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    Systems-Level Network Pharmacology analysis of Terminalia chebula (Haritaki): Deciphering Multi-Target Mechanisms Across Inflammation-Metabolism-Immunity Axis

    Peraira Jackulin Josephraj1★, Shubham Koundalkar2, Pooja Kumari3, Jain Priyanka Vinodbhai4, Kinjal Panchal5, Ruddri Raval6, Prabhavati Kalu7★ Corresponding author

    1. 1Department of Dravyaguna Vijnana, Rama Ayurvedic Medical College and Hospital, Kanpur, Uttar Pradesh, INDIA.
    2. 2Department of Dravyaguna Vijnana, SBG Ayurvedic Medical College, Belgaum, Karnataka, INDIA
    3. 3Department of Dravyaguna Vijnana, Shiv Shakti Ayurvedic Medical College and Hospital, Bhikhi, Punjab, INDIA.
    4. 4Department of Dravyaguna Vijnana, Institute of Teaching and Research in Ayurveda, Jamnagar, Gujarat, INDIA
    5. 5Department of Shalakya Tantra, Shree Swaminarayan Ayurveda College, Kalol, Gujarat, INDIA
    6. 6Department of Swasthavritta Evam Yoga, Shri V M Mehta Institute of Ayurveda, Rajkot, Gujarat, INDIA.
    7. 7Department of Shalya Tantra, Rajarajeshwari Ayurvedic Medical College, Post Graduate Centre and Hospital, Humnabad, Karnataka, INDIA.

    CORRESPONDENCE

    Peraira Jackulin Josephraj

    Department of Dravyaguna Vijnana, Rama Ayurvedic Medical College and Hospital, Kanpur, Uttar Pradesh, INDIA.

    jackulineperaira@gmail.com

    Received: 09-02-2026; Revised: 16-04-2026; Accepted: 22-06-2026.

    Volume 18, Issue 4 · pp. 1349–1359 · PUBLISHED Oct-Dec 2026 · DOI: 10.5530/pres.20260290

    ABSTRACT

    Background Chronic inflammatory disorders result from complex interactions among inflammation, metabolic dysregulation, and immune imbalance. Conventional single-target therapies often fail to address this complexity. Terminalia chebula (Haritaki), an important Ayurvedic medicinal plant, is traditionally used for inflammatory, metabolic, and immune-related disorders; however, its integrated molecular mechanisms remain insufficiently understood. Objectives This study aimed to elucidate the systems-level, multi-target mechanisms of Terminalia chebula across the inflammation-metabolism-immunity axis using network pharmacology and molecular docking approaches. Materials and Methods Bioactive compounds of T. chebula were screened from IMMPAT and Dr. Duke databases using ADME criteria. Potential targets were predicted using SwissTargetPrediction and BindingDB, while disease-related targets were obtained from GeneCards. Overlapping targets were analyzed through compound-target and protein-protein interaction networks, followed by hub gene identification and KEGG pathway enrichment. Molecular docking was performed to validate ligand-target interactions. Results Thirty-two bioactive compounds and 150 overlapping targets were identified. Key phytoconstituents included quercetin, ellagic acid, and fatty acids, while hub genes comprised TNF, BCL2, ESR1, MMP9, EGFR, SRC, and CASP3. Enriched pathways involved Rap1, PI3K-Akt, MAPK, and PPAR signaling. Molecular docking demonstrated favorable binding affinities (≤-4 kcal/mol), supporting stable ligand-protein interactions. Conclusion Terminalia chebula exerts therapeutic effects through coordinated multi-component, multi-target modulation of interconnected inflammatory, metabolic, and immune pathways, providing a scientific basis for its traditional use in complex chronic disorders.

