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. 2025 Oct 2;15:34445. doi: 10.1038/s41598-025-17562-5

Exploration of isolated actives from Coleus amboinicus leaves as anticancer agents: in vitro testing, network pharmacology studies, and molecular docking

Kasta Gurning 1,2, Yehezkiel Steven Kurniawan 1, Friska Septiani Silitonga 1,3, Gian Primahana 4, Endang Astuti 1, Winarto Haryadi 1,✉
PMCID: PMC12491499  PMID: 41039037

Abstract

Cancer is one of the serious health problems and is a major cause of death worldwide. Therefore, the development of anticancer agents is urgently needed. A potential and novel anticancer agent was successfully isolated from Coleus amboinicus (C. amboinicus) leaves (16-hydroxy-7α-acetoxyroyleanone compound). This study aims to explore the cytotoxic activity of the isolated compound against various cancer cells in vitro, network pharmacology studies on pathways in cancer, and molecular docking. Cytotoxicity testing using the tetrazolium microculture technique (MTT test) against MCF-7, A549, HeLa, and Du-145 cancer cell lines and the normal cell line (CV-1). Network pharmacology studies were carried out using bioinformatics with a database focused on three main proteins in the pathway in cancer and molecular docking was carried out computationally. The cytotoxic activity (IC50) of the active compound against MCF-7, A549, HeLa, and Du-145 cells was 4.22, 18.10, 6.31, and 4.67 μg/mL, respectively, while the IC50 value in CV-1 cells was 19.27 μg/mL. The study of the network pharmacology approach to the cancer pathway of each cancer target revealed that the active compounds target the same three main proteins, namely MMP2, PPARG, and BCl2. In addition, the results of molecular docking showed a high binding affinity between the active compounds and the receptors of the target cancer therapy targets. In general, based on the data and analysis carried out, it shows that the bioactive compounds provide promising anticancer activity, especially for breast and prostate cancer drug agents, because the bioactive compounds provide lower IC50 and higher SI value than cisplatin as the standard drug, as indicated by the statistical analysis.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-17562-5.

Keywords: Coleus amboinicus, Breast cancer, NSCLC, Cervical cancer, Prostate cancer, Pathway in cancer

Subject terms: Biotechnology, Cancer, Plant sciences, Molecular medicine

Introduction

Coleus amboinicus Lour. is a family of Lamiaceae and this herb is widely found in Africa, Asia, and Australia, including Indonesia1. This plant gives the name bangun-bangun and is consumed as a vegetable and spice and is mixed to stimulate milk productivity for breastfeeding mothers in the Batak tribe in Sumatra Province, Indonesia2. In India, it is traditionally used as a medicine for malaria, inflammation, cough, fever, chronic asthma, kidney, bronchitis, gallstones1, cough, constipation, colds, headaches, and skin disorders3. This plant has various abundant phytochemical compounds, such as flavonoids, terpenoids, phenolics, flavonoids, flavone glycosides, isoprenoids, and essential oils, which provide various activities such as anticancer, antioxidant, antibacterial, antitumor, anti-inflammatory, antidiabetic, antirheumatic, antimicrobial, and various other therapeutic effects1,3–7.

The diverse therapeutic effects, especially in the development of potential anticancer agents, encourage the isolation of bioactive compounds from the leaf parts of this plant and the exploration of the potential cytotoxic activity as anticancer agents. Previous studies have successfully isolated royleanone derivative compounds from the leaf parts of Coleus amboinicus, Lour. (C. amboinicus), namely 16-hydroxy-7α-acetoxyroyleanone, syn. 16-hydroxy-7O-acetylhorminone, and showed activity as an antioxidant2. Royleanone compounds and their derivatives have been successfully identified from various plants, such as 7α-acetoxyroyleanone, horminone, 7-ketoroyleanone, 7α-ethoxyroyleanone8,9, 6β-hydroxyroyleanone, 7O-formylhorminone, 6β,7α-dihydroxyroyleanone, 7α-acetoxy-6β-hydroxyroyleanone, 7α-acyloxy-6β-hydroxyroyleanone, 7α-formyloxy-6β-hydroxyroyleanone, 6β-formyloxy-7α-hydroxyroyleanone, and 6β-hydroxy-7α-methoxyroyleanone10. The compounds royleanone, 7α-acetoxyroyleanone, horminone, 7-ketoroyleanone, and 7α-ethoxyroyleanone have cytotoxic activity against pancreatic cancer cells (MIAPaCa-2) and melanoma cancer cells (MV-3)9. While 7α-acetoxyroyleanone and horminone compounds against chronic myelogenous leukemia cells (K562) and breast cancer cells (MCF-7)11. Based on the information, the isolated compound was further explored for in vitro cytotoxic activity against MCF-7, lung cancer cells (A549), cervical cancer cells (HeLa), prostate cancer cells (Du145), and normal cells (CV-1) using the MTT method and tissue pharmacology studies.

Cytotoxic testing is carried out as an initial assay regarding the potential activity of compounds against target diseases and to estimate the side effects caused. In addition, cytotoxic testing can determine whether the cytotoxic effects caused are cytostatic (stopping cell growth or division) or cytocidal (killing cells)12,13. Network pharmacology studies are carried out to guide the acquisition of key proteins affected by active compounds for therapeutic applications of targeting cancer disease pathways. This study provides a comprehensive molecular mapping of the pharmacological journey of the isolated compounds in their application as cancer therapeutic agents. The network pharmacology approach provides simultaneous information in evaluating molecular interference from the disease under study and drug mechanisms, as well as efficacy in certain diseases. Systematically, the use of network pharmacology provides an analysis of all possible target configurations of combinations of drugs and proteins associated with the disease through protein–protein (PPI) networks14.

The obtained protein is then studied for interaction with active compounds like ligands to form complex compounds using molecular docking. Molecular docking of complex compounds with a network pharmacology approach is a rational step to study the mechanism of compounds against protein targets in disease pathways in cancer by evaluating interaction methods, ligand orientation to the active site of the target protein, and ligand-target protein binding affinity11. The use of molecular docking allows the identification of new compounds that are therapeutically interesting, predicting ligand-target interactions at the molecular level15.

Materials and methods

Materials

The 16-hydroxy-7α-acetoxyroyleanone (syn. 16-hydroxy-7-O-acetylhorminone) compound is isolated from C. amboinicus leaves2, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), trypsin-EDTA, ELISA microplate readers, dimethyl sulfoxide (DMSO) Merck D1435, cisplatin (EDQM C22100000), doxorubicin, 96-well plate (Nest Brand 701001), phosphate buffered saline (PBS, Gibco 18912-014), MCF-7 cells, A549 cells, HeLa cells, Du145 cells, CV-1 cells, Roswell Park Memorial Institute (RPMI-1640), PrestoBlue™ cell viability reagent (Thermofisher A132), DMEM-H medium (Gibco 11875-093), and fetal bovine serum (FBS, Gibco 10270-106) were used in this study.

