Integrated in silico dissection of ABC transporter- mediated hepatoprotective by apigenin and luteolin from Eclipta prostrata using network pharmacology, molecular docking and ADME-toxicity profiling

Integrated in silico dissection of ABC transporter- mediated hepatoprotective by apigenin and luteolin from Eclipta prostrata using network pharmacology, molecular docking and ADME-toxicity profiling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated in silico dissection of ABC transporter- mediated hepatoprotective by apigenin and luteolin from Eclipta prostrata using network pharmacology, molecular docking and ADME-toxicity profiling Mr. R. Pughazendhi, Dr. V.S. Chandrasekaran, Dr.M. Muthukumaran, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8453253/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Liver disease poses a significant global health challenge, necessitation the discover of safer, multi-target natural hepatoprotective agents. In this work, the hepatoprotective attributes of Apigenin and Luteolin, major flavonoids from Eclipta prostrata L., using an integrated in silico framework. A combination of network-based target exploration, pharmacokinetic and toxicological prediction, and structure-based molecular docking were employed to elucidate their mechanisms. ADME-toxicity predictions indicated favourable drug likeness, good oral absorption, and low toxicity for both compounds, suggesting a promising safety profile for therapeutic application. Network and Venn analysis identified 12 common overlapping targets, including XDH, MAOA, ALOX5, GSK3B, PARP1, ABCG2, TOP1, ESR1, ACHE, PTGS2, ABCC1, AND CETR linking these phytochemicals to lover-associated pathological mechanisms. Functional enrichment analysis using gene ontology and KEGG pathway revealed involvement in xenobiotic detoxification, oxidative stress management, ABC transporter activity, bile secretion and metabolic balance. Molecular docking demonstrated stable and strong interaction of Apigenin and Luteolin with key ABC transporters (CFTR, ABCC1 and ABCG2), mediated by hydrogen bonding, pi stacking, and hydrophobic forces. Apigenin exhibited the highest affinity for CFTR Luteolin showed comparable binding and strong interaction with ABCG2, suggesting the role in enhancing toxic efflux and effector hepatoprotective. These finding indicate the Apigenin and Luteolin may act as promising natural hepatoprotective molecules through transporter modulation, antioxidant and anti-inflammatory pathway. The study provides molecular insight supporting the development of natural multi target apoptotic agent, with experimental validation recommended for clinical translation. Pharmacokinetics Toxicology Drug Discovery, Design, & Development Eclipta prostrata. Apigenin Luteolin Hepatoprotective ABC transporter Network Pharmacology ADME-toxicity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Liver disease continues to represent a major global health burden, resulting in substantial morbidity and mortality worldwide due to the cirrhosis, hepatitis and drug induced liver injury (DILI) (Duo et al., 2025 ; Feng et al., 2025; Zhang et al., 2025 ). As the central organ responsible for xenobiotic metabolism and detoxification, the liver is particularly vulnerable to oxidative stress arising from exposure to reactive oxygen species, metabolic bu-products and liver injury (Jadhav et al., 2025 ). conventional hepatoprotective agent, such as silymarin and ursodeoxycholic acids, provide limited therapeutic efficacy and may cause adverse effect, emphasizing the need for safer, multi target natural alternatives (Jaffar et al., 2024 ; Lee et al., 2022 ; Okovityi et al., 2022 ; Pandey et al., 2023 ; Rajappa et al., 2024 ; Tighe et al., 2020 ). Eclipta prostrata (L.) L. (Asteraceae), has long been utilized in ayurvedic and traditional Chinese medicinal system and is widely recognized for its strong hepatoprotective properties (Pan et al., 2021 ; Timalsina & Devkota, 2021 ; Vinyagam et al., 2020 ). Phytochemical analyses have disclosed that its major bioactive constituents include the flavonoid composition, with Apigenin and luteolin as prominent constituents, which are widely recognized for their antioxidant and anti- inflammatory properties and liver-protective activities. These flavonoids act by modulating key molecular aims involved in oxidative stress, inflammation, and detoxification, thereby supporting hepatic function (Wang et al., 2024 ; Yao et al., 2023 ). Accumulating evidence indicates that Apigenin and luteolin are capable of modulating ATP-binding cassette (ABC) transporters, such as p-glycoprotein (ABCB1), MRP2 (ABCC2), and BCRP (ABCG2), which are essential for the hepatic efflux of xenobiotics and bile acids (Fan et al., 2020 ; Li et al., 2020 ; Sharma et al., 2020 ; Tvrdý et al., 2021 ; Zhu et al., 2021 ). Dysregulation of these transporters is closely linked with hepatotoxicity and impaired drug clearance. Thus, natural compounds competent of restoring ABC transporter activity could offer a new therapeutic strategy for liver protection. To systematically explore the hepatoprotective potential of these flavonoids, computational tools such as network pharmacology, molecular docking, and ADME–toxicity prediction have become increasingly valuable (Bergen et al., 2025; Han et al., 2025 ; Jadhav et al., 2025 ; Xiao et al., 20 25)). These in silico methods enable identification of target pathways, prediction of molecular interactions, and evaluation of pharmacokinetic safety profiles prior to experimental validation. Hence, the present investigation seeks to evaluate the hepatoprotective potential of Apigenin and Luteolin derived from Eclipta prostrata using an integrated in silico approach combining network pharmacology, molecular docking, and ADME–toxicity profiling. The central hypothesis is that these flavonoids modulate ABC transporter–mediated hepatic detoxification pathways, thereby enhancing liver protection against oxidative and xenobiotic stress. The outcome of this investigation is anticipated to yield molecular-level insights that facilitate the development of natural, multi-target hepatoprotective agents. 2. MATERIALS AND METHODS 2.1 Ligand Acquisition and Optimization 2.1.1 Retrieval of Phytochemical Structures and Energy Minimization Apigenin and luteolin where identified as major compound of Eclipta prostrata using the IMPPAT database (IMPPAT | IMPPAT: Indian Medicinal Plants, Phytochemistry and Therapeutics) (Azene et al., 2025 ; Ingle et al., 2024 ; Kaur et al., 2025 ; Sinha et al., 2022 ). The structure was downloaded from the PubChem (PubChem) (SDF/SMILES) and opened in the Molegro Molecular viewer (MMV) to check the chemical layout and convert them into the 3D format. Before further analysis, both structures were energy-minimized MMV to remove the stain and stabilize the molecular geometry ((Dittrich et al., 2020 ; Liebschner et al., 2023 ; Tsypin et al., 2023 ). Drug likeness was assessed through the mole soft (Molsoft L.L.C.: Drug-Likeness and molecular property prediction.), where key physio chemical properties ( Molecular weight, LogP, H-bond donors/acceptors and TPSA) were found within acceptable range, allowing both ligand to be considered suitable for docking and pharmacology studies ((Gu et al., 2024 ; Pirie et al., 2024 ; Raj et al., 2024 ). 2.2 ADME and Toxicity profile pharmacokinetic behaviour of Apigenin and luteolin was evaluated using Swiss ADME (SwissADME), were key parameter including gastrointestinal absorption, lipophilicity (LogP), Topological polar surface area (TPSA), hydrogen bond donor and acceptors and bioavailability score for examined (Azzam, 2022 ; Bakchi et al., 2022 ; Fagerholm & Hellberg, 2025 ). compound suitability was sacked against Lipinski’s rule of five to determine the oral drug-likeness, and both ligands matched acceptable range in most criteria, indicating good potential for systemic absorption and drug development feasibility (Bakchi et al., 2022 ). Toxicological assessment was performed using prTox-II (ProTox-3.0 - Prediction of TOXicity of chemicals) where predictive models where used to estimate acute toxicity (LD50), hepatotoxicity, mutagenicity, cytotoxicity and carcinogenic risk (Banerjee et al., 2024 ; Bergen et al., 2025; Lee et al., 2025 ; Sá et al., 2022 ).. The compound was classified under the safe toxicity class with no severe alert flagged in primary prediction filters, suggestion that apigenin and luteolin passes a favourable safety profile for further biological facilitation (Banerjee et al., 2024 ). 2.3 Network pharmacology Analysis 2.3.1 Prediction of Potential Molecular Targets Two screening approach were used to identify potential target. in the first step Swiss target prediction(SwissTargetPrediction) was used for ligand-based prediction where the smiley of Apigenin and luteolin were uploaded and protein with higher probability score shortlisted (Daina & Zoete, 2024 ; Galati et al., 2021 ; He et al., 2025 ; Shaikh et al., 2021 ). In the second step diseases related target were collected from the Gene Cards (GeneCards - Human Genes | Gene Database | Gene Search) by searching terms like hepatotoxicity, liver injury and detoxification (Ayyadurai et al., 2023 ; Bergen et al., 2025; Datta et al., 2023 ; Teschke & Xuan, 2022 ; Zhu et al., 2025 ). Gene where filter based on the relevance score and only those strongly linked to the liver function was selected. The result from both methods were then compared and common high confidence gene were taken forward for the further network and docking studies (Jadhav et al., 2025 ). 2.3.2 Identification of common target by using Venn diagram After obtaining the target and genes, both datasets were compared to find the gene common to the two lists. A Venn diagram (Venny 2.1.0) tool was used to visualize their intersection between the ligand predicted target and the hepatotoxicity related genes (Bao et al., 2020 ; Sindhu et al., 2024 ; Wen et al., 2024 ; Yang et al., 2023 ). The intersecting genes from the diagram were selected as final potential target, as they were related both to the compound and to liver associate pathways. The common targets were then taken forward for network construction and docking analysis. 2.3.3 PPI network construction The common target was imported into STRING (STRING: functional protein association networks) to generate a protein-protein interaction (PPI) network, using homo-sapiens as selector Organism with confidence score ≥ 0.7 (Szklarczyk et al., 2022 , 2024 ). The interaction file was then exported and visualized in ytoscape_v3.10.3, where network parameters, including node degree and centrality were analysed to identify the key hub genes (Doncheva et al., 2022 ; Majeed & Mukhtar, 2023 ; Zhou et al., 2023 ). The core nodes were considered significant for their potential involvement in the hepatoprotection and detoxification pathways and were used for further pathway and docking evaluation. 2.3.4 GO and KEGG enrichment analysis The shortlist target genes was suspected to GO (Gene Ontology) and KEGG enrichment analysis using ShinyGO (ShinyGO 0.85), with homo sapiens selected as the reference species (Agbana et al., 2023 ; Arba, 2025 ; Trivedi et al., 2023 ). Enriched biological process (BP), molecular functions (MF), cellular component (CC) as well as KEGG pathway, were identified using an FDR < 0.05 cut off. The top ranked pathway related to detoxification, bile acid transport, oxidative regulation and ABC transporter activity were recorded and the generator enrichment plots were used to interpret the hepatoprotective relevance of apigenin and Luteolin (Barzegari et al., 2024 ; Dhongadi et al., 2023 ; Jadhav et al., 2025 ; Liu et al., 2025 ). 2.4 Molecular docking The ABC transporter proteins, selected for their role in hepatic detoxification, where download from RCSB protein Data Bank (PDB) (rcsb.org) (Burley, 2021 ; Burley et al., 2022 , 2024; Rose et al., 2020 ; Wankowicz, 2025 ). The structures were cleaned using Molegro Molecular viewer (MMV) by removing water molecules, co crystallised ligands and unwanted heteroatom (Dere et al., 2025 ). The minimized ligand structure where the docked against the prepared protein receptors using Swissdock (SwissDock), producing multiple finding confirmations ((Bugnon et al., 2024 ; Gu et al., 2025 ; Hossain, 2024 ; Martis & Téletchéa, 2025 ). The finest binding pose for each protein-ligands complex was selected based on affinity and full fitness score (Sarıgün et al., 2025 ). Protein-ligand interaction such as hydrogen bonding and hydrophobic contacts were subsequently examined and visualized using BIOVIA studio visualizer to interpret active site engagement and docking stability (Baroroh et al., 2023 ; Errington et al., 2025 ; Tran-Nguyen & Camproux, 2025 ; Verma et al., 2025 ; Wu et al., 2024 ). 