NJ026-0404

HPLC Quantification and In Silico Analysis of Polyphenols from Moringa oleifera Targeting FPR2 in Epithelial Ovarian Cancer 

Chang-Dae Lee1 and Sanghyun Lee1,2*

1Department of Plant Science and Technology, Chung-Ang University, Anseong 17546, Republic of Korea. 2Natural Product Institute of Science and Technology, Anseong 17546, Republic of Korea.

Correspondence to: Sanghyun Lee, slee@cau.ac.kr

Received: April 6, 2026; Revised: April 30, 2026; Accepted: May 6, 2026; Published: May 7, 2026

NATPRO J. 2026, 3, 9-16 

https://doi.org/10.23177/NJ026.0404 

Copyright ©  The Asian Society of Natural Products

Abstract

This study investigated the polyphenolic composition of Moringa oleifera and evaluated its potential biological relevance for epithelial ovarian cancer (EOC) using an integrated analytical and computational approach. High-performance liquid chromatography (HPLC) analysis revealed that (+)-catechin and quercetin 3-glucuronide were the major compounds, with concentrations of 32.12 mg/g and 19.14 mg/g, respectively. Network pharmacology and molecular docking were employed to predict the molecular targets and elucidate the pharmacological mechanisms of these compounds in the context of EOC. The results suggested that these compounds may interact with formyl peptide receptor 2 (FPR2), a key receptor highly implicated in EOC pathogenesis and inflammation-related signaling pathways. Both (+)-catechin and quercetin 3-glucuronide exhibited strong binding affinities to FPR2 in docking simulations, with scores of –8.7 and –10.7 kcal/mol, respectively, indicating possible involvement in modulating FPR2-related biological processes in EOC. These findings provide basic information on the polyphenolic profile of M. oleifera and suggest its potential as a source of bioactive compounds with potential applications to EOC-related biological processes. However, further experimental studies are required to validate these observations.

Keywords

epithelial ovarian cancer, high-performance liquid chromatography, molecular docking, quercetin 3-glucuronide

Introduction

Epithelial ovarian cancer (EOC) is the most common form of ovarian cancer, accounting for over 95% of ovarian malignancies [1]. EOC is characterized by an insidious onset and a lack of early symptoms, often leading to late-stage diagnosis in most patients [2]. Although the exact etiology of EOC remains unclear, its pathogenesis is widely attributed to a multifactorial interplay of risk factors, including advanced age, early menarche, late menopause, obesity, and smoking [3]. Evaluation of ovarian masses typically involves clinical assessment, imaging, and tumor marker tests to assess the risk of malignancy, followed by histological confirmation of cancer [4–6]. Treatment strategies are determined based on patient characteristics and tumor stage/histology and commonly include surgical debulking and systemic chemotherapy, with or without targeted therapies such as bevacizumab, PARP inhibitors, and immunotherapy [7]. However, despite these advances, EOC remains associated with high recurrence rates, chemoresistance, and treatment-related toxicity, underscoring the urgent need for alternative therapeutic approaches. Novel approaches also involve neoadjuvant therapy, interval debulking, and heated intraperitoneal chemotherapy. The management of EOC is inherently complex and typically involves a combination of pharmacologic and non-pharmacologic interventions aimed at achieving remission, minimizing complications, and improving quality of life [8,9]. This clinical reality necessitates the exploration of novel therapeutic agents, and natural products, with their vast chemical diversity and multi-target capability, represent a promising frontier for developing effective and less toxic adjunct therapies.

