Antioxidant and antiproliferation activity of cancer cells from Euphorbia hirta, Euphorbia tirucalli, and Euphorbia tithymaloides: in vitro and in silico approaches

Atividade antioxidante e antiproliferativa de células cancerígenas de Euphorbia hirta, Euphorbia tirucalli e Euphorbia tithymaloides: abordagens in vitro e in silico

Abstract

Euphorbiaceae is a plant family that is reported to have many medicinal properties. Euphorbia hirta (EH), Euphorbia tirucalli (ET), and Euphorbia tithymaloides (ETH) are species of Euphorbiaceae that are widely grown in Indonesia but whose pharmacological effects as antioxidants and anticancer agents have not been widely studied. This study assessed the antioxidant and antiproliferative properties of extracts from these plants. Measurements methods were performed using in vitro and in silico methods. The free radicals used in this study were 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). Assays of these compounds were used to evaluate antiproliferative activity against breast and cervical cancer cells. Liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-MS/MS-QTOF) was used to identify the compounds in the three plant samples. The results showed that all samples contained alkaloids, flavonoids, and terpenoids and inhibited free radicals with IC₅₀ values ​​ of 10.18 (EH), 83.79 (ET), and 125.10 ppm (ETH). ETH inhibited cancer cell proliferation with IC₅₀ values ​​of 593.08 ppm (breast cancer cells) and 194.86 ppm (cervical cancer cells). In silico evaluation showed that the compounds in the extract have the potential to inhibit cancer cell growth by inhibiting the regulation of vascular endothelial growth factor receptor 2 and Bcl-2. The results of this study present a promising opportunity for the development of natural antioxidant sources and novel anticancer drugs based on Euphorbiaceae plants.

Keywords:
antioxidant; antiproliferation; chemotaxonomy; Euphorbia hirta; Euphorbia tirucalli; Euphorbia tithymaloides

Resumo

Euphorbiaceae é uma família de plantas com diversas propriedades medicinais. Euphorbia hirta (EH), Euphorbia tirucalli (ET) e Euphorbia tithymaloides (ETH) são espécies dessa família amplamente cultivadas na Indonésia, mas cujos efeitos farmacológicos como antioxidantes e anticancerígenos ainda não foram suficientemente estudados. Este estudo avaliou as propriedades antioxidantes e antiproliferativas de extratos dessas plantas. As medições foram realizadas in vitro e in silico. Os radicais livres utilizados foram o DPPH (2,2-difenil-1-picrilhidrazil) e o MTT [brometo de 3-(4,5-dimetiltiazol-2-il)-2,5-difeniltetrazólio] para avaliar a atividade antiproliferativa em células de câncer de mama e de colo do útero. A cromatografia líquida acoplada à espectrometria de massas com analisador de tempo de voo quadrupolo (LC-MS/MS-QTOF) foi utilizada para identificar os compostos presentes nas três amostras vegetais. Os resultados da análise mostraram que todas as amostras continham alcaloides, flavonoides e terpenoides, e inibiram radicais livres com valores de IC₅₀ de 10,18 (EH), 83,79 (ET) e 125,10 ppm (ETH). O ETH inibiu a proliferação de células cancerígenas com valores de IC₅₀ de 593,08 ppm (células de câncer de mama) e 194,86 ppm (células de câncer cervical). A avaliação in silico mostrou que os compostos do extrato têm potencial para inibir o crescimento de células cancerígenas por meio da inibição da regulação do receptor 2 do fator de crescimento endotelial vascular e de Bcl-2. Os resultados deste estudo apresentam uma oportunidade promissora para o desenvolvimento de fontes naturais de antioxidantes e novos fármacos anticancerígenos baseados em plantas da família Euphorbiaceae.

Palavras-chave:
antioxidante; antiproliferativo; quimiotaxonomia; Euphorbia hirta; Euphorbia tirucalli; Euphorbia tithymaloides

1. Introduction

Cancer is a serious global health issue. This disease has a significant impact on both developed and developing countries, with a sustained increase in its year-to-year incidence rate (Siegel et al., 2023). In Indonesia, cancer is a disease that incurs significant expenses for the national health insurance program. The number of individuals affected by the disease and the incidence of specific types of cancer vary according to geographical location (Santoso et al., 2021). A study of hospital-based cancer registry data from multiple Indonesian regions found that the majority of cancer patients were over 39 years of age, female, and diagnosed with breast or cervical cancer (Gondhowiardjo et al., 2021). Ecological studies on cancer prevalence indicate that an increase in the consumption of preserved meat, chicken or fish, high-fat or high-cholesterol foods such as fried foods, conventional smoking, electronic cigarettes, diabetes and hypertension in a geographic region is associated with higher cancer prevalence (Santoso et al., 2021).