Extraction, separation, and purification of extract leaves

Briefly, the sample of C. amboinicus leaf powder was extracted with methanol (Merck), filtered, and the residue was re-macerated for 2 repetitions. The obtained liquid methanol extract was concentrated using a rotary vacuum evaporator at 50 °C to obtain a thick extract. The thick extract was partitioned gradually with various polarity properties of the solvent, starting with n-hexane, chloroform, and ethyl acetate. The ethyl acetate extract was concentrated and continued with purification using gravity column chromatography with a silica gel stationary phase and a mobile phase with a combination of n-hexane and ethyl acetate. The polarity of the mobile phase used increased gradually, and at a ratio of n-hexane (10): ethyl acetate (1), a vase-shaped isolate (crystal) that was tapered and yellow in color was obtained2. The isolated isolates were subjected for thin layer chromatography (Rf) and Electrothermal IA9300 (melting point). The structure elucidation was done with UV-Vis spectrometry, FT-IR (KBr), 1D and 2D-NMR (1H & 13C-NMR, COSY, HMBC, and HMQC), and GC–MS.

In vitro cytotoxicity testing of cancer cells

The cancer cells used were MCF-7, A549, HeLa, and Du145, as well as normal cells (CV-1) obtained and studied at the Integrated Laboratory of Universitas Padjadjaran, Bandung, Indonesia. The media were prepared and cultured using RPMI and DMEM-H fluids containing 10% PBS and 50 µg/mL antibiotics. Cell cultures were incubated at 37 °C in 5% CO2 atmosphere until the minimum cell growth percentage reached 70%. Cytotoxicity testing of MCF-7, HeLa, A549, Du145, and CV-1 cells used various concentrations of isolates at 0.90, 1.95, 3.91, 7.81, 15.63, 31.25, 62.50, and 125 µg/mL. The solvent used to create various concentrations of isolates is 2% DMSO. The used positive controls are cisplatin and doxorubicin drugs. The test method refers to the previous research6.

Network pharmacological study

SMILE data from the 16-hydroxy-7α-acetoxyroyleanone compound were uploaded to the SWISS target prediction database site (http://www.swisstargetprediction.ch/) to obtain gene data, downloaded in CSV format, filtered, and integrated using Microsoft Excel 365. The obtained genes provide information on the target proteins of candidate drug agents from compounds against therapeutic target proteins from target cancer diseases. The therapeutic targets of cancer diseases that were determined included “breast cancer,” “non-small cell lung cancer,” “cervical cancer,” and “prostate cancer,” which were obtained separately from the GeneCards database, the human gene database (https://www.genecards.org/), the Therapeutic Target Database (TTD; https://db.idrblab.net/ttd/), Online Mendelian Inheritance in Man (OMIM; https://www.omim.org/), and DisGeNET (https://www.disgenet.org/). Compound genes and genes of each cancer disease were integrated separately using the VENNY diagram (https://www.biotools.fr/misc/venny) to obtain potential genes5,7,16.

Potential genes between compounds and each selected therapeutic cancer disease were uploaded, constructed, and analyzed using the STRING database (https://string-db.org/) with the “homo sapiens” setting with high confidence (0.40) to obtain a protein–protein interaction (PPI) network. The PPI results from STRING data were exported in TSV form, constructed, and further analyzed using Cytoscape 3.10.3 software, and the determination of genes as key protein targets using the CytoHubba plugin. Potential genes from compounds and each cancer therapy were further analyzed by gene ontology (GO) pathway enrichment, including GO biological process, GO cellular component, GO molecular function, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway using the ShinyGO 0.82 bioinformatics database (https://bioinformatics.sdstate.edu/go/) with the “human” setting, an FDR cutoff of 0.05, and ten pathways from each pathway were set. Three proteins were identified as the primary targets of each cancer therapy based on the KEGG pathway, namely “pathways in cancer”17,18, and then continued with an analysis of the interaction and binding affinity energy between compounds and proteins in complex molecules by molecular docking.

Molecular docking

The analysis of interactions between the isolates (ligands) and main proteins in the network of cancer pharmacology pathways (receptors) aims to explore intermolecular interactions, affinity, and predictions of the types of bonds that occur theoretically. The isolate structure was optimized for energy using GaussView 5.0 with ground state method settings, DFT, B3LYP, and basis set 3-21G5. The selected primary target proteins for each cancer therapy were downloaded from the Protein Data Bank (PDB; http://www.rcsb.org/) database. Re-docking studies were performed with native proteins for each target cancer protein. Target proteins for breast cancer were prepared in grid boxes measuring 41.56 × 42.42 × 39.91 Å (MMP-2, PDB ID 3AYU), and 44.49 × 48.47 × 56.86 Å (PPARG, PDB ID 4Y29), 47.16 × 38.83 × 37.00 Å (BCl2, PDB ID 4MAN). Target proteins for lung cancer is prepared in a grid box measuring 42.79 × 43.04 × 39.21 Å (MMP-2, PDB ID 7XJO), 13.09 × 55.82 × 16.79 Å (PPARG, PDB ID 6DGL), and 46.53 × 33.69 × 51.84 Å (BCl2, PDB ID 6GL8). Target proteins for cervical cancer is prepared in a grid box measuring 44.49 × 49.57 × 41.23 Å (MMP-2, PDB ID 1HOV), 74.62 × 72.43 × 73.00 Å (PPARG, PDB ID 6C5T), and 35.93 × 43.21 × 41.47 Å (BCl2, PDB ID 4AQ3). Target proteins for prostate cancer were prepared in a grid box measuring 37.82 × 42.33 × 40.24 Å (MMP-2, PDB ID 7XGJ), 51.53 × 58.00 × 52.2200 Å (PPARG, PDB ID 2HFP), and 38.04 × 39.55 × 40.12 Å (BCl2, PDB ID 4AQ3) with each distance of 0.375 Å for 100 runs of the generic Lamarck algorithm using AutoDock4.2 software. If the root-mean-square deviation (RMSD) value is < 2.00 Å, it indicates that the re-docking process is valid19. Molecular docking studies were carried out using similar parameters to the re-docking investigation. Molecular docking of isolates (ligands) and target proteins followed the grid size in the re-docking process using PyRx virtual screening tools and AutoDock Vina software. Molecular docking results were combined with PyMOL DLP 3D and visualized using AutoDock Tools and Discovery Studio Visualizer7.

