INTRODUCTION
Heart failure (HF) has emerged as a global concern, with a notable increase over the past three decades [1]. This rise is particularly pronounced in the Southeast Asian population, affecting approximately 10 million people in Indonesia [2].
Currently, medications such as Angiotensin Receptor Blockers, Angiotensin-Converting Enzyme Inhibitors, Angiotensin Receptor-Neprysilin Inhibitors, and Kalium-sparing diuretics are standard treatments for chronic HF patients [3]. Despite their efficacy, some patients could not tolerate their side effects—cough [4], hyperkalemia [5], angioedema [6], and renal insufficiency [7] and contraindication—pregnancy, renal stenosis, and HF with hypotension patients [7,8]. This fact emphasizes the need to explore alternative or complementary therapy, especially through natural resources.
A. paniculata, also called sambiloto or “King Bitter,” is a commonly found plant in Southeast Asia [9] and a prioritized medicinal plant that has become used as part of “jamu pahitan” in Indonesia [9,10]. This plant is rich in diterpenoid and flavonoid contents [11], which have many health effects [9] and are known to be safe [12]. Previous studies and some clinical trials [10,13–15] have revealed sambiloto’s efficacy, with antioxidant [16], anti-inflammatory [17], antimicrobial [18], antimalarial, anticancer [19], antiviral [20,21], and antidiabetic properties [10,22]. Some preclinical studies also proved that A. paniculata water extract inhibits the expression of key inflammatory markers such as cyclooxygenase-2, nuclear factor-kappa B (NF-?B), phosphorylated inhibitor of kappa B, and interleukin-6 (IL-6) in the hearts of mice subjected to cardiac remodeling induced by a high-fat diet [23]. Additionally, in rat models of ischemia-reperfusion (I-R), hydroalcoholic extracts of A. paniculata improved cardiac dysfunction. They restored the expression of intrinsic antioxidant proteins [24]. Similar improvements in cardiac dysfunction also occurred in mongrel dogs subjected to I-R when water extract of A. paniculata [25].
Cardiac hypertrophy, an HF precursor, is typically triggered by risk factors associated with metabolic syndrome, such as hypertension, dyslipidemia, obesity, and diabetes mellitus [26–28]. These factors contribute to a gradual but persistent increase in oxidative stress and a proinflammatory state. The Renin-Angiotensin-Aldosterone System is a key pathway [29,30] for initiating pathological cardiomyocyte hypertrophy through oxidative stress [31] and inflammatory processes [32].
Regarding inflammation regulation, the NLR family pyrin domain-containing 3 (NLRP3) inflammasome has become a main target of therapy in pathological cardiac remodeling [33,34]. NLRP3 activation seems to be linked to the upregulation of NF-κB [35] and the downregulation of nuclear factor erythroid 2-related factor (Nrf2). These are important transcription factors that crosstalk the expression of intrinsic antioxidants, anti-inflammatory and proinflammatory cytokines [36,37]. The Nrf2/NF-κB/NLRP3 signaling pathway is a promising target in modulating cardiac hypertrophy by A. paniculata.
Our research group’s systematic review [38] found that A. paniculata and its bioactive compound, Andrographolide, have anticardiac hypertrophy properties, particularly by enhancing antioxidant activity and reducing the proinflammatory markers intracellularly. Andrographolide could upregulate Nrf2 myocardial infarction animal models [39]. Our previous research also showed the cardioprotective effect of dried A. paniculata ethanolic extract (APE) on doxorubicin-induced cardiotoxicity rats by reducing the mRNA expression of NLRP3 and NF-κB, further improving cardiac dysfunction [40,41]. Although APE reportedly has anti-inflammation and antioxidant effects, to the best of our knowledge, its potency in inhibiting hypertrophy of cardiomyocytes induced by angiotensin-II (Ang-II) through Nrf2/NLRP3/NF-κB signaling has not yet been conducted. Based on current conditions, we aimed to investigate APE potency in mitigating cardiac hypertrophy progression using Ang-II treated H9c2 cells and capture its possibility of molecular interaction and potential candidates of the drug from bioactive compounds screened through in silico analysis.
MATERIAL AND METHODS
Material plants extraction procedure
APE was previously donated and formulated by PT Konimex (Sukaharjo, Indonesia) as a condensed paste extract. A. paniculata leaves from Semarang, Central Java, Indonesia, and a voucher specimen was deposited in the Faculty of Pharmacy, Universitas Gadjah Mada. Dried leaves were crushed into fine particles. Furthermore, dried plant material (1 kg) underwent percolation with 10 l of 90% ethanol, and the solvent was evaporated at 60°C under vacuum, as described in our previous study [40,41]. The formed condensed paste extract did not further dry using a fluid bed granulator, as explained in our in-press report. This condensed paste did not undergo Andrographolide profiling; thus, qualitative phytochemical screening, gas-chromatography mass-spectrometry (GC-MS) profiling, and total triterpenoid analysis were done.
Cells, chemical, and reagent
The H9c2 (2-1) rat cardiomyoblast (ATCC0, CRL-1446, CVCL-0286) cell line was provided by the Faculty of Medicine, Universitas Padjajaran, located in Bandung, West Java. Dulbecco’s modified Eagle medium (DMEM 1.0 g/l, L-glutamine, and sodium pyruvate, Cat. No. 10-014-CV), Fetal Bovine Serum (FBS, US Approved, Cat. No. 35-011-CV), and penicillin-streptomycin (Cat. No. 30-002-CI) were purchased from Corning® (Arizona, USA). Ang-II (Ang-II, HY-13948), 2′,7′-dichlorodihydrofluorscein (DCFH2-DA, Cat. No. HY-D0940), and dihydroethidium (DHE, HY-D0079) were obtained from Medchem Express (New Jersey, USA). Irbesartan was acquired from PT. Combiphar, Indonesia. Furthermore, 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) (Vybrant® MTT Cell Proliferation Assay Kit, V-13154) and dimethyl sulfoxide (DMSO) were acquired from Thermo Fisher (Massachusetts, USA). Wright-Giemsa solutions (Cat. No. WG16) from Sigma-Aldrich® (Burlington, USA).
