INTRODUCTION:

Malaria is a vector-borne disease caused by Plasmodium parasites and transmitted through the bites of infected female Anopheles mosquitoes¹. The term “malaria,” derived from the Italian malaria meaning “bad air,” reflects early misconceptions regarding the disease’s origin². Despite advances in treatment, malaria remains widespread, with a complex life cycle involving both mosquitoes (sexual phase) and humans (asexual phase). Symptoms include fever, chills, fatigue, splenomegaly, and anemia³, arising from interactions among the parasite, vector, and host⁴.

Endemic to tropical and subtropical regions, malaria is caused by five human-infecting Plasmodium species (P. falciparum, P. vivax, P. malariae, P. knowlesi, P. ovale), notably P. falciparum, responsible for ~95% of cases and nearly all severe complications^5,6. Global incidence continues to rise, reaching 263 million cases and 597,000 deaths in 2023, with children under five accounting for 76% of deaths in Africa⁷. WHO classifies malaria as a major public health threat, especially in developing nations. However, drug resistance to chloroquine (CQ), atovaquone, amodiaquine (AQ), and sulfadoxine-pyrimethamine (SP) combinations, as well as artemisinin derivatives, may undermine control efforts^8,9. Strategies focusing on parasite elimination, inhibition of host–parasite interactions, and rational drug development underscore that the treatment, prevention, and control of malaria remain major global challenges^10-12.

The WHO has also reported a significant global rise in the use of traditional and complementary medicine (TCM), with 88% of member countries recognizing its importance for the prevention and treatment of chronic diseases related to modern lifestyles¹³. Herbal medicine, rooted in ancient practices, continues to be widely trusted for its perceived efficacy and safety¹⁴. Unlike synthetic drugs, herbal extracts contain a complex mixture of bioactive compounds. Understanding the therapeutic roles of these compounds is essential to optimizing efficacy and minimizing potential side effects^15,16. This is due to the possibility of malaria recurrence, even after clinical recovery and the resolution of parasitemia through the use of antimalarial drugs¹⁷.

Fatoua pilosa Gaudich., locally known as Loworen, is a traditional medicinal plant from Timor Island, East Nusa Tenggara (NTT), Indonesia. Among the Tetun ethnic group, it is commonly used to treat malaria by consuming a decoction made from its roots^18,19. Previous studies have demonstrated its moderate inhibitory activity against chloroquine sensitive P. falciparum 3D7, with an IC₅₀ value of 24.92 µg/mL^19,20. In addition, a decoction of F. pilosa roots is used as a diuretic in the community of Tompu, Central Sulawesi²¹. In addition to its antimalarial and diuretic properties, F. pilosa also exhibits antioxidant, antibacterial, antifungal, anti-inflammatory, and muscle-relaxant activities^22,23. This study investigates the antimalarial potential of F. pilosa using network pharmacology and in silico approaches to support its ethnomedicinal use.

MATERIALS AND METHODS:

Materials:

The bioactive constituents of F. pilosa were identified based on gas chromatography–mass spectrometry (GC–MS) data reported in a previous study by Taek et al.¹⁹. Potential protein targets associated with these constituents were predicted using the Similarity Ensemble Approach (SEA; https://sea.bkslab.org/) and SwissTargetPrediction (http://www.swisstargetprediction.ch/). Protein targets related to malaria were retrieved from the Online Mendelian Inheritance in Man (OMIM; https://omim.org/), GeneCards (https://www.genecards.org), and DisGeNET (https://www.disgenet.org/) databases^24,25.

Tools:

The computational tools used in this study included a personal computer with the following specifications: Asus brand, Windows 11 64-bit operating system, AMD Ryzen™ 7 7435HS 3.1 GHz processor, 8 GB RAM, and 500 GB SSD storage. The primary software utilized included Molegro Virtual Docker (MVD) version 5, ChemOffice Professional 22.0, Chem3D 22.0, and data from the Protein Data Bank (PDB) for protein structural analysis. Additional software tools used were Cytoscape v3.9.1 (https://cytoscape.org/) and several online databases: PubChem (https://pubchem.ncbi.nlm.nih.gov/), UniProt (https://www.uniprot.org/), STRING (https://www.string-db.org/), Enrichr (https://amp.pharm.mssm.edu/Enrichr/), and Metascape (https://metascape.org/gp/index.html#/main/step1).