Statistical analysis

Cytotoxic activity data are reported as mean ± standard error (SEM) with three repetitions. Differences in cytotoxic test data were evaluated statistically using one-way analysis of variance (ANOVA), followed by Tukey’s post hoc multiple comparison test (cytotoxic activity between cells) using GraphPad Prisma 10.01 software. The statistical significance level was set at a p-value of less than 0.05 (p < 0.05), which was considered statistically significant (*p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

Results

Structures eludication of compounds

16-hydroxy-7α-acetoxyroyleanone; syn. 16-hydroxy-7-O-acetylhorminone (Fig. 1), C22H28O6, 50 mg, melting point: 214–215 °C, UV(MeOH): λmax 210 and 274 nm, GC–MS: 100% purity of m/z 390, 348, 330, and 43 g/mol. 1H NMR (500 MHz, TMS, ppm): δ 2.62–2.66 tt, (2H, 3 J = 5.2 Hz; 4.6 Hz), 1.47–1.50 tt, (2H, 3 J = 5.2 Hz; 4.6 Hz), 1.57 t (2H, J = 3.85 Hz), 1.83–1.87 tt (1H, 3 J = 5.2 Hz; 4.6 Hz), 1.78 s (2H, widened), 5.66 d (1H), 3.17 m (1H), 4.32 s (2H, widened), 1.20 d, (3H, J = 12.03 Hz), 1.24 d, (6H, J = 5 Hz), 0.94 s (3H), 2.05 s (3H), 7,21 s brs. 13C NMR (400 MHz, CDCl3, ppm): δ 38.54 (C1), 24.36 (C2), 42.45 (C3), 33.86 (C4), 49.93 (C5), 21.69 (C6), 68.93 (C7), 137.27 (C8), 150.09 (C9), 38.81 (C10), 183.47 (C11), 152.08 (C12), 124.86 (C13), 185.94 (C14), 20.03 (C15), 67.24 (C16), 19.15 (C17), 33.72 (C18), 24.01 (C19), 19.90 (C20), 169.77 (C1′), and 21.13 (C2′) (Figs. S1–S10).

Fig. 1.

Fig. 1

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The 2D and 3D structures of 16-hidroxy-7α-acetoxyroyleanone (syn. 16-hydroxy-7-O-acetylhorminone). Black, red, and grey colors represent carbon, oxygen, and hydrogen atoms.

Cytotoxicity testing using the MMT assay

The cytotoxicity of the compound 16-hydroxy-7α-acetoxyroyleanone (syn. 16-hydroxy-7-O-acetylhorminone) was evaluated against MCF-7, A549, HeLa, Du145, and CV-1 cell lines using the MTT assay method shown in Fig. 2. The viability of each cancer cell is shown in Fig. 2a, the activity value (IC50) of the statistical analysis results is shown in Fig. 2b–c, and the morphology of the cytotoxic test of the compound against cancer cells is shown in Fig. 2d. The cytotoxic activity value (IC50) for normal CV-1 cells was 19.27 µg/mL, and each cancer cell was 4.22 µg/mL (MCF-7 cells) with a selectivity index (SI) value of 4.57; 18.10 µg/mL (A549 cells) with an SI value of 1.07; 6.31 µg/mL (HeLa cells) with an SI value of 3.05; and 4.67 µg/mL (Du-145 cells) with an SI value of 4.13. The graphs and R2 values in determining the cytotoxic activity values of the compounds are presented in Fig. S11. The predicted absorption, distribution, metabolism, excretion, and toxicology (ADMET) of the isolates are listed in Table 1.

Fig. 2.

Fig. 2

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Cytotoxic evaluation of the 16-hydroxy-7α-acetoxyroyleanone compound by the MTT test method: (a) Viability of each cell, (b) IC50 value of the compound against cancer cells and CV-1 cells, (c) IC50 value of the compound against cancer cells compared with the control (drug), (d) Morphology of cancer cell testing. Data are presented as mean ± standard error, with significance p < 0.05 and considered statistically significant (*p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

Table 1.

ADMET profile prediction of 16-hydroxy-7α-acetoxyroyleanone compound.

No. Parameters ADMET data source
pkCSM SwissADME ADMETLab3.0
1. MW (g/mol) 390.476 390.47 390.20
2. Log P 3.043 2.780 2.305
3. H-bond acceptor 6 6 6
4. H-bond donors 2 2 2
5. Rotatable bond 3 4 4
6. TPSA (Å2) 165.193 100.90 100.90
7. GI absorption 73.667% High 77.8%
8. BBB permeant − 0.141 No No
9. P-gp substrate Yes Yes NA
10. CYP1A2 inhibitor No No No
11. CYP2C19 inhibitor No No No
12. CYP2C9 inhibitor No No No
13. CYP2D6 inhibitor No No No
14. CYP3A4 inhibitor No No No
15. % Intestinal absorption 84.209 NA NA
16. Ames’s toxicity No NA 0.442
17. Hepatotoxicity No No NA
18. Max. tolerated dose (mg/kg/day) − 0.089 NA NA
19. Drug likeness NA Yes NA
20. Oral bioavailability NA 0.560 NA
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Network pharmacological study to obtain the main target protein

The gene of the 16-hydroxy-7α-acetoxyroyleanone as the active compound was obtained using the SwissTargetPrediction database, and 50 genes were obtained. Information on target cancer disease genes was obtained using the GeneCards, TTD, OMIM, and DisGeNET databases. Breast cancer disease obtained 14,557 genes, 5750 non-small cell lung cancer (NSCLC) genes, 9184 cervical cancer genes, and 11,420 prostate cancer genes. The gene of the 16-hydroxy-7α-acetoxyroyleanone compound was integrated with the genes of each cancer disease separately using a Venn diagram to obtain target genes between the active compound and cancer disease (Table 2), and then protein–protein interactions (PPI) were built using the STRING database (Fig. 3a–b).

Table 2.

Integration of the 16-hydroxy-7α-acetoxyroyleanone compound gene into the respective genes for cancer disease therapy.

No. Target cancer type Genes Total
1. Breast cancer OPRD1, FABP4, FABP3, FABP5, OPRM1, AMPD3, PABPC1, ITGAL, PIM1, ACE, ECE1, AGTR1, CCR1, MME, MMP13, MMP1, ADAMTS5, MMP14, HAO2, PPARG, PTGER4, IDO1, HCAR2, CCKBR, GPR35, CYP26A1, GSTM1, KIF11, PTGER2, PTPN1, BCL2L2, BCL2, BCL2A1, BRD4, THRA, THRB, AMPD2, PPARA, EDNRB, GRIN1, CTSA, LTB4R, PSMB5, AVPR2, AVPR1A, ITGA4, ITGB1, OXTR, PCSK7, MMP2 50
2. Non-small cell lung cancer (NSCLC) FABP4, FABP3, FABP5, OPRM1, PABPC1, ITGAL, PIM1, ACE, ECE1, AGTR1, CCR1, MME, MMP13, MMP1, ADAMTS5, MMP14, PPARG, PTGER4, IDO1, CCKBR, GSTM1, KIF11, PTGER2, PTPN1, BCL2L2, BCL2, BCL2A1, BRD4, THRA, THRB, PPARA, EDNRB, GRIN1, PSMB5, AVPR2, ITGB1, MMP2 37
3. Cervical cancer OPRD1, FABP4, FABP3, FABP5, OPRM1, PABPC1, ITGAL, ACE, ECE1, AGTR1, CCR1, MME, MMP13, MMP1, ADAMTS5, MMP14, PPARG, PTGER4, IDO1, GPR35, CYP26A1, GSTM1, KIF11, PTGER2, PTPN1, BCL2L2, BCL2, BCL2A1, BRD4, THRA, THRB, PPARA, EDNRB, GRIN1, CTSA, LTB4R, PSMB5, ITGA4, ITGB1, OXTR, MMP2 41
4. Prostate cancer FABP4, FABP3, FABP5, OPRM1, PABPC1, ITGAL, PIM1, ACE, ECE1, AGTR1, CCR1, MME, MMP13, MMP1, ADAMTS5, MMP14, PPARG, PTGER4, IDO1, CCKBR, CYP26A1, GSTM1, KIF11, PTGER2, PTPN1, BCL2L2, BCL2, BCL2A1, BRD4, THRA, THRB, PPARA, EDNRB, GRIN1, CTSA, PSMB5, AVPR2, AVPR1A, ITGA4, ITGB1, OXTR, PCSK7, MMP2 43
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Fig. 3.

