Synthesis and exploration of breast cancer agent from curcumin analogs based on 3-benzyloxybenzaldehyde

2.1 Materials

The reagents, including 3-benzyloxybenzaldehyde, N-methyl-4-piperidone, were obtained from Macklin, and 4-piperidone monohydrate hydrochloride from Sigma. The other chemicals, i.e., ethanol, sodium hydroxide, potassium hydroxide, hydrochloric acid, and acetone, are in pro analysis specification. The materials used in the anticancer evaluation include Dulbecco’s Modified Eagle Medium (DMEM), Roswell Park Memorial Institute (RPMI), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), trypsin-EDTA, phosphate buffered saline (PBS), sodium dodecyl sulfate (SDS), cancer cell lines T47D, HER2, MCF-7, 4T1, Vero, doxorubicin, dimethyl sulfoxide (DMSO), and immunocytochemistry kit.

2.2 Instrumentation

The synthesis is conducted in a sonicator (Cole-Palmer) and a hotplate (Thermo Scientific). Structure elucidation of curcumin analogs was carried out using a thin-layer chromatography (TLC) scanner (CAMAG TLC Scanner), a Fourier transform infrared (FTIR) spectrophotometer with attenuated total reflectance (ATR), nuclear magnetic resonance (1H-NMR (JEOL 500 MHz), and ¹³C-NMR (JEOL 125 MHz) spectrometers). The deuterated solvents, i.e., CDCl₃, were used for NMR measurement. Measurement using a microplate reader (Bio-Rad, Benchmark), an inverted and a light microscope (Olympus).

2.3 Method for curcumin analogue synthesis

2.4 Cytotoxicity activity

Cancer cell lines T47D, HER2, MCF-7, 4T1, and normal cell Vero were cultured in DMEM and RPMI (T47D) at 37°C under 5% CO₂. The cell suspension (10⁶/mL) was prepared. A total of 100 μL/well was inserted into a 96-well plate. The incubation process was carried out for 24 h to allow cells to reattach. Compounds C1 and C2 were prepared in DMSO at a concentration of 10.000 μg/mL. Each sample was diluted into culture medium in five serial concentrations: 200, 100, 50, 25, and 12.5 μg/mL. A total of 100 μL of the respective test concentration was then filled into the wells. The incubation was performed for the next 24 h, and the MTT assay was carried out to assess the cell viability. The MTT solution in PBS was prepared, and 1 mL of this aliquot was diluted to a volume of 10 mL with culture medium. An aliquot (100 μL) of diluted MTT was inserted into each well. The incubation was conducted for another 4 h. Finally, 100 μL of SDS stopper 10% in 0.1 M HCl was filled into each well. The plates were kept at room temperature overnight. The ELISA reader was used to record absorbance readings at 595 nm (Gurning et al., 2024).

The IC₅₀ values were determined by plotting cell viability against compound concentration to generate a dose–response curve. The concentration at which 50% inhibition of cell growth occurred, compared to untreated controls, was taken as the IC₅₀ value. The selective cytotoxicity of the compounds toward each cancer cell over normal cells was evaluated by calculating the selectivity index (SI). SI was determined by dividing the IC₅₀ value obtained from the normal cell line by the IC₅₀ value from the cancer cell line, using the following formula:

2.5 Immunocytochemical test

An immuncytochemical test was performed using p53 and BCL-2 monoclonal antibodies with the breast cancer cells (T47D, HER2, MCF-7, and 4T1). All the cells were prepared in a plate with 24 wells. Then, 1000 μL complete media, DMEM (HER2, MCF-7, and 4T1) and RPMI (T47D), were added to the plate with one control and three variations of concentration depending on IC₅₀ value. Subsequently, 24 well plates with transferred cells were incubated at 37°C with 5% CO₂ for 24 h. After incubation, the media were discharged and washed with PBS and methanol. Afterward, 100 μL paramount block was added and incubated for 10 min, then washed with water. The wells were added with 100 μL paramount ostium blocking and incubated for 20 min, pipette until completely removed. The next step was adding 100 μL primary antibody (p53 and BCL-2), incubating it for an hour, and washing with PBS three times. Paramount secondary links were added and incubated for 30 min, washed with PBS three times. The same procedure was conducted for paramount HRP. Each well was inoculated with DAB concentrate and DAB buffer and incubated for 10 min, and then washed with water. Thereafter, hematoxylin was added with a volume of 100 μL for 10-20 seconds and washed with water. The 24-well plates were washed with PBS and water. Cover slips were placed on top of the object glass and then dropped with alcohol and xylol. When it is dried, drop the glue and cover the slide (Astuti et al., 2015). Representative images of cells were obtained at 400× magnification using an Olympus light microscope equipped with an Optilab camera. Image analysis was performed using Image Raster software. For each treatment group, a representative image containing approximately 900 cells was counted per group. The scale bar represents 100 μm. Cell counts were further analyzed and expressed as percentages, calculated as follows:

2.6 Pharmacokinetic profile prediction

Pharmacokinetic profile prediction (ADMET) was performed through the pkCSM and ADMETlab 2.0 (http://admet.scbdd.com/) websites by entering the Simplified Molecular Input Line Entry System (SMILES) codes of curcumin and synthesized curcumin analogs, downloaded from the PubChem website (www.pubchem.ncbi.nlm.nih.gov) (Kawashima et al., 2023).

2.7 Network pharmacology study

Each curcumin analog (compounds C1 and C2) was identified as a potential target for therapeutic use using the SwissTargetPrediction database (http://www.swisstargetprediction.ch/) and determining the species “homo sapiens.” Data was filtered by removing duplicates and using genes that had probability values. Identification of disease target genes using the keyword “breast cancer” was obtained from the GeneCards database (https://www.genecards.org/), DisGeNET (https://www.disgenet.org/), OMIM (https://www.omim.org/), and TTD (http://db.idrblab.net/ttd/). The genes of each curcumin analog compound with breast cancer genes were integrated using the VENNY 2.1 diagram (https://bioinfogp.cnb.csic.es/tools/venny/), and the intersecting genes were uploaded to the STRING database (https://string-db.org/) with the setting “homo sapiens,” confidence level 0.4. Data were downloaded in TSV format, constructed, and further analyzed using Cytoscape 3.10.2 software to obtain protein-protein interactions of compounds-targets-diseases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the intersecting genes were uploaded to the ShinyGO 0.81 database (http://bioinformatics.sdstate.edu/go/) with the determination of the species “human” and FDR cutoff 0.05. GO analysis with the approach of biological processes (BP), cellular components (CC), and molecular functions (MF) parameters. KEGG was identified in network pharmacology that focused on cancer pathways to obtain genes that are target proteins in breast cancer treatment therapy (Haryadi et al., 2025, 2024).

2.8 Molecular docking

Proteins resulting from bioinformatics approach studies were tracked through the website www.rcsb.org to obtain the PDB ID. Protein structures and standard ligands were prepared using the Autodock tool. Curcumin analog compounds and curcumin guide compounds were modeled and optimized using GaussView 5.0 software. Molecular docking simulations were performed using Autodock Vina. The parameters reviewed in molecular docking were affinity energy (more negative values indicate higher stability), RMSD values < 2.00 Å, and interactions between ligands and receptor proteins. Analysis of the most stable conformational interactions was visualized using BIOVIA Discovery Studio 2019 Client software (Haryadi and Pranowo, 2023).

