Mechanistic insights into the anti-aging potential of Centella asiatica via network pharmacology and molecular docking

1. INTRODUCTION

Aging is marked by a decline in tissue and organ repair, leading to reduced physiological reserves and the development of age-related diseases [1]. Research on aging focuses on understanding its physiological origin, biological responses, and potential interventions to delay aging [2,3]. The UN World Social Report predicts that by 2050, the global population aged 65 and above will reach 1.6 billion, highlighting the need for interventions to promote successful aging [4]. Lifestyle choices, such as a healthy diet and regular exercise, are essential in enhancing longevity, while herbal supplements with antioxidant and anti-inflammatory properties show promise in delaying aging [5–8]. Centella asiatica, known for its antioxidant polyphenols, has been studied for its anti-aging effects, including telomere preservation and enhanced survival in Drosophila melanogaster, although its bioactive compounds and mechanisms remain unclear [9–11].

Nowadays, drug discovery uses more computational approaches to better understand the underlying interactions between various drugs and their targets. This marks a shift from traditional methods, focusing only on one drug/one target/one therapeutic effect, to a more modern method. Among the modern methods is network pharmacology, which employs computational power to systematically map the molecular interactions of drug molecules within the biological system, followed by molecular docking, which predicts potential in vivo interactions. Its application extends beyond drug discovery to include drug repurposing, where known compounds are evaluated for new therapeutic use through unbiased analysis of all potential targets [12]. Thus, to confirm the previous research on C. asiatica’s potency as an anti-aging agent and to further identify the bioactive compounds and their mechanism of action contributing to those effects, network pharmacology and molecular docking analysis were conducted to explore the mechanism underlying its therapeutic potential as an anti-aging agent.

2. MATERIALS AND METHODS

The network pharmacology and molecular docking analysis were conducted using various databases/software, as listed in Table 1. The research workflow is illustrated in Figure 1.

Figure 1. Research workflow. [Click here to view]

Table 1. Database and software used in study.

2.1. Bioactive compounds data collection

The bioactive compounds associated with C. asiatica were collected from two databases, which are TCMSP (https://old.tcmsp-e.com/tcmsp.php) and BATMAN-TCM 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/) [13,14]. The keyword used in both databases was ”Centella asiatica”. The bioactive compounds were then selected based on their oral bioavailability and Lipinski’s rules of five characteristics, which were assessed using data from the SwissADME database. The criteria for oral bioavailability were set at a minimum of 30%, while drug-likeness shows not more than one violation of Lipinski’s rules of five [15,16]. Some bioactive compounds, mostly high molecular weight compounds, cannot be analyzed using this tool; however, these compounds were included in the analysis if there is any published in vitro or in vivo data that shows their anti-aging properties [17].

2.2. Bioactive compounds-related protein targets data collection

Protein targets associated with bioactive compounds were predicted using several databases, including STP (http://www.swisstargetprediction.ch/), PharmMapper (https://www.lilab-ecust.cn/pharmmapper/), and HIT (https://hit2.badd-cao.net/) [18–22]. In STP, protein target prediction utilized the canonical SMILES of each bioactive compound. The species was restricted to Homo sapiens. For PharmMapper, the predictions were based on the 2D structures of the compounds in sdf format. Maximum generated conformations and Number of Reserved Matched Targets were set to 1,000, and targets set were set to ”Human Protein Targets Only”. While in HIT, only protein targets from compounds with a similarity value of 1.0 to the input canonical SMILES were included in the analysis. These protein targets were then supplemented with their UniProt ID and gene names from UniProt (https://www.uniprot.org/) for further identification and analysis purposes [23].

2.3. Aging-related protein targets data collection

Protein targets associated with aging were collected from GeneCards (https://www.genecards.org/) using the ”cellular aging” keyword, to capture genes and proteins involved in cellular-level aging mechanisms and avoid the overly broad scope of aging. Additional protein targets were sourced from a publication on the aging interactome, which presents an interactome-based approach to aging by identifying protein–protein interaction networks associated with aging processes and complements the GeneCards-derived targets by adding interaction-based biological context [24,25]. These protein targets were then supplemented with their UniProt ID and gene names from UniProt (https://www.uniprot.org/) for further identification and analysis purposes [23].

2.4. Protein–protein interaction network construction

The protein–protein interaction network was constructed using the STRING version 2.1.1 (https://apps.cytoscape.org/apps/stringapp) plugin in Cytoscape version 3.10 [26]. The network was built by intersecting the aging-related protein target network with the bioactive compound-related protein target network [17]. To ensure the reliability of the interactions included, only those with a combined STRING score greater than 0.7 were retained, reflecting a high-confidence threshold commonly applied in PPI network analyses. Further analysis was conducted in Cytoscape for visualization and interpretation of the interaction.