3 RESULT AND DISCUSSION 3.1 ADME profile and drug likeness evaluation SwissADME analysis (Table 1 ) confirmed that Apigenin and Luteolin comply with Lipinski’s rules, with no violation, indicating favourable oral bioavailability. both molecules exhibited high GI absorption, tolerable LogP values and moderate TPSA, suggesting good membrane permeability. This bioavailability score (0.55) for both compounds further supports their potential as orally active hepatoprotective activity. The absence of blood brain barrier permeability predicts reduced CNS toxicity, which is desirable for liver targeting molecules. Overall, both compounds demonstrate drug like behaviour with acceptable physicochemical balance, making them suitable for further talking and biological investigation. Table 1 ADME- drug likeness profile of Apigenin and Luteolin Parameter Apigenin Luteolin Molecular Weight (g/mol) 270.24 286.24 LogP 3.22 2.78 H-Bond Donors 3 4 H-Bond Acceptors 4 5 TPSA (Ų) 90.9 111.13 Lipinski Rule Violations 0 0 Bioavailability Score 0.55 0.55 GI Absorption High High BBB No No Result Summary Drug-like Drug-like 3.2 Toxicity evaluation The toxicity is screening outcomes presented in Table 2 indicate the both Apigenin and luteolin fall under toxicity class 5, reflecting minimal acute toxicity with high LD 50 threshold (2500 mg/kg for Apigenin and 3919 mg/kg for Luteolin). Both compounds were predicted to exhibit no hepatotoxic or cytotoxic and immunologically safe, supporting their suitability for liver related therapeutic applications. Carcinogenicity and mutagenicity remained inactive for Apigenin, whereas luteolin show only low risk prediction, which is still with-in tolerable range for natural bio-active compounds. In this analysis, the safety index recommended that both flavonoid permits positive pharmacological tolerance with negligeable toxicity concerns, reinforcing their potential for further biological evaluation. Table 2 Toxicity assessment of Apigenin and Luteolin 3.3 network pharmacology 3.3.1 Target identification and overlapping gene screening Targets screening was initially performed using SwissTargetPridiction for Apigenin and luteolin generating 13 and 28 compound associated gene respectively. Disease related liver injury and hepatotoxicity genes retrieved from GeneCard resulted in a much large catalogue (> 400 genes). Intercepting both dataset using Venn analysis identified 12 common overlapping target (Table 3 and Fig. 1 A), including XDH, MAOA, ALOX5, GSK3B, PARP1, ABCG2, TOP1, ESR1, ACHE, PTGS2, ABCC1 and CFTR. These shared gene represent the core pharmacological nodes linking Eclipta prostrata phytochemicals with liver associated pathological mechanism. 3.3.2 Protein-Protein Interaction (PPI) Network Construction and Hub Gene Selection PPI analysis using STRING database revealed robust connectivity among the overlapping target genes, establishing an extremely collaborative constellation (Fig. 1 B). degree based network topology further ascertained hub genes with criticality, amongst which PTGS2, GSK3B, ABCG2, ABCC1, ESR1, CFTR materialized as dominant regulators (Fig. 1 C). The hubs are pertinent in inflammation, detoxification, oxidative stress regulation, xenobiotic efflux and fibrotic signalling, demonstrating that both flavonoids wield multi nodes hepatoprotective behaviour rather than single target action. Table 3 Identification of compound-associated, disease related and overlapping core target for Apigenin and Luteolin Ligand Apigenin Luteolin NOX4, AKR1B1, CDK5R1/CDK5, XDH, MAOA, FLT3, CA2, CCNB3/CDK1/CCNB1/CCNB2, ALOX5, ADORA1, CA7, GLO1, APP, SYK, GSK3B, PARP1, TTR, MMP9, CA12, MMP2, CA4, MMP12, CD38, CYP1B1, ABCG2, AKR1B10, TNKS2, TNKS, TOP1, ARG1 NOX4, AKR1B1, CDK5R1/CDK5, XDH, MAOA, FLT3, CYP19A1, ESR1, CCNB3/CDK1/CCNB1/CCNB2, ACHE, ADORA1, PTGS2, ESR2, CDK6, ADORA2A, SYK, GSK3B, ABCC1, HSD17B1, TTR, CSNK2A1, CFTR, CYP1B1, ABCG2, AKR1B10, TNKS2, TNKS GeneCards Disease-Related Genes NAT2, CYP2E1, GPT, SLC17A5, GSTM1, CYP3A4, CYP2C9, CYP2B6, F2, CYP1A2, ABCB1, TPMT, TNF, GGT1, ALB, CYP2C19, NR1I2, POLG, CYP2D6, GSTP1, SLCO1B1, NFE2L2, CYP3A5, ABCG2, ALPP, MTHFR, CYP1A1, IL4, ABCB11, ABCC2, IL4R, UGT1A1, IL10, SOD1, DNM1L, CES1, HGF, LPL, GSS, HMOX1, NQO1, ABCB7, NR1H2, NR1I3, IL6, PPARG, APOA1, UGT2B7, CYP2C8, AKR1A1, RASGRP1, KCNIP4, ARHGAP24, EGFR, AHR, HLA-A, SLPI, PTGS2, INS, KRT18, NOS2, SOD2, CES2, ACHE, CASP9, NR1H4, SORD, PPIG, INSR, TP53, MAOB, XDH, LEPR, IGF1, SLCO1B3, CYP7A1, COMT, CD36, GYS1, NOS3, IGF2, CTLA4, GPX4, IRS1, IRS2, LEP, APOC3, GPX1, GYS2, RDH5, UCP1, ADRB3, GPX3, LMX1A, IRS4, ETNK2, MT-CO2, HLA-B, KDR, CYP2A6, ABCC3, NFKB1, PARP1, UGT1A4, IL2, MAPK8, ATP8B1, CAT, IL1B, BCHE, KRT8, MAPK1, SIRT1, HLA-DRB1, SMAD2, TGFB1, GSR, CPT1A, SDHB, TSPAN12, ESR1, MCL1, POMC, XPO1, CD33, NR0B1, CYP7B1, GCLC, COX5A, HRH2, PRL, GCLM, HLA-DQA1, SHBG, TTF2, CYP27A1, ERBB2, PTPN1, TGFA, ABCG1, HLA-C, LTC4S, HPX, BACH1, IL1RAPL2, AFP, GPT2, MDM2, STAT1, ALK, CAV1, SIRT3, ERCC1, MB, F3, APOC2, MKI67, OSMR, AASS, EHHADH, GSTM2, SLC26A1, CP, CFTR, GSK3B, TERT, TNFRSF1A, TNNI3, GLUL, CSF2, DLK1, COL18A1, TXNRD1, MMAB, GSTA1, BAAT, SPINK1, CUX2, APEH, OXA1L, RIPOR2, CD4, CRP, SEPSECS, FTCD, DNAL1, PDGFRA, NLRP3, SCARB1, ABCB4, HMGCS2, PDK4, SLC7A11, IL11, KYAT1, FIP1L1, ITPA, IFNG, SLC19A1, HMGCR, TNFSF10, CSF3, TYMS, HIF1A, POR, PPARA, CYCS, FDFT1, IFNA2, AADAC, EDNRA, EPHX2, UGT1A9, HLA-DQB1, APCS, FANCI, DNMT1, JUN, RPS6KB1, CASP3, TOP1, ABCC1, FASLG, NAT1, MT2A, SLC22A1, SLCO2B1, CCND1, HSP90AA1, BCL2, LDLR, AIFM1, BAX, EIF2AK2, GUSB, MAOA, TNFRSF10B, ALDOB, CCR5, GC, BID, CHKA, RBP4, ADH1B, SLC10A1, SLC22A2, FPGS, ADH1C, HLA-DRB5, RET, CASP8, MPO, ADA, CALR, EGF, CXCR2, ITGB2, ALOX5, ANPEP, APOE, ASS1, CD44, CYP17A1, G6PD, HSPA5, ICAM1, MAP3K5, MAPK3, MAPK9, P4HB, PCSK9, PLG, PNP, RB1, STAT2, ATP7B, CASP2, DHFR, HPRT1, KNG1, ODC1, PIK3C2A, PPARGC1A, ADIPOQ, CETP, DHODH, BAD, GOT1, SLC8A1, TFAM, WARS2, ADORA3, AOX1, CHGA, EIF3A, RIPK3, RPS27A, ADH7, AKR1D1, EPO, GGH, UBE2B, CXCL2, GDF6, PEX6, SLC22A7, IFNA1, IL18BP, SLC47A1, ADGRF5, MTOR, AKT2, CTNNB1, NTRK3, HSPB1, ITGB3, MAPK14, DPYD, ADAM17, CPT2, DNMT3B, EDNRB, FAS, HMGB1, IL2RA, IL6R, KCNH2, LMNA, TF, CCL2, CD40LG, CDC25C, HNF4A, KEAP1, NR3C1, OPRM1, PRKAA2, RHOA, SERPINA1, SQSTM1, STK11, EPCAM, ITGA5, TNFRSF1B, CD79A, CDK1, CHRNA2, CUL3, EPHA3, ERCC2, F8, F9, HSF1, KLK3, PPARD, TDO2, TFAP2A, TLR1, VEGFC, ALAD, ANXA5, APOB, CFL1, FTO, GZMB, IRF1, MTHFD1, NR1H3, OTC, PRDX2, PRKAA1, SGPL1, ACOX1, BCL2L11, CALM1, EGR1, EHMT2, HBB, PCK2, PDX1, PTPA, SLC1A5, TJP1, TKT, ATF3, BAK1, BMP6, CFD, CPOX, CXADR, CXCL10, E2F1, GNMT, HFE, HMBS, IDUA, NAGLU, NCOR1, PRDX5, SPTBN1, SQLE, UROD, ALAS1, CALCA, CD80, CXCL1, ENDOG, EPHX1, HBA1, HSPA4, IL17A, IL18, KLF6, KRT19, KRT7, PFKFB3, SELL, SET, UBC, ABCG8, AOC1, APOA5, BCL3, CPA6, NDUFS4, NR0B2, PNPLA3, S100A11, SULT2A1, TACR2, TCN2, TWNK, CABIN1, PAPSS2, SMN1, TRIT1, AKR7A2, CXCL5, GNRH1, GSTZ1, JUNB, KLRK1, KRIT1, MTF1, NCOA6, SLC10A2, CXCL9, DAPK2, FGF21, NPY4R, SESN2, SLCO1A2, CHRNA9, CROT, LACTB, CKAP2, PDLIM3, SH3BP5, SLC16A4, SLC51A Common Overlapping Genes (Venn Intersection) XDH, MAOA, ALOX5, GSK3B, PARP1, ABCG2, TOP1, ESR1, ACHE, PTGS2, ABCC1, CFTR 3.3.3 GO, KEGG and Clustering functional pathway interpretation GO enrichment (Fig. 2 A) molecular biology (MB) revealed that the identified target genes were predominantly involved in xenobiotic response, oxidative stress management, transmembrane efflux and cellular detoxification, indicating a strong association with hepatoprotection. Molecular function (Fig. 2 B) term further highlight ATP binding, ABC transporter activity, oxidoreductase functions and anion transport, suggesting improved toxin clearance and antioxidant defence. KEGG pathway analysis (Fig. 3 A&B) consistently positioned ABC transporter as the top entrenched pathway. followed by metabolic and inflammatory signalling cascades including phenylalanine metabolism, arachidonic acid metabolism, NF-kP signalling, bile secretion and serotonergic pathway, confirming multi-pathway regulatory potential. Dendrogram clustering (BP, MF, KEGG) further grouped this function into integrated modules linked to detoxification, efflux transport, inflammatory suppression and metabolic restoration, were hub nodes such as ESR1, PTGS2, GSK3B, ABCC1, ABCG2, and CFTR play central regulatory role. collectively the enrichment results strongly support that Apigenin and Luteolin exert hepatoprotective action via transporter mediated, detoxification, toxin clearance oxidative and inflammation control and metabolic homeostasis, reinforcing the therapeutic potential in liver injury. 3.4 Molecular docking Analysis Molecular docking was performed to assess the interaction strength and functional relevance of the screened target. Apigenin and Luteolin were individually docked against the three major ABC transporter protein involved in the hepatic xenobiotic clearance CFTR(2PZF), ABCC1(4C3Z) ana ABCG2(8QCM). The docking affinity value with the protein roles has summariser in Table 4 and the corresponding binding interaction views are illustrated in Fig. 4 and Fig. 5 . Both compounds successfully inserted into the catalytic cavity of all the three transporters farming staple binding architectures Apigenin exhibited the strongest affinity towards CFTR (-8.198 kcal/mol) followed by ABCC1 (-7.106) and ABCG2 (-6.077). likewise, luteolin displayed comparable potency, particularly toward CFTR (-8.081) and ABCG2 (-6.188), reflecting closed structural compatibility with the transporter residues. These scores indicate a favourable ligand transporter fit implying that both phytochemicals may enhance transport efficiency and attenuate intracellular toxin build up during the hepatocellular stress. Interaction profiling elevated that complex stabilization was mediator predominantly through conventional hydrogen bond, carbon hydrogen bond, van der Waals attractions and hydrophobic contract including π-Sigma, π-alkyl and π-π stacking. such as interaction promote firm anchoring inside the binding groove, reducing the probability of ligand displacement. Only minimal unfavourable donor-donor and acceptor-acceptor clashes were observed and were largely out balance by favourable bonding patterns confirming stable and strain free docking. Structurally, Apigenin CFTR (Fig. 4 A) displayed strong hydrogen bonding complemented with hydrophobic occupancy supporting it is high affinity. Apigenin ABCC1 (Fig. 4 B) and Apigenin ABCG2 (Fig. 4 C) interaction including multiple van der Waals and aromatic stabilising contact, enhancing cavity detention. For Luteolin enhanced π-stacking interaction in ABCG2 (Fig. 5 C) suggested better aromatic compatibility, compare to apigenin whereas CFTR and ABCC1 complexes also demonstrated satisfactory stabilization. In this docking outcome strongly correlated with network pharmacology Geo and KEGG enrichment result, confirming that Apigenin and luteolin effective target ABC mediated detoxification, bile/ion transport, xenobiotic efflux and redux regulations. Table 4 Molecular docking score of selected Phyto ligands with ABC transporter proteins showing target role, binding strength (kcal/mol) and structural PDB ID ligands Pathway Target protein Common name Role PDB ID Affinity (kcak/mol) Apigenin ABC transporter CFTR Cystic fibrosis transmembrane conductance regulator Ion channel / transport 2PZF -8.198 (2) ABCC1 ATP binding cassette subfamily C member 1 Transport protein 4C3Z -7.106 (1) ABCG2 ATP binding cassette Super-family G member 2 ABC transporter 8QCM −6.077 (3) Luteolin CFTR Cystic fibrosis transmembrane conductance regulator Ion channel / transport 2PZF -8.081 (2) ABCC1 ATP binding cassette subfamily C member 1 Transport protein 4C3Z -7.003(3) ABCG2 ATP binding cassette Super-family G member 2 ABC transporter 8QCM -6.188 (1) 4 CONCLUSION This analysis combined network pharmacology, ADME-tox profiling combined with molecular with molecular docking analysis was employed to assess the hepatoprotective potential of Apigenin and Luteolin derived from Eclipta prostrata . ADME-tox result confirmed good informed good drug likeness, strong absorption and low toxicity (class 5). Indicating favourable safety for oral publication. network and Venn analysis identified 12 overlapping liver related targets, with hub gene including ABCC1, ABCG2, CFTR, PTGS2, ESR, GSK3B, PARP1, TOP1, ALOX5, AND XDH. GO and KEGG enrichment demonstrate the involvement in xenobiotic detoxification, oxidative stress defence, ABC efflux activity, bile secretion and metabolic balance. Highlighting multi target therapeutic potential. Docking result showed stable ligand- protein complex, where both flavonoids strongly interact with CFTR, ABCC1 and ABCG2 supported by hydrogen bonding, π-stacking and hydrophobic