12‑200 25082261
    [Google Scholar]
  70. Mignogna C. Staropoli N. Botta C. Aurora Kinase A expression predicts platinum-resistance and adverse outcome in high-grade serous ovarian carcinoma patients. J. Ovarian Res. 2016 9 1 31 10.1186/s13048‑016‑0238‑7 27209210
    [Google Scholar]
  71. Kim M.K. Min D.J. Wright G. Goldlust I. Annunziata C.M. Loss of compensatory pro-survival and anti-apoptotic modulator, IKKε, sensitizes ovarian cancer cells to CHEK1 loss through an increased level of p21. Oncotarget 2014 5 24 12788 12802 10.18632/oncotarget.2665 25474241
    [Google Scholar]
  72. Meng Y. Chen C.W. Yung M.M.H. DUOXA1-mediated ROS production promotes cisplatin resistance by activating ATR-Chk1 pathway in ovarian cancer. Cancer Lett. 2018 428 104 116 10.1016/j.canlet.2018.04.029 29704517
    [Google Scholar]
/content/journals/cpd/10.2174/0113816128385767250808102022
Loading
/content/journals/cpd/10.2174/0113816128385767250808102022
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test