The biological mechanisms of cancer involve aberrant metabolic processes within cells, accompanied by alterations in the energy production metabolic pathway and the biosynthesis pathway of intermediate compounds. These metabolic changes are driven by the necessity to support the rapid proliferation of tumor cells, resulting in alterations to the composition and concentration of extracellular and intracellular compounds. Moreover, these metabolic changes affect cellular signal transduction and gene expression, facilitating cancer cell growth (You et al., 2023). The metabolic processes in cancer cells are reorganized, resulting in phenotypic characteristics that differ from those of normal cells. These characteristics include processes such as cell proliferation, migration, and angiogenesis (Liu et al., 2023). In addition, high concentrations of free radicals (reactive oxygen or nitrogen species) in cells have been demonstrated to induce oxidative stress. This condition is associated with the dysregulation of redox signals and genes that regulate apoptosis and cell proliferation (Xing et al., 2022). As reported in previous studies, a high concentration of free radicals has been detected in some cancer cells (Uniyal et al., 2024).

Bcl-2, vascular endothelial growth factor (VEGF), and vascular endothelial growth factor receptor (VEGFR) are proteins that facilitate the growth of cancer cells and are targets for anticancer therapy (Jia et al., 2024; Eissa et al., 2025). Bcl-2 is a member of the B-cell lymphoma 2 family protein that plays a role in cell apoptosis. It consists of both pro-apoptotic and pro-survival proteins, and the shift in the function of this protein towards a prosurvival balance allows cancer cells to avoid apoptosis, in contrast to normal cells (Souers et al., 2013). VEGF and VEGFR are key mediators of angiogenesis, which is integral to normal cell growth. However, angiogenesis is also involved in cancer cell metastasis, ensuring the supply of nutrients and oxygen during proliferation through the development of new blood vessels (Sullivan and Brekken, 2010). Increased VEGF expression has been widely confirmed in cancer cells and is associated with disease aggressiveness (Fontanella et al., 2014).

Chemotherapy is a cancer treatment that has demonstrated efficacy in specific malignancies. However, its effectiveness is constrained by drug resistance and adverse effects on normal tissue and cells (Kumar et al., 2017). This poses a significant challenge in the development of anticancer drugs and has prompted the development of more effective and selective alternative medicines derived from herbal plants. Alternative medicine, which is based on traditional medicine and utilizes plant-based remedies, constitutes a significant component of health services. Its utilization in the prevention and treatment of diseases has a long-standing history in numerous countries (Liu, 2021). In Indonesia, which has a rich diversity of plant species, there is a long tradition of using medicinal plants and spices in traditional medicine systems. Plants from the Euphorbiaceae family are one example of this (Adnan et al., 2022).

Euphorbiaceae are distributed in tropical and subtropical regions from Africa to America, and consist of more than 2000 species in the form of herbs, shrubs, trees resembling succulents or cacti (Kgosiemang et al., 2025). The white sap produced by these plants is toxic and has pharmacological effects. The phytochemical content of these plants, including euphrol, cycloartenol, tirucalol, and triterpenoids, has been shown to possess a range of bioactivities, including antibacterial, antioxidant, anticancer, anti-inflammatory, antiviral, and antifungal properties. Therefore, this genus has promising potential in medicine (Villanueva et al., 2015; Aleksandrov et al., 2019; Kgosiemang et al., 2025).

The Euphorbiaceae family has been widely distributed and cultivated throughout Indonesia, with empirical utilization in traditional medicine (Naki et al., 2023; Wirasisya et al. 2023; Pahmi et al., 2025). Euphorbia hirta, Euphorbia tirucalli, and Euphorbia tithymaloides have been used in traditional medicine (Singh and Sudha, 2018; Kemboi et al., 2020). These plants exhibit antioxidant activity as free radical scavengers and inhibitors of cellular oxidative stress (Foda et al., 2022; Nawaz et al., 2022). Euphorbia hirta has demonstrated the capacity to function as an antitumor agent with a 70% inhibition rate of cancer cells in vivo, as evidenced by active compounds such as phenols and flavonoids (Pavithra et al., 2024; Costa et al., 2025). All parts of the Euphorbia tirucalli plant, including the sap, possess pharmacological properties. The plant has demonstrated analgesic, anthelmintic, anti-arthritis, antimicrobial, anti-inflammatory, antioxidant, antiviral, and anticancer activities (Mali and Panchal, 2017). Furthermore, Euphorbia tithymaloides contains diterpenoids, which have been found to possess bioactivity as antidiabetic and antiproliferative agents for cancer cells (cervical, liver, blood, breast, and lung cancers) (Khang et al., 2023).