force. Apigenin exhibited highest affinity towards CFTR (-8.198 kcal/mol), while Luteolin showed comparable binding (-8.081 kcal/mol) and strong interaction with ABCG2 suggesting their role in enhancing toxic efflux and production of hepatocytes. In the end of the study the finding indicates that Apigenin and Luteolin may serve as promising natural hepatoprotective molecules acting through transporter modulation and antioxidant, anti-inflammatory pathway. experimental validation is recommended to modulate this in silico insight into the therapeutic application. References Azzam KA (2022) SwissADME and pkCSM Webservers Predictors: an integrated Online Platform for Accurate and Comprehensive Predictions for In Silico ADME/T Properties of Artemisinin and its Derivatives. Kompleksnoe Ispolzovanie Mineralnogo Syra = Complex Use Mineral Resour 325(2):14. https://doi.org/10.31643/2023/6445.13 Agbana YL, Abu T, Akinleye TE, Adeniji JA, Ogbole OO (2023) Integrating network pharmacology and pharmacological evaluation to investigate the anticancer effects of Duranta erecta Linn. Verbenaceae in breast cancer. Res Square (Research Square). https://doi.org/10.21203/rs.3.rs-2462558/v1 Ayyadurai VAS, Deonikar P, Fields C (2023) Mechanistic Understanding of D-Glucaric Acid to Support Liver Detoxification Essential to Muscle Health Using a Computational Systems Biology Approach. Nutrients 15(3):733. https://doi.org/10.3390/nu15030733 Arba M (2025) The Therapeutic Potential of Methanolic Leaf Extract of Syzygium cumini in Managing Type 2 Diabetes Mellitus based on Network Pharmacology Study. DergiPark (Istanbul University) . https://dergipark.org.tr/en/pub/tcandtc/issue/91764/1615590 Azene M, Olaokun OO, Iweriebor BC, Mkolo NM, Obi CL, Alemayehu WS, Assefa S, Abebe Z, Habtamu A, Baye K (2025) Metabolomics and databases driven approach of identification of phytochemicals from seed of Salvia schimperi using high-performance liquid chromatography tandem mass spectrometry. PLoS ONE 20(11). https://doi.org/10.1371/journal.pone.0335929 Bao M, Dai Y, Chen X, Liao S, Feng W, Lin C, Yu Y, Mo Z, Liu Y (2020) Network pharmacology study of vaccarin for the prevention and treatment of postmenopausal osteoporosis. Res Square (Research Square). https://doi.org/10.21203/rs.3.rs-79318/v1 Burley SK (2021) RCSB Protein Data Bank: celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Acta Crystallographica Section A Foundations and Advances , 77 . https://doi.org/10.1107/s0108767321090310 Bakchi B, Krishna AD, Sreecharan E, Ganesh VBJ, Niharika MP, Maharshi S, Puttagunta SB, Sigalapalli DK, Bhandare RR, Shaik AB (2022) An overview on applications of SwissADME web tool in the design and development of anticancer, antitubercular and antimicrobial agents: A medicinal chemist’s perspective. J Mol Struct 1259:132712. https://doi.org/10.1016/j.molstruc.2022.132712 Burley SK, Berman HM, Duarte JM, Feng Z, Flatt JW, Hudson BP, Lowe R, Peisach E, Piehl DW, Rose Y, Šali A, Sekharan M, Shao C, Vallat B, Voigt M, Westbrook JD, Young J, Zardecki C (2022) Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students [Review of Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students ]. Biomolecules , 12 (10), 1425. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/biom12101425 Baroroh U, Muscifa ZS, Destiarani W, Rohmatullah FG, Yusuf M (2023) Molecular interaction analysis and visualization of protein-ligand docking using Biovia Discovery Studio Visualizer. Indonesian J Comput Biology (IJCB) 2(1):22. https://doi.org/10.24198/ijcb.v2i1.46322 Banerjee P, Kemmler E, Dunkel M, Preißner R (2024) ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research , 52 . https://doi.org/10.1093/nar/gkae303 Barzegari E, Ghanbari-Movahed M, Shafiei S, Farzaei MH, Echeverría J (2024) Natural flavonoids for the amelioration of acetaminophen-induced hepatotoxicity: a systems pharmacology-based study. Chem Pap 78(11):6573. https://doi.org/10.1007/s11696-024-03556-6 Bugnon M, Röhrig UF, Goullieux M, Perez MAS, Daina A, Michielin O, Zoete V (2024) SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina. Nucleic Acids Research , 52 . https://doi.org/10.1093/nar/gkae300 Burley, S. K., Bhatt, R. K., Bhikadiya, C., Bi, C., Biester, A., Biswas, P., Bittrich,S., Blaumann, S., Brown, R., Chao, H., Chithari, V. R., Craig, P., Crichlow, G. V.,Duarte, J. M., Dutta, S., Feng, Z., Flatt, J. W., Ghosh, S., Goodsell, D. S., … Zardecki,C. (2024). Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank. Nucleic Acids Research, 53. https://doi.org/10.1093/nar/gkae1091 Burley, S. K., Bhatt, R. K., Bhikadiya, C., Bi, C., Biester, A., Biswas, P., Bittrich,S., Blaumann, S., Brown, R., Chao, H., Chithari, V. R., Craig, P., Crichlow, G. V.,Duarte, J. M., Dutta, S., Feng, Z., Flatt, J. W., Ghosh, S., Goodsell, D. S., … Zardecki,C. (2024). Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank. Nucleic Acids Research, 53. https://doi.org/10.1093/nar/gkae1091 Dittrich B, Chan S, Wiggin SB, Stevens JS, Pidcock E (2020) Fast energy minimization of the CCDC drug-subset structures by molecule-in-cluster computations allows independent structure validation and model completion. CrystEngComm 22(43):7420. https://doi.org/10.1039/d0ce00488j Doncheva NT, Morris JH, Holze H, Kirsch R, Nastou K, Cuesta-Astroz Y, Rattei T, Szklarczyk D, von Mering C, Jensen LJ (2022) Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks. J Proteome Res 22(2):637. https://doi.org/10.1021/acs.jproteome.2c00651 Datta S, Aggarwal D, Sehrawat N, Yadav M, Sharma V, Sharma A, Zghair AN, Dhama K, Sharma A, Kumar V, Sharma AK, Wang H (2023) Hepatoprotective effects of natural drugs: Current trends, scope, relevance and future perspectives [Review of Hepatoprotective effects of natural drugs: Current trends, scope, relevance and future perspectives ]. Phytomedicine 121:155100 Elsevier BV. https://doi.org/10.1016/j.phymed.2023.155100 Dhongadi MR, Pattanashetti L, Patil VS (2023) Elucidating the hepatoprotective mechanisms of Vernonia cinerea phytoactives in drug-induced liver injury by computational approach of network pharmacology, molecular docking and dynamic simulation studies. Res Square (Research Square). https://doi.org/10.21203/rs.3.rs-3237131/v1 Daina A, Zoete V (2024) Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning. Commun Chem 7(1):105. https://doi.org/10.1038/s42004-024-01179-2 Dere D, Pehlivan SN, Silva AD da, de Azevedo WF (2025) Hands-On Docking with Molegro Virtual Docker. Methods in Molecular Biology , 2984 , 125. https://doi.org/10.1007/978-1-0716-4949-7_9 Duo H, You J, Du S, Yu M, Wu S, Yue P, Cui X, Huang Y, Luo J, Pan H, Ye Q (2025) Liver cirrhosis in 2021: Global Burden of Disease study. PLoS ONE 20(7). https://doi.org/10.1371/journal.pone.0328493 Errington D, Schneider C, Bouysset C, Dreyer FA (2025) Assessing interaction recovery of predicted protein-ligand poses. J Cheminform 17(1):76. https://doi.org/10.1186/s13321-025-01011-6 Fan X, Bai J, Hu M, Xu Y, Zhao S, Sun Y, Wang B, Hu J, Li Y (2020) Drug interaction study of flavonoids toward OATP1B1 and their 3D structure activity relationship analysis for predicting hepatoprotective effects. Toxicology 437:152445. https://doi.org/10.1016/j.tox.2020.152445 Fagerholm U, Hellberg S (2025) Human ADME/PK is lost in translation and prediction from in silico to in vitro to in vivo. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2025.02.17.638712 Fagerholm U, Hellberg S (2025) Human ADME/PK is lost in translation and prediction from in silico to in vitro to in vivo. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2025.02.17.638712 Galati S, Stefano MD, Martinelli E, Poli G, Tuccinardi T (2021) Recent Advances in In Silico Target Fishing [Review of Recent Advances in In Silico Target Fishing ]. Molecules 26(17):5124. https://doi.org/10.3390/molecules26175124 . Multidisciplinary Digital Publishing Institute Gu Y, Wang Y, Zhu K, Li W, Liu G, Tang Y (2024) DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles. J Cheminform 16(1). https://doi.org/10.1186/s13321-024-00800-9 Gu S, Shen C, Zhang X, Sun H, Cai H, Luo H, Zhao H, Liu B, Du H, Zhao Y, Fu C, Zhai S, Deng Y, Liu H, Hou T, Kang Y (2025) Benchmarking AI-powered docking methods from the perspective of virtual screening. Nat Mach Intell 7(3):509. https://doi.org/10.1038/s42256-025-00993-0 Hossain MI (2024) Swiss Dock is a Novel Web Technology That Simplifies Molecular Docking. SSRN Electron J. https://doi.org/10.2139/ssrn.5019569 Han J, Zhung W, Jang I, Lee J, Kang MJ, Lee TD, Kwack SJ, Kim K, Hwang D, Lee B-W, Kim HS, Kim WY, Lee S (2025) HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model. J Cheminform 17(1):48. https://doi.org/10.1186/s13321-025-00992-8 He T, Caba K, Ballester PL (2025) A precise comparison of molecular target prediction methods. Digit Discovery. https://doi.org/10.1039/d5dd00199d Ingle SG, Gade A, Hedawoo GB (2024) Systematic review on phytochemicals structure and activity databases. Phytomedicine Plus 4(4):100644. https://doi.org/10.1016/j.phyplu.2024.100644 Jaffar HM, Al-Asmari F, Khan FA, Rahim MA, Zongo E (2024) Silymarin: Unveiling its pharmacological spectrum and therapeutic potential in liver diseases—A comprehensive narrative review [Review of Silymarin: Unveiling its pharmacological spectrum and therapeutic potential in liver diseases—A comprehensive narrative review ]. Food Science & Nutrition , 12 (5), 3097. Wiley. https://doi.org/10.1002/fsn3.4010 Jadhav P, Thomas A, Pathan M, Chaudhari SY, Wavhale R, Chitlange SS (2025) Unlocking the therapeutic potential of unexplored phytocompounds as hepatoprotective agents through integration of network pharmacology and in-silico analysis. Sci Rep 15(1):8425. https://doi.org/10.1038/s41598-025-92868-y Kaur H, Gupta M, Ahmed Z, Nargotra A (2025) Psychoactive Plant Database: a phytochemical resource for neurological drug discovery. Front Pharmacol 16:1569127. https://doi.org/10.3389/fphar.2025.1569127 Li X, Xing H, Qin Z, Yang J, Wang P, Zhang X, Yao Z, Yao X (2020) Potential metabolism determinants and drug–drug interactions of a natural flavanone bavachinin. RSC Adv 10(58):35141. https://doi.org/10.1039/d0ra06961b Lee HA, Chang YW, Sung PS, Yoon EL, Lee HW, Yoo J, Lee Y, An J, Song DS, Cho YY, Kim SU, Kim YJ (2022) Therapeutic mechanisms and beneficial effects of non-antidiabetic drugs in chronic liver diseases [Review of Therapeutic mechanisms and beneficial effects of non-antidiabetic drugs in chronic liver diseases ]. Clinical and Molecular Hepatology , 28 (3), 425. Korean Association for the Study of the Liver. https://doi.org/10.3350/cmh.2022.0186 Liebschner D, Moriarty NW, Poon BK, Adams PD (2023) In situ ligand restraints from quantum-mechanical methods. Acta Crystallogr Sect D Struct Biology 79(2):100. https://doi.org/10.1107/s2059798323000025 Lee H, Kim J, Kim J, Lee Y (2025) Recent advances in AI-based toxicity prediction for drug discovery [Review of Recent advances in AI-based toxicity prediction for drug discovery ]. Front Chem 13:1632046. https://doi.org/10.3389/fchem.2025.1632046 . Frontiers Media Liu X, Wang R, Li Y, Zhang J, Li J, Li S, Ye Q, Cao R, Wang W, Luo W, Beng S, Duan X, Peng C (2025) Development and preliminary mechanistic analysis of compound effervescent hepatoprotective granules. Frontiers in Nutrition , 12 . https://doi.org/10.3389/fnut.2025.1639561 Majeed A, Mukhtar S (2023) Protein–Protein Interaction Network Exploration Using Cytoscape. Methods Mol Biol 2690:419. https://doi.org/10.1007/978-1-0716-3327-4_32 Martis EAF, Téletchéa S (2025) Ten quick tips to perform meaningful and reproducible molecular docking calculations. PLoS Comput Biol 21(5). https://doi.org/10.1371/journal.pcbi.1013030 Okovityi SV, Raikhelson KL, Prikhodko VA (2022) Combined hepatoprotective pharmacotherapy for liver disease. Experimental Clin Gastroenterol 7:5. https://doi.org/10.31146/1682-8658-ecg-203-7-5-20 Pan B, Pan W, Zheng L, Xia C (2021) Pharmacological Mechanisms Underlying the Hepatoprotective Effects of Ecliptae herba on Hepatocellular Carcinoma. Evidence-Based Complementary and Alternative Medicine , 2021 , 1. https://doi.org/10.1155/2021/5591402 Pandey B, Baral R, Kaundinnyayana A, Panta S (2023) Promising hepatoprotective agents from the natural sources: a study of scientific evidence. Egypt Liver J 13(1). https://doi.org/10.1186/s43066-023-00248-w Pirie R, Stanway-Gordon HA, Stewart HL, Wilson KL, Patton S, Tyerman J, Cole DJ, Fowler K, Waring MJ (2024) An analysis of the physicochemical properties of oral drugs from 2000 to 2022. RSC Med Chem 15(9):3125. https://doi.org/10.1039/d4md00160e Rose Y, Duarte JM, Lowe R, Segura J, Bi C, Bhikadiya C, Chen L, Rose A, Bittrich S, Burley SK, Westbrook JD (2020) the PDB Archive. J Mol Biol 433(11):166704. https://doi.org/10.1016/j.jmb.2020.11.003 . RCSB