2.9 Statistical analysis

Statistical analysis was performed on the activity test data using GraphPad Prism 10.0.1 software. The difference in the average anticancer activity between test cells was performed using analysis of variance (ANOVA) and continued with Tukey’s post hoc test. Breast anticancer activity data are expressed as average ± standard deviation (means ± SD), with a significance level of p < 0.05 considered statistically significant (*p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

3.1 Synthesis of curcumin analogs based on 3-benzyloxybenzaldehyde

Curcumin is a compound with abundant pharmacological properties. Its structure consists of four parts: the β-diketone group, the aryl group, the active methylene group, and two double bonds. Modification of its structures involves transforming its β-diketone into monoketone form, which can improve its stability. Curcumin analogs are synthetic molecules with a similar structure to curcumin but with structural modifications. They exhibit pharmacological properties that are like or superior to the parent compound, curcumin (Yang et al., 2013). The synthesis of its analogs was achieved by the Claisen-Schmidt aldol condensation using a base catalyst, with slight modifications, as illustrated in Fig. 1. Its reaction was monitored using TLC, and the products of synthesis were analyzed using a TLC-scanner along with melting point determination. The elucidation of its structure is conducted using FTIR, ¹H-NMR, and ¹³C-NMR. The synthesis of curcumin analogs C1 and C2 results indicate that the colors are slightly different, with C2 exhibiting a lighter yellow compared to C1. This subtle difference is attributed to the minor modification of structure between the two compounds. The difference in color of curcumin analogs is possibly caused by the phenolic groups present in the compounds. C1 contains a methyl group in the nitrogen, which acts as an electron-donating. This can increase electron density in the conjugated system, resulting in a more intense color of the compound.

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Fig. 1.

Scheme for the synthesis of curcumin analogs C1 and C2.

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The elucidation of C1 and C2 compounds using FTIR exhibit strong peaks of 1582 cm^-1 (C1) and 1581 cm^-1 (C2), representing C=C_aliphatic. The peaks at 1667 cm⁻¹ correspond to C=O stretching, which typically appears at around 1715 cm⁻¹. However, the conjugation with the C=C double bond and the reduction in the character of the two double bonds lead to a decrease in these values (Pana et al., 2014). The aromatic groups of C1 and C2 are possibly distinguished by the peaks at 1440, 1451, and 1495 cm^-1. Additionally, another characteristic was noted in the peak corresponding to the meta-substituted structure of C1 and C2, which displayed three medium bands in the range of 685–779 cm⁻¹ (Fig. 2). Moreover, the ¹H-NMR spectra of C1 and C2 confirmed the successful formation of the compound’s structures. The presence of carbon atoms in compounds was elucidated through the ¹³C-NMR spectra, and both compounds show the same number of carbons as the structure, which are 16 peaks for C1 and fourteen for C2.

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Fig. 2.

FTIR spectra of curcumin analogue C1 and C2.

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3.2 Cytotoxicity activity

Cytotoxicity assay was conducted using breast cancer cell lines (T47D, HER2, MCF-7, 4T1) and a normal cell line (Vero) against curcumin analogs, monoketones C1 and C2, and a commercial drug (doxorubicin) as a positive control. The IC₅₀ values were classified under high cytotoxic activity (IC₅₀ < 20 μg/mL), moderate cytotoxic activity (20 μg/mL < IC₅₀ < 100 μg/mL), and no cytotoxic activity (IC₅₀ > 100 μg/mL) (Fikroh et al., 2023). In the study, both C1 and C2 showed significant cytotoxicity toward breast cancer cells, suggesting their promise as potential anticancer compounds. C1 exhibited high cytotoxicity against 4T1 (IC₅₀ = 5.45 μg/mL) and moderate activity against T47D (28.34 μg/mL), MCF-7 (60.06 μg/mL), HER2 (91.89 μg/mL). C2 showed high cytotoxicity against HER2 (1.73 μg/mL) and T47D (5.88 μg/mL). In comparison, doxorubicin showed higher potency in most cancer cell lines (IC₅₀: T47D = 0.35 μg/mL, HER2 = 0.02 μg/mL, MCF-7 = 28.63 μg/mL, 4T1 = 6.51 μg/mL. Although C2 exhibited high cytotoxicity, C2 displayed considerable cytotoxicity in normal Vero cells, which is undesirable (Fig. 3).