2.5. Protein–protein interaction network analysis

The protein–protein interaction network was analyzed using CytoNCA version 2.1.6 (https://apps.cytoscape.org/apps/cytonca) plugin in Cytoscape version 3.10 for its degree centrality (DC) and betweenness centrality (BC) calculation, which were used to further determine the core protein within the protein–protein interaction network [27]. To cluster the network into cluster(s) with highly interconnected regions, the network was analyzed using the MCODE version 2.0.3 (https://apps.cytoscape.org/apps/mcode) plugin in Cytoscape version 3.10 [28].

2.6. Gene ontology and pathway enrichment

The gene ontology and pathway of the core proteins were identified using the Enrichr (https://maayanlab.cloud/Enrichr/) database [29–31]. The pathway with a p-value of less than 0.05 is considered to be potentially relevant for those core proteins. Multiple testing correction was performed using the Benjamini–Hochberg procedure to control the false discovery rate, and the adjusted p-values are reported accordingly. The pathway was obtained from the KEGG database (https://www.kegg.jp/kegg/pathway.html), and the KEGG mapper database (https://www.genome.jp/kegg/mapper/) was used to pinpoint the core protein targets in the selected pathway [32–34].

A two-step ClueGO analysis was conducted to understand the molecular functions and biological processes associated with the selected pathway. The first analysis only focused on the molecular functions of the core protein targets. This was done to gain a general understanding of how the core proteins might work in vivo. The second analysis was conducted using pathway-relevant protein targets, exploring both molecular functions and biological processes of those proteins. This was done further to predict their specific contribution within the selected pathway. Both analyses were done using the ClueGO version 2.5.10 plugin (https://apps.cytoscape.org/apps/cluego) in Cytoscape version 3.10 [35]. The visualization of the results was done on the same platform. ClueGO groups proteins based on their shared functions or interactions to show the interconnectedness of the proteins, influencing the whole process. Therefore, it gives insight into the most relevant hallmarks of aging influenced by these proteins and how they might be targeted to slow the aging process or to treat age-related diseases.

2.7. Molecular docking analysis

Molecular docking analysis used C. asiatica core bioactive compounds and reference compound Resveratrol as ligands, and aging-associated core protein targets as proteins. The core bioactive compounds have the most relevant number of core protein targets within the pathway, whereas the core protein targets are the core proteins that contribute to the pathway. The 3D structures of the protein were collected from PDB (https://www.rcsb.org/), whereas the 2D structures of the ligands were collected from PubChem (https://pubchem.ncbi.nlm.nih.gov/) [15,36,37]. Initially, each protein structure was prepared using Discovery Studio before being used in the molecular docking analysis. The molecular docking analysis was done using the CB Dock (https://cadd.labshare.cn/) website with an auto-blind docking feature [38,39]. Due to the exploratory nature of the work and the complexity of defining suitable control compounds for broadly characterized aging-related protein targets, this study does not include validation using negative controls. Therefore, blind docking and binding affinity analysis were employed as an initial validation step for identifying potential binding regions and scores.

3. RESULTS

3.1. Data collection

The number of bioactive compounds data collected from TCMSP and BATMAN are 141. After removing some duplicates, the final set of bioactive compounds associated with C. asiatica was further evaluated for its oral bioavailability, and Lipinski’s rule of five violations was 134 bioactive compounds. Among these, some high molecular weight compounds, such as madecassoside and asiaticoside, could not be analyzed using SwissADME. However, based on the literature review, these compounds have shown anti-aging potential and were included in the analysis [11,17]. Following this screening, a total of 73 compounds fulfill the oral bioavailability and Lipinski’s rules of five violation requirements. These compounds were selected to be further analyzed in this network pharmacology study.

Protein targets associated with the selected bioactive compounds were obtained from STP, PharmMapper, and HIT databases, resulting in 19,053 protein targets in total. From the initial dataset of 19,053 targets, identical entries were removed based on UniProt ID, resulting in 1,361 unique targets, regarded as C. asiatica-related protein targets. As for protein targets related to aging, 224 protein targets were collected from GeneCard using ”cellular aging” keywords. Another 483 were collected from Randhawa and Kumar [25] aging interactome publication. After removing duplicates, the aging-related protein target used in the analysis was 634 protein targets.

3.2. Protein–protein interaction network construction

Each category of protein targets, aging-related and C. asiatica-related, was constructed using the STRING database into two different networks, resulting in an aging-related network and a C. asiatica-related network. Protein–protein interaction networks were then constructed by intersecting these two networks using Cytoscape, resulting in C. asiatica and an aging network with 240 nodes and 7,493 edges, as visualized using Cytoscape, shown in Figure 2. In this network, nodes represent protein targets, while edges represent the interaction between protein targets.