Moringa oleifera is an ancient medicinal plant cultivated in tropical and subtropical regions, particularly in countries such as India, Pakistan, and Nepal [10]. Rich in polyphenols, phenolic acids, flavonoids, and isothiocyanates, M. oleifera exhibits a broad spectrum of bioactive properties, including anticancer, antibacterial, anti-inflammatory, and antioxidant effects [11,12]. Often referred to as the “miracle tree,” M. oleifera has traditionally been used to treat conditions such as asthma, diabetes, skin infections, and hypertension [13,14]. Previous studies have demonstrated that M. oleifera inhibits lipopolysaccharide (LPS)-induced inflammation in RAW 264.7 cells by suppressing cytokines such as TNFα, IL-8, and IL-6, and by modulating the NF-κB signaling pathway [15]. In a previous study, screening of M. oleifera leaf extracts revealed that (+)-catechin, ellagic acid, and quercetin 3-glucuronide are among the most predominant phytochemicals [16]. These three compounds were specifically selected as reference markers for the current study based on their high relative abundance in the M. oleifera leaf extract, their structural representation of diverse major polyphenol subclasses, and their well-documented anti-inflammatory and multi-target capabilities [14–16]. Given the growing recognition of polyphenols as modulators of cancer-related signaling pathways, these compounds may also hold relevance for EOC therapy, although direct evidence remains limited.

This study investigates the phytochemical profile of M. oleifera as a source of bioactive flavonoids relevant to EOC treatment, with a particular focus on quantifying (+)-catechin, ellagic acid, and quercetin 3-glucuronide. It also examines the pharmacological relevance of (+)-catechin and quercetin 3-glucuronide using network pharmacology and molecular docking analyses. A key focus is their potential interactions with formyl peptide receptor 2 (FPR2), a receptor implicated in EOC-related inflammatory pathways. Growing evidence has revealed a strong association between FPR2 and EOC pathogenesis [17]. FPR2 is significantly overexpressed in EOC tissues and is positively correlated with several clinicopathological parameters, including FIGO stage, histological grade, and tumor subtype. Through this integrated approach, the study aims to provide preliminary evidence supporting the therapeutic potential of M. oleifera and to identify novel plant-derived compounds for use in complementary EOC therapy.

Materials and methods

Plant materials and extraction

M. oleifera was cultivated and identified by Dr. Chun Geon Park at Hwaseong Agro-Farm (Hwaseong, Korea). Ten grams of dried M. oleifera leaves (MOL) were ground and extracted with 50 mL of 70% EtOH at 37 °C for 24 hours in a darkroom. This extraction process was repeated three times [16]. The pooled extract was concentrated using a rotary vacuum evaporator at 50 °C and then dissolved in distilled water prior to use. The final extract yield was approximately 5%–6%.

Instruments and reagents

High-performance liquid chromatography (HPLC) analysis was performed using a Waters Alliance 2695 Separations Module (Waters, Milford, MA, USA) equipped with a photodiode array (PDA) detector (Waters 996 PDA Detector, Milford, MA, USA), a pump, and an auto-sampler. Separation was carried out using an YMC Pack Pro C18 column (4.6 × 250 mm, 5 μm). HPLC-grade solvents, including water, acetonitrile (ACN), trifluoroacetic acid (TFA), and MeOH, were purchased from J.T. Baker (Phillipsburg, New Jersey, USA). EtOH was obtained from Samchun Chemicals (Pyeongtaek, Korea). Standard compounds, including (+)-catechin (1), ellagic acid (2), and quercetin 3-glucuronide (3), were provided by the Natural Product Institute of Science and Technology (www.nist.re.kr), Anseong, Korea (Figure 1).

HPLC/PDA conditions

Quantitative analysis of MOL extract was performed using an HPLC system equipped with a YMC Pack Pro C18 column (4.6 × 250 mm, 5 μm), following previously reported methods [18,19]. The sample was dissolved in MeOH at a concentration of 10 mg/mL. Gradient elution was carried out using 0.1% TFA in water (solvent A) and ACN (solvent B) under the following conditions: 0–10 min, 83% A; 25 min, 76% A; 40 min, 40% A; 41 min, 0% A; 45 min, 0% A; 50 min, 83% A; and 60 min, 83% A. The column temperature was maintained at 35 °C. The injection volume was 10 μL, and the flow rate was set at 1.0 mL/min. UV detection was performed at 270 nm.

Calibration curve

Standard solutions were prepared by dissolving each reference compound in MeOH at a concentration of 1 mg/mL. Working solutions for the calibration curve were prepared by serial dilution of the stock solutions to the desired concentrations. All standard and sample solutions were filtered through a 0.45 μm polyvinylidene fluoride membrane prior to analysis. The concentrations of each compound in the MOL extract were quantified using calibration curves based on peak area (Y) versus concentration (X, µg/mL). Data are expressed as mean ± standard deviation (SD) (n = 3) (Table 1).