Methods:

Network pharmacology:

Identification and screening of F. pilosa and Malaria Related Protein Targets:

Bioactive compound targets from F. pilosa were identified using SEA and SwissTargetPrediction, based on the canonical SMILES obtained from PubChem. Duplicate entries were eliminated, and the remaining protein target names were standardized according to the UniProt database. Likewise, malaria-associated targets were collected using the keyword “malaria” from the OMIM, GeneCards, and DisGeNET databases, then screened to remove redundancies and harmonized based on UniProt annotations.

Construction of Compound–Target and General Target Networks:

Interaction networks between F. pilosa bioactive compounds and their respective targets were constructed in Cytoscape v3.9.1, with nodes representing compounds and proteins, and edges indicating interactions. The intersection between F. pilosa targets and malaria related proteins was used to generate a general target network. Network analysis was performed using the CytoNCA plugin with a filtering criterion of degree centrality (DC) ≥ median value. The most influential compound, based on the number of connected core targets, was selected as the main bioactive component.

Protein-Protein Interaction (PPI) Network and Analysis Enrichment:

The protein-protein interaction (PPI) network involving F. pilosa targets and malaria-related proteins was constructed using the STRING database (Homo sapiens, confidence score ≥ 0.4) and visualized via Cytoscape v3.9.1. To identify core targets, intersecting proteins were analyzed using the CytoNCA plugin based on four centrality measures: degree, eigenvector, betweenness, and closeness. Only proteins with values exceeding the median for all metrics were retained. Subsequently, these core targets underwent Gene Ontology (GO) and pathway enrichment analysis using Metascape and Enrichr to clarify their involvement in biological processes, molecular functions, cellular components, and signaling pathways

Pharmacokinetic and Toxicity Analysis:

The pharmacokinetic profiles of the bioactive compounds were assessed using SwissADME (http://www.swissadme.ch/) to evaluate absorption, distribution, metabolism, and excretion (ADME) parameters, alongside Lipinski’s Rule of Five. Additionally, toxicological properties, including LD₅₀ and toxicity classes, were predicted via the ProTox-III server (https://tox-new.charite.de/protox_III/) based on molecular structures.

Molecular docking:

Protein Preparation:

The three-dimensional crystal structure of Falcipain-3 (PDB ID: 3BWK) was obtained from the Protein Data Bank (https://www.rcsb.org). Preparation of the protein involved the removal of water molecules and co-crystallized ligands using BIOVIA Discovery Studio 2021 Client. The refined structure was subsequently exported in .pdb format for further computational studies²⁶.

Ligand Preparation:

Two dimensional structures of the compounds were obtained from PubChem. Water molecules were removed and the structures were optimized using ChemDraw 22.0 and Chem3D 22.0. Three dimensional geometry optimization was performed using the MMFF94 force field, and the optimized structures were saved in SDF format²⁷.

Docking and Interactions Analysis:

Blind docking was performed using AutoDock Vina embedded in CB-Dock2 platform (https://cadd.labshare.cn/cb-dock2/index.php), was employed to conduct molecular docking simulations. It integrates the CurPocket tool, which automatically predicts binding pockets based on protein surface curvature by calculating cavity center and size, allowing accurate identification of ligand-binding sites without prior knowledge of the active site²⁸. This step helps direct docking toward the most probable interaction regions.

Pre-processed protein and ligand structures were uploaded, and the platform automatically carried out cavity detection and docking analysis, yielding data such as cavity dimensions, residue numbers, and Vina binding scores. This method enhances the understanding of ligand–protein interactions and aids in identifying lead compounds with strong binding affinities and therapeutic potential against malaria. In addition, two-dimensional interaction profiles of selected bioactive compounds were visualized using BIOVIA Discovery Studio 2021 Client software, offering further insight into their binding behavior and specificity^29,30.