Figure 2. Protein–protein interaction (PPI) network of intersecting protein targets between Centella asiatica-related targets and aging-related targets, highlighting shared key proteins that may contribute to its potential anti-aging mechanisms. The network consists of 240 nodes and 7,493 edges. Nodes in square shape represent protein targets, while edges represent interactions between these proteins. [Click here to view]

3.3. Protein–Protein Interaction Network analysis

To identify core proteins in the network—meaning the proteins that are crucial to the network—a CytoNCA centrality analysis was used. These core proteins are regarded as important because of their position in the topological structure of the network. Metrics such as DC and BC were used to determine the importance of each protein. These metrics were calculated and applied to filter the proteins. Ideally, core proteins are highly connected within the network; therefore, they hold a key role in the overall mechanism. In this study, a protein is considered a core protein if it has a DC value that is more than twice the median of the DC and twice the median of the BC. A total of 28 protein targets met the requirements and were therefore classified as core proteins of the network, as outlined in Table 2.

Table 2. Core protein targets.

In research related to aging, DC and BC values are regarded as more relevant parameters because DC shows the number of direct interactions of a protein with another protein in a network. DC value directly describes how important a protein is in a network because a protein with a high DC value tends to influence more biological pathways. On the other hand, in the aging process, many pathways are related interdependently. In this condition, a high BC value becomes important to identify proteins that are critical in connecting various biological processes. Proteins with high BC value are hubs between different pathways and help protect complex biological mechanism integration.

These core proteins were then mapped into a core protein network, as shown in Figure 3. To identify whether the network consists of several clusters, the network was analyzed using MCODE. The result from MCODE analysis shows that the network belongs only to one cluster, which consists of 28 protein targets.

Figure 3. Core protein–protein interaction (PPI) network representing key protein targets associated with Centella asiatica and aging. Nodes represent proteins, while edges represent interactions between them. The core network consists of highly interconnected proteins that may play central roles in mediating the biological effects of Centella asiatica in aging-related pathways. [Click here to view]

3.4. Gene ontology and pathway enrichment

Enrichment analysis was done on these 28 protein targets to identify the most relevant cellular components, molecular function, biological process, and biological pathway in a broader biological context. Statistically, a significant result is marked with an adjusted p-value at a maximum of 0.05. The top 10 cellular components, molecular function, and biological process results are outlined in Table 3.

Table 3. Top 10 enriched GO terms in the cellular components (CC), molecular function (MF), and biological process (BP) categories for the core proteins.

For the biological pathway, the enrichment analysis shows that the most significant pathway is related to cancer, with a p-value of 2.73E-15 (FDR-adjusted). This result aligns with the understanding that aging and cancer share a lot of common biological pathways, such as cellular senescence, inflammation, apoptosis, and DNA damage. This finding reinforces the interconnectedness of aging and tumorigenesis. However, in the context of this study, the cellular senescence pathway is emphasized because it directly describes the mechanism of aging and connects widely to the hallmarks of aging. This pathway is also a highly significant pathway with a p-value of 4.89E-06 (FDR-adjusted).

ClueGO grouped the molecular function of the core protein targets into eight groups. The top three groups, namely Group 5, 7, and 6, with p values of 9.09E-12, 2.12E-10, and 1.78E-09, are related to telomerase activity, nitric oxide synthase activity, and transcription coregulator binding, respectively. The result of this analysis is outlined in Table 4 and visualized in Figure 4.

Figure 4. Top three ClueGO analysis results for the core protein groups in the molecular function category categorizing proteins based on their key biological roles in aging related mechanisms. The results highlight: (a) Group related to telomerase activity; (b) Group related to nitric-oxide synthase activity; and (c) Group related to Transcription coregulator binding. Bold labels indicate the most relevant functioins within each group. The network layout is structured to emphasize functional relationships, with proteins involved in similar biological processes positioned in closed proximity. [Click here to view]

Table 4. Top 3 ClueGO analysis results for the core protein groups in the molecular function category.

A second analysis using ClueGO was conducted to study the molecular function and biological process of seven protein targets associated with said cellular senescence pathway. This analysis helps identify which aging-related mechanisms these proteins influence, specifically related to the cellular senescence hallmark. The results of the analysis are visualized in Figure 5 and outlined in Table 5 for molecular function groups, and Figure 6 and Table 6 for biological process.

Figure 5. ClueGO analysis results for the cellular senescence pathway-associated protein groups in the molecular function category. The network layout enhances the visualization of functional relationships, grouping related molecular functions to highlight key biological process involved in cellular senescence. Bold labels indicate the most relevant functional terms. [Click here to view]

Figure 6. ClueGO analysis results for the cellular senescence pathway-associated protein groups in the Biological Process category. The network represents biological process enriched in the identified protein targets, proving insight into their potential roles in cellular senescence. Bold labels indicate the most relevant biological process. [Click here to view]

Table 6. ClueGO analysis results for the cellular senescence pathway-associated protein groups in the biological process category.

Table 7. Molecular docking results for protein-ligand complexes.