Protein Data Bank: Architectural Advances Towards Integrated Searching and Efficient Access to Macromolecular Structure Data from Raj AS, Acharya R, Patel B, Pawar S (2024) Harnessing Technology for Drug Discovery: Computational Approaches to Toxicokinetics and ADME in Medicinal Plants. Eur J Med Plants 35(4):20. https://doi.org/10.9734/ejmp/2024/v35i41194 Rajappa R, Nallupillai P, Krishna KL, Ramesh M, Annegowda HV (2024) Exploring the Mechanisms and Dosages of Herbal Hepatoprotective Agents. Pharmacognosy Res 16(4):706. https://doi.org/10.5530/pres.16.4.82 Sharma K, Mishra K, Senapati KK, Danciu C (2020) Bioactive Compounds in Nutraceutical and Functional Food for Good Human Health. In IntechOpen eBooks . IntechOpen. https://doi.org/10.5772/intechopen.78846 Shaikh F, Tai HK, Desai N, Siu SWI (2021) LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds. J Cheminform 13(1):44. https://doi.org/10.1186/s13321-021-00523-1 de Sá AGC, Long Y, Portelli S, Pires DEV, Ascher DB (2022) toxCSM: comprehensive prediction of small molecule toxicity profiles. Brief Bioinform 23(5). https://doi.org/10.1093/bib/bbac337 Sinha S, Hazarika A, Johari S, Neog B, Rajkhowa S, Biswas A (2022) IMPDB: Indian Medicinal Phytochemical Database Curated for Drug Designing. J Comput Biophys Chem 21(6):709. https://doi.org/10.1142/s2737416522500302 Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, Bork P, Jensen LJ, von Mering C (2022) The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research , 51 . https://doi.org/10.1093/nar/gkac1000 Sindhu M, Thakur U, Latwal S, Muwal D, Rao A (2024) Network pharmacology reveals physiological manifestations of viperid envenomation and role of nitric oxide in their treatment. Res Square (Research Square). https://doi.org/10.21203/rs.3.rs-4512510/v1 Sarıgün A, Uyar B, Franke V, Akalin A (2025) PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning. MDC Repository (Max-Delbrueck-Center for Molecular Medicine) . https://doi.org/10.48550/arxiv.2506.20043 Szklarczyk D, Nastou K, Koutrouli M, Kirsch R, Mehryary F, Hachilif R, Hu D, Peluso ME, Huang Q, Fang T, Doncheva NT, Pyysalo S, Bork P, Jensen LJ, von Mering C (2024) The STRING database in 2025: protein networks with directionality of regulation. Nucleic Acids Research , 53 . https://doi.org/10.1093/nar/gkae1113 Tighe S, Akhtar D, Iqbal U, Ahmed A (2020) Chronic Liver Disease and Silymarin: A Biochemical and Clinical Review [Review of Chronic Liver Disease and Silymarin: A Biochemical and Clinical Review ]. J Clin Translational Hepatol 8(4):1. https://doi.org/10.14218/jcth.2020.00012 Timalsina D, Devkota HP (2021) Eclipta prostrata (L.) L. (Asteraceae): Ethnomedicinal Uses, Chemical Constituents, and Biological Activities [Review of Eclipta prostrata (L.) L. (Asteraceae): Ethnomedicinal Uses, Chemical Constituents, and Biological Activities ]. Biomolecules 11(11):1738. https://doi.org/10.3390/biom11111738 . Multidisciplinary Digital Publishing Institute Tvrdý V, Pourová J, Jirkovský E, Křen V, Valentová K, Mladěnka P (2021) Systematic review of pharmacokinetics and potential pharmacokinetic interactions of flavonolignans from silymarin [Review of Systematic review of pharmacokinetics and potential pharmacokinetic interactions of flavonolignans from silymarin ]. Med Res Rev 41(4):2195. https://doi.org/10.1002/med.21791 . Wiley Teschke R, Xuan TD (2022) Heavy Metals, Halogenated Hydrocarbons, Phthalates, Glyphosate, Cordycepin, Alcohol, Drugs, and Herbs, Assessed for Liver Injury and Mechanistic Steps [Review of Heavy Metals, Halogenated Hydrocarbons, Phthalates, Glyphosate, Cordycepin, Alcohol, Drugs, and Herbs, Assessed for Liver Injury and Mechanistic Steps ]. Frontiers in Bioscience-Landmark , 27 (11), 314. IMR Press. https://doi.org/10.31083/j.fbl2711314 Trivedi K, Rathaur P, Parmar N, Pancholi S, Gelat B, Chettiar SS, Patel A, Jhala D (2023) Exploring Mechanism of Actions for Eugenol and Beta- Caryophyllene to Combat Colorectal Cancer Chemotherapy Using Network Pharmacology. Res Square (Research Square). https://doi.org/10.21203/rs.3.rs-2767165/v1 Tsypin A, Ugadiarov L, Khrabrov K, Avetisian M, Telepov A, Rumiantsev E, Skrynnik A, Panov AI, Vetrov D, Tutubalina E, Kadurin A (2023) Gradual Optimization Learning for Conformational Energy Minimization. arXiv (Cornell University) . https://doi.org/10.48550/arxiv.2311.06295 Tran-Nguyen V, Camproux A (2025) Computational modeling of protein–ligand interactions: From binding site identification to pose prediction and beyond [Review of Computational modeling of protein–ligand interactions: From binding site identification to pose prediction and beyond ]. Curr Opin Struct Biol 95:103152 Elsevier BV. https://doi.org/10.1016/j.sbi.2025.103152 Vinyagam R, Kumar P, Lee KE, Xu B, Matin MN, Kang SG (2020) Biological and Functional Properties of Wedelolactone in Human Chronic Diseases. Phyton 90(1):1. https://doi.org/10.32604/phyton.2020.013388 Verma VV, Vimal S, Mishra MK, Sharma VK (2025) A comprehensive review on structural insights through molecular visualization: tools, applications, and limitations [Review of A comprehensive review on structural insights through molecular visualization: tools, applications, and limitations ]. Journal of Molecular Modeling , 31 (6), 173. Springer Science + Business Media. https://doi.org/10.1007/s00894-025-06402-y Wang C, Feng X, Li W, Chen L, Wang X, Lan Y-T, Tang R, Jiang T, Zheng L, Liu G (2024) Apigenin as an emerging hepatoprotective agent: current status and future perspectives [Review of Apigenin as an emerging hepatoprotective agent: current status and future perspectives ]. Front Pharmacol 15:1508060 Frontiers Media. https://doi.org/10.3389/fphar.2024.1508060 Wen X, Cheng M, Song Z, Hu J, Liang X, Lang W, Yang M, Zhou R, Hao Y (2024) Molecular mechanism of honeysuckle + forsythia in treatment of acute lung injury based on network pharmacology. Biomedical Rep 20(2). https://doi.org/10.3892/br.2024.1720 Wu N, Zhang R, Peng X, Fang L, Chen K, Jestilä JS (2024) Elucidation of protein–ligand interactions by multiple trajectory analysis methods. Phys Chem Chem Phys 26(8):6903. https://doi.org/10.1039/d3cp03492e Wankowicz SA (2025) Ten rules for a structural bioinformatic analysis. PLoS Comput Biol 21(10). https://doi.org/10.1371/journal.pcbi.1013094 Xiao T, Liu Y, Hu K, Guo K, Zhang M, Wang T, Lei W, Wang W, Zhou S, Hu Y, Su R (2025) Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics. PLoS Comput Biol 21(9). https://doi.org/10.1371/journal.pcbi.1013423 Yang X, Wang C-X, Wang J, Wu S, Yong Q, Li K, Yang J (2023) In silico evidence implicating novel mechanisms of Prunella vulgaris L. as a potential botanical drug against COVID-19-associated acute kidney injury. Frontiers in Pharmacology , 14 . https://doi.org/10.3389/fphar.2023.1188086 Yao C, Dai S, Wang C, Fu K, Wu R, Zhao X, Yao Y, Li Y (2023) Luteolin as a potential hepatoprotective drug: Molecular mechanisms and treatment strategies [Review of Luteolin as a potential hepatoprotective drug: Molecular mechanisms and treatment strategies ]. Biomed Pharmacother 167:115464 Elsevier BV. https://doi.org/10.1016/j.biopha.2023.115464 Zhu Y-D, Guan X, Chen J, Peng S, Finel M, Zhao Y-Y, Wang R, Bi H, Lei M, Wang D, Ge G (2021) Neobavaisoflavone Induces Bilirubin Metabolizing Enzyme UGT1A1 via PPARα and PPARγ. Front Pharmacol 11:628314. https://doi.org/10.3389/fphar.2020.628314 Zhou H, Xu M, Hu P, Li Y, Ren C, Li M, Yang P, Wang S, Liu X (2023) Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms. Front Immunol 14:1172724. https://doi.org/10.3389/fimmu.2023.1172724 Zhang Y, Luo M, Ming Y (2025) Global burden of cirrhosis and other chronic liver diseases caused by specific etiologies from 1990 to 2021. BMC Gastroenterol 25(1):641. https://doi.org/10.1186/s12876-025-04264-5 Zhu Q, Xie X, Fang L, Huang C, Li J (2025) Chronic alcohol intake disrupts cytochrome P450 enzyme activity in alcoholic fatty liver disease: insights into metabolic alterations and therapeutic targets. Front Chem 13:1509785. https://doi.org/10.3389/fchem.2025.1509785 Additional Declarations The authors declare no competing interests. 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A) TOP enriched biological process B) molecular function\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8453253/v1/a6ef4520067e04a654337abb.png"},{"id":99317696,"identity":"aa8c8589-34c5-497f-94dd-f7d4ce8f47f8","added_by":"auto","created_at":"2025-12-31 16:30:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4351564,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway and signalling map of target. A) KEGG bar plot showing top enriched pathway with transporters ranked B) KEGG pathway interaction map highlighting the signalling connectivity of enriched genes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8453253/v1/b5d06ec2c9846d2135d8a93b.png"},{"id":99156420,"identity":"decb9b71-c596-4a2f-b068-a2b90ccd1814","added_by":"auto","created_at":"2025-12-29 11:41:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5019803,"visible":true,"origin":"","legend":"\u003cp\u003edocking interaction profile of Apigenin with ABC-transporter targets (CFTR, ABCC1 and ABCG2). (A) Binding mode of Apigenin vs CFTR (PDB: 2PZF) showing hydrogen-bonding and pocket fit visualization. (B) Interaction of Apigenin vs ABCC1 (PDB: 4C3Z) represented with surface topology and ligand orientation within active cavity. (C) Docked conformation of Apigenin vs ABCG2 (PDB: 8QCM) highlighting hydrophobic contacts, van der Waals forces and π-stacking interactions\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8453253/v1/c85d51c311d455dbae217f35.png"},{"id":99315689,"identity":"cffa03b9-d0fe-4ead-84b5-06376e6c699f","added_by":"auto","created_at":"2025-12-31 16:27:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5025982,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking interaction profile of Luteolin with ABC-transporter targets (CFTR, ABCC1 and ABCG2). (A) Luteolin–CFTR complex (PDB: 2PZF) illustrating hydrogen bonding, ligand pose orientation and binding within the channel pocket. (B) Luteolin–ABCC1 complex (PDB: 4C3Z) presenting interaction surface mapping and ligand accommodation within the transporter active cavity. (C) Luteolin–ABCG2 complex (PDB: 8QCM) demonstrating hydrophobic interactions, van der Waals stabilization and π–stacking contributions within the core binding region.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8453253/v1/3e2e79e4c899a4b15bac7369.png"},{"id":99788229,"identity":"1aa2c971-34de-4376-ad69-7c625c1287ae","added_by":"auto","created_at":"2026-01-08 12:45:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21202170,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8453253/v1/fab467a0-fd26-4a4b-9f6c-e66df7ff616d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegrated in silico dissection of ABC transporter- mediated hepatoprotective by apigenin and luteolin from \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEclipta prostrata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e using network pharmacology, molecular docking and ADME-toxicity profiling\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eLiver disease continues to represent a major global health burden, resulting in substantial morbidity and mortality worldwide due to the cirrhosis, hepatitis and drug induced liver injury (DILI) (Duo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Feng et al., 2025; Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As the central organ responsible for xenobiotic metabolism and detoxification, the liver is particularly vulnerable to oxidative stress arising from exposure to reactive oxygen species, metabolic bu-products and liver injury (Jadhav et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). conventional hepatoprotective agent, such as silymarin and ursodeoxycholic acids, provide limited therapeutic efficacy and may cause adverse effect, emphasizing the need for safer, multi target natural alternatives (Jaffar et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Okovityi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pandey et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rajappa et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tighe et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eEclipta prostrata\u003c/em\u003e (L.) L. (Asteraceae), has long been utilized in ayurvedic and traditional Chinese medicinal system and is widely recognized for its strong hepatoprotective properties (Pan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Timalsina \u0026amp; Devkota, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vinyagam et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Phytochemical analyses have disclosed that its major bioactive constituents include the flavonoid composition, with Apigenin and luteolin as prominent constituents, which are widely recognized for their antioxidant and anti- inflammatory properties and liver-protective activities. These flavonoids act by modulating key molecular aims involved in oxidative stress, inflammation, and detoxification, thereby supporting hepatic function (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccumulating evidence indicates that Apigenin and luteolin are capable of modulating ATP-binding cassette (ABC) transporters, such as p-glycoprotein (ABCB1), MRP2 (ABCC2), and BCRP (ABCG2), which are essential for the hepatic efflux of xenobiotics and bile acids (Fan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tvrd\u0026yacute; et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Dysregulation of these transporters is closely linked with hepatotoxicity and impaired drug clearance. Thus, natural compounds competent of restoring ABC transporter activity could offer a new therapeutic strategy for liver protection.