The number of studies evaluating the potential of alternative medicines derived from Euphorbia for antioxidant and anticancer effects is limited. This is a significant gap in the literature because the presence of free radicals supports cancer cell growth. This study aimed to characterize the metabolite profiles of three Indonesian Euphorbia species (Euphorbia hirta, Euphorbia tirucalli, and Euphorbia tithymaloides) and evaluate their antioxidant and antiproliferative activities through in vitro assays, followed by in silico drug-likeness screening, toxicity prediction, and molecular docking to identify potential anticancer compounds.

2. Materials and Methods

2.1. Materials

Fresh leaves and stems of Euphorbia hirta, Euphorbia tirucalli, and Euphorbia tithymaloides were obtained from Banda Aceh (5°33’0”N 95°19’3”E), and taxonomic identification was performed at the Department of Biology, Syiah Kuala University, Indonesia. Before extraction, the samples were air-dried for three weeks. The chemical materials used in this study were absolute grade for analysis.

2.2. Extraction

Each sample was extracted using the maceration method for 72 h. The sample (100 g) was soaked with 1 L of methanol (Merck) (1:10 w/v ratio). The filtrate obtained was evaporated at 50 °C (Asworo and Widwiastuti, 2023). The results were designated Euphorbia hirta extract (EH), Euphorbia tirucalli extract (ET), and Euphorbia tithymaloides extract (ETH).

2.3. Metabolite profiling

The samples were analysed using liquid chromatography- quadrupole time-of-flight mass spectrometry (LC-MS/MS-QTOF). The column used was an ACQUITY UPLC HSS T3 Column (100Å, 1.8 µm, 2.1 mm X 100 mm). The operation and ionization mode was Tof MS^E and ESI (-)/ ESI (+) with an acquisition range of 50-1200 Da. A total of 10 µL of the sample was injected into the column with an eluent composed of 0.1% formic acid in acetonitrile and 0.1% formic acid in aquabidest. The flow rate was set at 0.6 mL/min with a gradient. The identification of metabolites was conducted using UNIFI software, which integrates a mass spectrum library derived from the peaks and retention times of each metabolite. The parameters applied in this process included a mass error reading of ≤5 ppm, an isotope match of MZ RMS of ≤6, and a metabolite intensity of ≥300 (Wang et al., 2021) (Supplementary Material).

2.4. Antioxidant activity

The free radical used in this method is DPPH (2,2-diphenyl-1-picrylhydrazyl) (Sigma) (Mostofa et al., 2023). The sample concentrations selected for this analysis were 1.5-50 ppm for EH and 15-500 ppm for ET and ETH. Each sample (1 mL) was mixed with 1 mL of DPPH (0.1 mM) and incubated for 30 minutes at room temperature. Subsequently, the absorbances were measured at 517 nm. A DPPH solution without sample was applied as a negative control and an ascorbic acid (Sigma) solution (0.3-12.5 ppm) as a positive antioxidant control. Low absorbance values ​​indicated high free radical inhibition. In this analysis, the IC₅₀ value was determined by linear regression between inhibition and sample concentration.