RESULT:

Assembly material active drug:

GC–MS analysis reported by Taek et al.¹⁹ revealed 19 distinct constituents in the F. pilosa extract (Table 1). A total of nineteen bioactive compounds were identified and assigned specific compound codes along with their corresponding PubChem Compound Identification Numbers (CIDs) for standardized reference. These include Angelicin (M1, CID: 10658), n-Hexadecanoic acid (M2, CID: 985), Heraclin (M3, CID: 2355), Seselin (M4, CID: 68229), Ethyl oleate (M5, CID: 5363269), Brayelin (M6, CID: 618370), Vincanine (M7, CID: 12313538), Strictamine (M8, CID: 21159178), Cycloartenol (M9, CID: 92110), Friedelin (M10, CID: 91472), Aristolone (M11, CID: 165536), β-Amyrin (M12, CID: 73145), Lupene-3-one (M13, CID: 92158), Lupeol (M14, CID: 259846), Urs-12-en-24-oic acid, 3-oxo-, methyl ester (M15, CID: 612822), Moretenol (M16, CID: 91746818), α-Amyrin acetate (M17, CID: 92842), 9,19-Cyclolanost-7-en-3-ol (M18, CID: 634328), and Lup-20(29)-en-3β-ol, acetate (M19, CID: 323074). The structural diversity of these compounds suggests multiple, potentially complementary pharmacological mechanisms.

Identification and screening of F. pilosa and Malaria Related Protein Targets:

The target genes of compound bioactive F. pilosa identified more carry on for evaluate potential therapeutic as an antimalarial. SEA and SwissTargetPrediction collectively assigned 2,700 putative human targets to the 19 F. pilosa constituents (1973 from SwissTargetPrediction; 727 from SEA). After deduplication, 966 unique protein coding genes remained (610 from SwissTargetPrediction and 356 from SEA) (Figure 1).

Figure 1: Collection of malaria related target proteins and F. pilosa, and removal of duplicate targets.

Through the OMIM, GeneCards, and DisGeNET databases, a total of 7,029 genes were initially identified as being associated with malaria (210 from OMIM, 6,809 from GeneCards, and 10 from DisGeNET). After the removal of duplicates, 6,912 unique malaria related genes were retained (93 from OMIM, 6,809 from GeneCards, and 10 from DisGeNET) (Figure 1). Comparative analysis with the predicted targets of F. pilosa bioactive compounds revealed 468 overlapping genes. These genes are considered the intersection between malaria pathogenesis and the pharmacological potential of F. pilosa, and may represent key therapeutic targets (Figure 2a). This overlap is illustrated in a Venn diagram (https://bioinformatics.psb.ugent.be/webtools/Venn/) (Figure 2b).

Figure 2: Related genes between F. pilosa and Malaria. (a) Protein-protein interaction networks (PPI) of 467 selected target genes were built using a STRING database with the high confidence (0.7), (b) Venn diagram of 467 target proteins related to F. pilosa and Malaria

Construction of Compound–Target and General Target Networks

A protein-protein interaction (PPI) network involving 468 selected genes was constructed via the STRING database and Cytoscape, applying a high-confidence threshold of 0.700 (Figure 2a). Network topology was further characterized using the CytoNCA plugin, complemented by clustering analysis to categorize genes according to pharmacological similarities. All identified clusters reached statistical significance (p<0.05), suggesting that these intra-cluster interactions are biologically meaningful³¹. Notably, Cluster 2 was prioritized for further investigation due to its specific association with malaria pathogenesis. This cluster, comprising 35 genes, exhibited a density of 0.50084 and a quality score of 0.41446 (p=0.00011). Based on degree centrality metrics (Table 1 and Figure 3), four pivotal hub genes were identified: Signal Transducer and Activator of Transcription 3 (STAT3), Protooncogene Tyrosine Protein Kinase (SRC), Epidermal Growth Factor Receptor (EGFR), and Janus Kinase 2 (JAK2).