Qualitative phytochemical analysis
The extract’s qualitative profiling was initially conducted using reagents, followed by quantitative analysis using GC-MS. Qualitative phytochemical analysis was performed using Mayer, Dragendroff, Liebermann-Burchard, Braymer, Keller-Killiani, and Shinoda methods to identify various compounds, including alkaloids, steroids, terpenoids, saponins, flavonoids, and tannins, respectively.
Terpenoid total analysis
A. paniculata are known to be rich in diterpenoid content, especially Andrographolide. Terpenoid total analysis assesses the total terpenoid concentration contained in this extract. The procedure is performed in triplicate using a colorimetric-based assay with an ultraviolet Spectrophotometer at 551 nm and presented as mg/g of extract mass. APE’s optical density (OD) will be compared with ursolic acid as a standard concentration.
Quantitative phytochemical analysis: GC-MS
This GC-MS is performed using untargeted metabolomic attempts to analyze all detectable compounds, including unknown ones [42]. Subsequently, the APE was subjected to (GC-MS, Shimadzu GC-MS-QP2020) to analyze the phytoconstituents present in the extract. The sample was prepared by the analysis using a direct capillary column SH-Rxi-5Sil MS with dimensions of 30 m × 0.25 mm × 0.25 m thickness. The column oven temperature was initially set at 50°C for 5 minutes, followed by a temperature ramp of 5°C/minute up to 280°C, which was maintained for 20 minutes. The injector and MS transfer lines were held at 280°C and 250°C, respectively. Helium was the carrier gas with a 1 ml/minute column flow rate. The solvent cut time was set at 2 minutes, and the diluted samples were injected using Shimadzu’s AOC-20i/s Autosampler. Along with sample preparation using the autosampler, the sample was filtered and diluted the sample beforehand to test the dynamic range and instrument’s sensitivity results. Shimadzu Electron Ionization mass spectra were collected in scan mode within the m/z 33–600 range. Identified compounds were compared with NIST17 mass spectral databases using retention time (RT) and mass spectra. The results obtained from GC-MS analysis included RT, % area, and similarity index (SI). The RT is defined as the time taken for the compound to travel through the GC column from injection to the detector measured in one minute. SI represents the quantification aspect of how close the mass spectrum detected in the unknown extract to the mass spectral database, whereas %area determined how much the concentration of specific detected in the test. APE solvent becomes an internal standard to calibrate and ensure the results validity of GC-MS testing.
Cell culture
H9c2 cells are ventricular embryonic rat BDIX cardiomyoblasts frequently used in cardiac hypertrophy studies models [43–45], showing similar responses to primary neonatal cardiomyocytes [46]. As stated in our previous study [47], the H9c2 cells were cultured in DMEM (Cat. No. 11885084) supplemented with 10% FBS (Cat. No. 10438026 and 1% penicillin-streptomycin solution. The cells were maintained at 37°C in a humidified atmosphere containing 5% CO2. Culturing was performed in T25 flasks for 7 days, with media changed every 3 days. Passaging was conducted when the cells reached 70%–80% confluency.
Cell viability and toxicity assay
The cell viability assay was conducted using the MTT method, with a seeding density of 5,000 cells per well. The viability of H9c2 cells was assessed indirectly by evaluating their metabolic activity, which involves converting MTT to formazan crystals within the mitochondria. The assay aimed to determine the optimal and safest dosage of APE for use in the treatment.
H9c2 cells were treated with various concentrations of APE ranging from 3.9 to 125 μg/ml for 48 hours and compared with a group without APE to assess cell viability. Following treatment, the cells were rinsed with phosphate-buffered saline (PBS) and incubated with MTT reagent according to the manufacturer’s instructions. Subsequently, the formazan crystals’ OD was measured using a microplate reader at an absorbance of 570 Å after being dissolved with DMSO for 10 minutes of incubation.
The APE toxicity profile was evaluated by determining its half-maximal inhibitory concentration (IC50) and comparing it with doxorubicin (negative control) and Irbesartan (positive control). The results were then categorized using established criteria to assess APE safety and efficacy for potential therapeutic use [48]. APE’s OD was transformed into IC50 and calculated using the following formula:
Hypertrophy induction experiment
For experimental procedures, the cells were seeded into various types of culture plates: a 96-well plate for cell viability assays and cell-size area (CSA) determination, a six-well plate for mRNA analysis, and a 12-well plate for oxidative stress assays. Before induction, the cells underwent serum starvation for 24 hours.
Subsequently, the cells were divided into four experimental groups for treatment purposes: control group (Con), negative control group treated with Ang-II, positive control group treated with Irbesartan (Irbe), and experimental group treated with APE.
Cell-size area determination
CSA, a commonly used parameter in many previous studies, calculates the 2D area of the cell interface to investigate hypertrophy. Wright-Giemsa staining was employed to obtain clear cell images and accurate calculations. This routine staining method uses methylene blue and eosin to stain the nucleus and cytoplasmic contents of cells. The methods we conducted are modified from previous research using hematoxylin-eosin staining [49] and Giemsa staining [50].