The biological processes are grouped into two groups, the white group (Group 0) with a p-value of 8.71E-13 and the grey group (Group 1) with a p-value of 5.07E-08. Both groups share overlapping functions, wherein the most significant function in both groups is the “leukocyte apoptotic process”, with the lowest p-value of 4.2E-09. Bold edges between the nodes show a strong relationship between biological functions.

3.5. Molecular docking analysis

The result of molecular docking analysis of Quercetin, Apigenin, Rutin, and Ursolic Acid toward PTEN, TP53, MAPK3, AKT1, MYC, SIRT1, and IL6, shows negative values, preferably less than −5, indicating that the high binding capacity of these bioactive compounds allows them to bind to these proteins readily. Among these proteins, Ursolic Acid more often shows different binding sites than the other three bioactive compounds. Additionally, the vina score of these four C. asiatica bioactive compounds indicates more negative binding scores compared to Resveratrol, which was used as a reference. The molecular docking result is shown in Table 7. The visualization of molecular docking of the protein and ligand complexes is shown in Table 8.

Table 8. Molecular docking results of seven core protein target associated with cellular senescence pathway, namely PTEN, TP53, MAPK3, AKT1, MYC, SIRT1, and IL6, with bioactive compounds from Centella asiatica and Resveratrol as reference compound, alongside with their binding sites and docking scores.

PTEN
Quercetin
Apigenin
Rutin
Ursolic Acid
Resveratrol
TP53
Quercetin
Apigenin
Rutin
Ursolic Acid
Resveratrol
MAPK3
Quercetin
Apigenin
Rutin
Ursolic Acid
Resveratrol
AKT1
Quercetin
Apigenin
Rutin
Ursolic Acid
Resveratrol
MYC
Quercetin
Apigenin
Rutin
Ursolic Acid
Resveratrol
SIRT1
Quercetin
Apigenin
Rutin
Ursolic Acid
Resveratrol
IL6
Quercetin
Apigenin
Rutin
Ursolic Acid
Resveratrol

4. DISCUSSION

The network pharmacology approach was employed to explore the molecular mechanisms underlying the anti-aging potential of C. asiatica [40]. Bioactive compounds from C. asiatica were screened and mapped to their predicted protein targets, which were then intersected with known aging-related targets. This intersection yielded 28 core proteins, identified through centrality analysis using degree and betweenness metrics. To gain insights into the biological relevance of these proteins, subsequent enrichment analysis revealed a strong association between these targets and key aging-related pathway, notably cellular senescence, apoptosis, and telomere maintenance. Functional clustering using ClueGO highlighted pathways related to transcriptional regulation, nitric oxide synthase activity, and telomerase regulation.

Seven of the 28 core proteins—TP53, PTEN, MAPK3, AKT1, MYC, IL6, and SIRT1—were mapped directly to the cellular senescence pathway (Fig. 7), suggesting their central role in meditating the anti-aging effects of C. asiatica. This is particularly important because cellular senescence represents a key hallmark of aging, wherein cells permanently exit the cell cycle in response to various stressors, including DNA damage, oxidative stress, and telomere attrition. While senescence serves as a protective mechanism to prevent malignant transformation, the accumulation of senescent cells over time contributes to age-related tissue dysfunction [41]. These cells adopt a pro-inflammatory secretory profile—known as the senescence-associated secretory phenotype—which promotes chronic inflammation and disrupts tissue regeneration [42].

Figure 7. Putative targets of Centella asiatica in the cellular senescence pathway. This figure shows the KEGG cellular senescence pathway with core protein targets identified through network pharmacology and KEGG pathway enrichment analysis using DAVID Bioinformatics. Protein highlighted in red represents predicted targets of Centella asiatica bioactive compounds, mapped onto the generic KEGG pathway for visualization purposes. [Click here to view]

Notably, among the senescence regulating proteins identified, MYC modulation has been previously observed in vitro studies involving C. asiatica, highlighting its potential to influence transcriptional networks related to cellular proliferation and stress response. Meanwhile, PTEN and p53 act as major gatekeepers of genomic integrity, regulating PI3K/AKT pathway and cell cycle arrest, respectively [43–45]. Centella asiatica appears to balance proliferative signals via AKT1 and MAPK3 with protective checkpoints such as PTEN and p53, ensuring growth only in healthy cells [11].

In addition to these findings, enrichment analysis revealed leukocyte apoptosis as one of the most significantly associated biological processes. Proper regulation of immune cell turnover is vital during aging, as impaired apoptosis can lead to persistence of dysfunctional leukocytes, which may evade death, become senescent, and contribute to a chronic inflammatory environment (inflammaging) [46]. By potentially modulating these apoptosis-related pathways, C. asiatica could help restore immune balance, mitigate excessive inflammation, and thereby slow the aging process at the systemic level.