Table 1: Network topology analysis of the results of grouping the 10 best target genes

Name	Degree	Clustering coeficient	Closeness centrality	Betweness centrality
STAT3	83	0.22656	0.45599	0.04149
SRC	80	0.21456	0.48478	0.11813
EGFR	77	0.24778	0.46137	0.03495
JAK2	53	0.31640	0.41951	0.01916
PIK3CA	50	0.32490	0.42074	0.01208

Figure 3: A protein-protein interaction network (PPI) of 35 target genes was selected based on cluster analysis associated with malaria disease, using the STRING database.

Protein-Protein Interaction (PPI) Network and Analysis Enrichment:

The interaction between F. pilosa, malaria, and various molecular pathways is illustrated in Figure 4a. A total of 35 genes from Cluster 2, which were identified as being associated with malaria, were further analyzed using the Enrichr and Metascape platforms. As shown in Figure 4b, the malaria related gene network was found to be enriched in several Gene Ontology (GO) biological processes, including the cell surface receptor protein tyrosine kinase signaling pathway, cell activation, and regulation of the MAPK cascade. These findings were further supported by enrichment analysis using Enrichr (Figure 4c).

Figure 4: a. Integration Results of Network Pharmacology Antimalarial and F. pilosa, b. results gene ontology (GO) enrichment analysis of metascape, c. KEGG pathway results from Enrich

Pharmacokinetic and Toxicity Analysis:

Pharmacokinetic and toxicological assessments of the 19 bioactive compounds were conducted using SwissADME and ProTox-III. The compounds were screened for oral bioavailability based on Lipinski’s Rule of Five, focusing on key descriptors: molecular weight (< 500 g/mol), lipophilicity (logP ≤ 5), hydrogen bond acceptors (HBA ≤ 10), and hydrogen bond donors (HBD ≤ 5). These parameters were utilized to estimate the potential for systemic absorption following oral intake^32,33.

In this study, seven compounds including M1, M3, M4, M6, M7, M8, and M11 did not violate at least one Lipinski parameter. However, all compounds were still considered potential candidates for further development, as each exhibited only a single violation while maintaining other favorable pharmacokinetic characteristics (Table 2). The Topological Polar Surface Area (TPSA) values of the compounds ranged from 17.07 to 52.28 Å². TPSA values below 140 Å² are generally associated with good intestinal absorption, while values below 90 Å² are predictive of the compound's ability to cross the blood-brain barrier³⁴. Notably, very low TPSA values (<25 Å²), observed in compounds such as M10, M11, and other triterpenes, suggest the potential for central nervous system penetration an advantage or risk depending on the intended therapeutic target.