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    INTRODUCTION

    Inflammation is a vital biological process that acts as the body's first line of defense against harmful stimuli such as infections, damaged cells, along with irritants (Chavda et al., 2024). The interaction of inflammatory factors and metabolism is critical for physiological balance and progression of diseases, including immune dysregulation (Hu et al., 2024). This interrelated inflammation-metabolism-immunity axis is responsible for the development of a wide range of diseases, such as metabolic syndrome, autoimmune diseases, cardiovascular disease, and chronic inflammatory conditions (Rathmell, 2012; Xu et al., 2025). Formerly, drug discovery and development was mostly based on the "one target, only one disease" strategy. However, clinical data reveal that single-target medications are challenging to interfere with the entire disease network, are now prone to drug resistance, and have a low safety profile in clinical use (Zimmermann et al., 2007). As a result, multi-target therapy strategies that can alter associated molecular networks at the systems level are gaining widespread acceptance. In Ayurveda, Haritaki (Terminalia chebula) is highly valued for its exceptional therapeutic properties, it is referred to as the "King of Medicines" and is consistently ranked among the best in Ayurveda (Ratha and Joshi, 2013). Key signaling pathways like NF-κB, MAPK, PI3K-Akt, innate immunological pathways, and cytokines networks-central elements in the inflammation-metabolism-immunity axis-have been shown to be modulated by T. chebula in both experimental and clinical investigations (Haghani et al., 2022; Lee et al., 2025; Lopez et al., 2017). Additionally, emerging evidence suggests to its function in immune cell control, gut microbiome modification, lipid and glucose metabolism, and redox balance, suggesting a systems-level therapeutic impact as opposed to discrete pharmacological effects (Agrawal and Kulkarni, 2023; Sadhupati et al., 2024). The broad therapeutic efficacy of the herb can be explained by identifying important hub genes, signaling modules, and synergistic interactions through the construction of compound-target-pathway-disease networks (Jin et al., 2021). In addition to bridging the gap between contemporary systems biology and ancient Ayurvedic knowledge, this method offers a scientific justification for the integrative use of Terminalia chebula in complicated chronic conditions. Therefore, the present study aims to use a systems-level network pharmacology analysis to elucidate Terminalia chebula's multi-target mechanisms across the interconnected inflammation-metabolism-immunity axis providing novel insights into its holistic therapeutic potential and supporting evidence-based application in integrative medicine.

    MATERIALS AND METHODS

    Bio-active compounds screening of T. chebula

    IMMPAT 2.0 and Dr. Duke's Ethnobotanical database were used to identify all the phytocompounds in Haritaki (Lans and Van Andel, 2020; Vivek-Ananth et al., 2023). ADME screening focused on GI absorption, bioavailability, and Lipinski rule to identify functionally active components in T. chebula. This was carried out using SwissADME database (Daina et al., 2017).

    Target Screening of T. chebula bioactive compounds

    Both SwissTargetPrediction (Daina et al., 2019) and BindingDB databases (Liu et al., 2024) were used to identify active ingredient targets in T. chebula. To simplify and standardize the analysis of data, the Uniprot library (Bateman et al., 2024) was employed to systematically convert the target's "protein name" into the "gene name."

    Prediction of Targets related to inflammation-metabolism-immunity

    The search plan comprised using the terms 'inflammation', 'metabolic disorder', and 'immune dysregulation' to obtain the relevant gene data set via GeneCard (Safran et al., 2021). The gene data collected via these searches were compiled, and duplicates were eliminated. Following data normalization with the UniProt library, we identified key targets for the inflammation-metabolism-immunity axis. An online mapping tool framework was used to create a Venn diagram correlating the active compounds of T. chebula with inflammation, metabolism, and immune targets.

    Construction of Compound-Target Network

    Using Cytoscape 3.7.2 to build the targeted compound network, the active compounds and their targets were imported, and a major compound-target-network diagram for Haritaki ameliorating inflammation-metabolic disorder-immune dysregulation was created.

    Establishment of Protein-Protein Interaction (PPI) Network and Hub Gene Prediction

    To build a PPI network with common targets, STRING database (Szklarczyk et al., 2024) was used, with 'Homo sapiens' as the species. The PPI network was then examined using Cytoscape. The cytohubba pluggin was used to undertake a thorough study of the network, allowing for the estimation of degree scores, MNC (maximum neighbourhood component), and MCC (Maximum Clique Centrality). After review, hub targets were chosen for in silico molecular docking.

    KEGG Enrichment Analysis and Pathway-Target Network Formation

    KEGG enrichment studies were carried out on the intersecting target genes via the String database. A pathway- target network was constructed to establish the link among the enriched pathways and their potential targets.

    Molecular Docking validation of hub genes and bio-active compounds

    Molecular docking, a prominent computer-based structural technique for drug development, anticipate ligand-target interactions on a molecular level. Molecular docking was employed to see if the active components discovered using network pharmacology could bind to the key targets. The 3D structure of the target protein was obtained from the PDB Database (Bittrich et al., 2023). Concurrently, the structure of the bio-active compounds of T. chebula was obtained from Pubchem (Kim et al., 2024). Protein preparation were carried out by dehydrogenation and incorporating polar bonds via BIOVIA DS. To verify the target protein was entirely encompassed by the docking box, we uploaded the receptor and ligand pdbqt structures into AutoDock and put the target protein to the center of the grid. Finally, BIOVIA software was utilized to visualize the results.