\u003c/p\u003e \u003cp\u003eTo systematically explore the hepatoprotective potential of these flavonoids, computational tools such as network pharmacology, molecular docking, and ADME\u0026ndash;toxicity prediction have become increasingly valuable (Bergen et al., 2025; Han et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jadhav et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., 20 25)). These in silico methods enable identification of target pathways, prediction of molecular interactions, and evaluation of pharmacokinetic safety profiles prior to experimental validation.\u003c/p\u003e \u003cp\u003eHence, the present investigation seeks to evaluate the hepatoprotective potential of Apigenin and Luteolin derived from \u003cem\u003eEclipta prostrata\u003c/em\u003e using an integrated in silico approach combining network pharmacology, molecular docking, and ADME\u0026ndash;toxicity profiling. The central hypothesis is that these flavonoids modulate ABC transporter\u0026ndash;mediated hepatic detoxification pathways, thereby enhancing liver protection against oxidative and xenobiotic stress. The outcome of this investigation is anticipated to yield molecular-level insights that facilitate the development of natural, multi-target hepatoprotective agents.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ligand Acquisition and Optimization\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Retrieval of Phytochemical Structures and Energy Minimization\u003c/h2\u003e \u003cp\u003eApigenin and luteolin where identified as major compound of Eclipta prostrata using the IMPPAT database (IMPPAT | IMPPAT: Indian Medicinal Plants, Phytochemistry and Therapeutics) (Azene et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ingle et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kaur et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sinha et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The structure was downloaded from the PubChem (PubChem) (SDF/SMILES) and opened in the Molegro Molecular viewer (MMV) to check the chemical layout and convert them into the 3D format. Before further analysis, both structures were energy-minimized MMV to remove the stain and stabilize the molecular geometry ((Dittrich et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liebschner et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tsypin et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Drug likeness was assessed through the mole soft (Molsoft L.L.C.: Drug-Likeness and molecular property prediction.), where key physio chemical properties ( Molecular weight, LogP, H-bond donors/acceptors and TPSA) were found within acceptable range, allowing both ligand to be considered suitable for docking and pharmacology studies ((Gu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pirie et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Raj et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 ADME and Toxicity profile\u003c/h2\u003e \u003cp\u003epharmacokinetic behaviour of Apigenin and luteolin was evaluated using Swiss ADME (SwissADME), were key parameter including gastrointestinal absorption, lipophilicity (LogP), Topological polar surface area (TPSA), hydrogen bond donor and acceptors and bioavailability score for examined (Azzam, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bakchi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fagerholm \u0026amp; Hellberg, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). compound suitability was sacked against Lipinski\u0026rsquo;s rule of five to determine the oral drug-likeness, and both ligands matched acceptable range in most criteria, indicating good potential for systemic absorption and drug development feasibility (Bakchi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Toxicological assessment was performed using prTox-II (ProTox-3.0 - Prediction of TOXicity of chemicals) where predictive models where used to estimate acute toxicity (LD50), hepatotoxicity, mutagenicity, cytotoxicity and carcinogenic risk (Banerjee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bergen et al., 2025; Lee et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; S\u0026aacute; et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).. The compound was classified under the safe toxicity class with no severe alert flagged in primary prediction filters, suggestion that apigenin and luteolin passes a favourable safety profile for further biological facilitation (Banerjee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Network pharmacology Analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Prediction of Potential Molecular Targets\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTwo screening approach were used to identify potential target. in the first step Swiss target prediction(SwissTargetPrediction) was used for ligand-based prediction where the smiley of Apigenin and luteolin were uploaded and protein with higher probability score shortlisted (Daina \u0026amp; Zoete, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Galati et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; He et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shaikh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the second step diseases related target were collected from the Gene Cards (GeneCards - Human Genes | Gene Database | Gene Search) by searching terms like hepatotoxicity, liver injury and detoxification (Ayyadurai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bergen et al., 2025; Datta et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Teschke \u0026amp; Xuan, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Gene where filter based on the relevance score and only those strongly linked to the liver function was selected. The result from both methods were then compared and common high confidence gene were taken forward for the further network and docking studies (Jadhav et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Identification of common target by using Venn diagram\u003c/h2\u003e \u003cp\u003eAfter obtaining the target and genes, both datasets were compared to find the gene common to the two lists. A Venn diagram (Venny 2.1.0) tool was used to visualize their intersection between the ligand predicted target and the hepatotoxicity related genes (Bao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sindhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The intersecting genes from the diagram were selected as final potential target, as they were related both to the compound and to liver associate pathways. The common targets were then taken forward for network construction and docking analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 PPI network construction\u003c/h2\u003e \u003cp\u003eThe common target was imported into STRING (STRING: functional protein association networks) to generate a protein-protein interaction (PPI) network, using homo-sapiens as selector Organism with confidence score\u0026thinsp;\u0026ge;\u0026thinsp;0.7 (Szklarczyk et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The interaction file was then exported and visualized in ytoscape_v3.10.3, where network parameters, including node degree and centrality were analysed to identify the key hub genes (Doncheva et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Majeed \u0026amp; Mukhtar, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The core nodes were considered significant for their potential involvement in the hepatoprotection and detoxification pathways and were used for further pathway and docking evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 GO and KEGG enrichment analysis\u003c/h2\u003e \u003cp\u003eThe shortlist target genes was suspected to GO (Gene Ontology) and KEGG enrichment analysis using ShinyGO (ShinyGO 0.85), with homo sapiens selected as the reference species (Agbana et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Arba, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Trivedi et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Enriched biological process (BP), molecular functions (MF), cellular component (CC) as well as KEGG pathway, were identified using an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cut off. The top ranked pathway related to detoxification, bile acid transport, oxidative regulation and ABC transporter activity were recorded and the generator enrichment plots were used to interpret the hepatoprotective relevance of apigenin and Luteolin (Barzegari et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dhongadi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jadhav et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Molecular docking\u003c/h2\u003e \u003cp\u003eThe ABC transporter proteins, selected for their role in hepatic detoxification, where download from RCSB protein Data Bank (PDB) (rcsb.org) (Burley, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Burley et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, 2024; Rose et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wankowicz, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The structures were cleaned using Molegro Molecular viewer (MMV) by removing water molecules, co crystallised ligands and unwanted heteroatom (Dere et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The minimized ligand structure where the docked against the prepared protein receptors using Swissdock (SwissDock), producing multiple finding confirmations ((Bugnon et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hossain, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Martis \u0026amp; T\u0026eacute;letch\u0026eacute;a, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The finest binding pose for each protein-ligands complex was selected based on affinity and full fitness score (Sarıg\u0026uuml;n et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Protein-ligand interaction such as hydrogen bonding and hydrophobic contacts were subsequently examined and visualized using BIOVIA studio visualizer to interpret active site engagement and docking stability (Baroroh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Errington et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tran-Nguyen \u0026amp; Camproux, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Verma et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULT AND DISCUSSION","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 ADME profile and drug likeness evaluation\u003c/h2\u003e\n \u003cp\u003eSwissADME analysis (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) confirmed that Apigenin and Luteolin comply with Lipinski\u0026rsquo;s rules, with no violation, indicating favourable oral bioavailability. both molecules exhibited high GI absorption, tolerable LogP values and moderate TPSA, suggesting good membrane permeability. This bioavailability score (0.55) for both compounds further supports their potential as orally active hepatoprotective activity. The absence of blood brain barrier permeability predicts reduced CNS toxicity, which is desirable for liver targeting molecules. Overall, both compounds demonstrate drug like behaviour with acceptable physicochemical balance, making them suitable for further talking and biological investigation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eADME- drug likeness profile of Apigenin and Luteolin\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eApigenin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLuteolin\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolecular Weight (g/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH-Bond Donors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH-Bond Acceptors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPSA (\u0026Aring;\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipinski Rule Violations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioavailability Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI Absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResult Summary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Toxicity evaluation\u003c/h2\u003e\n \u003cp\u003eThe toxicity is screening outcomes presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e indicate the both Apigenin and luteolin fall under toxicity class 5, reflecting minimal acute toxicity with high LD 50 threshold (2500 mg/kg for Apigenin and 3919 mg/kg for Luteolin). Both compounds were predicted to exhibit no hepatotoxic or cytotoxic and immunologically safe, supporting their suitability for liver related therapeutic applications. Carcinogenicity and mutagenicity remained inactive for Apigenin, whereas luteolin show only low risk prediction, which is still with-in tolerable range for natural bio-active compounds. In this analysis, the safety index recommended that both flavonoid permits positive pharmacological tolerance with negligeable toxicity concerns, reinforcing their potential for further biological evaluation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Toxicity assessment of Apigenin and Luteolin\u003c/div\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1767008116.png\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 network pharmacology\u003c/h2\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Target identification and overlapping gene screening\u003c/h2\u003e\n \u003cp\u003eTargets screening was initially performed using SwissTargetPridiction for Apigenin and luteolin generating 13 and 28 compound associated gene respectively. Disease related liver injury and hepatotoxicity genes retrieved from GeneCard resulted in a much large catalogue (\u0026gt;\u0026thinsp;400 genes). Intercepting both dataset using Venn analysis identified 12 common overlapping target (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA), including XDH, MAOA, ALOX5, GSK3B, PARP1, ABCG2, TOP1, ESR1, ACHE, PTGS2, ABCC1 and CFTR. These shared gene represent the core pharmacological nodes linking \u003cem\u003eEclipta prostrata\u003c/em\u003e phytochemicals with liver associated pathological mechanism.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Protein-Protein Interaction (PPI) Network Construction and Hub Gene Selection\u003c/h2\u003e\n \u003cp\u003ePPI analysis using STRING database revealed robust connectivity among the overlapping target genes, establishing an extremely collaborative constellation (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). degree based network topology further ascertained hub genes with criticality, amongst which PTGS2, GSK3B, ABCG2, ABCC1, ESR1, CFTR materialized as dominant regulators (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). The hubs are pertinent in inflammation, detoxification, oxidative stress regulation, xenobiotic efflux and fibrotic signalling, demonstrating that both flavonoids wield multi nodes hepatoprotective behaviour rather than single target action.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIdentification of compound-associated, disease related and overlapping core target for Apigenin and Luteolin\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eApigenin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLuteolin\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNOX4, AKR1B1, CDK5R1/CDK5, XDH, MAOA, FLT3, CA2, CCNB3/CDK1/CCNB1/CCNB2, ALOX5, ADORA1, CA7, GLO1, APP, SYK, GSK3B, PARP1, TTR, MMP9, CA12, MMP2, CA4, MMP12, CD38, CYP1B1, ABCG2, AKR1B10, TNKS2, TNKS, TOP1, ARG1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNOX4, AKR1B1, CDK5R1/CDK5, XDH, MAOA, FLT3, CYP19A1, ESR1, CCNB3/CDK1/CCNB1/CCNB2, ACHE, ADORA1, PTGS2, ESR2, CDK6, ADORA2A, SYK, GSK3B, ABCC1, HSD17B1, TTR, CSNK2A1, CFTR, CYP1B1, ABCG2, AKR1B10, TNKS2, TNKS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneCards Disease-Related Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNAT2, CYP2E1, GPT, SLC17A5, GSTM1, CYP3A4, CYP2C9, CYP2B6, F2, CYP1A2, ABCB1, TPMT, TNF, GGT1, ALB, CYP2C19, NR1I2, POLG, CYP2D6, GSTP1, SLCO1B1, NFE2L2, CYP3A5, ABCG2, ALPP, MTHFR, CYP1A1, IL4, ABCB11, ABCC2, IL4R, UGT1A1, IL10, SOD1, DNM1L, CES1, HGF, LPL, GSS, HMOX1, NQO1, ABCB7, NR1H2, NR1I3, IL6, PPARG, APOA1, UGT2B7, CYP2C8, AKR1A1, RASGRP1, KCNIP4, ARHGAP24, EGFR, AHR, HLA-A, SLPI, PTGS2, INS, KRT18, NOS2, SOD2, CES2, ACHE, CASP9, NR1H4, SORD, PPIG, INSR, TP53, MAOB, XDH, LEPR, IGF1, SLCO1B3, CYP7A1, COMT, CD36, GYS1, NOS3, IGF2, CTLA4, GPX4, IRS1, IRS2, LEP, APOC3, GPX1, GYS2, RDH5, UCP1, ADRB3, GPX3, LMX1A, IRS4, ETNK2, MT-CO2, HLA-B, KDR, CYP2A6, ABCC3, NFKB1, PARP1, UGT1A4, IL2, MAPK8, ATP8B1, CAT, IL1B, BCHE, KRT8, MAPK1, SIRT1, HLA-DRB1, SMAD2, TGFB1, GSR, CPT1A, SDHB, TSPAN12, ESR1, MCL1, POMC, XPO1, CD33, NR0B1, CYP7B1, GCLC, COX5A, HRH2, PRL, GCLM, HLA-DQA1, SHBG, TTF2, CYP27A1, ERBB2, PTPN1, TGFA, ABCG1, HLA-C, LTC4S, HPX, BACH1, IL1RAPL2, AFP, GPT2, MDM2, STAT1, ALK, CAV1, SIRT3, ERCC1, MB, F3, APOC2, MKI67, OSMR, AASS, EHHADH, GSTM2, SLC26A1, CP, CFTR, GSK3B, TERT, TNFRSF1A, TNNI3, GLUL, CSF2, DLK1, COL18A1, TXNRD1, MMAB, GSTA1, BAAT, SPINK1, CUX2, APEH, OXA1L, RIPOR2, CD4, CRP, SEPSECS, FTCD, DNAL1, PDGFRA, NLRP3, SCARB1, ABCB4, HMGCS2, PDK4, SLC7A11, IL11, KYAT1, FIP1L1, ITPA, IFNG, SLC19A1, HMGCR, TNFSF10, CSF3, TYMS, HIF1A, POR, PPARA, CYCS, FDFT1, IFNA2, AADAC, EDNRA, EPHX2, UGT1A9, HLA-DQB1, APCS, FANCI, DNMT1, JUN, RPS6KB1, CASP3, TOP1, ABCC1, FASLG, NAT1, MT2A, SLC22A1, SLCO2B1, CCND1, HSP90AA1, BCL2, LDLR, AIFM1, BAX, EIF2AK2, GUSB, MAOA, TNFRSF10B, ALDOB, CCR5, GC, BID, CHKA, RBP4, ADH1B, SLC10A1, SLC22A2, FPGS, ADH1C, HLA-DRB5, RET, CASP8, MPO, ADA, CALR, EGF, CXCR2, ITGB2, ALOX5, ANPEP, APOE, ASS1, CD44, CYP17A1, G6PD, HSPA5, ICAM1, MAP3K5, MAPK3, MAPK9, P4HB, PCSK9, PLG, PNP, RB1, STAT2, ATP7B, CASP2, DHFR, HPRT1, KNG1, ODC1, PIK3C2A, PPARGC1A, ADIPOQ, CETP, DHODH, BAD, GOT1, SLC8A1, TFAM, WARS2, ADORA3, AOX1, CHGA, EIF3A, RIPK3, RPS27A, ADH7, AKR1D1, EPO, GGH, UBE2B, CXCL2, GDF6, PEX6, SLC22A7, IFNA1, IL18BP, SLC47A1, ADGRF5, MTOR, AKT2, CTNNB1, NTRK3, HSPB1, ITGB3, MAPK14, DPYD, ADAM17, CPT2, DNMT3B, EDNRB, FAS, HMGB1, IL2RA, IL6R, KCNH2, LMNA, TF, CCL2, CD40LG, CDC25C, HNF4A, KEAP1, NR3C1, OPRM1, PRKAA2, RHOA, SERPINA1, SQSTM1, STK11, EPCAM, ITGA5, TNFRSF1B, CD79A, CDK1, CHRNA2, CUL3, EPHA3, ERCC2, F8, F9, HSF1, KLK3, PPARD, TDO2, TFAP2A, TLR1, VEGFC, ALAD, ANXA5, APOB, CFL1, FTO, GZMB, IRF1, MTHFD1, NR1H3, OTC, PRDX2, PRKAA1, SGPL1, ACOX1, BCL2L11, CALM1, EGR1, EHMT2, HBB, PCK2, PDX1, PTPA, SLC1A5, TJP1, TKT, ATF3, BAK1, BMP6, CFD, CPOX, CXADR, CXCL10, E2F1, GNMT, HFE, HMBS, IDUA, NAGLU, NCOR1, PRDX5, SPTBN1, SQLE, UROD, ALAS1, CALCA, CD80, CXCL1, ENDOG, EPHX1, HBA1, HSPA4, IL17A, IL18, KLF6, KRT19, KRT7, PFKFB3, SELL, SET, UBC, ABCG8, AOC1, APOA5, BCL3, CPA6, NDUFS4, NR0B2, PNPLA3, S100A11, SULT2A1, TACR2, TCN2, TWNK, CABIN1, PAPSS2, SMN1, TRIT1, AKR7A2, CXCL5, GNRH1, GSTZ1, JUNB, KLRK1, KRIT1, MTF1, NCOA6, SLC10A2, CXCL9, DAPK2, FGF21, NPY4R, SESN2, SLCO1A2, CHRNA9, CROT, LACTB, CKAP2, PDLIM3, SH3BP5, SLC16A4, SLC51A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommon Overlapping Genes (Venn Intersection)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eXDH, MAOA, ALOX5, GSK3B, PARP1, ABCG2, TOP1, ESR1, ACHE, PTGS2, ABCC1, CFTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 GO, KEGG and Clustering functional pathway interpretation\u003c/h2\u003e\n \u003cp\u003eGO enrichment (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA) molecular biology (MB) revealed that the identified target genes were predominantly involved in xenobiotic response, oxidative stress management, transmembrane efflux and cellular detoxification, indicating a strong association with hepatoprotection. Molecular function (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB) term further highlight ATP binding, ABC transporter activity, oxidoreductase functions and anion transport, suggesting improved toxin clearance and antioxidant defence. KEGG pathway analysis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026amp;B) consistently positioned ABC transporter as the top entrenched pathway. followed by metabolic and inflammatory signalling cascades including phenylalanine metabolism, arachidonic acid metabolism, NF-kP signalling, bile secretion and serotonergic pathway, confirming multi-pathway regulatory potential. Dendrogram clustering (BP, MF, KEGG) further grouped this function into integrated modules linked to detoxification, efflux transport, inflammatory suppression and metabolic restoration, were hub nodes such as ESR1, PTGS2, GSK3B, ABCC1, ABCG2, and CFTR play central regulatory role. collectively the enrichment results strongly support that Apigenin and Luteolin exert hepatoprotective action via transporter mediated, detoxification, toxin clearance oxidative and inflammation control and metabolic homeostasis, reinforcing the therapeutic potential in liver injury.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Molecular docking Analysis\u003c/h2\u003e\n \u003cp\u003eMolecular docking was performed to assess the interaction strength and functional relevance of the screened target. Apigenin and Luteolin were individually docked against the three major ABC transporter protein involved in the hepatic xenobiotic clearance CFTR(2PZF), ABCC1(4C3Z) ana ABCG2(8QCM). The docking affinity value with the protein roles has summariser in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and the corresponding binding interaction views are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eBoth compounds successfully inserted into the catalytic cavity of all the three transporters farming staple binding architectures Apigenin exhibited the strongest affinity towards CFTR (-8.198 kcal/mol) followed by ABCC1 (-7.106) and ABCG2 (-6.077). likewise, luteolin displayed comparable potency, particularly toward CFTR (-8.081) and ABCG2 (-6.188), reflecting closed structural compatibility with the transporter residues. These scores indicate a favourable ligand transporter fit implying that both phytochemicals may enhance transport efficiency and attenuate intracellular toxin build up during the hepatocellular stress.\u003c/p\u003e\n \u003cp\u003eInteraction profiling elevated that complex stabilization was mediator predominantly through conventional hydrogen bond, carbon hydrogen bond, van der Waals attractions and hydrophobic contract including \u0026pi;-Sigma, \u0026pi;-alkyl and \u0026pi;-\u0026pi; stacking. such as interaction promote firm anchoring inside the binding groove, reducing the probability of ligand displacement. Only minimal unfavourable donor-donor and acceptor-acceptor clashes were observed and were largely out balance by favourable bonding patterns confirming stable and strain free docking.\u003c/p\u003e\n \u003cp\u003eStructurally, Apigenin CFTR (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) displayed strong hydrogen bonding complemented with hydrophobic occupancy supporting it is high affinity. Apigenin ABCC1 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB) and Apigenin ABCG2 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC) interaction including multiple van der Waals and aromatic stabilising contact, enhancing cavity detention. For Luteolin enhanced \u0026pi;-stacking interaction in ABCG2 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC) suggested better aromatic compatibility, compare to apigenin whereas CFTR and ABCC1 complexes also demonstrated satisfactory stabilization.\u003c/p\u003e\n \u003cp\u003eIn this docking outcome strongly correlated with network pharmacology Geo and KEGG enrichment result, confirming that Apigenin and luteolin effective target ABC mediated detoxification, bile/ion transport, xenobiotic efflux and redux regulations.