Table 2. Pharmacokinetics and toxicity analysis of F. pilosa compounds

Compound	Molecular weight (g/mol)	Log P (<4.15)	HBA (<10)	HBD (<5)	Lipinski	TPSA (Å²)	Predicted LD ₅₀(mg/kg)	Class toxicity
M1	186.16	1.48	3	0	Yes; 0 violation	43.35	322	4
M2	256.42	4.19	2	1	Yes; 1 violation: LOGP>4.15	37.30	900	4
M3	216.19	1.18	4	0	Yes; 0 violation	52.28	8100	6
M4	228.24	2.37	3	0	Yes; 0 violation	39.44	3850	5
M5	310.51	5.03	2	0	Yes; 1 violation: LOGP>4.15	26.30	5000	5
M6	258.27	2.04	4	0	Yes; 0 violation	48.67	3850	5
M7	292.37	2.63	2	1	Yes; 0 violation	32.34	325	4
M8	322.40	2.82	4	0	Yes; 0 violation	41.90	1	1
M9	426.72	6.92	1	1	Yes; 1 violation: LOGP>4.15	20.23	3450	5
M10	426.72	6.92	1	0	Yes; 1 violation: LOGP>4.15	17.07	500	4
M11	218.33	3.56	1	0	Yes; 0 violation	17.07	1870	4
M12	426.72	6.92	1	1	Yes; 1 violation: LOGP>4.15	20.23	70000	6
M13	424.70	6.82	1	0	Yes; 1 violation: LOGP>4.15	17.07	5000	5
M14	426.72	6.92	1	1	Yes; 1 violation: LOGP>4.15	20.23	2000	4
M15	468.71	5.92	3	0	Yes; 1 violation: LOGP>4.15	43.37	5000	5
M16	468.75	7.08	2	0	Yes; 1 violation: LOGP>4.15	26.30	5000	5
M17	468.75	7.08	2	0	Yes; 1 violation: LOGP>4.15	26.30	3460	5
M18	426.72	6.92	1	1	Yes; 1 violation: LOGP>4.15	20.23	2000	4
M19	468.75	7.08	2	0	Yes; 1 violation: LOGP>4.15	26.30	5000	5

Based on the molecular docking results, compound M9 showed a MolDock score of -10.1 kcal/mol, which is more negative compared to the native ligand (-8.7 kcal/mol). It can be said that M9 is considered to have promising antimalarial potential, as indicated by the favorable binding energy⁵⁰. Visualization of the docking results of the native ligand and M9 using Biovia can be seen in Figure 5.


M0	M9

Figure 5: 2D visualization amino acid compound F. pilosa against Falcipain-3 protein

DISCUSSION:

Malaria is an infectious disease caused by Plasmodium parasites that are transmitted through the bites of infected female Anopheles mosquitoes. Among the various species, Plasmodium falciparum is associated with the most severe and life-threatening form of the disease⁵¹. Its life cycle comprises an incubation period of approximately 10 days within the mosquito vector and 7–20 days in the human host. Following transmission via mosquito saliva, sporozoites enter the bloodstream and migrate to the liver, where they invade hepatocytes and undergo repeated asexual replication through schizogony⁵². The resulting merozoites are subsequently released into the circulation and infect erythrocytes. Continuous intraerythrocytic replication can give rise to gametocytes, representing the sexual stage of the parasite⁴.

The Plasmodium life cycle is highly intricate, involving both vertebrate and invertebrate hosts and encompassing sexual as well as asexual phases. This biological complexity poses significant challenges for antimalarial drug and vaccine development. Although artemisinin-based therapies remain the most effective and are extensively employed in combination regimens, the emergence of drug resistance highlights the urgent need for novel antimalarial agents that offer high efficacy, low toxicity, and reduced production costs^53,54.

Immune responses to P. falciparum antigens accumulate over time, correlating with reduced parasitemia and hospitalization rates. The development of T follicular helper (Tfh) cells, which are crucial in coordinating immune responses, is regulated by cytokines such as interleukin-6 (IL-6), IL-21, and IL-27 through STAT3 signaling⁵⁵. IL-6 signaling via STAT3 promotes Tfh cell programming while limiting Th1 differentiation⁵⁶, and IL-27 induces IL-21 production to further support Tfh cell development. Deficiency or mutation in STAT3 impairs Tfh cell differentiation^57,58, highlighting STAT3’s essential role in immune regulation during malaria infection⁵⁹.

Significant progress has been made in identifying drug targets for P. falciparum, particularly through target based approaches⁶⁰. One critical survival mechanism during the erythrocytic stage involves hemoglobin degradation within host red blood cells. This process provides essential amino acids for parasite metabolism and is mediated by proteolytic enzymes inside the digestive vacuole⁶¹. These proteases are validated therapeutic targets¹¹ and are grouped into (i) proteins directly involved in erythrocyte invasion and lysis and (ii) enzymes such as aspartic proteases (plasmepsins) and cysteine proteases (falcipains), which degrade hemoglobin⁶².