    Ethical Statement

    As this study was based on in silico network pharmacology analysis using publicly available databases (IMMPAT, Dr. Duke, GeneCards), ethical approval was not required for this research.

    Statistical Analysis

    Data normalization and standardization were conducted using the UniProt library. In the PPI network, topological properties including degree scores, MNC, and MCC were used to identify hub genes. Pathway enrichment results were filtered using a False Discovery Rate (FDR) to ensure statistical significance.

    RESULTS

    Bioactive compounds screening of T. chebula

    IMMPAT yielded 89 chemical components for T. chebula, while Dr Duke's database yielded 97. Chemical formula of T. chebula phytochemicals was validated by PubChem. Table 1 shows that 32 phytocompounds that met the ADME demand were identified as possible active phytocomponents utilizing SwissADME.

    Table 1: Bioactive phytochemicals of Haritaki identified via SwissADME.
    Haritaki CompoundPubChem CIDGI absorptionLipinski violationBioavailability score
    3-Dehydroshikimic acid439774High00.55
    Anthraquinone6780High00.55
    Arjugenin12444386High10.56
    Arjunolic Acid73641High00.56
    Ascorbic Acid54670067High00.56
    Caffeic Acid689043High00.56
    Colosolic acid15917996High10.56
    Corosolic acid6918774High10.56
    Ellagic Acid5281855High00.55
    Ethyl gallate13250High00.55
    Ferulic acid445858High00.55
    Flavylium145858High00.55
    Gallic Acid370High00.56
    Linoleic Acid5280450High00.55
    L-Rhamnose25310High00.55
    Maslinic Acid73659High10.56
    Myristic Acid11005High00.85
    Oleic Acid445639High10.85
    Oxalic Acid971High00.85
    Palmitic Acid985High10.85
    Palmitoleic Acid445638High00.85
    p-Coumaric acid637542High00.85
    Phloroglucinol359High00.55
    Proline145742High00.55
    Pyrogallol1057High00.55
    Quercetin5280343High00.55
    Ricinoleic acid643684High00.85
    Shikimic Acid8742High00.56
    Stearic Acid5281High10.85
    Succinic Acid1110High00.85
    Syringic acid10742High00.56
    Vanillic Acid8468High00.85

    Target Screening of T. chebula bioactive compounds

    Target screening of biologically active T. chebula compounds revealed 77 targets from the BindingDB database and 172 from SwissTargetPrediction. After de-duplication, thirty-two promising compounds from T. chebula were predicted to target 212 proteins.

    Prediction of Targets related to inflammation-metabolism-immunity

    A comprehensive search of the GeneCards database revealed 6375 targets linked with inflammation, 17411 targets linked to metabolic disorders, and 8858 targets linked with immunological dysregulation. A Venn diagram (Figure 1) identified 150 targets associated with T. chebula chemicals and inflammation, metabolism, and immunological dysregulation.

    Figure 1: Venn diagram of Haritaki bioactive targets overlapping inflamation-metabolism-immunity axis.

    Construction of Compound-Target Network

    Over 32 T. chebula compounds linked with the 150 common targets were imported into Cytoscape, resulting in a comprehensive Compound-Target (CT) network (Figure 2). The composite network has 182 nodes and 339 edges, an average of 3.725 neighbors, and network heterogeneity along with centralization of 1.198 and 0.141, respectively. It demonstrated a highly linked structure of multi-targeting chemicals and proteins. The main compounds were selected based on the order of their topological properties, with numerous compounds scoring reasonably high degree (>11). These included Quercetin, Ellagic Acid, Palmitoleic Acid, Oleic Acid, Linoleic Acid, Ricinoleic Acid, Shikimic Acid, L-Rhamnose, Anthraquinone, and Flavylium.

    Figure 2: Compound-Target Network of Haritaki Across Inflammation-Metabolism-Immunity Axis.

    Establishment of Protein-Protein Interaction (PPI) Network and Hub Gene Prediction

    The PPI network for T. chebula chemicals and inflammation-metabolism disorder-immune dysregulation showed significant connections (at a confidence level of 0.7), as shown in the Figure 3. Based on MCC, MNC, and Degree values, the top seven ranking hub genes were BCL2, ESR1, TNF, MMP9, EGFR, SRC, and CASP3 (Figure 4).

    Figure 3: PPI of 150 targets associated with T. chebula chemicals and inflammation, metabolism, and immunological dysregulation.
    Figure 4: Hub gene obtained via Cytohubba based on Degree (A), MCC (B), MNC (C). Venn digram of core hub genes (D).