2.5. Antiproliferative cancer cell assay

The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) (Invitrogen, Thermo Fisher Scientific) assay was used to evaluate the antiproliferative activity of the samples against cancer cells (Mostofa et al., 2023). This assay used MCF7 cancer cells (breast cancer cells), HeLa (cervical cancer cells), and fibroblast cells (NIH/3T3) as normal cells obtained from the Cell Culture Laboratory at The Research Center of Raw Materials for Drugs and Traditional Medicine-LAPTIAB, National Research and Innovation Agency (BRIN), Puspiptek Serpong in South Tangerang, Banten, Indonesia. Each cell line was cultured in high-glucose Dulbecco’s minimal Eagle’s medium containing 10% foetal bovine serum, 1% penicillin-streptomycin, and 0.5% amphotericin, until 80-90% confluence. The number of cells per well on the plate was 10000 cells in 100 μL of growth medium. Cells were incubated for 24 h (37 °C; 5% CO₂). A 96-well plate containing the control medium (without cells and sample), negative control (cells without sample), and sample was used for the analysis. Approximately 100 μL of samples (125, 250, 500, and 1000 ppm) were mixed with cells on a plate and incubated for 24 h (37 °C; 5% CO₂). The subsequent step involved washing the cells with phosphate-buffered saline, adding 0.5 mg/mL MTT and incubating for 4 h (37 °C; 5% CO₂). Before measuring the absorbance at 570 nm, 10% sodium dodecyl sulphate was added to prevent the formation of formazan purple crystals from the reaction of MTT with mitochondria in surviving cells. Higher absorbance values indicate a higher percentage of viable cells. The percentage of proliferation inhibition was estimated as the difference in cell viability between the sample and the maximum cell viability (100%). The selectivity index (SI) was calculated as the ratio of the IC₅₀ value for normal cells to the IC₅₀ value for cancer cells. A compound is considered selective against cancer cells with SI value ≥ 2 (Widiandani et al., 2023).

2.6. Drug-likeness and toxicity prediction

Drug-likeness screening of the metabolites in each sample was performed using SwissADME (2026). The parameters applied in the analysis included the Lipinski, Ghose, Veber, Egan, and Muegge rules. A high metabolite similarity to the drug was indicated by the absence of violations of these regulations. Metabolites that passed the drug similarity screening were subjected to toxicity prediction using ProTox 3.0 (Prediction of Toxicity of Chemicals) (Banerjee et al., 2026). This analysis is important for drug development (Wahyuningsih et al., 2025).

2.7. Docking analysis

Molecular docking analysis was conducted to observe the interaction of metabolites contained in the samples with cancer therapy target proteins in silico. The proteins used in this analysis were vascular endothelial growth factor receptor 2 (VEGFR2) (PDB ID: 3EWH) and Bcl-2 (PDB ID: 4LVT). The 3D structures of protein were obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB, 2026). The ligands used were metabolites from samples that passed drug-like and toxicity screenings in previous analyses. The 3D structures of the ligands used in this analysis were obtained from the PubChem database (NLM, 2026). The preparation process before docking involved separating the co-crystal ligands, removing water molecules, removing unwanted ligands, and adding hydrogen to the protein using the PyMOL Molecular Graphics System 2.5.4. Subsequently, the re-docking of the receptor with the co-crystal ligand was performed to validate the docking method. The Open Babel application in PyRx-Phyton Prescription 0.8 was used to minimize the energy of each ligand using the MMFF94 force field. This objective was to avert interference during the docking simulations. The grid box position was set at the center of the ligand co-crystal according to the protein 3EWH (coordinates X: 14.7401, Y: -5.7558, Z: 8.6223) and 4LVT (coordinates X: 7.2119, Y: -2.5929, Z: -7.9128). Docking analysis and binding affinity evaluation of each ligand were performed using AutoDock Vina, integrated with PyRx-Phyton Prescription 0.8 software. The interactions between the ligands and proteins were visualized using BIOVIA Discovery Studio Visualizer V21.1.0.20298 (Kudatarkar et al., 2021).

2.8. Statistical analysis

The data are presented as the mean value ± standard deviation (SD) from three replicates (n = 3). The Shapiro-Wilk test was utilised to evaluate the normality of the data. The Kruskal-Wallis test and Dunn's post-hoc test at a 95% confidence level were utilised to compare means. The analyses were performed using R software version 4.1.3 (R Development Core Team, 2022).

3. Results and Discussion

3.1. Metabolite profile

Results of this analysis showed that there were 248 metabolites contained in ET, EH, and ETH, consisting of alkaloids, flavonoids, and terpenoids (Supplementary file). A total of 54 metabolites were contained in ET only, 62 in EH, 51 in ETH, 23 metabolites were contained in both EH and ET, 15 metabolites were contained in both ET and ETH, and 23 metabolites were contained in both EH and ETH. Furthermore, 20 metabolites were contained in all samples tested in this study (Figure 1). The metabolite profiles of Euphorbiaceae plants in this study were consistent with previous studies, indicating the presence of flavonoids in all parts of these plants, including roots, stems, leaves, and shoots, at varying concentrations (Magozwi et al., 2021; Dehghani et al., 2024). Varying concentrations of alkaloids have been found in the roots and leaves of Euphorbia hirta (Das et al., 2022). In addition, Euphorbia tithymaloides and Euphorbia tirucalli contained terpenoids, including sesquiterpenes and triterpenoids (Duong et al., 2019; Patil et al., 2023).