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Fig. 3.

Anticancer activity against various types of breast cancer cells of (a) compound C1, (b) compound C2, and (c) doxorubicin.

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The results of this study are in line with previous research, showing that structural modification of curcumin into monoketone analogs with aromatic substitutions can yield compounds with potent cytotoxic activity and favorable toxicity profiles. For example, 1-(4-chlorobenzoyl)-3,5-bis((E)-4-methoxylbenzylidene)piperidin-4-one (CMBP) exhibited strong anticancer potential with IC₅₀ = 1.6 μg/mL against MCF-7 cells (Fathima et al., 2024). Other curcumin analogs, such as PGV-0 and PGV-1, also showed promising cytotoxicity; PGV-0 had IC₅₀ = 13.76 μg/mL and PGV-1 had IC₅₀ = 38.21 μg/mL against 4T1 cells, compared to curcumin with IC₅₀ = 34.34 μg/mL (Murwanti et al., 2020). Similarly, (Eryanti et al., 2015; Eryanti et al., 2018) synthesized analogs from 4-piperidone and N-methyl-4-piperidone with various aromatic substitutions, resulting in IC₅₀ against T47D values ranging from 4–45 μg/mL depending on the type and position of the substituent.

The classification of the SI is as follows: SI < 1 indicates non-selective activity; low selectivity ranges from 1 to 3; moderate selectivity ranges from 3 to 6; and high selectivity corresponds to SI values > 6 (Sancha et al., 2023). The SI values presented in Table 1 indicate that C1 exhibits high selectivity toward the 4T1 cell line (SI = 15.46), while showing low to moderate selectivity against T47D (2.97), MCF-7 (1.40), and HER2 (0.92). In contrast, C2 demonstrates non-selective activity across all breast cancer cell lines (SI < 1). It indicates that it may also have toxic effects on normal cells, which is not desirable for a breast anticancer agent. When compared to doxorubicin, which shows very high selectivity in T47D (51.34) and HER2 (898.5), but low selectivity in MCF-7 (0.63) and 4T1 (2.76), C1 shows a more favorable selectivity profile toward MCF-7 and 4T1 cells. Based on these results, the analysis of activity and selectivity index, then the curcumin C1 analog compound, was continued with immunocytochemical testing. Although cytotoxicity was assessed over 24 hours in this study, longer exposures (48–72 h) could provide additional information on delayed or cumulative effects (Komissarova et al., 2005). Future studies, including extended time-course experiments, are recommended to better characterize the long-term efficacy and safety of the synthesized curcumin analogs.

3.3 Immunocytochemistry test

Immunocytochemistry is a technique used to detect antigens within cells by binding them to specific antibodies. This study examined the expression of BCL-2 and p53 proteins in four breast cancer cell lines (T47D, HER2, MCF-7, 4T1), utilizing two specific antibodies (anti-BCL-2 and anti-p53). Experiments were conducted with control (untreated) samples and three different concentrations of a curcumin analogs. The expression of BCL-2 and p53 was indicated by brown staining in the cell nucleus, representing the binding of monoclonal antibodies to the proteins. The outcomes have been summarized in Table 2.

In the analysis of breast cancer cell lines, curcumin analog C1 increased p53 expression in all tested cell lines. In the 4T1 cell line, treatment with 2.725 μg/mL of the curcumin analog C1 increased p53 protein expression up to 86.36%. Similarly, increased p53 expression was also observed in MCF-7, HER2, and T47D cell lines, particularly at higher concentrations of C1, indicating that the compound may help reactivate p53 function across different breast cancer subtypes. This enhancement in p53 protein levels is expected to restore the tumor-suppressing function of p53, which is typically observed in normal cells, and the increasing of apoptosis. On the other hand, a low score of p53 indicates the mutation of its gene. The p53 is a protein that acts as a crucial tumor suppressor by controlling cell division and preventing cells from proliferating excessively or uncontrollably. Mutations in p53 are highly prevalent in human cancers, making it a prime target for cancer therapies (Marei et al., 2021).