Identification of target genes and overlap with disease targets

Potential protein targets for molecular docking were predicted using the SwissTargetPrediction tool (http://www.swisstargetprediction.ch) [20] and the SuperPred web server (https://prediction.charite.de) [21], in combination with the GeneCards database (https://www.genecards.org) [22]. SwissTargetPrediction utilized structural similarity based on SMILES input, while SuperPred applied machine learning to predict protein targets and disease associations. The GeneCards database was queried using the keyword “EOC” to retrieve disease-associated genes. Predicted protein targets were compared with EOC-related genes using Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) [23] to identify overlapping, disease-relevant targets.

Protein-protein interaction (PPI) network analysis

PPI analysis was performed using the STRING database (https://string-db.org/) [24], with Homo sapiens selected as the reference organism. Interaction datasets were filtered using a high-confidence score threshold (≥ 0.4), and both predicted and experimentally validated interactions were included. The resulting network was analyzed to identify hub proteins based on centrality metrics, including degree, betweenness, and clustering coefficients.

Key target screening

To facilitate further analysis and visualization, the interaction networks were imported into the Cytoscape software (Version 3.10.2; https://cytoscape.org/) [25] via the STRING plugin, using a confidence score threshold of 0.4. Centrality metrics (degree, betweenness, and closeness) were calculated using the NetworkAnalyzer tool. Proteins with the highest values in these parameters were identified as key regulatory nodes within the network. To ensure a systematic and unbiased prioritization of therapeutic targets, proteins were ranked based on these topological parameters, with a particular emphasis on betweenness centrality. Betweenness centrality is a crucial metric for identifying "bottleneck" nodes that control the flow of information between different biological modules. Targets exhibiting the highest betweenness centrality, along with above-median degree and closeness values, were identified as core regulatory hubs and selected for downstream validation.

Gene ontology (GO) and pathway enrichment analysis

To estimate lipid peroxidation, we quantified the Gene function and pathway enrichment analyses were performed using the Metascape web platform (https://metascape.org/) [26]. Statistically significant terms were identified using the hypergeometric test with Benjamini-Hochberg p-value correction across multiple databases, including GO, Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Molecular Signatures Database (MSigDB). Enriched terms were automatically grouped into non-redundant clusters based on a Kappa similarity score (threshold 0.3), with each cluster represented by the most significant term (lowest p-value).

In silico absorption, distribution, metabolism and excretion (ADME) prediction

The pharmacokinetic profiles of (+)-catechin (1) and quercetin 3-glucuronide (3) were predicted using the SwissADME web tool (http://www.swissadme.ch/) [27]. The SMILES notations of both compounds were retrieved from PubChem and submitted to SwissADME to evaluate their physicochemical properties, lipophilicity, water solubility, oral bioavailability, and drug-likeness.

Molecular docking simulation

Molecular docking was performed following a modified version of a previously reported protocol to evaluate the binding interactions between (+)-catechin (1), quercetin 3-glucuronide (3), and the target receptor FPR2 [28]. The docking simulations were executed using AutoDock Vina (version 1.2.1; https://vina.scripps.edu/) [29] integrated within the AMDock Tools (version 1.5.2; https://github.com/Valdes-Tresanco-MS/AMDock-win) graphical interface [30]. The 3D structures of the ligands were downloaded in PDB format from the PubChem database (https://pubchem.ncbi.nlm.nih.gov), while the crystal structure of FPR2 (PDB ID: 6LW5) was retrieved from the RCSB Protein Data Bank (www.rcsb.org). In accordance with standard AutoDock Vina protocols, the receptor was treated as rigid, while the ligands were allowed full conformational flexibility. To specifically target the known binding pocket of the FPR2, the grid box was defined with dimensions of 23 Å × 23 Å × 23 Å. The Vina search parameters were set to an exhaustiveness of 8, and a maximum of 10 binding poses were generated per run. The docked conformations were evaluated using the Vina empirical scoring function, and the pose with the lowest predicted binding energy (kcal/mol) was selected as the most probable binding mode. Redocking of the native ligand was performed to validate the docking protocol, with a root-mean-square deviation (RMSD) of < 2.0 Å considered indicative of acceptable accuracy [31]. Visualization of docking interactions was performed using PyMOL (www.pymol.org).