Figure 1
Distribution of metabolites in the ET (Euphorbia tirucalli), EH (Euphorbia hirta), and (ETH Euphorbia tithymaloides) extracts using LC-MS/MS-QTOF analysis.

3.2. Antioxidant activity

The three extracts exhibited concentration-dependent DPPH radical-scavenging activity, with IC₅₀ values of 10.18, 83.79, and 125.10 ppm for EH, ET, and ETH, respectively (Figure 2). Based on these values, EH showed the strongest antioxidant activity among the tested extracts, although its activity remained lower than that of ascorbic acid, which exhibited an IC₅₀ of 2.71 ppm. A lower IC₅₀ indicates that a smaller concentration of an extract is required to achieve 50% radical-scavenging activity and therefore reflects stronger antioxidant capacity (Mostofa et al., 2023).

Figure 2
Antioxidant activity of EH (A), ET (B), ETH (C), and ascorbic acid (positive control) (D). EH: Euphorbia hirta, ET: Euphorbia tirucalli, ETH: Euphorbia tithymaloides. The results of triplicate experiments have been presented with standard deviation.

The differences in antioxidant activity among EH, ET, and ETH may be associated with their distinct metabolite compositions. The extracts contained alkaloids, flavonoids, and terpenoids, groups of secondary metabolites that have been reported to contribute to antioxidant activity (Indriaty et al., 2023; Sabaragamuwa and Perera, 2023). Consistent with these findings, previous studies have demonstrated free-radical-scavenging activity in the roots, stems, and leaves of E. hirta and E. tirucalli (Nawaz et al., 2022). However, the antioxidant capacity of a crude extract is determined not only by the presence of individual metabolites but also by their relative abundance and interactions. The weaker activity of the extracts compared with ascorbic acid may therefore reflect differences in the composition and antioxidant efficiency of the metabolites present in the crude extracts. In addition, the individual constituents may not act synergistically to the same extent in donating electrons or hydrogen atoms to DPPH radicals (Abbas et al., 2022; Al-Nemi et al., 2022). In contrast, ascorbic acid is a single, well-characterized antioxidant compound and has been reported to exhibit DPPH-scavenging IC₅₀ values ranging from 0.2 to 22.5 ppm (Martino-Morales et al., 2020). These differences indicate that the antioxidant activity observed in the present study is likely influenced by the qualitative and quantitative composition of the extracts rather than simply by the presence of antioxidant metabolite classes.

3.3. Antiproliferation activity

The antiproliferative activity of the extracts differed markedly among the three Euphorbia species and between cancer cell lines (Figures 3A and 3B). ETH showed the greatest inhibitory effect against both MCF7 and HeLa cells, with IC₅₀ values of 593.08 and 194.86 ppm, respectively, whereas the IC₅₀ values of EH and ET were above 1000 ppm. The stronger activity of ETH against HeLa cells suggests that its constituents may have greater activity toward cervical cancer cells than toward MCF7 cells. The differences among the extracts may be related to their distinct metabolite compositions, as observed in the metabolite profiling analysis. Thus, the antiproliferative effects of the extracts may depend not only on the plant family but also on species-specific chemical composition and the relative abundance of bioactive constituents.

Figure 3
Antiproliferative activity of EH, ET, and ETH against (A) MCF7 cell (breast cancer); (B) HeLa cell (cervical cancer); (C) NIH/3T3 cell (non cancer cell). EH: Euphorbia hirta, ET: Euphorbia tirucalli, ETH: Euphorbia tithymaloides. The results of triplicate experiments have been presented with standard deviation.

The response of the NIH/3T3 non-cancerous cell line provided an important perspective on the biological relevance of these findings. EH and ET showed IC₅₀ values above 1000 ppm, indicating low cytotoxicity toward NIH/3T3 cells under the conditions tested, whereas ETH produced substantially greater inhibition, with an IC₅₀ of 287.98 ppm and 90.29% inhibition at 1000 ppm (Figure 3C). Natural products with IC₅₀ values above 1000 ppm have been reported to exhibit little or no detectable proliferation-inhibitory or cytotoxic activity in cell-based assays (Pramanik et al., 2023). However, the low cytotoxicity of EH and ET toward NIH/3T3 cells was accompanied by limited activity against MCF7 and HeLa cells, indicating that their apparent safety was not associated with meaningful antiproliferative potency. In contrast, although ETH demonstrated stronger activity against both cancer cell lines, its activity toward NIH/3T3 cells resulted in SI values below 2, indicating insufficient selectivity for the cancer cells (Table 1). Therefore, the biological activity of ETH should be interpreted cautiously, because its greater antiproliferative potency was accompanied by a potential risk of toxicity toward non-cancerous cells. Further investigation should focus on identifying the constituents responsible for the antiproliferative activity and determining concentrations that maximize activity against cancer cells while minimizing effects on normal cells.