    KEGG Enrichment Analysis and Pathway-Target Network Formation

    A pathway enrichment analysis was done using the anticipated 150 common targets to investigate pathways important to the Inflammation-Metabolism-Immunity Axis. A total of 170 pathways were found, and the top 20 were statistically extracted as shown in Table 2. These included the Rap1 signaling route, PI3K-Akt signaling pathway, MAPK signaling pathway, PPAR signaling pathway, and the endocrine resistance pathway (Figure 5). Additional Pathway-Target networks were built, as illustrated in Figure 6.

    Figure 5: Pathway analysis illustrated via Bubble plot.
    Figure 6: Enriched Pathway-Target Network.
    Table 2: Pathway annotation obtained via STRING KEGG analysis.
    Enriched pathwayTotal gene countObserved gene countFDR
    Pathways in cancer515391.97 × 10⁻²⁴
    EGFR tyrosine kinase inhibitor resistance77141.16 × 10⁻¹²
    Proteoglycans in cancer194184.10 × 10⁻¹²
    Rap1 signaling pathway201185.40 × 10⁻¹²
    PI3K-Akt signaling pathway349225.40 × 10⁻¹²
    AGE-RAGE signaling pathway in diabetic complications96145.91 × 10⁻¹²
    Prostate cancer97131.05 × 10⁻¹⁰
    Focal adhesion195162.19 × 10⁻¹⁰
    MAPK signaling pathway286185.65 × 10⁻¹⁰
    Endocrine resistance94128.13 × 10⁻¹⁰
    PPAR signaling pathway75111.34 × 10⁻⁹
    MicroRNAs in cancer159141.42 × 10⁻⁹
    HIF-1 signaling pathway102121.52 × 10⁻⁹
    Steroid hormone biosynthesis60102.61 × 10⁻⁹
    Serotonergic synapse108122.61 × 10⁻⁹
    Ras signaling pathway225158.19 × 10⁻⁹
    Adherens junction69108.19 × 10⁻⁹
    Bladder cancer4084.41 × 10⁻⁸
    Melanoma7291.58 × 10⁻⁷
    Ovarian steroidogenesis5081.79 × 10⁻⁷

    Molecular Docking validation of hub genes and bio-active compounds

    The ten top-ranked compounds (Quercetin, Ellagic Acid, Palmitoleic Acid, Oleic Acid, Linoleic Acid, Ricinoleic Acid, Shikimic Acid, L-Rhamnose, Anthraquinone, and Flavylium) predicted in the CT network were docked with seven hub targets relevant to the Inflammation-Metabolism-Immunity Axis (BCL2, ESR1, TNF, MMP9, EGFR, SRC, and CASP3). Most compounds had favorable binding energy scores (≤-4 kcal/mol). The Table 3 depicts the binding affinity metrics for hub genes and T. chebula bioactive compounds. Figure 7 shows the 3D docking poses and 2D Ligand Protein interaction photos of each target against the Haritaki phyto-constituent with the highest binding affinity.

    Figure 7: 3D and 2D view of Haritaki bioactive- hub gene with highest binding affinity.
    Table 3: Binding affinity score of Haritaki bioactive-core hub gene.
    Bioactive chemicalsPost docking binding affinity(kcal-1) with hub genes
    BCL2ESR1TNFMMP9EGFRSRCCASP3
    Flavylium-7.4-8.7-7.1-6.8-8.1-8.1-5.9
    L-Rhamnose-4.5-5-4-5.1-5.2-4.7-4.6
    Palmitoleic Acid-4.7-6.2-5.1-4.5-5.7-6.1-4.9
    Oleic Acid-5.3-4.3-4.8-4.6-5.9-6.3-4
    Quercetin-6.9-8.5-6.4-6.8-8.7-8.3-6.6
    Linoleic Acid-4.9-6.9-5-5.6-6.1-6.2-4.2
    Ellagic Acid-6.4-7.6-6.1-7.8-8.5-9-6.1
    Ricinoleic acid-4.8-6.5-5.3-4.4-6.1-5.9-4.4
    Anthraquinone-7.1-8.6-5.9-6.9-7.5-8.6-5.5
    Shikimic Acid-5.4-6.2-4.5-5.4-5.2-5.4-4.5