Regarding BCL-2 expression, treatment with curcumin analog C1 resulted in a variable response across the cell lines. In T47D cells, BCL-2 expression slightly decreased at curcumin analog C1 concentrations of 28.34 μg/mL and 14.17 μg/mL. In the HER2 breast cancer cell line, BCL-2 expression was also suppressed; treatment with 183.78 μg/mL of C1 reduced BCL-2 protein expression by 3.64%. Interestingly, in 4T1 cells, BCL-2 expression markedly increased at higher concentrations, suggesting a possible cell-type-specific response. BCL-2 serves as a central regulator of apoptosis in response to environmental factors and diverse stress signals. It modulates and facilitates the mitochondrial processes that contribute to cell death through the intrinsic apoptosis pathway. This pathway is essential for normal embryonic development and acts as a critical mechanism for cancer prevention. Prior to the induction of apoptosis, BCL-2 plays a vital role in maintaining normal cellular physiology, including neural activity, autophagy, calcium regulation, mitochondrial dynamics and energy production, and other key processes essential for healthy cellular function (Marie Hardwick and Soane, 2013). The downregulation of BCL-2 in certain cell lines may indicate that curcumin analogue C1 promotes apoptosis through the intrinsic pathway. The result of this study shows that C1 can increase p53 expression and suppress BCL-2 expression in some of the breast cancer cell lines, although the effect may vary depending on the cellular context. Therefore, compound C1 demonstrates potential as a breast cancer therapeutic agent (Fig. 4).

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Fig. 4.

Protein expression of curcumin analog compound C1 from various breast cancer cells (400×). Scale bar = 100 μm (a) T47D; (b) HER2; (c) MCF-7; (d) 4T1. Sign (→) is a positive expression cell (brown color).

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Fig. 4.

Protein expression of curcumin analog compound C1 from various breast cancer cells (400×). Scale bar = 100 μm (a) T47D; (b) HER2; (c) MCF-7; (d) 4T1. Sign (→) is a positive expression cell (brown color).

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Fig. 4.

Protein expression of curcumin analog compound C1 from various breast cancer cells (400×). Scale bar = 100 μm (a) T47D; (b) HER2; (c) MCF-7; (d) 4T1. Sign (→) is a positive expression cell (brown color).

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Fig. 4.

Protein expression of curcumin analog compound C1 from various breast cancer cells (400×). Scale bar = 100 μm (a) T47D; (b) HER2; (c) MCF-7; (d) 4T1. Sign (→) is a positive expression cell (brown color).

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3.4 Pharmacokinetic prediction

Predicting pharmacokinetics is crucial for understanding how a drug compound will function in the body, encompassing absorption, distribution, metabolism, excretion, and toxicity (ADMET). Using a prediction model with computer-based tools such as pkCSM and ADMETlab 2.0 provides important insights that aid in selecting the best candidates and allow for further optimization to achieve effective clinical results. For a compound to be a strong drug candidate, it must reach sufficient levels at the target site and remain there long enough to produce the intended biological effect.