Table 1
The IC₅₀ and selectivity index (SI) of Euphorbia hirta (EH), Euphorbia tirucalli (ET), Euphorbia tithymaloides (ETH) against cancer cells (MCF7 and HeLa).

Samples	IC50			SI
	MCF7	HeLa	NIH/3T3 (non cancer)	MCF7	HeLa
EH	>1000	>1000	>1000	nd	nd
ET	>1000	>1000	>1000	nd	nd
ETH	593.08	194.86	287.98	0.48	1.47

• nd = not detected.

The observed activity is consistent with previous reports describing the antitumor potential of Euphorbiaceae species and the toxic effects associated with latex from some members of this family. In particular, E. hirta and E. tirucalli have been investigated for anticancer activity against different cancer cell types, with phenolic and terpenoid constituents proposed as important contributors to their biological effects (Amtaghri et al., 2022; Jiménez-González et al., 2023). Nevertheless, the weak antiproliferative activity of EH and ET in the present study indicates that the occurrence of potentially bioactive metabolite classes within a species does not necessarily translate into strong cytotoxic activity in a particular cancer model. This discrepancy may reflect differences in metabolite composition, extract polarity, concentration, cancer-cell susceptibility, or the relative contribution of individual compounds. Therefore, rather than supporting the direct designation of these extracts as anticancer agents, the present findings indicate that E. tithymaloides warrants further investigation as a source of antiproliferative constituents, particularly those with improved selectivity toward cancer cells.

3.4. Drug-likeness and toxicity prediction

Among the 248 metabolites identified across the three extracts, 86 satisfied the predefined drug-likeness criteria based on the Lipinski, Ghose, Veber, Egan, and Muegge rules (Figure 4A). This finding indicates that a substantial proportion of the detected metabolites possess physicochemical characteristics compatible with drug-like compounds. Of these metabolites, 29 were additionally predicted to be inactive against the evaluated toxicity endpoints, including hepatotoxicity, neurotoxicity, cardiotoxicity, cytotoxicity, carcinogenicity, and mutagenicity (Figure 4B). These compounds were therefore prioritized for subsequent molecular docking analysis against proteins relevant to anticancer therapy.

Figure 4
(A) Drug-likeness screening using Lipinski, Ghose, Veber, Egan, and Muegge rules; (B) Toxicity prediction for the possibility of inducing hepatotoxicity, neurotoxicity, cardiotoxicity, cytotoxicity, carcinogenicity, and mutagenicity.

Drug-likeness assessment provides an initial indication of whether the physicochemical and structural characteristics of a compound are compatible with those commonly observed in drug candidates. The five complementary rules used in this study evaluate different molecular properties associated with drug-like behavior and pharmacokinetic suitability (Daina et al., 2017). However, satisfying these criteria does not establish pharmacological efficacy or guarantee favorable pharmacokinetics in vivo. Similarly, the predicted absence of toxicity should be regarded as a preliminary safety indication rather than evidence of biological safety, because computational toxicity models may not fully reproduce the complex biological processes underlying adverse effects (Banerjee et al., 2024). Therefore, the 29 metabolites identified in this analysis should be considered prioritized candidates for further investigation rather than confirmed drug candidates. Their biological activity and safety require validation using appropriate experimental assays.

3.5. Validation of docking analysis

The redocking results supported the reliability of the selected docking protocol, with RMSD values of 1.381 Å for 3EWH and 1.739 Å for 4LVT, both below the commonly accepted threshold of 2 Å (Shivanika et al., 2020). The relatively small deviations between the redocked and experimentally determined ligand conformations, together with the comparable ligand poses observed in Figure 5, indicate that the selected grid-box positions were able to reproduce the experimentally observed binding modes. This validation provides a reasonable basis for applying the same docking protocol to the metabolites identified in the extracts. Nevertheless, the RMSD criterion primarily assesses the ability of the docking protocol to reproduce a known ligand pose and does not by itself confirm the biological activity or therapeutic potential of the tested compounds.

Figure 5
The validation (redocking) analysis result. (A) Co-crystal ligand of protein 3EWH; (B) Co-crystal ligand of protein 4LVT. Red: before redocking, green: after redocking.