Fig. 3

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Network pharmacological study of the 16-hydroxy-7α-acetoxyroyleanone compound against various cancer diseases: (a) Venn diagram showing the integration of compound genes with cancer diseases; and (b) protein–protein interactions (PPIs) using STRING.

Gene ontology (GO) functional enrichment pathway analysis based on GO biological process, GO cellular component, and GO molecular function classifications. GO and KEGG classifications used an FDR cutoff of 0.05. The results of GO and KEGG enrichment analysis for the top 10 signaling pathways in the enrichment function bubble diagram for each target cancer are shown in Fig. 4a–d and Table 3.

Fig. 4.

Fig. 4

Fig. 4

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GO analysis of enrichment functions of biological processes (BP), cellular components (CC), molecular functions (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) for each target cancer: (a) breast cancer; (b) non-small cell lung cancer (NSCLC); (c) cervical cancer; and (d) prostate cancer.

Table 3.

Top 10 enrichment analyses for GO and KEGG of each target cancer.

Target cancer Category Term Name Count Pathway genes Enrichment FDR Fold enrichment
Breast cancer BP GO:0001990 Reg. of systemic arterial blood pressure by hormone 7 40 1.66E−09 77.00337
BP GO:0003044 Reg. of systemic arterial blood pressure mediated by a chemical signal 7 51 5.97E−09 60.3948
BP GO:0050886 Endocrine proc 9 93 4.39E−10 42.58251
BP GO:0042310 Vasoconstriction 8 83 4.22E−09 42.41149
BP GO:0007204 Positive reg. of cytosolic calcium ion concentration 12 342 4.19E−09 15.43927
BP GO:0044057 Reg. of system proc 17 584 8.53E−12 12.80878
BP GO:0042592 Homeostatic proc 23 1916 1.86E−09 5.282068
BP GO:0051239 Reg. of multicellular organismal proc 28 3024 1.66E−09 4.074252
BP GO:0010033 Response to organic substance 30 3269 3.79E−10 4.038109
BP GO:0065008 Reg. of biological quality 35 4103 8.53E−12 3.753515
CC GO:0034668 Integrin alpha4-beta1 complex 2 3 0.001002 293.3462
CC GO:0097444 Spine apparatus 2 3 0.001002 293.3462
CC GO:0097136 Bcl-2 family protein complex 2 10 0.009027 88.00385
CC GO:0008305 Integrin complex 3 31 0.002512 42.58251
CC GO:0098636 Protein complex involved in cell adhesion 3 53 0.009027 24.90675
CC GO:0009897 External side of plasma membrane 6 459 0.016799 5.751885
CC GO:0005925 Focal adhesion 6 475 0.016799 5.558138
CC GO:0098552 Side of membrane 8 757 0.009472 4.650137
CC GO:0005887 Integral component of plasma membrane 19 1894 3.55E−06 4.414132
CC GO:0031226 Intrinsic component of plasma membrane 19 1978 3.58E−06 4.226676
MF GO:0004953 Icosanoid receptor activity 5 17 1.62E−08 129.4174
MF GO:0008528 G protein-coupled peptide receptor activity 12 156 2.74E−13 33.84763
MF GO:0042562 Hormone binding 7 93 5.42E−08 33.11973
MF GO:0001653 Peptide receptor activity 12 162 2.74E−13 32.59402
MF GO:0004222 Metalloendopeptidase activity 8 126 1.91E−08 27.93773
MF GO:0042277 Peptide binding 12 402 5.20E−09 13.1349
MF GO:0033218 Amide binding 12 481 2.25E−08 10.97761
MF GO:0004930 G protein-coupled receptor activity 16 1029 2.25E−08 6.841893
MF GO:0038023 Signaling receptor activity 23 1908 3.56E−10 5.304215
MF GO:0060089 Molecular transducer activity 23 1908 3.56E−10 5.304215
KEGG Path:hsa04614 Renin-angiotensin system 4 23 7.93E−06 76.52508
KEGG Path:hsa03320 PPAR signaling pathway 6 75 1.24E−06 35.20154
KEGG Path:hsa04924 Renin secretion 4 69 0.000478 25.50836
KEGG Path:hsa04080 Neuroactive ligand-receptor interaction 15 362 3.00E−13 18.23284
KEGG Path:hsa04933 AGE-RAGE signaling pathway in diabetic complications 4 100 0.001764 17.60077
KEGG Path:hsa04670 Leukocyte transendothelial migration 4 114 0.002501 15.43927
KEGG Path:hsa04926 Relaxin signaling pathway 4 129 0.003302 13.64401
KEGG Path:hsa04020 Calcium signaling pathway 6 240 0.000478 11.00048
KEGG Path:hsa04024 cAMP signaling pathway 5 221 0.002501 9.955186
KEGG Path:hsa05200 Pathways in cancer 11 530 1.24E−06 9.132475
NSCLC BP GO:0044057 Reg. of system proc 13 584 7.41E−09 13.40361
BP GO:0006954 Inflammatory response 14 892 3.15E−08 9.450496
BP GO:0009725 Response to hormone 14 898 3.15E−08 9.387352
BP GO:0048878 Chemical homeostasis 15 1241 1.14E−07 7.27798
BP GO:0009719 Response to endogenous stimulus 17 1660 8.26E−08 6.166408
BP GO:0042592 Homeostatic proc 18 1916 8.26E−08 5.656768
BP GO:0051239 Reg. of multicellular organismal proc 23 3024 7.41E−09 4.579704
BP GO:0010033 Response to organic substance 24 3269 7.41E−09 4.420666
BP GO:0065008 Reg. of biological quality 26 4103 7.41E−09 3.815603
BP GO:0042221 Response to chemical 26 4821 1.14E−07 3.247339
z GO:0097136 Bcl-2 family protein complex 2 10 0.009308 120.4263
CC GO:0008305 Integrin complex 2 31 0.021713 38.8472
CC GO:0005741 Mitochondrial outer membrane 4 222 0.021713 10.84922
CC GO:0009897 External side of plasma membrane 5 459 0.021713 6.559168
CC GO:0005925 Focal adhesion 5 475 0.021713 6.338227
CC GO:0005768 Endosome 8 1232 0.021713 3.909945
CC GO:0005887 Integral component of plasma membrane 12 1894 0.0063 3.814984
CC GO:0031226 Intrinsic component of plasma membrane 12 1978 0.0063 3.652972
CC GO:0005615 Extracellular space 14 3577 0.021713 2.356679
CC GO:0005576 Extracellular region 17 4673 0.021713 2.190506
MF GO:0004222 Metalloendopeptidase activity 8 126 8.60E−09 38.23058
MF GO:0008237 Metallopeptidase activity 8 204 1.99E−07 23.613
MF GO:0008528 G protein-coupled peptide receptor activity 6 156 6.29E−06 23.15891
MF GO:0042277 Peptide binding 9 402 9.06E−07 13.48056
MF GO:0033218 Amide binding 9 481 3.52E−06 11.2665
MF GO:0004175 Endopeptidase activity 9 497 3.98E−06 10.90379
MF GO:0008270 Zinc ion binding 13 947 1.99E−07 8.265798
MF GO:0046914 Transition metal ion binding 14 1247 4.00E−07 6.760098
MF GO:0038023 Signaling receptor activity 15 1908 5.03E−06 4.733739
MF GO:0060089 Molecular transducer activity 15 1908 5.03E−06 4.733739
KEGG Path:hsa04614 Renin-angiotensin system 3 23 0.000215 78.5389
KEGG Path:hsa03320 PPAR signaling pathway 6 75 1.26E−07 48.17053
KEGG Path:hsa04924 Renin secretion 4 69 0.00019 34.90618
KEGG Path:hsa04933 AGE-RAGE signaling pathway in diabetic complications 4 100 0.000554 24.08526
KEGG Path:hsa04926 Relaxin signaling pathway 4 129 0.001288 18.67075
KEGG Path:hsa04928 Parathyroid hormone synthesis secretion and action 3 106 0.010651 17.04146