After the cells were treated with Ang-II, with or without Irbesartan or APE, in 96-well plates, they were washed twice with ice-cold PBS. Subsequently, 100% ice-cold methanol was added to each well and allowed to air-dry. The cells were then stained with methylene blue for 30 minutes and washed with distilled water. Eosin staining was performed for another 30 minutes, followed by washing with distilled water. The stained cells were observed under an inverted microscope, and their CSAs were measured in five different regions in each group. A total of 150 cells, using five random 20× objective lens fields in each group, were calculated for their CSA using ImageJ® software by three blinded independent observers who conducted the measurements.
Oxidative stress parameters: DCFH2-DA and DHE assay
The DCFH2-DA and DHE are fluorescent probes that are routinely used to detect the generation of superoxide anions and hydrogen peroxide through microplate-based methods. A total of 20,000 cells per well in a 6-well plate for each group were trypsinized and washed with PBS three times. After centrifugation, the cells were incubated with DCFH-DA at 5 μM and DHE at 10 μM for 30 minutes at 37°C. Subsequently, the cells were washed again with PBS three times.
Following the incubation period, the OD in the fluorometry was measured at excitation/emission wavelengths of 492/517 nm for DCFH2-DA and 518/616 nm for DHE, respectively. The OD readings were then converted into fold changes of fluorescence intensity, normalized with the blank OD readings, and compared with those of the control group.
Quantitative real-time polymerase chain reaction
A total of 1 × 106 cultured cells per well for each group were subjected to total RNA extraction using the Direct Zol RNA MiniPrep Plus kit with TRI reagent (Zymo Research, USA, R2053). Subsequently, 500 μg of extracted RNA was reverse transcribed using the ReverTra Ace qPCR RT Master Mix (Toyobo BioTech, Japan, FSQ-301) according to the manufacturer’s instructions.
The relative gene expression levels of brain natriuretic peptide (BNP), AT1R, NLRP3, NF-?B, interleukin-1β (IL1B), Nrf2, and superoxide dismutase 1 (SOD1) were determined using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) with an SYBR Green PCR kit (Toyobo BioTech, Japan, QPS201). β-actin was used as a housekeeping gene for normalization. The fold-change expression of each gene was quantified using Livak’s method. Table 1 lists the primers used for RT-qPCR.
 | Table 1. List of primer. [Click here to view] |
Molecular docking analysis
Molecular docking was done using Molegro Virtual Docker (MVD, Molexus, Odder, Denmark) [51]. Docking was initiated to confirm the interaction between the detected metabolite, especially the most abundant one to Kelch-like ECH-associated protein 1 (Keap1) (a regulator protein of Nrf2), the ATP-binding domain within NLRP3 domain, and the inhibitor of kappa B kinase (IKK kinase) (a regulator protein of NF-?B). Before initiating the docking process, we will download the 3D structures of each ligand in.sdf format from the PubChem Website (https://pubchem.ncbi.nlm.nih.gov/). Additionally, receptor files for Keap1 (PDB ID: 4I7B), NLRP3 (PDB ID: 7ALV), and IKK kinase (PDB ID: 4KIK) will come from the RCSB website (https://www.rcsb.org/). Following optimization of the receptor structures, the native ligands will be re-docked to their respective positions, ensuring the validity of the docking process with a root mean square deviation (RMSD) threshold of less than 2 Å.
MVD employs both internal and external energy of bonds created by the ligand on the binding site. Each compound will be docked into the same binding coordinates, and their binding affinity will be evaluated using the MolDockScore (kJ/mol). A negative MolDockScore indicates a strong binding affinity of the ligand to the target protein, providing valuable insights into potential therapeutic interactions.
Chemoinformatic analysis: Molecular fingerprint using machine learning
MFP is part of chemoinformatics performed to predict bioactivity and liphophilicity metrics in specific compounds against protein targets using publicly available databases. This research uses data from Chemical European Molecular Biology Laboratory (CHEMBL) accessed through DataWarrior®. The IC50 and ligand efficiency lipophilicity (LELP) incorporate bioactivity with the compound’s physiochemical feature. SkelSpheres are used as molecular descriptors.
First, the Keap1 inhibitor dataset (CHEMBL Q14145; Uniprot K7E49), NLRP3 NACHT domain inhibitor (CHEMBL Q96P20; Uniprot), and IKK kinase inhibitor (CHEMBL O14920; O15111; Q9Y6K9; Uniprot) compounds were initially prepared using the CHEMBL database, which manually curates bioactive chemical molecules. Data containing IC50 values were extracted and separated. The clustering of compounds to determine testing and training data was performed using the SkelSpheres descriptor with a similarity score threshold of 0.9. The dataset was divided into representative and non-representative data. Representative data were designated for testing purposes, while non-representative data were allocated for training purposes. For predictive model validation, the IC50 values were transformed into Log IC50, and LELP was calculated as one important parameter in drug discovery, combining bioactivity with the physiochemical feature of the drug [52].
In the testing data, the Log IC50 values were considered the actual Log IC50 (aLog IC50) or actual LELP (aLELP) values. In contrast, the values predicted by the predictive model/machine learning were considered the predicted Log IC50 (pLog IC50) or predicted LELP (pLELP) values. To validate the Log IC50 and LELP prediction models, support vector regression (SVR) was employed for the data training. The models’ performance was evaluated by measuring the R-square (R2, R2 > 0.5) values and Pearson correlation coefficients (r) [53,54]. These metrics were calculated by comparing pLog IC50 or pLELP and aLog IC50 or pLELP values of the Keap1, IKK, and NLRP3 inhibitor compounds in the testing data. These steps are also done for the LELP parameter. After the system is validated, we input canonical the simplified molecular-input line-entry system of APE metabolites and then run the prediction through our built dataset. The IC50 of each compound will be categorized based on established criteria [48], whereas metabolites with ideal LELP are within the range −10 and +10 [52].