    DISCUSSION

    The present study reflect that Haritaki have a multi-compound, multi-targeted, and multi- biological pathway effect. The comprehensive network of Haritaki Compound-Target and Pathway- Target along with PPI depicts mechanistic insight to immune dysregulation, metabolic imbalance, and persistent inflammation treatment. The results of this study show 150 potential targets for the modulation of Inflammation-Metabolism-Immunity Axis with Haritaki bioactive compounds. The seven hub targets, SRC, BCL2, TNF, CASP3, EGFR, ESR1 and MMP9, were also discovered in the network diagram as the potential targets. Previous study suggest that these genes play critical roles in inflammatory signaling, immune cell activation, survival, and apoptosis pathways (Byeon et al., 2012; Hobbs et al., 2011; Lee et al., 2007; Nehra et al., 2010; Zeng et al., 2016). Such multi-target regulation may explain Haritaki's long-term efficacy in chronic, complex disease conditions (Sagar et al., 2025). TNF, MMP9, EGFR, SRC, and ESR1 are the primary immunomodulatory regulators, affecting cytokine production, immune cell migration, epithelial-immune interactions, and immunological tolerance (Gilad et al., 2022; He et al., 2023; Holbrook et al., 2019; Kassi and Moutsatsou, 2010; Lu et al., 2014). Previous research states that modulation of TNF possibly mediated by polyphenols such as quercetin and ellagic acid, may reduce chronic low-grade inflammation while preserving immune response (Gupta et al., 2014). BCL2 and CASP3, on the other hand, are linked to molecular regulators of apoptosis, allowing for the elimination of immune cells through apoptosis (Hussar, 2022). Importantly, the combined activation of immunomodulatory and apoptotic targets supports immunological recalibration rather than indiscriminate suppressive, which promotes immune homeostasis (Hotamisligil, 2017; Sakaguchi, 2002). This balanced control is consistent with the Ayurvedic notion of Rasayana, which emphasizes increasing Vyadhikshamatva (disease resistance) while avoiding immune exhaustion. The incorporation of molecular docking results enhances these network predictions. Haritaki's bioactive compounds have high binding affinities (≤-4 kcal/mol) to essential protein targets in inflammation, metabolism, and immunological function. Pathway enrichment analysis found strong convergence in the RAP1, PI3K-Akt, and MAPK signaling pathways, emphasizing the molecular link between inflammation and metabolism (Johnson and Lapadat, 2002; Raaijmakers and Bos, 2009). Rap1 signaling promotes immune activation through NF-κB-mediated cytokine production, while also modulating glucose-lipid metabolism, sensitivity to insulin, along with fatty acid oxidation (Jaśkiewicz et al., 2018). The PI3K-Akt pathway is a key regulator of insulin, growth factor, and immune signaling, integrating metabolic functions like glucose absorption and lipogenesis with cell survival and inflammatory regulation (Acosta-Martinez and Cabail, 2022). Furthermore, the MAPK pathway, particularly through p38 and JNK, connects stress and inflammatory stimuli to inflammatory cytokines and immune activation while also regulating cellular energy detection and metabolic adaptability (Pua et al., 2022). Modulation of these interconnected pathways suggests that Haritaki may mitigate inflammation-driven metabolic derangements. From a translational perspective, the identified multi-target and multi-pathway profile supports the potential application of Haritaki in complex disease clusters such as metabolic syndrome, atherosclerosis, non-alcoholic fatty liver disease, and chronic inflammatory disorders, (Bag et al., 2013) where single-target pharmacological approaches often fail to achieve durable outcomes. The network identifies potential biomarkers, such as TNF-α, phosphorylated Akt, p38 MAPK, and caspase-3 activity, for further experimental validation in vitro, in vivo, and clinical trials. Nonetheless, this study features shortcomings typical of in silico network pharmacology, such as its dependence on computerized target prediction and a lack of dose-response contextualization (Li et al., 2023). Thus, biochemical, cell-based, and clinical validation are required to establish expected interactions and therapeutic significance.

    CONCLUSION

    The present systems pharmacology investigation demonstrates that Haritaki (Terminalia chebula) exerts its therapeutic potential through multi-component, multi-target interactions, supported by stable molecular binding and network-level pathway regulation. These findings provide a scientific basis for its traditional use and highlight its promise as a therapeutic candidate for complex chronic disorders involving inflammation-metabolism-immunity crosstalk. Further experimental and clinical validation is warranted.

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    Josephraj, P. J., Koundalkar, S., Kumari, P., Vinodbhai, J. P., Panchal, K., Raval, R., & Kalu, P. (2026). Systems-Level Network Pharmacology analysis of Terminalia chebula (Haritaki): Deciphering Multi-Target Mechanisms Across Inflammation-Metabolism-Immunity Axis. Pharmacognosy Research, 18(4), 1349–1359. https://doi.org/10.5530/pres.20260290