Results & Discussion

HPLC/PDA analysis of the MOL sample revealed significant amounts of (+)-catechin (1) and quercetin 3-glucuronide (3), whereas ellagic acid (2) was detected only in trace quantities (Table 2 and Figure 2). The UV spectra of the detected peaks in the MOL extract were extracted from the PDA data and confirmed to match those of the standards (Figure 2). This quantitative profiling of major compounds provides a crucial phytochemical basis for understanding the biological activities of MOL and prioritizing target compounds for subsequent in silico investigations [32].

Quantifying active compound levels is essential for evaluating their bioavailability and pharmacokinetics, which in turn are critical for understanding the biological activities of MOL, particularly in relation to its chemical composition [33]. Therefore, this study sought to identify individual polyphenolic compounds known to contribute to the bioactivity of MOL. Among the three detected bioactives, (+)-catechin (1) was the most abundant, with a content of 32.12 mg/g, followed by quercetin 3-glucuronide (3) at 19.14 mg/g. In contrast, ellagic acid (2) was present only in trace amounts. Despite its low concentration, ellagic acid (2) is recognized for potent antioxidant and antiproliferative activities [34]. However, to ensure in silico investigations meaningfully reflect the actual extract's potential, we prioritized major, quantifiable constituents. Ellagic acid was excluded from subsequent in silico analysis due to its trace-level abundance in the extract, which limits its relevance in representing the major phytochemical profile. Traditionally, EOC was thought to originate from the ovarian surface epithelium [2]. However, growing evidence has revealed a strong association between FPR2 and EOC pathogenesis [17]. FPR2 is significantly overexpressed in EOC tissues and is positively correlated with several clinicopathological parameters, including FIGO stage, histological grade, and tumor subtype. Notably, high FPR2 expression is associated with poor prognosis and may serve as an independent prognostic biomarker [17]. Moreover, FPR2 overexpression enhances the invasive and metastatic potential of ovarian cancer cells, while its pharmacological inhibition markedly reduces cellular motility [35].

To further investigate the therapeutic potential of (+)-catechin (1) and quercetin 3-glucuronide (3), particularly in relation to EOC, this study applied systematic network pharmacology and molecular docking simulations targeting FPR2. A total of 58 overlapping targets associated with the two compounds and EOC were identified (Figure 3). To interpret the broader biological relevance of these 58 core targets, functional enrichment analysis was performed. GO and pathway analyses revealed that these targets are highly enriched in biological processes critical to immune regulation, such as "neutrophil degranulation" and "signaling by interleukins" (Figure 4). This systematic functional profiling confirms that the overlapping targets represent a biologically meaningful pharmacological network, providing a strong rationale for focusing on G protein-coupled receptors (GPCRs) like FPR2. A raw PPI network was initially constructed using the STRING database (Figure 5). Because default layouts often cluster nodes based on local confidence rather than global importance, the resulting network was imported into Cytoscape for rigorous topological analysis. Centrality parameters, with a particular emphasis on betweenness centrality, were calculated to systematically identify key regulatory proteins acting as critical network bottlenecks (Figure 6). This data-driven analysis highlighted FPR2 as a primary core target exhibiting exceptionally high centrality metrics. Mapping the compound targets within this refined PPI network provided systemic insights into their biological significance. FPR2 is known to play a critical role in the body’s defense against bacterial infections and inflammation associated with liver diseases such as liver fibrosis, nonalcoholic fatty liver disease, and liver cancer [36]. This underscores the importance of FPR2 in the pathophysiology of liver diseases. Moreover, FPR2 expression is significantly elevated in breast cancer tumor tissues compared to normal tissues and is correlated with poor prognosis [37]. It should be noted that network pharmacology analysis relies on database-derived target prediction and may be influenced by algorithmic bias and data completeness. Therefore, the identified targets should be interpreted as preliminary and hypothesis-generating rather than definitive biological evidence.