Computational pharmacokinetic and toxicity analysis
Physicochemical properties influence drug candidates’ administration, distribution, metabolism, excretion, and toxicities (ADMETs). In addition, drug-likeness (DL) prediction was performed using Lipinski’s Rule of Five [55] which assesses molecular properties related to drug absorption and permeation. SwissADME (https://swissadme.ch) and pKCSM (https://biosig.lab.uq.edu.au/pkcsm/prediction) were employed to predict DL and ADMET variables, respectively, by inputting canonical smiles. Compounds that do not violate Lipinski’s Rule of Five have undergone molecular docking and molecular fingerprint (MFP) analysis. For each detected compound, further toxicity predictions were made using Protox 3 (https://comptox.charite.de/protox3/index.php?site=compound_input).
Statistical analysis
All in vitro data collected were analyzed using GraphPad Prism 10 (GraphPad Software Inc.). Descriptive statistics were presented as mean ± SEM. Bivariate analysis was performed using analysis of variance followed by Bonferroni analysis for multiple comparisons. Statistical significance was determined at a p-value threshold below 0.05. This statistical analysis was also done for the validation methods of MFP for predicting the IC50 and LELP parameters for the entire compound using R2 and Spearman correlation the result will be provided in supplementary materials.
RESULTS
APE qualitative phytochemical, GC-MS, and terpenoid analysis
The qualitative phytochemical analysis identified tannin, flavonoid, terpenoid, and steroid using specific reagents in this extract (Table 2). The results were then confirmed through GC-MS (Fig. 1), and thirty metabolites were detected (Table 3). Among these thirty compounds, gamma-sitosterol (26.99%), Andrographolide (12.28%), Cortolone (11.0%), stigmasterol (10.05%), and 2,6,10,15,19,23-pentamethyl-2,6,18,22-tetracosatetraen-10,15-diol (PMT) (6.08%) become the most abundant metabolites in extract. Mass-spectral of each predominant compound present in the supplementary file. Our experiments identified the total terpenoid concentration in this extract reaching 52.1 ± 0.2% mg/mg.
 | Table 2. Phytochemical screening results. [Click here to view] |
 | Figure 1. GC-MS analysis of A. paniculata ethanolic extract (APE). [Click here to view] |
 | Table 3. List of compound in AP ethanolic extract detected in GC-MS. [Click here to view] |
Viability and cytotoxicity profile of APE in H9c2 cardiomyoblast
APE started to reduce the H9c2 viability even in the lowest tested dosage (3.9 μg/ml), as presented in Figure 2A. Using the exact APE dosage group, we tried adding Ang-II 1 μM to see the change in H9c2 viability (Fig. 2b). However, the changes are not different, and the reduction of viability was observed even in the lowest tested dosage of extract, with relatively similar IC50 (APE only vs. APE with Ang-II, 17.65 vs. 22.68 μg/ml), classified as strong cytotoxicity (10–100 μg/ml), based on established criteria [48] (Fig. 2c). The H9c2 viability is only reduced in the 2.5 μM (Fig. 2d) for Ang-II dosage determination. Regarding the cytotoxicity profile, doxorubicin has more cytotoxicity activity than APE, as demonstrated in this research, which reduces cell viability starting from 0.12 μM (Fig. 2e). Irbesartan 1 μM is the selected dosage for positive control based on a previous study and did not reduce cell viability (Fig. 2f) [56]. Based on our IC50 result above, we used approximately the lowest half of IC50 detected, becoming 9 μg/ml concentration of APE as tested dosage to find APE antihypertrophic activity. In addition, this dosage showed a minimum reduction of H9c2 viability.
 | Figure 2. Viability and cytotoxicity testing of H9c2 cells, (A) viability test of APE, (B) viability test of APE with Ang-II 1 μM, (C) cytotoxicity testing of APE and APE with Ang-II 1 μM, (D) viability testing of Ang-II, (E) viability testing of doxorubicin, and (F) viability testing of doxorubicin. Ang-II: angiotensin-II. (n = triplicate); APE = A. paniculata ethanolic extract; *p < 0.05. [Click here to view] |
The effect of APE on CSA and mRNA expression BNP and angiotensin receptor 1 (AT1R)
The H9c2 exposed by Ang-II significantly demonstrated increasing CSA and upregulation of BNP and AT1R mRNA expression compared to the control group. The APE significantly reduced the CSA of Ang-II treated H9c2 compared to the Ang-II group, even though the reduction is not much like the Irbesartan group (Fig. 3a and b, see supplementary file). Furthermore, this finding is also supported by the decrease in the expression of BNP (Fig. 3c) and AT1R in the APE group (Fig. 3d).
 | Figure 3. Hypertrophic testing results (A) Wright-Giemsa staining (B) cell-size area (C) BNP fold-change expression, and (D) AT1R fold-change expression; #p < 0.05 versus Ang-II, *p < 0.05 versus Ang-II+Irbesartan, and Ang-II+APE. AT1R = angiotensin-II reeptor 1; BNP = brain natriuretic peptide. (n = triplicate). [Click here to view] |
The effect of APE in superoxide anion and hydrogen peroxide production on Ang-II-treated H9c2 cells
The Ang-II-treated H9c2 showed significantly increased intracellular superoxide anion and hydrogen peroxide production. Superoxide anion (Fig. 4a) and hydrogen peroxide (Fig. 4b) were markedly reduced after the cells were treated with APE (p < 0.05), even though for reactive oxygen species (ROS), the reduction was not as much as the Irbesartan group.
 | Figure 4. Oxidative stress parameters (a) relative superoxide anion level (DCFHDA), (b) relative hydrogen peroxide level (DHE), (c) Nrf2 mRNA fold-change expression, and (d) SOD1 mRNA fold-change expression; #p < 0.05 versus Ang-II, *p < 0.05 versus Ang-II+Irbesartan, and Ang-II+APE. DCFH2-DA = 2’,7’-dichlorodihydrofluorscein; DHE = dihydroethidium. (n = triplicate). [Click here to view] |
The effect of APE in the Nrf2 and SOD1 the mRNA expression
Following the Ang-II exposure, Nrf2 and SOD1 mRNA expression were significantly suppressed in Ang-II-treated H9c2 cells (p < 0.05). Furthermore, the APE reverted the mRNA expression of both genes significantly (p < 0.05), comparable to Irbesartan, which shows that the APE recovered the antioxidant defense mechanism (Fig. 4c and d).