Falcipain-2 (FP2) and falcipain-3 (FP3) are key cysteine proteases involved in hemoglobin hydrolysis. Notably, FP3 shows higher hemoglobinolytic activity than FP2 and is predominantly expressed during the trophozoite stage, making it an attractive antimalarial target^11,63. STAT3 plays an essential role in the immune response to Plasmodium falciparum infection, particularly in regulating memory T-cell cytokine plasticity, thereby supporting host homeostasis and infection control⁶⁴. STAT3 can be activated by pro-inflammatory, anti-inflammatory, and cellular stress stimuli, allowing it to function in both pro- and anti-inflammatory pathways⁶⁵.

Malaria-derived toxins function as immunogens that trigger the secretion of cytokines like TNF-α and IL-1, leading to fever and upregulated endothelial receptor expression⁶⁶. Throughout the erythrocytic stage, the host’s immune response deploys pro-inflammatory cytokines, such as IFN-γ and various interleukins (IL-12 (p70), IL-8, IL-6, IL-1β), to suppress parasitic replication. The equilibrium between these and regulatory cytokines (notably TGF-β and IL-10) is vital, any disruption to this balance can result in hyperinflammation, which is a primary driver of severe malaria and higher mortality rates⁶⁷.

F. pilosa an medicinal plant of Timor Island Indonesia, is traditionally used by the Tetun ethnic group for malaria treatment by consuming decoctions made from its roots^18,19. One of its bioactive compounds, Cycloartenol (M9), showed the strongest binding affinity to falcipain-3 in silico, compared to other compounds and also native ligands. In addition to this antimalarial potential, M9 has also been reported to have antihyperlipidemic activity⁶⁸, anticancer^69. Further investigation into the pharmacological potential of F. pilosa, especially compound M9, is needed to explore its role as a promising therapeutic agent.

CONCLUSION:

This study demonstrates the potential of F. pilosa as a promising source of antimalarial drug candidates through an in silico approach. From 19 identified bioactive compounds, 468 overlapping target genes were found to be associated with both the plant and malaria, with STAT3 identified as a key regulatory gene involved in immune response. Network analysis and pathway enrichment support the pharmacological relevance of these targets. Pharmacokinetic and toxicity evaluations showed that most compounds exhibit favorable oral bioavailability and blood-brain barrier permeability. Among them, Cycloartenol (M9) showed the highest binding affinity to falcipain-3, suggesting it as the most promising candidate for further investigation.

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

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Received on 04.08.2025 Revised on 13.12.2025

Accepted on 09.02.2026 Published on 01.07.2026

Available online from July 04, 2026

Research J. Pharmacy and Technology. 2026;19(7):3341-3350.

DOI: 10.52711/0974-360X.2026.00475

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Ligan	CurPocket ID	Vina score (kcal/mol)	Cavity volume (Å3)	Center (x, y, z)	Docking size (x, y, z)	Amino acid residue
Native ligan (M0)	C1	-8.7	37844	-3, -30, 29	35, 35, 35	GLY92; ASN182 TYR90; GLY91 TYR93
M9	C5	-10.1	354	15, -34, 45	24, 24, 24	TYR168; TYR173 PRO120; CYS89
M10	C1	-9.5	37844	-3, -30, 29	35, 35, 35	ALA46; LEU47
M12	C1	-8.8	37844	-3, -30, 29	35, 35, 35	LYS43; VAL42; PRO41
M13	C4	-9.4	391	-17, -34, 18	22, 22, 22	ASP81 TYR168 PRO120
M14	C4	-9.4	391	-17, -34, 18	22, 22, 22	ASP81 TYR168 PRO120; VAL80
M16	C5	-9.0	354	15, -34, 45	24, 24, 24	GLY88 TYR168; TYR173; TYR90 PRO120; VAL80
M17	C4	-9.7	391	-17, -34, 18	24, 24, 24	TYR168; TYR90 PRO120; CYS89; VAL80
M19	C5	-9.0	354	15, -34, 45	23, 23, 23	PRO120; CYS89; VAL80