Aging is also marked by functional decline in glial cells, especially oligodendrocytes and microglia, which play critical roles in maintaining neuronal integrity. Oligodendrocytes are responsible for the formation and maintenance of the myelin sheath, essential for rapid signal conduction and metabolic support to neurons. Aging reduces the capacity of oligodendrocyte precursor cells to remyelinate damaged axons, leading to demyelination and associated cognitive deficits [47].

Moreover, chronic oxidative stress in aging promotes persistent activation of microglia. While microglia are initially protective—clearing myelin debris and supporting repair—prolonged activation can result in a senescent microglial phenotype. These senescent microglia exhibit impaired phagocytic ability and secrete pro-inflammatory cytokines such as IL-1B, TNF-a, and IL-6, perpetuating neuroinflammation and contributing to neurodegeneration [48,49]. Experimental evidence supports the neuroprotective role of Centella asiatica, particularly its aqueous extract, which improves learning and memory in aged mice. These effects are likely mediated by its antioxidant properties and ability to modulate glial cell function [50]. Our findings, in conjunction with previous in vivo results, suggest that C. asiatica may protect against age-related cognitive decline by supporting oligodendrocyte integrity, reducing oxidative damage, and preventing chronic glial senescence [50].

Alongside neural aging, mitochondrial dysfunction represents another intrinsic factor that accelerates aging. As a cellular powerhouse, mitochondria generate ATP through redox processes that can lead to the accumulation of reactive oxygen species, mutation in mitochondrial DNA, and progressive mitochondrial decay [51]. This dysfunction is linked with increased oxidative stress, aberrant apoptosis regulation, and energy deficits in aging tissues. Notably, aging brains show increased neurons with cytochrome c oxidase deficiency in areas such as the substantia nigra and hippocampus, further implicating mitochondrial failure in neurodegeneration. Centella asiatica, through its antioxidant constituents, may help ameliorate these mitochondrial effects by reducing oxidative stress and supporting mitochondrial integrity [51,52].

Furthermore, the vascular system is another major target of aging-related deterioration. Endothelial dysfunction, vascular inflammation, and reduced nitric oxide (NO) bioavailability are prominent features that contribute to cardiovascular diseases in the elderly. In our ClueGO analysis, nitric oxide synthase activity emerged as one of the most significantly enriched molecular functions targeted by C. asiatica’s bioactive compounds. Nitric oxide plays a key role in maintaining vascular tone, inhibiting platelet aggregation, and preventing leukocyte adhesion. Dysregulation of NO pathways leads to impaired vasodilation and heightened inflammatory responses, which accelerate vascular aging and increase the risk of oxidative stress [53]. Centella asiatica may help preserve endothelial function and vascular homeostasis. These molecular insights are consistent with prior studies showing C. asiatica’s cardioprotective effects, including attenuation of cardiac hypertrophy, fibrosis, and ischemia-reperfusion injury. The plant’s antioxidant, anti-inflammatory, and vasomodulatory properties make it a promising candidate for mitigating cardiovascular aging and its related pathologies [53].

The therapeutic potential of C. asiatica lies in its rich repertoire of bioactive compounds, particularly the combination of polyphenols—such as Quercetin, Apigenin, and Rutin—and triterpenoid, including Ursolic Acid, Asiaticoside, Madecassoside, and Asiatic Acid. These compounds act on various molecular targets, many of which are associated with aging hallmarks, particularly cellular senescence and inflammatory signaling [9]. Among the 28 core proteins identified in this study, several—including TP53, AKT1, PTEN, IL6, and SIRT1—are modulated by multiple compounds, suggesting coordinated regulation across intersecting pathways.

Among these compounds, Quercetin, Apigenin, Rutin, and Ursolic Acid emerged as the most relevant based on network analysis results. Their potential synergy with more abundant triterpenoids found in C. asiatica, such as Asiaticoside, Madecassoside, and Asiatic Acid, further highlights the value of exploring this plant as a multi-component anti-aging agent. Quercetin, Apigenin, and Rutin are known for their strong antioxidant and anti-inflammatory properties [9]. They modulate pathways involved in DNA repair, apoptosis, and cytokine suppression, contributing to delayed cellular senescence. Meanwhile, triterpenoids such as Ursolic Acid and Asiaticoside exhibit pronounced sirtuin-activiting properties. Sirtuins, especially SIRT1 and SIRT2, are key regulators of chromatin remodeling, DNA repair, metabolic homeostasis, and mitochondrial integrity. The activation of these proteins has been linked to increased cellular longevity and improved stress resistance [54,55].

The interplay among these mechanisms is largely mediated by telomerase regulation. Telomerase, via its catalytic subunit TERT, maintains telomere length and delays replicative senescence. Evidence from studies on C. asiatica extract DLBS1649 shows that it prevents age-related telomerase suppression and maintains TERT expression, indicating potential for delaying telomere-driven senescence. The interaction between telomerase activation, p53 modulation, and sirtuin signaling supports a multi-layered protective effect that balances prosurvival signals with genomic surveillance [11].