The effect of APE in the NF-?B, NLRP3, and IL-1β the mRNA expression
Ang-II exposure to H9c2 in this study showed upregulation of intracellular NF-?B, NLRP3, and IL-1β mRNA expression. Furthermore, the APE reverted the mRNA expression of NF-?B (Fig. 5a), NLRP3 (Fig. 5b), and IL-1β (Fig. 5c) in Ang-II-treated H9c2 significantly (p < 0.05) and comparable with Irbesartan showing that the APE activity in modulating the proinflammation signaling.
 | Figure 5. Proinflammation parameters, (a) Nf-?B mRNA fold-change expression, (b) NLRP3 mRNA fold-change expression, and (c) IL-1β mRNA fold-change expression; #p < 0.05 vsersu Ang-II, *p < 0.05 versus Ang-II+Irbesartan, and Ang-II+APE. IL-1β = Interleukin-1β; Nf-?B = Nuclear Factor-Kappa B; NLRP3 = NLR family pyrin domain-containing 3. (n = triplicate). [Click here to view] |
DL properties of secondary metabolites detected in APE
DL properties reflect characteristics of molecules that indicate their suitability to become safe and effective drugs. We used Lipinski’s Rule of Five, which consists of the number of molecular weights no more than 500 Da, Hydrogen acceptor below 10, Hydrogen donor below 5, and CLogP less than 4.15 through SwissADME prediction and summarized the results for entire secondary metabolites in Table 4. Since 29 metabolites have good drug-like properties, except tetrapentacone, they will further continue molecular docking and fingerprint analysis.
 | Table 4. Druglikeness feature of entire metabolites. [Click here to view] |
Predominant metabolites in APE have negative energy binding to the Keap1, IKK, and NLRP3 in molecular docking
Before performing molecular docking, the protein quality is checked. We strictly use.pdb files with Resolution <3 Å and good Ramachandran criteria for each.pdb file. Since the selected.pdb files have their native ligands, the redocking methods were used to check the validity, which is proven by the RMSD <2 Å for Keap1 (Fig. 6a), IKK (Fig. 6b), and NLRP3 (Fig. 6c). As present in our in vitro model, Irbesartan could modulate mRNA expression of NF-?B, NLRP3, and Nrf2. Hence, we also used Irbesartan as a standard drug in molecular docking and compared its MolDock and Rerank Score to the detected metabolites. The exact direction of the binding site of each protein is summarized in Table 5. Among the 30 compounds docked to Keap1, IKK, and NLRP3, PMT has the lowest MolDock and Rerank Score compared to other metabolites detected in GC-MS (see supplementary materials). Irbesartan shows low binding energy against IKK and NLRP3 as a positive control but not for Keap1. In this study, PMT showed much lower binding energy than the other molecules found in the APE. Furthermore, the entire metabolite showed a negative MolDock Score and Rerank Score, as well as the five predominant metabolites to the Keap1, IKK, and NLRP3 that judge their spontaneous binding to the active site of the corresponding protein (Table 6).
 | Figure 6. Validation of redocking in molecular docking (A) Keap1 redocking, (B) IKK redocking, and (C) NLRP3 redocking, which shows RMSD <2 Å. IKK = inhibitor of Kappa B kinase; Keap1 = Kelch-like ECH associated protein 1; NLRP3 = NLR family pyrin domain-containing 3 (NLRP3) inflammasome; RMSD = root mean square deviation. [Click here to view] |
 | Table 5. Optimal binding site of Keap1, IKK Kinase, and NLRP3 protein. [Click here to view] |
 | Table 6. Molecular docking results of predominant compound to Keap1, IKK, and NLRP3. [Click here to view] |
As the lowest ligand to interact with Keap1, the hydrogen bond site created by PMT (Arg415 and Ser602) is different from Irbesartan (Asn387 and Ser555), but similar to the native ligand, but not for other predominant compounds. Stigmasterol had more hydrogen bonds than gamma-sitosterol, Andrographolide, and Cortolone. Furthermore, even Cortolone has higher Moldock and Rerank scores than other predominant compounds. It interacts with binding sites through hydrogen bonds with almost similar amino acids with native ligands at Ser602 (Fig. 7).
 | Figure 7. Two and three-dimensional visualization of the interaction of the five selected compounds to the Keap1 protein binding site (A) stigmasterol, (B) cortolone, (C) gamma-sitosterol, (D) PMT, and (E) andrographolide. Keap1 = Kelch-like ECH associated protein 1; PMT: 2,6,10,15,19,23-Pentamethyl-2,6,18,22-tetracosatetraen-10,15-diol. [Click here to view] |
Different interactions with IKK protein, even PMT having the lowest binding energy and the amino acid contributing to the hydrogen bond (Asp166, Lys44), are different from those of the native ligand. Cortolone has higher binding energy and more hydrogen bonds than other predominant compounds through Glu97, Cys99, and Glu149 (Fig. 8).