KEGG Path:hsa04080 Neuroactive ligand-receptor interaction 10 362 4.07E−08 16.63347
KEGG Path:hsa05200 Pathways in cancer 11 530 4.67E−08 12.49707
KEGG Path:hsa05415 Diabetic cardiomyopathy 4 203 0.006442 11.86466
KEGG Path:hsa04020 Calcium signaling pathway 4 240 0.010651 10.03553
Cervical cancer BP GO:0044057 Reg. of system proc 15 584 2.18E−10 13.66737
BP GO:0007610 Behavior 13 659 3.60E−08 10.49698
BP GO:0006954 Inflammatory response 16 892 2.14E−09 9.544687
BP GO:0009725 Response to hormone 15 898 1.45E−08 8.888357
BP GO:0009719 Response to endogenous stimulus 19 1660 1.31E−08 6.090488
BP GO:0010033 Response to organic substance 27 3269 4.59E−10 4.394965
BP GO:0051239 Reg. of multicellular organismal proc 24 3024 1.51E−08 4.223145
BP GO:0065008 Reg. of biological quality 28 4103 5.47E−09 3.631308
BP GO:0006950 Response to stress 29 4424 5.40E−09 3.488104
BP GO:0042221 Response to chemical 29 4821 2.18E−08 3.200865
CC GO:0034668 Integrin alpha4-beta1 complex 2 3 0.000641 354.7442
CC GO:0097444 Spine apparatus 2 3 0.000641 354.7442
CC GO:0097136 Bcl-2 family protein complex 2 10 0.004767 106.4233
CC GO:0008305 Integrin complex 3 31 0.001329 51.49512
CC GO:0098636 Protein complex involved in cell adhesion 3 53 0.004767 30.11979
CC GO:0009897 External side of plasma membrane 6 459 0.005648 6.955768
CC GO:0005925 Focal adhesion 6 475 0.006113 6.721469
CC GO:0098552 Side of membrane 8 757 0.00297 5.623422
CC GO:0005887 Integral component of plasma membrane 17 1894 4.62E−06 4.776123
CC GO:0031226 Intrinsic component of plasma membrane 17 1978 4.62E−06 4.573295
MF GO:0004953 Icosanoid receptor activity 5 17 8.52E−09 156.5048
MF GO:0004222 Metalloendopeptidase activity 8 126 8.44E−09 33.78516
MF GO:0008528 G protein-coupled peptide receptor activity 9 156 2.49E−09 30.69902
MF GO:0001653 Peptide receptor activity 9 162 2.49E−09 29.56202
MF GO:0008237 Metallopeptidase activity 8 204 1.68E−07 20.86731
MF GO:0042277 Peptide binding 9 402 1.47E−06 11.91305
MF GO:0008270 Zinc ion binding 13 947 4.22E−07 7.304659
MF GO:0046914 Transition metal ion binding 14 1247 1.13E−06 5.97404
MF GO:0038023 Signaling receptor activity 19 1908 1.67E−08 5.298852
MF GO:0060089 Molecular transducer activity 19 1908 1.67E−08 5.298852
KEGG Path:hsa04614 Renin-angiotensin system 4 23 3.47E−06 92.54196
KEGG Path:hsa03320 PPAR signaling pathway 6 75 4.17E−07 42.5693
KEGG Path:hsa04924 Renin secretion 4 69 0.000255 30.84732
KEGG Path:hsa04670 Leukocyte transendothelial migration 4 114 0.001542 18.67075
KEGG Path:hsa05410 Hypertrophic cardiomyopathy 3 90 0.010729 17.73721
KEGG Path:hsa04080 Neuroactive ligand-receptor interaction 12 362 3.02E−10 17.63921
KEGG Path:hsa04926 Relaxin signaling pathway 4 129 0.002136 16.49973
KEGG Path:hsa05415 Diabetic cardiomyopathy 4 203 0.010545 10.48505
KEGG Path:hsa05200 Pathways in cancer 10 530 1.96E−06 10.03993
KEGG Path:hsa04024 cAMP signaling pathway 4 221 0.011592 9.631064
Prostate cancer BP GO:0001990 Reg. of systemic arterial blood pressure by hormone 7 40 6.92E−10 91.003977
BP GO:0003044 Reg. of systemic arterial blood pressure mediated by a chemical signal 7 51 2.12E−09 71.375668
BP GO:0042310 Vasoconstriction 8 83 1.79E−09 50.122673
BP GO:0050886 Endocrine proc 8 93 2.53E−09 44.733138
BP GO:0044057 Reg. of system proc 15 584 1.09E−10 13.356748
BP GO:0009725 Response to hormone 16 898 1.79E−09 9.265438
BP GO:0009719 Response to endogenous stimulus 20 1660 1.79E−09 6.265334
BP GO:0010033 Response to organic substance 28 3269 1.03E−10 4.454156
BP GO:0051239 Reg. of multicellular organismal proc 25 3024 2.82E−09 4.299130
BP GO:0065008 Reg. of biological quality 31 4103 7.77E−11 3.929004
CC GO:0034668 Integrin alpha4-beta1 complex 2 3 0.000884 346.681818
CC GO:0097136 Bcl-2 family protein complex 2 10 0.005637 104.004546
CC GO:0008305 Integrin complex 3 31 0.001759 50.324780
CC GO:0098636 Protein complex involved in cell adhesion 3 53 0.005637 29.435249
CC GO:0009897 External side of plasma membrane 6 459 0.007147 6.797683
CC GO:0005925 Focal adhesion 6 475 0.007597 6.568708
CC GO:0030055 Cell-substrate junction 6 484 0.007597 6.446563
CC GO:0098552 Side of membrane 8 757 0.00418 5.495617
CC GO:0005887 Integral component of plasma membrane 15 1894 0.000268 4.118448
CC GO:0031226 Intrinsic component of plasma membrane 15 1978 0.000268 3.943549
MF GO:0042562 Hormone binding 7 93 4.35E−08 39.141500
MF GO:0004222 Metalloendopeptidase activity 8 126 3.04E−08 33.017320
MF GO:0008528 G protein-coupled peptide receptor activity 8 156 4.35E−08 26.667830
MF GO:0001653 Peptide receptor activity 8 162 4.70E−08 25.680130
MF GO:0008237 Metallopeptidase activity 8 204 1.61E−07 20.393050
MF GO:0042277 Peptide binding 11 402 3.04E−08 14.229480
MF GO:0033218 Amide binding 11 481 6.70E−08 11.892410
MF GO:0046914 Transition metal ion binding 15 1247 1.63E−07 6.255285
MF GO:0038023 Signaling receptor activity 18 1908 1.61E−07 4.905875
MF GO:0060089 Molecular transducer activity 18 1908 1.61E−07 4.905875
KEGG Path:hsa04614 Renin-angiotensin system 4 23 3.87E−06 90.438740
KEGG Path:hsa03320 PPAR signaling pathway 6 75 3.26E−07 41.601820
KEGG Path:hsa04924 Renin secretion 4 69 0.000236 30.146250
KEGG Path:hsa04933 AGE-RAGE signaling pathway in diabetic complications 4 100 0.000879 20.800910
KEGG Path:hsa04670 Leukocyte transendothelial migration 4 114 0.001284 18.246410
KEGG Path:hsa04080 Neuroactive ligand-receptor interaction 12 362 4.14E−10 17.238320
KEGG Path:hsa04926 Relaxin signaling pathway 4 129 0.001844 16.124740
KEGG Path:hsa04020 Calcium signaling pathway 6 240 0.000191 13.000570
KEGG Path:hsa05200 Pathways in cancer 11 530 2.69E−07 10.792920
KEGG Path:hsa05415 Diabetic cardiomyopathy 4 203 0.009339 10.246750
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The determination of the main protein for each cancer disease are focused on the pathways in cancer from the results of KEGG enrichment analysis. Proteins involved in the pathways in cancer for the active compounds of all cancer disease targets provide the same protein, namely 10 proteins (AGTR1, EDNRB, GSTM1, ITGB1, MMP2, PPARG, PTGER2, PTGER4, and BCL2), as shown in Table S2. The acquisition of the main protein target in the cancer pathway is determined by three proteins shown in Fig. 5a–b and Tables S3–S6. Relevant targets of the compound 16-hydroxy-7α-acetoxyroyleanone and signaling pathways in cancer from each target cancer studied are shown in Figs. S12–S15.

