Bioactive compounds of Plukenetia volubilis L. as c-Met tyrosine kinase inhibitors for gastric anticancer: in silico study

Silma Lutfi Farwah; Saeful Amin; Ade Yeni Aprillia

doi:10.35814/jifi.v24i2.2152

Authors

Silma Lutfi Department of Pharmacy, Faculty of Pharmacy, University of Bakti Tunas Husada, Tasikmalaya City, 46115, Indonesia
Saeful Amin Department of Pharmacy, Faculty of Pharmacy, University of Bakti Tunas Husada, Tasikmalaya City, 46115, Indonesia
Ade Yeni Aprillia Department of Pharmacy, Faculty of Pharmacy, University of Bakti Tunas Husada, Tasikmalaya City, 46115, Indonesia

DOI:

https://doi.org/10.35814/jifi.v24i2.2152

Keywords:

Apigenin, c-Met tyrosine kinase, gastric cancer, molecular dynamic, Plukenetia volubilis L.

Abstract

Gastric cancer remains one of the most aggressive malignancies worldwide due to its high incidence and mortality rates. Although tyrosine kinase inhibitors have demonstrated therapeutic efficacy, their use is often associated with adverse effects, highlighting the need for safer anticancer agents. Plukenetia volubilis L. is known to be rich in bioactive compounds, particularly fatty acids and phenolic compounds with natural antioxidant activity. Accordingly, this study aimed to evaluate the potential of Plukenetia volubilis L. compounds as gastric anticancer candidates through inhibition of the c-Met tyrosine kinase receptor (PDB ID: 3DKC) using an integrated in silico approach. To this end, screening drug-likeness using swissADME, molecular docking using the PyRx algorithm, molecular dynamics simulation using AMBER 22, and ADMET prediction were performed using pkCSM. Among the 28 identified compounds, ten met the drug-likeness criteria for oral drug candidates. Redocking validation using adenosine-5′-triphosphate produced a root mean square deviation value of 1.31 Å, indicating a reliable docking method. Molecular docking results show that apigenin, pinoresinol, mycophenolic acid, and oleuropein aglycone exhibit the strongest binding affinities toward c-Met tyrosine kinase, with binding free energies of −7.77, −7.57, −7.24, and −6.90 (kcal/mol), respectively. Further analysis demonstrates that apigenin forms the most stable ligand–receptor complex during molecular dynamics simulation and possesses favorable pharmacokinetic properties with no predicted hepatotoxicity based on absorption, distribution, metabolism, excretion, and toxicity analysis. In conclusion, apigenin emerges as the most promising gastric anticancer candidate from Plukenetia volubilis L. through c-Met tyrosine kinase inhibition and warrants further experimental validation.

References

[1] D. M. Hausman and D. M. Hausman, “What Is Cancer ?,” vol. 62, no. 4, pp. 778–784, 2019.

[2] C. Juni, “Kanker lambung : kenali penyebab sampai pencegahannya,” vol. 3, no. 3, pp. 144–152, 2020, doi: 10.18051/JBiomedKes.2020.v3.144-152.

[3] L. Zhou, B. Han, Y. Yuan, Z. Dong, Y. Shi, and R. Zheng, “The global burden of stomach cancer and its risk factors from 1990 to 2021 : findings from the Global Burden of Disease Study 2021,” 2025.

[4] H. Sung et al., “Global Cancer Statistics 2020 : GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” vol. 71, no. 3, pp. 209–249, 2021, doi: 10.3322/caac.21660.

[5] Y. Zhang, L. Shen, and Z. Peng, “Advances in MET tyrosine kinase inhibitors in gastric cancer,” 2024, doi: 10.20892/j.issn.2095-3941.2024.0044.

[6] M. Zhao, F. Wei, G. Sun, Y. Wen, and J. Zeng, “Natural compounds targeting glycolysis as promising therapeutics for gastric cancer : A review,” no. November, pp. 1–21, 2022, doi: 10.3389/fphar.2022.1004383.

[7] F. Ramos-Escudero, M. T. Morales, M. Ramos Escudero, A. M. Muñoz, K. Cancino Chavez, and A. G. Asuero, “Assessment of phenolic and volatile compounds of commercial Sacha inchi oils and sensory evaluation,” Food Res. Int., vol. 140, p. 110022, 2021, doi: 10.1016/j.foodres.2020.110022.

[8] N. Apriana, Riska, N. Bialangi, L. Alio, Y. K. Salimi, L. Ode Aman, and A. Kadir Kilo, “Identifikasi senyawa metabolit sekunder dan analisis total fenolik ekstrak metanol daun sacha inchi (Plukenetia volubilis linneo) yang tumbuh di Gorontalo,” J. Pendidikan, Kim. Fis. dan Biol., vol. 1, no. 3, pp. 193–207, 2025.

[9] N. A. R. Mhd Rodzi and L. K. Lee, Sacha Inchi (Plukenetia volubilis L.): recent insight on phytochemistry, pharmacology, organoleptic, safety and toxicity perspectives, vol. 8, no. 9. Elsevier Ltd, 2022. doi: 10.1016/j.heliyon.2022.e10572.

[10] N. Anis, R. Mhd, and L. K. Lee, Sacha Inchi (Plukenetia volubilis L.): recent insight on phytochemistry, pharmacology, organoleptic, safety and toxicity perspectives. Elsevier Ltd, 2022. doi: 10.1016/j.heliyon.2022.e10572.