 | Figure 8. Two and three-dimensional visualization of the interaction of the five selected compounds to the IKK protein binding site (A) andrographolide, (B) gamma-sitosterol, (C) cortolone, (D) stigmasterol, and (E) PMT. IKK = inhibitor of Kappa B kinase; PMT: 2,6,10,15,19,23-Pentamethyl-2,6,18,22-tetracosatetraen-10,15-diol. [Click here to view] |
PMT also showed lower binding energy with NLRP3 than Irbesartan (−161.702 kJ/mol) through hydrogen bonds formed through Arg578. Interestingly, Andrographolide formed hydrogen bonds with five amino acids in the NLRP3 active site (Ala228, Arg351, Pro352, Val353, and Asp662). In addition, Cortolone shows three hydrogen bonds through Thr439, Arg578, and Glu629 amino acids (Fig. 9).
 | Figure 9. Two and three-dimensional visualization of the interaction of the five selected compounds to the NLRP3 kinase protein binding site. (A) andrographolide, (B) cortolone, (C) stigmasterol, (D) PMT, and (E) gamma-sitosterol. NLRP3 = NLR family pyrin domain-containing 3; PMT = 2,6,10,15,19,23-pentamethyl-2,6,18,22-tetracosatetraen-10,15-diol. [Click here to view] |
Predominant metabolites in APE have good predicted bioactivity and LELP to the Keap1, IKK kinase, and NLRP3
The Keap1, IKK, and NLRP3 inhibitor data retrieved from DataWarrior® are 1721, 78, and 908 data, respectively. After data cleansing, including omitting the non-IC50 and blank data, it resulted in 306, 59, and 527 compounds for Keap1, IKK, and NLRP3 inhibitors. The data then split based on the similarity (0.9) yielded 76, 15, and 241 representative compounds for each included protein. The non-representative data will be used as training. The data validation results were summarized in Figure 10 for each dataset of proteins. R2 and Spearman correlation were employed to determine data validation and showed our SVR prediction that each Log IC50 and LELP dataset has R2 above >0.5 and Spearman correlation >0.5, which shows good correlation and data linearity as described in a previous publication [53,54]. Details of each compound found in APE with their predicted IC50 and LELP against Keap1, IKK, and NLRP3 are summarized in our electronic data supplementation.
 | Figure 10. Validation of data training of compound present in ChEMBl database for MFP analysis prediction in Log IC50 and LELP. (A) Keap1 Log IC50, (B) IKK Log IC50, (C) NLRP3 Log IC50, (D) Keap1 LELP, (E) IKK LELP, and (F) NLRP3 LELP, which showing R2 > 0.5 and r > 0.5. IC50 = half maximal inhibitory concentration; IKK = inhibitor of Kappa B kinase; Keap1 = Kelch-like ECH associated protein 1; LELP = ligand efficiency liphophilicity; NLRP3 = NLR family pyrin domain-containing 3. [Click here to view] |
Based on the SkelSpheres descriptor of predominant detected compounds, the pIC50 of each compound has good activity (1–20 μM), except PMT, which shows excellent activity (0.2 μM) against Keap1 protein. Despite having good bioactivity, only Andrographolide and Cortolone showed an ideal LELP value (below 10) against Keap1, IKK, and NLRP3 (Table 7).
 | Table 7. MFP analysis of IC50 and LELP of predominant cmpound against Keap1, IKK, and NLRP3. [Click here to view] |
Computational prediction of pharmacokinetics and toxicity of predominant compound
Table 8 summarizes the pharmacokinetic and toxicity profiles for all compounds. Andrographolide and Cortolone exhibit good oral absorption with low blood–brain barrier penetration. Regarding metabolism, PMT inhibits CYP1A2 and CYP3A4, while stigmasterol inhibits CYP2D6. The remaining compounds show no significant interactions with CYP enzymes. Regarding excretion, Andrographolide and Cortolone are P-glycoprotein substrates, whereas the other three compounds are not, suggesting they cannot clear effectively through the liver or kidneys.
 | Table 8. ADMET properties of predominant compound. [Click here to view] |
In terms of toxicity, stigmasterol and gamma-sitosterol have a high predicted acute toxicity level, with both showing an LD50 of 890 mg/kgbw. Andrographolide has an LD50 of 1,890 mg/kgbw, Cortolone 3,000 mg/kgbw, and PMT 5,000 mg/kgbw. Most compounds are not expected to cause hepatotoxicity, nephrotoxicity, cardiotoxicity, mutagenicity, carcinogenicity, or cytotoxicity, except for Andrographolide and Cortolone, which show predicted cardiotoxic activity.
DISCUSSION
Our current research demonstrates that Ang-II at 1 μM induces hypertrophy in H9c2 cells, characterized by increased CSA and elevated BNP and AT1R mRNA levels. This hypertrophic response was accompanied by activation of intracellular proinflammatory pathways, evidenced by upregulated mRNA expression of NLRP3, NF-?B, and IL-1β. Concurrently, we observed a redox imbalance with elevated ROS and H2O2 levels and reduced mRNA expression of Nrf2 and SOD1. Treatment of Ang-II-exposed H9c2 cells with APE reduced CSA and hypertrophic marker expression, effectively suppressing the NLRP3/NF-?B/IL-1β pathway. Additionally, APE restored redox balance by upregulating Nrf2 and SOD1 mRNA expression comparable to Irbesartan, which also exhibitted anticardiac hypertrophy, antioxidant, and anti-inflammation activity in similar pathways [57,58]. To the best of our knowledge, this study is the first to report the efficacy of APE in mitigating cardiac hypertrophy via the NLRP3/NF-?B/IL-1β pathway and alleviating oxidative stress through Nrf2 signaling.