Fig. 5.

Fig. 5

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An enrichment analysis of the determination of the main target proteins based on KEGG in cancer pathways; (a) PPI of genes in involved pathways in cancer (blue highlights and in the middle) analyzed by the CytoHubba plugin; and (b) 3 top interacting target genes.

Molecular docking study

A molecular docking study was conducted on the top three target genes obtained from the results of network pharmacological studies focused on pharmacological pathways (pathways in cancer). The top three genes that are proteins in each cancer are matrix metalloproteinase-2 (MMP2), peroxisome proliferator-activated receptor gamma (PPARG), and B-cell lymphoma 2 (BCl2). The energy optimization results of the 16-hydroxy-7α-acetoxyroyleanone compound are − 1300.609 kJ/mol with a dipole moment of 3.4830 Debye5. The structure of the main target protein was downloaded from the PDB database. Before continuing the molecular docking process, the original and native proteins were first re-docked to obtain the best pose of the complex compound (RMSD < 2 Å) on the grid box that has been set for each target protein. Based on the best pose of the complex compound with the smallest RMSD used as the basis for arrangement in molecular docking. The RMSD of each native in the target protein complex compound is shown in Table 4 and Fig. S16. The molecular docking of the 16-hydroxy-7α-acetoxyroyleanone compound against the proteins of each cancer disease is shown in Fig. 6.

Table 4.

The PDB ID of the protein receptor and the RMSD value of the native ligand for each protein target.

No. Target cancers MMP2 PPARG BCL2
PDB ID RMSD (Å) PDB ID RMSD (Å) PDB ID RMSD (Å)
1. Breast cancer 3AYU20 0.38 4Y2921 0.62 4MAN22 1.76
2. NSCLC 7XJO23 0.43 6DGL24 0.41 6GL825 0.68
3. Cervical cancer 1HOV26 1.16 6C5T27 0.96 4AQ328 0.90
4. Prostate cancer 7XGJ29 1.05 2HFP30 0.37 2W3L31 0.48
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Fig. 6.

Fig. 6

Fig. 6

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Molecular study of the 16-hydroxy-7α-acetoxyroyleanone compound against the main target protein for cancer therapy.

Discussion

Given the absence of reports on the exploration of the compound 16-hydroxy-7α-acetoxyroyleanone (syn. 16-hydroxy-7-O-acetylhorminon) isolated from Coleus amboinicus leaves as an anticancer agent, this study was conducted. This compound in previous studies showed potential as an antioxidant2, targeting the peroxisome proliferator-activated receptor gamma (PPARG) protein from bioinformatic and molecular docking studies5. This study is a continuation in revealing cytotoxic activity against breast cancer cells (MCF-7), lung cancer cells (A549), cervical cancer cells (HeLa), prostate cancer cells (Du-145), and against normal cells (CV-1). In addition, we also reveal the prediction of the pharmacological effects of the compound on target cancer diseases through a network pharmacology approach accompanied by a study of molecular interactions with three main proteins (ligand-receptors) from each target cancer disease by molecular docking. Globally, cancer is a frightening health problem, widely diagnosed, and is included in the top three causes of death32,33. The development of drug discovery for various cancers continues to be a serious concern, considering the side effects of conventional treatments (chemotherapy, radiation, immunotherapy, and surgery). Recent reports support that the use of active compounds sourced from natural ingredients provides effective activity in the treatment of several cancers. In addition, the natural compound also prevents side effects from the use of chemotherapy and prolongs the survival and quality of life of patients33,34.