Importantly, this combinatorial mechanism underscores the unique advantage of Centella asiatica as an anti-aging agent. Herbal extracts that contain only individual components, such as Quercetin or Ursolic Acid alone, may not replicate this synergistic effect. The integrated action of both polyphenols and triterpenoids appears essential for comprehensive protection against cellular aging, particularly in maintaining genome integrity and preventing chronic inflammation.

Nevertheless, despite the promising biological activities of C. asiatica compounds, pharmaceutical application requires careful consideration of pharmacokinetic challenges. Many of the key bioactive compounds, such as Rutin and Ursolic Acid, possess high molecular weight or high lipophilicity (logP>5), which may limit aqueous solubility and oral bioavailability. Nevertheless, these barriers can be addressed through formulation strategies such as cyclodextrin complexation, lipid-based nanocarriers, or solid dispersion systems to enhance solubility and intestinal absorption [56].

To strengthen the pharmacological relevance of the network analysis, molecular docking analysis was performed. The results demonstrated that Quercetin, Apigenin, Rutin, and Ursolic Acid exhibit strong binding affinity (AG < –5 kcal/mol) to senescence-associated proteins [17]. These interactions reinforce the functional relevance of the network pharmacology predictions, confirming that the identified compounds have not only theoretical but also structural compatibility with their protein targets. However, as with any blind docking approach, the results should be interpreted with caution due to limitations such as reliance on predicted cavities. Nonetheless, this method provides a valuable first step for exploring potential binding regions on targets that are still underexplored.

Together, these findings suggest that the anti-aging potential of C. asiatica is supported by both biological plausibility and computational validation. Its bioactive compounds potentially act through multiple pathways—particularly senescence regulation, sirtuin activation, and telomerase maintenance—and can be optimized for therapeutic use through formulation science. This integrated, multi-target approach strengthens the rationale for further investigation and development of C. asiatica-based anti-aging interventions.

5. CONCLUSION

Based on current findings, C. asiatica bioactive compounds, which are predicted to have the most relevant anti-aging features, are Quercetin, Apigenin, Rutin, and Ursolic Acid—which act on key proteins within the cellular senescence pathway, including PTEN, TP53, MAPK3, AKT1, MYC, SIRT1, and IL6. These compounds are predicted to have potency as anti-aging through the cellular senescence pathway, in which they contribute to the regulation of cellular senescence, apoptosis, dysfunctional mitochondrial, and telomerase activity in ways that promote cellular homeostasis and longevity. Notably, the synergistic action between polyphenols and triterpenoids—especially sirtuin-activating compounds like Asiaticoside and Madecassoside—further reinforces Centella asiatica’s multifaceted anti-aging effects, including its neuroprotective and vasoprotective potential.

Additionally, molecular docking supports the network pharmacology findings, with all four compounds showing strong binding affinity to senescence-related targets. These findings provide a strong foundation for future in vitro and in vivo research regarding specific bioactive compounds of C. asiatica as anti-aging agents, thereby enabling a more focused and efficient longevity experimental design to validate their efficacy and mechanism of action. The exploratory nature of these findings has been acknowledged, and network pharmacology and enrichment analyses are recognized as providing a systems-level perspective on potential molecular mechanisms, forming the basis for subsequent biological validation.

6. LIST OF ABBREVIATIONS

ADME: Absorption, distribution, metabolism, and excretion; BC: Betweenness centrality; DC: Degree centrality; KEGG: Kyoto Encyclopedia of Genes and Genomes; MAPK3: Mitogen-activated protein Kinase 3; PI3K: Phosphoinositide 3-Kinase; PPI: Protein–Protein Interaction; PTEN: Phosphatase and Tensin Homolog; SASP: Senescence-Associated Secretory Phenotype; SIRT1: Sirtuin 1.

7. ACKNOWLEDGMENTS

The authors thank Santi Tan and Jeni Rustan for their valuable contributions, the Strategic Planning team for their assistance with the submission, and Dexa Medica for supporting the facilitation of this research.

8. 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.

9. FINANCIAL SUPPORT

This research was funded by PT Dexa Medica.

10. CONFLICTS OF INTEREST

This study was funded internally by PT Dexa Medica. One of the authors, Raymond Rubianto Tjandrawinata, is an employee of PT Dexa Medica. The company’s involvement was limited to providing financial support and had no influence on the study design, data collection, data analysis, interpretation of the results, or the decision to publish. The authors declare that there are no additional financial or personal relationships that could have influenced the work.

11. ETHICAL APPROVALS

This study does not involve experiments on animals or human subjects.

12. DATA AVAILABILITY

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

13. 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.

14. USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY

The authors declare that they have not used artificial intelligence (AI)-tools for writing and editing of the manuscript, and no images were manipulated using AI.