Previous studies using cardiac hypertrophy and doxorubicin cardiac injury models emphasize that whole A. paniculata extract mitigates cardiac disease by suppressing the proinflammation and oxidative stress pathways [23,24,40,59]. Furthermore, our previous studies confirmed that dried APE containing 8.98% Andrographolide reduces serum BNP and cardiac troponin I levels in vivo doxorubicin rat models [41]. We consider APE’s abilities to inhibit hypertrophy progression of H9c2 through modulating Nrf2/NF-?B/NLRP3 in this research as part of proving its mechanism to modulate proinflammation and oxidative stress pathways.
A. paniculata contains various bioactive compounds such as terpenoids, flavonoids, alkaloids, steroids, tannins, and saponins. The presence of these compounds can vary based on the plant part [60], geographic location [61], extraction solvents [62], and methods used [60]. Our analysis of APE identified terpenoids, flavonoids, steroids, and tannins. However, alkaloids, saponins, and glycosides were not detected, which differs from previous studies that suggest alkaloid, saponin, and glycoside presence [62]. Notably, Andrographolide, a diterpene lactone, was the second most abundant compound (12.28% by GC-MS analysis). Despite the abundant terpenoid content in this extract, limitations of GC-MS in detecting high molecular weight and highly polar compounds prevented the identification of other terpenoid forms, such as neoandrographolide, 14-deoxyandrographolide, and andrographanin [9,60]. Future studies may benefit from using alternative methods, such as nuclear magnetic resonance spectroscopy or high-performance liquid chromatography, particularly in identifying new compounds like PMT, to validate these findings.
Inflammation and oxidative stress are closely linked to cardiac remodeling [32,35]. Chronic ROS elevation can initiate inflammation in HF, exacerbated by neurohumoral activation, such as Ang-II stimulation of cardiomyocytes [26]. Ang-II activates NF-?B, a pivotal mediator that senses increased intracellular ROS production and primes the NLRP3 inflammasome signaling pathway, resulting in IL-1β mRNA expression [35]. In our Ang-II-treated H9c2 cells, activation of NF-?B (p50), NLRP3, and IL-1β was observed, consistent with prior research. Under normal conditions, Nrf2 responds to ROS-induced cellular stress by dissociating from Keap1, binding to antioxidant response elements (AREs), and upregulating intrinsic antioxidant mRNA transcription. However, NF-?B binding to the ?B site can inhibit Nrf2-ARE signaling, further downregulating antioxidant mRNA transcription [36]. Additionally, exceeding the capacity of this signaling pathway to counteract redox imbalance may help downregulate Nrf2 and SOD1 presented in our Ang-II-treated H9c2 cells.
In our study, APE effectively attenuated Ang-II-induced H9c2 cell hypertrophy by suppressing proinflammatory signaling through NF-?B/NLRP3/IL-1β. Nrf2 can suppress the NLRP3 signaling by reducing ROS and inducing anti-inflammation protein, NADPH Quinone Dehydrogenase-1 that directly inhibits NLRP3 priming [33,34], which becomes one probable reason caused by the increase of Nrf2 mRNA expression and decrease of NLRP3 mRNA expression. In this situation, NF-κB downregulation is also involved, as NLRP3 activation is initiated by priming signaling from NF-?B. Previous studies have shown that APE increases Nrf2/ARE signaling [63,64] and reduces NF-?B/NLRP3/IL-1β [65] signaling in non-cardiovascular disease models. Our study is the first to demonstrate enhanced Nrf2/ARE signaling in cardiac hypertrophy using APE. Although we did not directly compare APE with isolated Andrographolide in terms of antihypertrophic [66,67], antioxidant [16,39,68], and anti-inflammatory [69] effects, previous research suggests that whole extract formulations often exhibit superior therapeutic effects, possibly due to synergistic interactions among multiple compounds. The above phenomenon may explain the potential synergistic effects of various compounds in APE, enhancing its pharmacological efficacy beyond that of isolated constituents [70,71].
To predict the mechanism and potential synergistic effects of compounds identified in APE in modulating the NF-?B/NLRP3/IL-1β axis and Nrf2/ARE, we next conducted in silico analyses. Our findings demonstrate that the entire compound mixture, particularly the five most abundant compounds—Gamma-sitosterol, Andrographolide, Cortolone, Stigmasterol, and PMT—spontaneously interact with the binding sites of IKK, NLRP3, and Keap1, exhibiting significant bioactivity as indicated by SkelSpheres molecule identifier. Notably, these compounds form strong hydrogen bonds with each protein they dock with. PMT had the most negative binding energy in molecular docking. PMT also exhibits hydrogen bond interactions like native ligands in Keap1 through Arg415 and Ser602 and NLRP3 through Arg578. The other four predominant compounds, which show higher binding energies than PMT, interact differently with the binding sites, similar to native ligands. This research is the first documented report on PMT’s potential to modulate NLRP3, NF-κB, and Nrf2 signaling pathways through in silico analysis.
Andrographolide and Cortolone can bind spontaneously to Keap1, IKK, and NLRP3. Even though Andrographolide exhibits different hydrogen bond interactions with IKK and Keap1, it forms five hydrogen bonds like native ligands with the NLRP3 NACHT domain, suggesting that Andrographolide, in nature, may interact spontaneously with NLRP3. Previous in vivo studies confirm Andrographolide’s inhibition of NLRP3 inflammasome signaling and its anti-inflammatory properties by suppressing NF-κB activation [72–74].