The results of the cytotoxic activity test (IC50) against CV-1 normal cells were 19.27 µg/mL; MCF-7 cells were 4.22 µg/mL with a selectivity index (SI) value of 4.57; A549 cells were 18.10 µg/mL with a SI value of 1.07; HeLa cells were 6.31 µg/mL with a SI value of 3.05; and Du-145 cells were 4.67 µg/mL with a SI value of 4.13. In the cytotoxic test against various cancer cells, controls were also used as a comparison to determine the potential activity of active isolates with commonly used cancer drugs. Common cancer drugs used as controls in this study were doxorubicin for A549 cells and cisplatin for MCF-7, HeLa, and Du-145 cells. Based on the cytotoxic activity test data, the active compound showed lower IC50 and higher SI value than cisplatin against MCF-7 and Du-145 cells, whereas the cytotoxic activity values of cisplatin against both cancer cells were 15.9 and 6.38 μg/mL, respectively. Its cytotoxic activity was weaker than doxorubicin (15.82 μg/mL) against A549 cells and also lower than cisplatin (5.7 μg/mL) against HeLa cells (Fig. 2b–c). The cytotoxic activity values of the active compound and the drug as a positive control for all cancer cells tested were in the strong category. These results show great potential for further research of this compound to be developed as new cancer drug agents, especially for the development of breast cancer and prostate cancer drugs. The selectivity index values of the active compound against MCF-7, Du-145, HeLa, and A549 cancer cells were 4.57, 4.10, 3.05, and 1.07, respectively. The selectivity index value of a compound tested gives a value greater than 3 (SI > 3), indicating selective cytotoxicity against the cancer cells tested and providing safety to normal cells35,36. Based on this description, the compound 16-hydroxy-7α-acetoxyroyleanone is selective against breast cancer cells, cervical cancer cells, and prostate cancer cells. Other studies reported that the leaf part of this plant extracted with methanol had an activity against WiDr cancer cells of 8.598 ± 2.68 µg/mL37, the water extract synthesized in the form of copper oxide nanoparticles (CA-CuO) showed an activity of 12.5 µg/mL against HCT 116 cells (colon cancer)38, and the TiO2 nanoparticle form had a killing activity of 92.37% at a dose of 100 µg/mL within 24 h against HeLa cells39, and the latest study of ethanol extract and its partition results from the leaf part showed cytotoxic activity against A549 and MCF-7 cells6,7.

The ADMET prediction study provides balanced information between potential drug activity and appropriate selectivity in the selection and clinical development of potential new drug candidates40. The physicochemical properties and bioavailability of drug candidates provide several drug-likeness scores, which can be considered as the first step to understand the probability between successful and failed drug candidates. A series of ADMET predictions is important before drug candidates are introduced for clinical trials, thus providing more comprehensive understanding information41. The ADMET prediction results of the 16-hydroxy-7α-acetoxyroyleanone compound are shown in Table 1. From the evaluation of pharmacokinetic, physicochemical, and drug-likeness parameters, no violations of the rules related to drug-likeness properties were found. This compound shows high gastrointestinal absorption, cannot cross the blood–brain barrier, is not a P-gp substrate, and does not affect inhibitors (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) so that this compound shows oral drug molecular similarity and good bioavailability. Based on Lipinski’s 5 rules: (1) molecular weight < 500 Daltons, (2) LogP value < 5, (3) number of hydrogen bond donors < 5, (4) number of hydrogen bond acceptors < 10, and (5) molar reactivity between 40 and 130: this compound also meets these rules.

Network pharmacology studies play an important role in systematic and comprehensive investigations that highlight various active ingredients, techniques/tools/databases related to drug discovery, and development for various diseases. Network pharmacology is a common approach used in the era of modern drug discovery42,43. The network pharmacology approach to potential drugs and disease targets can be explored, and the mechanisms and pathways between the two can be understood using existing databases44. Based on the results of MTT testing against various cancer cells, the compound showed promising activity as new anticancer agents. This study was continued with exploration with a network pharmacology approach to cancer pathways, interaction studies, and binding energy between compounds and proteins (ligand-receptors) by molecular docking. The results of network pharmacology investigations of pathways in cancer against four cancer disease targets (breast, NSCLC, cervical, and prostate) 16-hydroxy-7α-acetoxyroyleanone compound targets three main proteins, namely MMP2, PPARG, and BCl2. Molecular docking investigations were continued to provide information on the binding energy and molecular interactions that occur between compounds and amino acid residues of proteins in their active sites.

MMP2 belongs to a family of zinc and calcium-containing enzymes in the degradation and remodeling of the extracellular matrix (ECM). Under normal conditions, MMP2 plays an important role together with MMP9 in ECM remodeling, while in cancer conditions, MMP2 promotes invasion and metastasis by facilitating the vascular endothelial growth factor (VEGF) pathway45. Targeting the MMP2 protein in cancer therapy should suppress its expression to inhibit the proliferation and invasion of cancer cells in the body20,46 and promote the induction of apoptosis (overexpression of Bax/Bcl-2) at the mRNA and protein levels47,48. Molecular docking studies of the isolated compound against the MMP2 protein in (1) breast cancer (PDB ID 3AYU) gave a binding affinity of − 6.5 kcal/mol and hydrogen bond with Ala85. The native ligand yielded binding affinity of − 8.5 kcal/mol and hydrogen bonds with Tyr73, Leu82, Ala85, Ala87, and Tyr142. (2) For NSCLC cancer (PDB ID 7XJO), the isolated compound showed a binding affinity of − 6.4 kcal/mol and hydrogen bonds with Gly136 and Thr144, while the native gave a binding affinity of − 9.2 kcal/mol and hydrogen interactions with Leu83, Ala84, Ala86, Ala88, Glu130, and Thr144, and doxorubicin (positive control) yielded a binding affinity of − 7.8 kcal/mol and hydrogen interactions with Ala86. (3) For cervical cancer (PDB ID 1HOV), the isolated compound yielded a binding affinity of − 8.1 kcal/mol and hydrogen interactions with Leu83, Ala84, and Val117, while the native ligand gave a binding affinity of − 8.3 kcal/mol and hydrogen bonds with Leu83 and Ala84. (4) For prostate cancer (PDB ID 7XGJ), the isolated compound showed a binding affinity − 7.0 kcal/mol and hydrogen bonds with Asp80 and Gly81. On the other hand, the native ligand exhibited a binding affinity of − 7.8 kcal/mol and hydrogen bonds with His121, His131, Pro135, Gly136, Leu138, Ile142, Thr144, and Thr146. The results of molecular docking studies on the MMP2 protein indicate that the binding affinity of the active compound does not have better stability than the native ligand and drug, but it generates crucial hydrogen interactions with key amino acid residues in the active site of the protein. This supports that the active compound plays its role as a cancer drug agent through the pro-apoptotic pathway that supports the induction of cancer cell apoptosis.