The predicted ADMET parameters have been detailed in Table 3, with human intestinal absorption (HIA) being crucial as it determines how an orally administered drug is absorbed from the digestive system into the bloodstream, directly impacting the drug’s bioavailability. Colon adenocarcinoma (Caco-2) cells assay is an in vitro permeability assay, developed to replicate characteristics of in vivo absorption. Drug compounds with high permeability are characterized by a Papp value ≥ 8×10⁻⁶ cm/s (log Papp ≥ 0.903×10⁻⁶ cm/s), while those with moderate-to-poor permeability have a Papp value < 8×10⁻⁶ cm/s (log Papp < 0.903×10⁻⁶ cm/s) (The et al., 2011). Compounds C1 and C2 exhibit high Caco-2 permeability, whereas curcumin demonstrates lower Caco-2 permeability, indicating that Compounds C1 and C2 have greater absorption potential than curcumin. This underscores HIA’s significance among ADMET parameters, where drugs with poor absorption are characterized by an HIA value of ≤ 29%, moderate absorption ranges from 30% to 79%, and high absorption is indicated by an HIA ≥ 80% (Pérez et al., 2004). The compounds C1 and C2 exhibit HIA values more than curcumin’s HIA value, suggesting that these compounds are likely to demonstrate better absorption profiles compared to curcumin.

Blood-brain barrier (BBB) permeability and volume of distribution (VD) are critical parameters for evaluating the distribution characteristics of drug candidates. A compound’s ability to cross the BBB is essential for assessing its potential impact on the central nervous system (CNS), as this barrier plays a major role in regulating the entry of substances into the brain. Compounds with a logBB value greater than -1 are classified as BBB+, indicating their capability to cross the BBB, while those with a logBB value of -1 or lower are categorized as BBB-, indicating they cannot penetrate this barrier. According to the data presented in Table 1, compounds C1, C2, and curcumin are classified as BBB+, suggesting their potential to reach and distribute within the CNS. 

Volume of distribution (VD) is a pharmacokinetic parameter representing the administered dose of a drug to its initial concentration in circulation, providing crucial insights into a drug’s in vivo distribution, including its binding to plasma proteins, distribution in body fluids, and uptake by tissues. A compound is considered to have an appropriate VD if its predicted VD ranges from 0.04 to 20 L/kg (Ahmad et al., 2023). The VD values for compounds C1 and C2 are both higher than curcumin, indicating that compounds C1 and C2 are predicted to distribute less in the blood and more in tissues compared to curcumin. This greater VD suggests that compounds C1 and C2 exhibit enhanced tissue distribution, potentially allowing for more effective targeting within tissues relative to curcumin. 

Drug metabolism parameters assess how enzymatic systems process drugs, influencing their duration and intensity of action in the body. Phase I reactions, which include oxidation, reduction, and hydrolysis, introduce or expose functional groups on drug molecules, enhancing their reactivity for further metabolism (Guengerich, 2008). The cytochrome P450 (CYP) enzyme family is the primary group responsible for drug biotransformation, with over 1,000 isoenzymes identified across different species. In humans, isoenzymes CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2 are particularly significant because they are involved in the metabolism of approximately 90% of all drugs (Sychev et al., 2018). ADMET predictions reveal that compounds C1, C2, and curcumin are metabolized by CYP3A4, but not by CYP2D6. Additionally, curcumin inhibits CYP1A2, CYP2C19, CYP2C9, and CYP3A4, while compound C1 inhibits CYP2C19, CYP2C9, and CYP3A4, and compound C2 inhibits CYP2D6 and CYP3A4.

Clearance (CL) quantitatively measures the irreversible removal of a drug from a specified matrix, typically blood or plasma (Smith et al., 2019). It is a vital pharmacokinetic parameter that, in conjunction with volume of distribution, influences a drug’s half-life and, consequently, its dosing regimen. As presented in Table 3, the CL values for compounds C1 and C2 are lower than that of curcumin, although the differences are not statistically significant. This finding implies that the elimination processes for these curcumin analogs may be extended in duration compared to curcumin. The toxicity prediction results for compounds C1, C2, and curcumin suggest that these compounds are not associated with Ames’s toxicity and carcinogenic effects.