3.6. Docking analysis

The docking analysis identified differences in the predicted binding affinities of the metabolites toward 3EWH and 4LVT (Table 2). Neobavaisoflavone showed the most favorable predicted binding affinity toward 3EWH (-9.57 kcal/mol), followed by kaempferol (-9.30 kcal/mol), whereas Nigakilactone I showed a less favorable score (-5.13 kcal/mol). For 4LVT, neobavaisoflavone showed the most favorable predicted binding affinity among the tested compounds (-8.08 kcal/mol), while desmodilactone showed the least favorable score (-5.25 kcal/mol). Nevertheless, all tested metabolites showed weaker predicted binding affinities than the corresponding co-crystal ligands of 3EWH (-14.30 kcal/mol) and 4LVT (-11.26 kcal/mol). Thus, neobavaisoflavone and kaempferol were prioritized based on their relatively favorable predicted interactions with 3EWH, while neobavaisoflavone also showed a favorable interaction with 4LVT.

Table 2
Ligand and protein binding energy results from molecular docking analysis.

Ligand	Protein-ligand Binding affinity (kcal/mol)
	Vascular Endothelial Growth Factor Receptor 2 (PDB ID: 3EWH)	Bcl-2 (PDB ID: 4LVT)
Co-crystal ligand	-14.3	-11.26
4-Hydroxy-2.4-dimethoxydihydrochalcone	-7.82	-7.1
6-Aldehydoisoophiopogonanone A	-9.11	-7.85
6-Hydroxykynurenic acid	-7.25	-6.08
Baimuxinal	-7.62	-6.85
Britannilactone	-7.22	-6.3
Chrysanthediol	-6.17	-6.2
Curcumenone	-7.7	-6.48
Deacetylpseudolaric acid A	-7.4	-7.64
Desmodilactone	-5.4	-5.25
Epicatechin	-8.29	-6.9
Flazin	-8.82	-7.6
Florilenalin	-6.97	-7.2
Genistein	-9.29	-7.2
Gentianamine	-6.17	-5.77
Inuchinenolide A	-7.88	-7.19
Istanbulin A	-7.38	-7.5
Kaempferol	-9.3**	-6.9
Licoricone	-8.97	-7.68
Linderane	-6.69	-7.55
Neobavaisoflavone	-9.57*	-8.08*
Nevadensin	-8.42	-7.4
Nigakilactone I	-5.13	-7.9**
Nobiletin	-7.26	-6.38
Nobilonine	-7.13	-6.76
Oxyphyllenone A	-6.01	-6.12
Tussilagonone	-7.95	-7.33
Vellerdiol	-6.9	-6.67
Viscidulin I	-8.49	-6.6
Vomifoliol	-6.87	-6.06

• ^*
  Ligand with the highest binding energy;
• ^**
  Ligand with the second highest binding energy.

The interaction profiles provided additional information on the potential binding modes of the prioritized compounds. Neobavaisoflavone and kaempferol occupied regions of the 3EWH binding site that overlapped with the binding region of the co-crystal ligand K11. Neobavaisoflavone formed interactions with 13 amino acid residues, including Leu840, Val848, and Lys868, whereas kaempferol interacted with six residues, including Leu840 and Val848 (Table 3 and Figure 6A). The greater number of interacting residues observed for neobavaisoflavone may contribute to its more favorable docking score, although the number of interactions alone does not determine binding strength. For 4LVT, neobavaisoflavone interacted predominantly through hydrophobic contacts with Phe101, Met112, Glu133, Leu134, and Ala146, without a hydrogen bond being detected. Nigakilactone I formed a hydrogen bond with Gly142 and hydrophobic interactions with Trp141, Leu198, and Tyr199. The interaction regions of these compounds were comparable to that of the co-crystal ligand 1XJ (Table 3 and Figure 6B), suggesting that the predicted poses were located within the relevant binding pocket.

Table 3
Ligand and protein interaction results from molecular docking analysis.