Peroxisome proliferator-activated receptor gamma (PPARG) in normal conditions functions as a transcription factor that regulates gene expression after ligand activation, increases fatty acid absorption, increases triglyceride formation, regulates insulin sensitivity in glucose metabolism, and regulates genes encoding proteins controlling metabolic homeostasis in various organs49. In addition, PPARG plays a role in regulating the response of the stress system, anxiolytics, and its dysregulation in body depression both in normal and diseased conditions50. PPARG activation provides antitumor effects in lung cancer by regulating lipid metabolism, cell cycle arrest, and apoptosis induction as well as inhibiting invasion and migration. Clinical data shows therapeutic agents for cancer patients with PPARG agonist potential, providing synergistic effects on cancer chemotherapy and radiotherapy24. PPARG in various cancer therapies acts as a good prognostic biomarker in immunotherapy targets51. Molecular docking study of the isolated compound against PPARG protein in (1) breast cancer (PDB ID VYAS) gave a binding affinity of − 6.8 kcal/mol and hydrogen bonds with Glu259, Ile262, and Arg280; and in (2) NSCLC cancer (PDB ID 6DGL), it exhibited a binding affinity of − 8.8 kcal/mol and generated hydrogen bonds with Glu291. The isolated compound generated a binding affinity of − 7.1 and − 7.8 kcal/mol targeting Glu123, Arg196, and Arg242; and His 266 via hydrogen bonds against PPARG protein in (3) cervical cancer (PDB ID 6C5T) and (4) prostate cancer (PDB ID 2HFP), respectively. The native ligand showed a binding affinity of − 8.5, − 10.0, − 9.6, and − 11.8 kcal/mol against PPARG in breast, NSCLC, cervical, and prostate cancer cells through the formation of hydrogen bonds with (Lys261, Ile262, Lys275, Arg280, Gln283, Ser342, and Ser464), (Leu228, Cys285, and Arg288), (His122), and (Ser289 and His449), respectively. Meanwhile, the doxorubicin (positive control) binding affinity − 8.2 kcal/mol and hydrogen interactions (Arg280, Cys285, and Ser342) in the active site of PPARG protein in NSCLC cells.

B-cell lymphoma 2 (BCl2) in the body functions to regulate the complex interactions between pro-apoptotic, pro-survival, and anti-apoptotic members and balances the decision between cell life and death. BCl2 protein also functions to regulate the intrinsic pathway and prevent the release of cytochrome C from mitochondria52,53. Targeting the BCl2 protein in cancer therapy continues to be developed by targeting malignant hematological tissues to induce apoptosis54. Abnormal overexpression of BCl2 is directly involved in maintaining the survival and growth of cancer cells and promoting therapeutic resistance. In addition, abnormal BCl2 activation is directly related to the development, metastasis, and recurrence of various cancers. Therefore, BCl2 activation is an important indicator for assessing the efficacy and prognosis of clinical treatment. In fact, the sensitivity of malignant tumor cells to apoptosis can be effectively improved by reducing BCl2 protein levels or inhibiting BCl2 activity55. Molecular docking studies were conducted to determine target cancer proteins using protein PDB ID codes supporting information where native ligand binding to the protein is pro-apoptotic. Molecular docking studies of the isolated compound against BCl2 protein revealed that it exhibited a binding affinity of − 7.0 kcal/mol by forming a hydrogen bond with Glu11 on (1) BCl2 breast cancer (PDB ID 4MAN); a binding affinity of − 7.2 kcal/mol by generating a hydrogen bond with Val43 on (2) BCl2 NSCLC cancer (PDB ID 6GL8); a binding affinity of − 8.1 kcal/mol by generating hydrogen bonds with Glu95, Leu96, and Arg105 on (3) BCl2 cervical cancer (PDB ID 4AQ3); and a binding affinity of − 7.4 kcal/mol by generating hydrogen bonds with Asp99 and Arg105 on (4) BCl2 prostate cancer (PDB ID 2W31). The doxorubicin as the positive control showed a binding affinity of − 8.9 kcal/mol by the formation of hydrogen bonds with Tyr16, Asp17, and Thr54 on (2) BCl2 NSCLC cancer cells. On the other hand, the native ligand yielded a binding affinity of − 10.9, − 7.4, − 8.8, and − 9.8 kcal/mol and it generated a hydrogen bond with Asp70, Asp100, and Leu103 against BCl2 proteins in breast, NSCLC, cervical, and prostate cancer cell lines.

Conclusion

The isolated 16-hydroxy-7α-acetoxyroyleanone, syn. 16-hydroxy-7O-acetylhorminone, compound showed weaker cytotoxic activity against A549, HeLa, and CV-1 cells with IC50 value of 18.10, 6.31, and 19.27 μg/mL, respectively. Furthermore, the compound yielded a stronger anticancer activity against MCF-7 and Du-145 cells with IC50 value of 4.22 and 4.67 μg/mL, which was more potent than cisplatin. Pharmacological studies on the cancer pathway of each target cancer diseases (breast, NSCLC, cervical, and prostate cancer) show that the compound targets the three main proteins, namely MMP2, PPARG, and BCl2. The presence of hydrogen bond interactions between the compound and key amino acids from the target proteins supports good interactions and binding ability. The isolated compound provides a promising application for the development as a cancer drug agent, especially for breast and prostate cancers, in the future because of its stronger anticancer profile compared with cisplatin as the standard drug. However, experimental validations through pharmacokinetic studies and in vivo model are still required to be investigated in the future.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (892.3KB, docx)
Supplementary Material 2 (1.3MB, docx)
Supplementary Material 3 (2.5MB, docx)
Supplementary Material 4 (2.5MB, docx)

Acknowledgements

The authors are grateful for financial support provided by the (1) Indonesian Education Scholarship (BPI), (2) Center for Higher Education Funding and Assessment (PPAPT), and (3) Indonesian Endowment Fund for Education (LPDP) to the author on behalf of Kasta Gurning (BPI Decree Number 02380/BPPT/BPI.06/9/2024), Friska Septiani Silitonga (BPI Decree Number 02111/J5.2.3./BPI.06/9/2022), and Yehezkiel Steven Kurniawan, are greatly appreciated. We deeply appreciate all the members of our team.

Author contributions

K.G. and Y.S.K. performed the experiments, collected data, interpreted and analyzed the data, wrote and prepared the original draft, revised, and edited the manuscript; F.S.S. performed experiments and collected data; G.P. helped with curation, formal analysis, and investigation; E.A. supervision, validation, and writing—review and editing; W.H. funding acquisition, validation, and writing—review and editing. All authors have read, reviewed, and approved of the final version of the article.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

Data are provided within the manuscript or supplementary information files. The datasets used and/or analyzed during the current study are included in this published article and its supplementary information files. Correspondence and requests for materials should be addressed to Kasta Gurning or Winarto Haryadi.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

10/21/2025

The original online version of this Article was revised: The Acknowledgments section in the original version of this Article was incorrect. It now reads: “The authors are grateful for financial support provided by the (1) Indonesian Education Scholarship (BPI), (2) Center for Higher Education Funding and Assessment (PPAPT), and (3) Indonesian Endowment Fund for Education (LPDP) to the author on behalf of Kasta Gurning (BPI Decree Number 02380/BPPT/BPI.06/9/2024), Friska Septiani Silitonga (BPI Decree Number 02111/J5.2.3./BPI.06/9/2022), and Yehezkiel Steven Kurniawan, are greatly appreciated. We deeply appreciate all the members of our team." The original Article has been corrected.

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Associated Data

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Supplementary Materials

Supplementary Material 1 (892.3KB, docx)
Supplementary Material 2 (1.3MB, docx)
Supplementary Material 3 (2.5MB, docx)
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Data Availability Statement

Data are provided within the manuscript or supplementary information files. The datasets used and/or analyzed during the current study are included in this published article and its supplementary information files. Correspondence and requests for materials should be addressed to Kasta Gurning or Winarto Haryadi.

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