Both (+)-catechin (1) and quercetin 3-glucuronide (3) exhibited high hydrophilicity, limited membrane permeability, and poor oral bioavailability. Docking simulations revealed highly favorable docking scores, suggesting potential structural compatibility of the compounds with FPR2 (Figure 7), with predicted binding energies of –8.7 kcal/mol and –10.7 kcal/mol for (+)-catechin (1) and quercetin 3-glucuronide (3), respectively (Table 3). However, as molecular docking is inherently an exploratory in silico approach, these findings suggest that both compounds may potentially interact with, rather than definitively bind to, FPR2, a receptor implicated in inflammation and cancer-related signaling pathways. The predicted interactions support their proposed anti-inflammatory and anticancer activities, supporting their potential biological relevance as bioactive constituents of MOL. These results further emphasize the importance of optimizing cultivation practices to enhance the accumulation of polyphenolic compounds such as (+)-catechin (1) and quercetin 3-glucuronide (3) in MOL.

(+)-Catechin (1) and quercetin 3-glucuronide (3) are low molecular weight flavonoids of botanical origin [38]. Unlike protein-based therapeutics such as monoclonal antibodies, these polyphenolic compounds lack complex tertiary structures and instead feature a conserved polyphenol core often conjugated with sugar moieties. Although they are unlikely to bind FPR2 through canonical antigen-antibody mechanisms, they may modulate FPR2-mediated signaling indirectly. The present study is fundamentally based on an in silico approach. Currently, direct experimental validation confirming these substances as canonical FPR2 ligands is lacking. Nonetheless, a theoretical connection can be inferred between these compounds and FPR2 function based on existing literature. According to previous studies, quercetin 3-glucuronide has been shown to exert anti-inflammatory properties in macrophages via the regulation of JNK/ERK signaling [39], whereas (+)-catechin broadly inhibits inflammatory mediators in microglia and acts through Akt cascades [40]. The biological significance of this interaction is supported by previous studies demonstrating that FPR2 pathway activity intersects with MAPKs depending on the cellular context. Consistent with prior research, FPR2 stimulation induces ERK1/2 pathway activity in neutrophils under ischemia-reperfusion injury conditions, and JNK is involved in ROS generation through the FPR2 pathway in granulocytes [41]. Both JNK and ERK signaling pathways have also been associated with regulating FPR2 expression in endothelial cells [42]. Thus, it is plausible that these flavonoids modulate inflammation, in part, via FPR2-dependent mechanisms. However, without in vitro or in vivo experimental evidence, this structural prediction remains strictly a hypothesis. This ability to influence biological pathways despite structural dissimilarity to conventional biologics highlights their potential as small-molecule modulators in FPR2-associated inflammatory diseases, including EOC.

These findings revealed that (+)-catechin (1) and quercetin 3-glucuronide (3), two major polyphenolic constituents of MOL, show theoretical docking score toward FPR2, a key receptor linked to EOC progression. In the context of other natural products, it is acknowledged that polyphenols from other plants, such as curcumin from turmeric and resveratrol from grapes, have also shown therapeutic potential against ovarian cancer. However, the present study offers a distinct contribution by identifying two specific polyphenols at high concentrations in M. oleifera and computationally linking them to a highly relevant therapeutic target, FPR2, with strong, predicted docking score. While other polyphenols often exhibit broader, less specific targets, our findings provide a hypothesis-generating framework suggesting a potential targeted interaction for the Moringa compounds against a receptor clinically correlated with EOC progression. Because these preliminary in silico data cannot establish a definitive mechanism of action, these findings strictly require further biochemical and cell-based validation to evaluate whether these compounds can serve as true adjunctive therapies targeting FPR2. This study underscores the importance of phytochemical standardization as a foundational step toward developing scientifically validated, MOL-based functional products in future experimental settings.

Research funding

This work was supported by the Natural Product Institute of Science and Technology, Anseong, Republic of Korea.

Competing interests

The authors declare that they have no conflicts of interest.

Acknowledgement

This work was supported by Gyeonggido Business & Science Accelerator (GBSA), Suwon, Republic of Korea.

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