Protein	Ligand	Binding Interaction
		H-bond	Hydrophobic bond
Vascular Endothelial Growth Factor Receptor 2 (PDB ID: 3EWH)	Co-crystal (K11)	Glu885, Cys919	Val848*, Ala866, Lys868*, Leu889, Ile892, Val898, Thr916, Leu1019
	Kaempferol	Cys919	Leu840*, Val848*, Ala866, Val899, Leu1035
	Neobavaisoflavone	Lys868*, Glu885, Thr916, Cys919	Leu840*, Val848*, Ala866, Leu889, Val899, Phe918, Leu1035, Cys1045, Phe1047
Bcl-2 (PDB ID: 4LVT)	Co-crystal (1XJ)	Arg104, Asn140, Trp141, Gly142	Phe101, Tyr105, Phe109, Met112, Leu134, Arg143*, Val145, Ala146, Val153, Tyr199
	Nigakilactone I	Gly142	Trp141, Leu198, Tyr199
	Neobavaisoflavone	-	Phe101, Met112, Glu133, Leu134, Ala146

• ^*
  Protein binding site.

Figure 6
Molecular docking visualization and interaction. (A) Interaction of 3EWH protein with co-crystal ligands, neobavaisoflavone, and kaempferol; (B) Interaction of 4LVT protein with crystal ligands, neobavaisoflavone, and nigakilactone I.

The predicted binding behavior may be influenced by the combination of hydrogen-bonding and hydrophobic interactions formed within the binding sites. Molecular docking is useful for predicting ligand-protein binding modes and relative binding affinities and can assist in prioritizing compounds for further investigation (Agu et al., 2023). Hydrogen bonds, hydrophobic interactions, and van der Waals forces can collectively contribute to ligand stabilization within a protein binding site (Kudatarkar et al., 2021). Hydrogen bonds are particularly important in molecular recognition and ligand binding because of their directional nature and contribution to the specificity of molecular interactions (Bulusu and Desiraju, 2020). However, docking scores should be interpreted as computational predictions rather than direct measurements of binding thermodynamics or biological efficacy. More favorable docking scores may indicate stronger predicted interactions within the computational model, but experimental studies are required to determine whether these interactions occur under biological conditions (Pantsar and Poso, 2018; Fan et al., 2019).

An important observation was the discrepancy between the docking and in vitro antiproliferative results. Neobavaisoflavone and Nigakilactone I were detected only in ET (E. tirucalli) and showed favorable predicted interactions with the target proteins, yet ET exhibited limited antiproliferative activity against both MCF7 and HeLa cells. This finding indicates that favorable ligand-protein docking scores do not necessarily translate into measurable cellular activity. Cellular uptake, membrane permeability, intracellular metabolism, target accessibility, compound abundance, and interactions among multiple constituents may influence the biological response of a crude extract. In contrast, kaempferol was detected in ETH (E. tithymaloides), which showed the strongest antiproliferative activity among the three extracts. Although this association is noteworthy, the present data do not establish kaempferol as the compound responsible for the observed activity because ETH contains multiple metabolites and its activity was accompanied by limited selectivity toward NIH/3T3 cells. Therefore, neobavaisoflavone, kaempferol, and Nigakilactone I should be regarded as computationally prioritized candidates rather than confirmed anticancer compounds. Isolation of the individual metabolites, followed by target-specific biochemical assays and cellular experiments, is required to determine their actual contribution to the antiproliferative activity. Further in vivo studies would subsequently be necessary to evaluate their biological efficacy and safety.

4. Conclusion

In conclusion, of the species analysed in this study, Euphorbia tithymaloides exhibited the strongest antiproliferative activity, whereas Euphorbia hirta demonstrated superior antioxidant capacity. Molecular docking suggested that neobavaisoflavone, kaempferol, and Nigakilactone I may contribute to these biological activities. Further mechanistic and in vivo studies are required to validate these findings.

Supplementary Material

Supplementary material accompanies this paper.

Table S1

Metabolites contained in the Euphorbia tirucalli, Euphorbia hirta, Euphorbia tithymaloides based on LC-MS/MS-QTOF analysis.

This material is available as part of the online article from https://doi.org/10.1590/1519-6984.306271

Acknowledgements

The authors would like to thank the financial support provided by the Ministry of Higher Education, Science, and Technology of Indonesia and the Institute for Research and Community Service of Universitas Syiah Kuala through Fundamental Regular Research Grant (Contract No: 113/C3/DT.05.00/PL/2025, May 28, 2025; 152/UN11.L1/PG.01.03/DPPM/2025, June 4, 2025) and all parties who contributed to this research.

Data Availability Statement

The entire dataset supporting the results of this study was published in the article itself.

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*

e-mail: [e-mail]

Editor:

Marcelo A.M. Esquisatto

Publication Dates

• Publication in this collection
  09 Oct 2026
• Date of issue
  2026

History

• Received
  09 Mar 2026
• Accepted
  13 Aug 2026