REFERENCES

1. Khan SS, Singer BD, Vaughan DE. Molecular and physiological manifestations and measurement of aging in humans. Aging Cell. 2017;16(4):624–33. CrossRef

2. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217. CrossRef

3. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: an expanding universe. Cell. 2023;186(7):2433–61. CrossRef

4. United Nations. World Social Report 2023: Leaving no one behind in an ageing world. New York, NY: United Nations Department of Economic and Social Affairs; 2023 [cited 2023 Sep 23]. Available from: https://desapublications.un.org/publications/world-social-report-2023-leaving-no-one-behind-ageing-world

5. Komatsu H, Yagasaki K, Kida H, Eguchi Y, Niimura H. Preparing for a paradigm shift in aging populations: listen to the oldest old. Int J Qual Stud Health Well-being. 2018;19(5):443–8. CrossRef

6. Sakaniwa R, Noguchi M, Imano H, Shirai K, Tamakoshi A, Iso H, et al. Impact of modifiable healthy lifestyle adoption on lifetime gain from middle to older age. Age Ageing. 2022;51(7):afac080. CrossRef

7. Cho SY, Lee HG, Kwon S, Park SU, Jung WS, Moon SK, et al. A systematic review of in vivo studies of the efficacy of herbal medicines for anti-aging in the last five years. Pharmaceutics. 2023;16(3):448. CrossRef

8. Argyropoulou A, Aligiannis N, Trougakos IP, Skaltsounis AL. Natural compounds with anti-ageing activity. Nat Prod Rep. 2013;30(11):1412. CrossRef

9. Zainol MK, Abd-Hamid A, Yusof S, Muse R. Antioxidative activity and total phenolic compounds of leaf, root and petiole of four accessions of Centella asiatica (L.) Urban. Food Chem. 2003;81(4):575–81. CrossRef

10. Park KS. Pharmacological effects of Centella asiatica on skin diseases: evidence and possible mechanisms. Evid Based Complement Alternat Med. 2021;2021:5462633. CrossRef

11. Karsono AH, Tandrasasmita OM, Berlian G, Tjandrawinata RR. Potential antiaging effects of DLBS1649, a Centella asiatica bioactive extract. Exp Pharmacol. 2021;13:299547. CrossRef

12. Chandran U, Mehendale N, Patil S, Chaguturu R, Patwardhan B. Network pharmacology. In: Chaguturu R, editor. Innovative approaches in drug discovery. Amsterdam, The Netherlands: Elsevier; 2017. pp. 127–64. CrossRef

13. Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6(1):1–6. CrossRef

14. Kong X, Liu C, Zhang Z, Cheng M, Mei Z, Li X, et al. BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins. Nucleic Acids Res. 2023;52:D1110–20. CrossRef

15. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. CrossRef

16. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615–23. CrossRef

17. Tan S, Yulandi A, Tjandrawinata RR. Network pharmacology study of Phyllanthus niruri: potential target proteins and their hepatoprotective activities. J Appl Pharm Sci. 2023;0(00):001–11. CrossRef

18. Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357–65. CrossRef

19. Liu X, Ouyang S, Yu B, Huang K, Liu Y, Gong J, et al. PharmMapper server: a web server for potential drug target identification via mapping approach. Nucleic Acids Res. 2010;38:W609–14. CrossRef

20. Wang X, Pan C, Gong J, Liu X, Li H. Enhancing the enrichment of pharmacophore-based target prediction for the polypharmacological profiles of drugs. J Chem Inf Model. 2016;56:1175–83. CrossRef

21. Wang X, Shen Y, Wang S, Li S, Zhang W, Liu X, et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017;45:W356–60. CrossRef

22. Yan D, Zheng G, Wang C, Chen Z, Mao T, Gao J, et al. HIT 2.0: an enahanced platform for Herbal Ingredients’ Targets. Nucleic Acids Res. 2022;50(D1):D1238–43. CrossRef

23. UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023;51:D523–31. CrossRef

24. Stelzer G, Rosen R, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards Suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54:1.30.1–33. CrossRef

25. Randhawa V, Kumar M. Analysis of aging-related protein interactome and cross-network module comparisons across tissues provide new insights into aging. Comput Biol Chem. 2021;92:107506. CrossRef

26. Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: network analysis and visualozation of proteomics data. J Proteome Res. 2019;18(2):623–32. CrossRef

27. Tang Y, Li M, Wang J, Pan Y, Wu FX. CytoNCA: a Cytoscape plugin for centrality analysis and evaluation of biological networks. BMC Bioinformatics. 2014;4:2. CrossRef

28. Bader GD, Hogue CWV. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2. CrossRef

29. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;128(14). CrossRef

30. Kuleshov MV, Jones MR, Roillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:(Web Server issue):W90–7. CrossRef

31. Xie Z, Bailey A, Kuleshov MV, Clarke DJB, Evangelista JE, Jenkins SL, et al. Gene set knowledge discovery with Enrichr. Current Protocols. 2021;1:e90. CrossRef

32. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

33. Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29(1):28–35. CrossRef

34. Kanehisa M, Sato Y, Kawashima M. KEGG Mapper for inferring tools for uncovering hidden features in biological data. Protein Sci. 2022;31(1):47–53. CrossRef

35. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091–3. CrossRef

36. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res. 2000;28:235–42. CrossRef

37. Kim S, Chen J, Cheng T, Gindulyte A, He S, Li Q, et al. PubChem 2023 update. Nuceic Acids Res. 2023;51(D1):D1373–80. CrossRef

38. Yang X, Liu Y, Gan J, Xiao ZX, Cao Y. FitDock: protein-ligand docking by template fitting. Brief Bioinform. 2022;23(3):bbac087. CrossRef

39. Liu Y, Yang X, Gan J, Chen S, Xiao ZX, Cao Y. CB-Dock2: improved protein-ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res. 2022;50(W1):W159–64. CrossRef

40. Noor F, Tahir ul Qamar M, Ashfaq UA, Albutti A, Alwashmi ASS, Aljasir MA. Network pharmacology approach for medicinal plants: review and assessment. Pharmaceutics. 2022;15:572. CrossRef

41. Di Micco R, Krizhanovsky V, Baker D, d’Adda di Fagagna F. Cellular senescence in ageing: from mechanisms to therapeutic oppoetunities. Nat Rev Mol Cell Biol. 2021;22(2):75–95. CrossRef

42. Desai A, Grolleau-Julius A, Yung R. Leukocyte function in the aging immune system. J Leukoc Biol. 2010;87(6):1001–9. CrossRef

43. d’Adda di Fagagna F, Reaper PM, Clay-Farrace L, Fiegler H, Carr P, von Zglinicki T, et al. A DNA damage checkpoint response in telomere-initiated senescence. Nature. 2003;426(6963):194–8. CrossRef

44. Beck J, Turnquist C, Horikawa I, Harris C. Targeting cellular senescence in cancer and aging: roles of p53 and its isoforms. Carcinogenesis. 2020;41(8):1017–29. CrossRef

45. Tait IS, Li Y, Lu J. PTEN, longevity and age related diseases. Biomedicines. 2013;1(91):17–48. CrossRef

46. Rea IM, Gibson DS, McGilligan C, McNerlan SE, Alexander HD, Ross OA. Age and age-related diseases: role of inflammation triggers and cytokines, Front Immunol. 2018;9:586. CrossRef

47. Lopez-Muguruza E, Matute C. Alterations of oligodendrocyte and myelin energy metabolism in multiple sclerosis. Int J Mol Sci. 2023;24(16):12912. CrossRef

48. Graciani AL, Gutierre MU, Coppi AA, Arida RM, Gutierre RC. Myelin, aging, and physical exercise. Neurobiol Aging. 2023;127:70–81. CrossRef

49. Murray CJ, Vecchiarelli HA, Tremblay ME. Enhancing axonal myelination in seniors: a review exploring the potential impact cannabis has on myelination in the aged brain. Front Aging Neurosci. 2023;15:1119552. CrossRef

50. Gray NE, Hack W, Brandes MW, Zweig JA, Yang L, Marney L, et al. Amelioration of age-related cognitive decline and anxiety in mice by Centella asiatica extract varies by sex, dose and mode of administration. Front Aging. 2024;5:1357922. CrossRef

51. Srivastava S. The mitochondria basis of aging and age-related disorders. Genes. 2017;8(12):398. CrossRef

52. Itoh K, Weis S, Mehrein P, Muller-Hocker J. Cytochrome c oxidase defects of the human substantia nigra in normal aging. Neurobiol Aging. 1996;17(6):843–8. CrossRef

53. Netala VR, Teertam SK, Li H, Zhang Z. A comprehensive review of cardiovascular disease management: cardiac biomarkers, imaging modalities, pharmacotherapy, surgical interventions, and herbal remedies. Cells. 2024;13(17):1471. CrossRef

54. Zhao M, Wu F, Tang Z, Yang X, Liu Y, Wang F, et al. Anti-inflammatory and antioxidant activity of ursolic acid: a systematic review and meta-analysis. Front Pharmacol. 2023;14:1256946. CrossRef

55. Iside C, Scafuro M, Nebbioso A, Altucci L. SIRT1 activation by natural phytochemicals: an overview. Front Pharmacol. 2020;11:1225. CrossRef

56. Christaki S, Spanidi E, Panagiotidou E, Athanasopoulou S, Kyriakoudi A, Mourtzinos I, et al. Cyclodextrins for the delivery of bioactive compounds from natural sources: medicinal, food and cosmetics applications. Pharmaceuticals. 2023;16(9):1274. CrossRef