[11] A. Parvaiz, “Synergistic Action of Tlp and Pr1 Proteins Revealed Their Enhanced Interaction With Fungal Cell-Wall Components: Evidence From in Silico Studies,” Agrobiol. Rec., vol. 22, pp. 129–140, 2025, doi: 10.47278/journal.abr/2025.055.

[12] R. Ruswanto, T. No, R. Mardianingrum, and D. Kesuma, “Heliyon Design , molecular docking , and molecular dynamics of thiourea-iron ( III ) metal complexes as NUDT5 inhibitors for breast cancer treatment,” vol. 8, no. July, 2022, doi: 10.1016/j.heliyon.2022.e10694.

[13] D. Atsna, N. Salsabila, T. Herlina, and D. Kurnia, “Antimicrobial Activities of Crocatin B From Piper crocatum as an Antithroat Infection : In Vitro and In Silico Studies,” pp. 1–13, 2026, doi: 10.1155/joch/5292752.

[14] M. E. Effiong, M. Bella-Omunagbe, I. S. Afolabi, and S. N. Chinedu, “Molecular Docking Appraisal of Pleurotus ostreatus Phytochemicals as Potential Inhibitors of PI3K/Akt Pathway for Breast Cancer Treatment,” Bioinform. Biol. Insights, vol. 19, 2025, doi: 10.1177/11779322251316864.

[15] S. Kumar et al., “Discovery of New Hydroxyethylamine Analogs against 3CL pro Protein Target of SARS-CoV-2 : Molecular Docking , Molecular Dynamics Simulation , and Structure − Activity Relationship Studies,” 2020, doi: 10.1021/acs.jcim.0c00326.

[16] M. R. Fadhil Pratama, H. Poerwono, and S. Siswodihardjo, “Introducing a two‐dimensional graph of docking score difference vs. similarity of ligand‐receptor interactions,” Indones. J. Biotechnol., vol. 26, no. 1, pp. 54–60, 2021, doi: 10.22146/IJBIOTECH.62194.

[17] M. Manish, S. Mishra, A. Anand, and N. Subbarao, “Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics,” Comput. Biol. Med., vol. 150, no. September, p. 106125, 2022, doi: 10.1016/j.compbiomed.2022.106125.

[18] C. G. Yang, T. Chen, W. T. Si, A. H. Wang, H. C. Ren, and L. Wang, “High-performance PBPK model for predicting CYP3A4 induction-mediated drug interactions: a refined and validated approach,” Front. Pharmacol., vol. 16, no. February, pp. 1–12, 2025, doi: 10.3389/fphar.2025.1521068.

[19] I. U. Khan, A. Singh, S. Singh, and B. P. S. Sagar, “Drug Induced Hepatotoxicity and Hepatoprotective Drugs : A Review,” vol. 8, no. 9, pp. 351–375, 2023.

[20] D. Fadhlurrohman and Ruswanto, “Study In Silico Senyawa Apigenin Saledri ( Avium graveolens ) Sebagai Antikanker Terhadap Kanker Payudara,” pp. 1–7, 2021.

[21] Kuschner, O’connor, and A. M. Butz, “Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions,” Physiol. Behav., vol. 176, no. 12, pp. 139–148, 2017, doi: 10.1111/cbdd.13084.Aromatic.

[22] R. Mardianingrum, S. R. N. Endah, E. Suhardiana, R. Ruswanto, and S. Siswandono, “Docking and molecular dynamic study of isoniazid derivatives as anti-tuberculosis drug candidate,” Chem. Data Collect., vol. 32, p. 100647, 2021, doi: 10.1016/j.cdc.2021.100647.

[23] F. Zare et al., “A combination of virtual screening, molecular dynamics simulation, MM/PBSA, ADMET, and DFT calculations to identify a potential DPP4 inhibitor,” Sci. Rep., vol. 14, no. 1, pp. 1–16, 2024, doi: 10.1038/s41598-024-58485-x.

[24] M. C. S. Wong, J. Huang, P. S. F. Chan, P. Choi, X. Q. Lao, and S. M. Chan, “Global Incidence and Mortality of Gastric Cancer , 1980-2018,” vol. 4, no. 7, pp. 1–14, 2021, doi: 10.1001/jamanetworkopen.2021.18457.

[25] A. T. McNutt, F. Bisiriyu, S. Song, A. Vyas, G. R. Hutchison, and D. R. Koes, “Conformer Generation for Structure-Based Drug Design: How Many and How Good?,” J. Chem. Inf. Model., vol. 63, no. 21, pp. 6598–6607, 2023, doi: 10.1021/acs.jcim.3c01245.

[26] I. C. Russell et al., “Molecular Basis of c-MET Inhibition by Approved Small Molecule Drugs: A Structural Perspective,” ACS Med. Chem. Lett., vol. 17, no. 3, pp. 590–597, 2026, doi: 10.1021/acsmedchemlett.5c00713.

[27] N. Wu, R. Zhang, X. Peng, L. Fang, K. Chen, and J. S. Jestilä, “Elucidation of protein-ligand interactions by multiple trajectory analysis methods,” Phys. Chem. Chem. Phys., vol. 26, no. 8, pp. 6903–6915, 2024, doi: 10.1039/d3cp03492e.

[28] S. Genheden and U. Ryde, “The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities,” Expert Opin. Drug Discov., vol. 10, no. 5, pp. 449–461, 2015, doi: 10.1517/17460441.2015.1032936.

[29] S. R. Zia, A. Coricello, and G. Bottegoni, “Increased throughput in methods for simulating protein ligand binding and unbinding,” Curr. Opin. Struct. Biol., vol. 87, no. Cv, p. 102871, 2024, doi: 10.1016/j.sbi.2024.102871.