Additionally, Cortolone, Stigmasterol, and gamma-sitosterol are phytosterols, plant-derived steroids. Preclinical studies frequently report that dietary phytosterols exert anti-inflammatory and antioxidant effects. In our in silico models, these compounds demonstrate spontaneous binding and predict significant bioactivity. For example, β-sitosterol, a derivative of gamma-sitosterol, modulates the PPARγ/NF-κB signaling pathway in myocardial ischemia/reperfusion injury. Studies also indicate that phytosterols activate several antioxidant enzymes, including catalase (CAT), SOD, glutathione , and glutathione Peroxidase [75].
Drug discovery involves comprehensive analysis, encompassing factors such as candidates’ physicochemical properties. Optimization extends beyond bioactivity to include pharmacokinetic properties, which are crucial for optimizing dosing strategies and identifying toxicity risks, particularly in clinical scenarios like HF [52]. Different compounds exhibit varying pharmacokinetic properties from absorption to excretion, predicted using computational tools such as SwissADME and Protox. MFP analysis, including LELP, guides the selection of lead compounds based on potency (IC50), physicochemical properties (lipophilicity, CLogP), and ADMET profile. Our findings highlight Andrographolide and Cortolone as drug-like candidates with favorable ADMET and LELP properties, despite predictions of potential cardiotoxicity. However, our findings did not directly compare the potency between APE and lead compounds in inhibiting hypertrophy. These results mainly suggest that the most five common compound showing good binding activity, as it present negative results of MolDock Score, but Andrographolide and Cortolone, even they did not become the most compound to have negative binding energy, the possessed good pIC50 and LELP. Regarding the similarity of binding mechanism. Despite the facts that previous study much discuss on how Andrographolide promotes NF-κB inhibition, somehow, our in silico studies did not reflect that as Andrographolide bind IKK with different site of hydrogen bond. Furthermore, it is interesting to see that andrographolide interact in the same binding site with native ligand of NLRP3 on Arg351. This might becoming a clue that Andrographolide interact directly with NLRP3 as it also possessed antiinflammation effect, which should be proven especially using enzyme-assay test.
Among all compounds detected, Andrographolide is the most suitable drug candidate that warrants being tested in clinical settings, as a lot of evidence from preclinical research has suggested it promotes anticardiac hypertrophy [39,66,67,76–79], has known pharmacokinetic [15], and drug-herbal interaction profile in previous studies [80]. In our in silico studies, Andrographolide showed good bioactivity to enhance Nrf2 proteins and inhibit the NF-?B and NLRP3 signaling with ideal LELP and ADMET properties. Cortolone also shows a similar feature in our study, even though its utility has not been yet explicitly discussed in many previous studies. In addition, despite the remaining compounds showing unfavorable physiochemical aspects of being a drug candidate, their use as an HF drug candidate is also potentially modified using certain strategies, especially PMT, a novel compound in this study. Drugs with high molecular weight and low absorption could potentiate their oral bioavailability by chemical modification or specific absorption enhancers using nanoparticle technology [81,82].
CONCLUSION
In conclusion, our study reveals that APE effectively inhibits hypertrophy in Ang-II-treated H9c2 cells by reducing inflammatory responses and restoring redox balance through the Nrf2/NLRP3/NF-κB signaling pathways. Ang-II exposure led to increased CSA, elevated BNP and AT1R mRNA levels, and activation of proinflammatory markers, accompanied by oxidative stress indicated by elevated ROS and decreased Nrf2 and SOD1 mRNA expression. APE treatment mitigated these effects in a way comparable to Irbesartan, demonstrating its unique ability to suppress the inflammatory pathway while enhancing antioxidant gene expression. It should be noted that successive solvation might give better results as it aims to find a more specific fraction of this extract, which will be planned in future studies.
Notably, entire five compounds, gamma-sitosterol, andrographolide, cortolone, stigmasterol, and PMT, were found to have good predicted molecular binding and bioactivity against the Nrf2/NLRP3/NF-κB signaling pathway to alleviate hypertrophy. Furthermore, among those five compounds, Andrographolide and Cortolone found in this APE become suitable drug candidate supported by in silico analyses which highlight their ideal interactions and bioactivity with key signaling proteins, added with good pharmacokinetic profiles. These results warrant further in vivo validation, such as enzyme assay studies, and exploring further therapeutic potential compounds found in this extract before further translating to the clinical scenario.
AUTHOR CONTRIBUTIONS
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. All the authors are eligible to be an author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.
ACKNOWLEDGMENT
We thank Mrs. Annisa, Wita, and Ajeng for providing technical assistance and PT Konimex Indonesia for affording the APE. We also thank Mas Rizky Anggun Adipurna Syamsunarno, MD, PhD, from UNPAD for gifting the H9c2 cells, Prof. Septelia Inawati and Sherin Saheera, MD, P.hD that has give significant contributions in research idea and technical conception, and Oluebebe Eziefule Magnificient for proofreading and correcting our manuscripts.
FINANCIAL SUPPORT
The entire research was supported by the PUTI Q2 2024 Grant from Universitas Indonesia, with a number NKB-658/UN2.RST/HKP.05.00/2024.
CONFLICTS OF INTEREST
The authors report no financial or any other conflicts of interest in this work.
DATA AVAILABILITY
The mass-spectral, original in silico data and full histology image are available in Supplementary Materials. Further inquiries of other in vitro data can be achieved with clear reason by directly contacting the corresponding author.
ETHICAL APPROVALS
This study does not involve experiments on animals or human subjects.
DATA AVAILABILITY
All data generated and analyzed are included in this research article.
PUBLISHER’S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.
USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY
The authors declares that they have not used artificial intelligence (AI)-tools for writing and editing of the manuscript, and no images were manipulated using AI.
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SUPPLEMENTARY MATERIAL:
The supplementary material can be accessed at the journal’s website: Link here [http://japsonline.com/Suppl-material-10.7324JAPS.2025.226923.rar].