Lipinski’s Rule of Five (RO5) is a parameter that predicts the drug-likeness of a chemical compound intended for oral administration by evaluating its biological activity. According to the RO5, a drug-like compound should have a molecular weight (MW) of less than 500 g/mol, a logP<5 indicating hydrophobicity, no more than five hydrogen bond donors (HBDs), and no more than 10 hydrogen bond acceptors (HBAs) (Elumalai et al., 2024; Smith et al., 2019). The analysis of monoketone curcumin analogs, as shown in Table 4, reveals that compound C1 does not meet these criteria due to a molecular weight above 500 Da and a logP>5, while compound C2 fails due to a logP>5. These findings suggest that both compounds may face challenges with oral bioavailability. Despite these limitations, bioavailability could be improved through advanced drug delivery methods, such as modifying nanoparticle surfaces or developing novel drug encapsulation techniques. These innovative systems have the potential to enhance absorption and therapeutic efficacy, making these curcumin analogs more suitable for clinical use. Although initial assessments point to limited bioavailability, continued research into advanced delivery systems may help overcome these obstacles, thereby increasing the therapeutic promise of these compounds.

3.5 Network pharmacology study

The target genes of each curcumin analog compound are compound C1 (73 genes), compound C2 (103 genes), and curcumin (68 genes). The total target genes of breast cancer disease from the database obtained are 13881, and each gene that intersects between curcumin analog compounds and breast cancer has been shown in Fig. 5(a). The intersection of target genes between each of them against breast cancer, namely compound C1 (69 genes), C2 (100 genes), and curcumin (64 genes), was built using the STRING database. Potential genes were determined based on the average shortest path length, betweenness centrality, closeness centrality, clustering coefficient, eccentricity, edge count, and indegree. The top ten for compound C1 were ABCC1, AKT1, ACACA, CPT1A, SCD, ACHE, ADORA1, ADORA3, and P2RY1; C2, namely ABCC1, ERBB2, PRKCB, AKT1, ABCG2, SMO, PDGFRB, KDR, KIT, and PARP1; and curcumin, namely ABCC1, STAT3, EGFR, SPHK1, BCL-2, SPHK2, AKT1, ADAM17, MMP8, and MMP13, have been shown in Fig. 5(b). Further analysis was carried out on the determination of the main target genes based on the pharmacological network in the cancer pathway. The main genes involved in the cancer pathway for curcumin analogs compound C1 are AKT1; C2 is AKT1; and curcumin is STAT3, have been shown in Fig. 5(c).

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Fig. 5.

Network pharmacology study of curcumin analog compounds on breast cancer therapy; (a) Venn diagram; (b) PPI compounds for breast cancer with Cytoscape; and (c) PPI-based pathway in cancer on network pharmacology.

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3.6 Molecular docking study

Molecular docking between curcumin analog compounds and breast cancer therapy is aimed at the main protein obtained from the study of network pharmacology. The target protein of curcumin analog compounds (compounds C1 and C2) is the same protein, AKT1, and uploaded from the protein data bank PDB ID: 6HHG (Homo sapiens) (Uhlenbrock et al., 2019), while the protein for curcumin compounds without substitution is STAT3. Pharmacologically, the network shows that the main protein of curcumin compounds is different from the main protein of its analog compounds in breast cancer therapy, so that the identification of molecular interactions with proteins is focused on the AKT1 protein (Fig. 6). The energy optimization of the compound structure, binding affinity energy, and molecular interaction between curcumin analog compounds and the main protein in breast cancer therapy have been shown in Table 5. The AKT1 protein has an important role in physiological placental development and cell growth proliferation (Hinz and Jücker, 2019), and the best therapy for cancer is by inhibiting the activation of its protein so that breast cancer induction occurs (Choi et al., 2019). Increasing efficacy in AKT1-targeted therapy also requires the addition of other therapies that function to prevent resistance and improve clinical response, thereby allowing targeting of compensatory mechanisms and/or increasing apoptosis (Shariati and Meric-Bernstam, 2019).

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Fig. 6.

Molecular docking of curcumin analog compounds, native ligands against AKT1 protein (PDB ID: 6HHG).

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