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMolecular docking score of selected Phyto ligands with ABC transporter proteins showing target role, binding strength (kcal/mol) and structural PDB ID\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eligands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathway\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget protein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCommon name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRole\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDB ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAffinity\u003c/p\u003e\n \u003cp\u003e(kcak/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eApigenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eABC transporter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCystic fibrosis transmembrane conductance regulator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon channel / transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2PZF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-8.198 (2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABCC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATP binding cassette subfamily C member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransport protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4C3Z\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-7.106 (1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABCG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATP binding cassette Super-family G member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABC transporter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8QCM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;6.077 (3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLuteolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCystic fibrosis transmembrane conductance regulator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon channel / transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2PZF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-8.081 (2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABCC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATP binding cassette subfamily C member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransport protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4C3Z\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-7.003(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABCG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATP binding cassette Super-family G member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABC transporter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8QCM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-6.188 (1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 CONCLUSION","content":"\u003cp\u003eThis analysis combined network pharmacology, ADME-tox profiling combined with molecular with molecular docking analysis was employed to assess the hepatoprotective potential of Apigenin and Luteolin derived from \u003cem\u003eEclipta prostrata\u003c/em\u003e. ADME-tox result confirmed good informed good drug likeness, strong absorption and low toxicity (class 5). Indicating favourable safety for oral publication. network and Venn analysis identified 12 overlapping liver related targets, with hub gene including ABCC1, ABCG2, CFTR, PTGS2, ESR, GSK3B, PARP1, TOP1, ALOX5, AND XDH. GO and KEGG enrichment demonstrate the involvement in xenobiotic detoxification, oxidative stress defence, ABC efflux activity, bile secretion and metabolic balance. Highlighting multi target therapeutic potential.\u003c/p\u003e \u003cp\u003eDocking result showed stable ligand- protein complex, where both flavonoids strongly interact with CFTR, ABCC1 and ABCG2 supported by hydrogen bonding, π-stacking and hydrophobic force. Apigenin exhibited highest affinity towards CFTR (-8.198 kcal/mol), while Luteolin showed comparable binding (-8.081 kcal/mol) and strong interaction with ABCG2 suggesting their role in enhancing toxic efflux and production of hepatocytes. In the end of the study the finding indicates that Apigenin and Luteolin may serve as promising natural hepatoprotective molecules acting through transporter modulation and antioxidant, anti-inflammatory pathway. experimental validation is recommended to modulate this in silico insight into the therapeutic application.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAzzam KA (2022) SwissADME and pkCSM Webservers Predictors: an integrated Online Platform for Accurate and Comprehensive Predictions for In Silico ADME/T Properties of Artemisinin and its Derivatives. Kompleksnoe Ispolzovanie Mineralnogo Syra = Complex Use Mineral Resour 325(2):14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31643/2023/6445.13\u003c/span\u003e\u003cspan address=\"10.31643/2023/6445.13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgbana YL, Abu T, Akinleye TE, Adeniji JA, Ogbole OO (2023) Integrating network pharmacology and pharmacological evaluation to investigate the anticancer effects of Duranta erecta Linn. Verbenaceae in breast cancer. Res Square (Research Square). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-2462558/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-2462558/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyyadurai VAS, Deonikar P, Fields C (2023) Mechanistic Understanding of D-Glucaric Acid to Support Liver Detoxification Essential to Muscle Health Using a Computational Systems Biology Approach. Nutrients 15(3):733. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu15030733\u003c/span\u003e\u003cspan address=\"10.3390/nu15030733\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArba M (2025) The Therapeutic Potential of Methanolic Leaf Extract of Syzygium cumini in Managing Type 2 Diabetes Mellitus based on Network Pharmacology Study. \u003cem\u003eDergiPark (Istanbul University)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dergipark.org.tr/en/pub/tcandtc/issue/91764/1615590\u003c/span\u003e\u003cspan address=\"https://dergipark.org.tr/en/pub/tcandtc/issue/91764/1615590\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzene M, Olaokun OO, Iweriebor BC, Mkolo NM, Obi CL, Alemayehu WS, Assefa S, Abebe Z, Habtamu A, Baye K (2025) Metabolomics and databases driven approach of identification of phytochemicals from seed of Salvia schimperi using high-performance liquid chromatography tandem mass spectrometry. PLoS ONE 20(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0335929\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0335929\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBao M, Dai Y, Chen X, Liao S, Feng W, Lin C, Yu Y, Mo Z, Liu Y (2020) Network pharmacology study of vaccarin for the prevention and treatment of postmenopausal osteoporosis. Res Square (Research Square). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-79318/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-79318/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurley SK (2021) RCSB Protein Data Bank: celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. \u003cem\u003eActa Crystallographica Section A Foundations and Advances\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1107/s0108767321090310\u003c/span\u003e\u003cspan address=\"10.1107/s0108767321090310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakchi B, Krishna AD, Sreecharan E, Ganesh VBJ, Niharika MP, Maharshi S, Puttagunta SB, Sigalapalli DK, Bhandare RR, Shaik AB (2022) An overview on applications of SwissADME web tool in the design and development of anticancer, antitubercular and antimicrobial agents: A medicinal chemist\u0026rsquo;s perspective. J Mol Struct 1259:132712. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molstruc.2022.132712\u003c/span\u003e\u003cspan address=\"10.1016/j.molstruc.2022.132712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurley SK, Berman HM, Duarte JM, Feng Z, Flatt JW, Hudson BP, Lowe R, Peisach E, Piehl DW, Rose Y, Šali A, Sekharan M, Shao C, Vallat B, Voigt M, Westbrook JD, Young J, Zardecki C (2022) Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students [Review of \u003cem\u003eProtein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students\u003c/em\u003e]. \u003cem\u003eBiomolecules\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(10), 1425. Multidisciplinary Digital Publishing Institute. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biom12101425\u003c/span\u003e\u003cspan address=\"10.3390/biom12101425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaroroh U, Muscifa ZS, Destiarani W, Rohmatullah FG, Yusuf M (2023) Molecular interaction analysis and visualization of protein-ligand docking using Biovia Discovery Studio Visualizer. Indonesian J Comput Biology (IJCB) 2(1):22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24198/ijcb.v2i1.46322\u003c/span\u003e\u003cspan address=\"10.24198/ijcb.v2i1.46322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee P, Kemmler E, Dunkel M, Prei\u0026szlig;ner R (2024) ProTox 3.0: a webserver for the prediction of toxicity of chemicals. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae303\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarzegari E, Ghanbari-Movahed M, Shafiei S, Farzaei MH, Echeverr\u0026iacute;a J (2024) Natural flavonoids for the amelioration of acetaminophen-induced hepatotoxicity: a systems pharmacology-based study. Chem Pap 78(11):6573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11696-024-03556-6\u003c/span\u003e\u003cspan address=\"10.1007/s11696-024-03556-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBugnon M, R\u0026ouml;hrig UF, Goullieux M, Perez MAS, Daina A, Michielin O, Zoete V (2024) SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae300\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurley, S. K., Bhatt, R. K., Bhikadiya, C., Bi, C., Biester, A., Biswas, P., Bittrich,S., Blaumann, S., Brown, R., Chao, H., Chithari, V. R., Craig, P., Crichlow, G. V.,Duarte, J. M., Dutta, S., Feng, Z., Flatt, J. W., Ghosh, S., Goodsell, D. S., \u0026hellip; Zardecki,C. (2024). Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank. Nucleic Acids Research, 53. https://doi.org/10.1093/nar/gkae1091\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurley, S. K., Bhatt, R. K., Bhikadiya, C., Bi, C., Biester, A., Biswas, P., Bittrich,S., Blaumann, S., Brown, R., Chao, H., Chithari, V. R., Craig, P., Crichlow, G. V.,Duarte, J. M., Dutta, S., Feng, Z., Flatt, J. W., Ghosh, S., Goodsell, D. S., \u0026hellip; Zardecki,C. (2024). Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank. Nucleic Acids Research, 53. https://doi.org/10.1093/nar/gkae1091\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDittrich B, Chan S, Wiggin SB, Stevens JS, Pidcock E (2020) Fast energy minimization of the CCDC drug-subset structures by molecule-in-cluster computations allows independent structure validation and model completion. CrystEngComm 22(43):7420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d0ce00488j\u003c/span\u003e\u003cspan address=\"10.1039/d0ce00488j\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoncheva NT, Morris JH, Holze H, Kirsch R, Nastou K, Cuesta-Astroz Y, Rattei T, Szklarczyk D, von Mering C, Jensen LJ (2022) Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks. J Proteome Res 22(2):637. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jproteome.2c00651\u003c/span\u003e\u003cspan address=\"10.1021/acs.jproteome.2c00651\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDatta S, Aggarwal D, Sehrawat N, Yadav M, Sharma V, Sharma A, Zghair AN, Dhama K, Sharma A, Kumar V, Sharma AK, Wang H (2023) Hepatoprotective effects of natural drugs: Current trends, scope, relevance and future perspectives [Review of \u003cem\u003eHepatoprotective effects of natural drugs: Current trends, scope, relevance and future perspectives\u003c/em\u003e]. Phytomedicine 121:155100 Elsevier BV. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phymed.2023.155100\u003c/span\u003e\u003cspan address=\"10.1016/j.phymed.2023.155100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhongadi MR, Pattanashetti L, Patil VS (2023) Elucidating the hepatoprotective mechanisms of Vernonia cinerea phytoactives in drug-induced liver injury by computational approach of network pharmacology, molecular docking and dynamic simulation studies. Res Square (Research Square). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-3237131/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-3237131/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina A, Zoete V (2024) Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning. Commun Chem 7(1):105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s42004-024-01179-2\u003c/span\u003e\u003cspan address=\"10.1038/s42004-024-01179-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDere D, Pehlivan SN, Silva AD da, de Azevedo WF (2025) Hands-On Docking with Molegro Virtual Docker. \u003cem\u003eMethods in Molecular Biology\u003c/em\u003e, \u003cem\u003e2984\u003c/em\u003e, 125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-0716-4949-7_9\u003c/span\u003e\u003cspan address=\"10.1007/978-1-0716-4949-7_9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuo H, You J, Du S, Yu M, Wu S, Yue P, Cui X, Huang Y, Luo J, Pan H, Ye Q (2025) Liver cirrhosis in 2021: Global Burden of Disease study. PLoS ONE 20(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0328493\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0328493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErrington D, Schneider C, Bouysset C, Dreyer FA (2025) Assessing interaction recovery of predicted protein-ligand poses. J Cheminform 17(1):76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13321-025-01011-6\u003c/span\u003e\u003cspan address=\"10.1186/s13321-025-01011-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan X, Bai J, Hu M, Xu Y, Zhao S, Sun Y, Wang B, Hu J, Li Y (2020) Drug interaction study of flavonoids toward OATP1B1 and their 3D structure activity relationship analysis for predicting hepatoprotective effects. Toxicology 437:152445. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tox.2020.152445\u003c/span\u003e\u003cspan address=\"10.1016/j.tox.2020.152445\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFagerholm U, Hellberg S (2025) Human ADME/PK is lost in translation and prediction from in silico to in vitro to in vivo. bioRxiv (Cold Spring Harbor Laboratory). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2025.02.17.638712\u003c/span\u003e\u003cspan address=\"10.1101/2025.02.17.638712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFagerholm U, Hellberg S (2025) Human ADME/PK is lost in translation and prediction from in silico to in vitro to in vivo. bioRxiv (Cold Spring Harbor Laboratory). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2025.02.17.638712\u003c/span\u003e\u003cspan address=\"10.1101/2025.02.17.638712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalati S, Stefano MD, Martinelli E, Poli G, Tuccinardi T (2021) Recent Advances in In Silico Target Fishing [Review of \u003cem\u003eRecent Advances in In Silico Target Fishing\u003c/em\u003e]. Molecules 26(17):5124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules26175124\u003c/span\u003e\u003cspan address=\"10.3390/molecules26175124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Multidisciplinary Digital Publishing Institute\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Y, Wang Y, Zhu K, Li W, Liu G, Tang Y (2024) DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles. J Cheminform 16(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13321-024-00800-9\u003c/span\u003e\u003cspan address=\"10.1186/s13321-024-00800-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu S, Shen C, Zhang X, Sun H, Cai H, Luo H, Zhao H, Liu B, Du H, Zhao Y, Fu C, Zhai S, Deng Y, Liu H, Hou T, Kang Y (2025) Benchmarking AI-powered docking methods from the perspective of virtual screening. Nat Mach Intell 7(3):509. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s42256-025-00993-0\u003c/span\u003e\u003cspan address=\"10.1038/s42256-025-00993-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MI (2024) Swiss Dock is a Novel Web Technology That Simplifies Molecular Docking. SSRN Electron J. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2139/ssrn.5019569\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.5019569\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan J, Zhung W, Jang I, Lee J, Kang MJ, Lee TD, Kwack SJ, Kim K, Hwang D, Lee B-W, Kim HS, Kim WY, Lee S (2025) HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model. J Cheminform 17(1):48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13321-025-00992-8\u003c/span\u003e\u003cspan address=\"10.1186/s13321-025-00992-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe T, Caba K, Ballester PL (2025) A precise comparison of molecular target prediction methods. Digit Discovery. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d5dd00199d\u003c/span\u003e\u003cspan address=\"10.1039/d5dd00199d\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIngle SG, Gade A, Hedawoo GB (2024) Systematic review on phytochemicals structure and activity databases. Phytomedicine Plus 4(4):100644. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phyplu.2024.100644\u003c/span\u003e\u003cspan address=\"10.1016/j.phyplu.2024.100644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaffar HM, Al-Asmari F, Khan FA, Rahim MA, Zongo E (2024) Silymarin: Unveiling its pharmacological spectrum and therapeutic potential in liver diseases\u0026mdash;A comprehensive narrative review [Review of \u003cem\u003eSilymarin: Unveiling its pharmacological spectrum and therapeutic potential in liver diseases\u0026mdash;A comprehensive narrative review\u003c/em\u003e]. \u003cem\u003eFood Science \u0026amp; Nutrition\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), 3097. Wiley. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/fsn3.4010\u003c/span\u003e\u003cspan address=\"10.1002/fsn3.4010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJadhav P, Thomas A, Pathan M, Chaudhari SY, Wavhale R, Chitlange SS (2025) Unlocking the therapeutic potential of unexplored phytocompounds as hepatoprotective agents through integration of network pharmacology and in-silico analysis. Sci Rep 15(1):8425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-92868-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-92868-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur H, Gupta M, Ahmed Z, Nargotra A (2025) Psychoactive Plant Database: a phytochemical resource for neurological drug discovery. Front Pharmacol 16:1569127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2025.1569127\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2025.1569127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Xing H, Qin Z, Yang J, Wang P, Zhang X, Yao Z, Yao X (2020) Potential metabolism determinants and drug\u0026ndash;drug interactions of a natural flavanone bavachinin. RSC Adv 10(58):35141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d0ra06961b\u003c/span\u003e\u003cspan address=\"10.1039/d0ra06961b\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HA, Chang YW, Sung PS, Yoon EL, Lee HW, Yoo J, Lee Y, An J, Song DS, Cho YY, Kim SU, Kim YJ (2022) Therapeutic mechanisms and beneficial effects of non-antidiabetic drugs in chronic liver diseases [Review of \u003cem\u003eTherapeutic mechanisms and beneficial effects of non-antidiabetic drugs in chronic liver diseases\u003c/em\u003e]. \u003cem\u003eClinical and Molecular Hepatology\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(3), 425. Korean Association for the Study of the Liver. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3350/cmh.2022.0186\u003c/span\u003e\u003cspan address=\"10.3350/cmh.2022.0186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiebschner D, Moriarty NW, Poon BK, Adams PD (2023) In situ ligand restraints from quantum-mechanical methods. Acta Crystallogr Sect D Struct Biology 79(2):100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1107/s2059798323000025\u003c/span\u003e\u003cspan address=\"10.1107/s2059798323000025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee H, Kim J, Kim J, Lee Y (2025) Recent advances in AI-based toxicity prediction for drug discovery [Review of \u003cem\u003eRecent advances in AI-based toxicity prediction for drug discovery\u003c/em\u003e]. Front Chem 13:1632046. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fchem.2025.1632046\u003c/span\u003e\u003cspan address=\"10.3389/fchem.2025.1632046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Frontiers Media\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Wang R, Li Y, Zhang J, Li J, Li S, Ye Q, Cao R, Wang W, Luo W, Beng S, Duan X, Peng C (2025) Development and preliminary mechanistic analysis of compound effervescent hepatoprotective granules. \u003cem\u003eFrontiers in Nutrition\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2025.1639561\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2025.1639561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajeed A, Mukhtar S (2023) Protein\u0026ndash;Protein Interaction Network Exploration Using Cytoscape. Methods Mol Biol 2690:419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-0716-3327-4_32\u003c/span\u003e\u003cspan address=\"10.1007/978-1-0716-3327-4_32\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartis EAF, T\u0026eacute;letch\u0026eacute;a S (2025) Ten quick tips to perform meaningful and reproducible molecular docking calculations. PLoS Comput Biol 21(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pcbi.1013030\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1013030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkovityi SV, Raikhelson KL, Prikhodko VA (2022) Combined hepatoprotective pharmacotherapy for liver disease. Experimental Clin Gastroenterol 7:5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31146/1682-8658-ecg-203-7-5-20\u003c/span\u003e\u003cspan address=\"10.31146/1682-8658-ecg-203-7-5-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan B, Pan W, Zheng L, Xia C (2021) Pharmacological Mechanisms Underlying the Hepatoprotective Effects of Ecliptae herba on Hepatocellular Carcinoma. \u003cem\u003eEvidence-Based Complementary and Alternative Medicine\u003c/em\u003e, \u003cem\u003e2021\u003c/em\u003e, 1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2021/5591402\u003c/span\u003e\u003cspan address=\"10.1155/2021/5591402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey B, Baral R, Kaundinnyayana A, Panta S (2023) Promising hepatoprotective agents from the natural sources: a study of scientific evidence. Egypt Liver J 13(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s43066-023-00248-w\u003c/span\u003e\u003cspan address=\"10.1186/s43066-023-00248-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirie R, Stanway-Gordon HA, Stewart HL, Wilson KL, Patton S, Tyerman J, Cole DJ, Fowler K, Waring MJ (2024) An analysis of the physicochemical properties of oral drugs from 2000 to 2022. RSC Med Chem 15(9):3125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d4md00160e\u003c/span\u003e\u003cspan address=\"10.1039/d4md00160e\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRose Y, Duarte JM, Lowe R, Segura J, Bi C, Bhikadiya C, Chen L, Rose A, Bittrich S, Burley SK, Westbrook JD (2020) the PDB Archive. J Mol Biol 433(11):166704. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmb.2020.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jmb.2020.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. RCSB Protein Data Bank: Architectural Advances Towards Integrated Searching and Efficient Access to Macromolecular Structure Data from\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaj AS, Acharya R, Patel B, Pawar S (2024) Harnessing Technology for Drug Discovery: Computational Approaches to Toxicokinetics and ADME in Medicinal Plants. Eur J Med Plants 35(4):20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/ejmp/2024/v35i41194\u003c/span\u003e\u003cspan address=\"10.9734/ejmp/2024/v35i41194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajappa R, Nallupillai P, Krishna KL, Ramesh M, Annegowda HV (2024) Exploring the Mechanisms and Dosages of Herbal Hepatoprotective Agents. Pharmacognosy Res 16(4):706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5530/pres.16.4.82\u003c/span\u003e\u003cspan address=\"10.5530/pres.16.4.82\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma K, Mishra K, Senapati KK, Danciu C (2020) Bioactive Compounds in Nutraceutical and Functional Food for Good Human Health. In \u003cem\u003eIntechOpen eBooks\u003c/em\u003e. IntechOpen. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5772/intechopen.78846\u003c/span\u003e\u003cspan address=\"10.5772/intechopen.78846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaikh F, Tai HK, Desai N, Siu SWI (2021) LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds. J Cheminform 13(1):44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13321-021-00523-1\u003c/span\u003e\u003cspan address=\"10.1186/s13321-021-00523-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede S\u0026aacute; AGC, Long Y, Portelli S, Pires DEV, Ascher DB (2022) toxCSM: comprehensive prediction of small molecule toxicity profiles. Brief Bioinform 23(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bib/bbac337\u003c/span\u003e\u003cspan address=\"10.1093/bib/bbac337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha S, Hazarika A, Johari S, Neog B, Rajkhowa S, Biswas A (2022) IMPDB: Indian Medicinal Phytochemical Database Curated for Drug Designing. J Comput Biophys Chem 21(6):709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1142/s2737416522500302\u003c/span\u003e\u003cspan address=\"10.1142/s2737416522500302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, Bork P, Jensen LJ, von Mering C (2022) The STRING database in 2023: protein\u0026ndash;protein association networks and functional enrichment analyses for any sequenced genome of interest. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkac1000\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac1000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSindhu M, Thakur U, Latwal S, Muwal D, Rao A (2024) Network pharmacology reveals physiological manifestations of viperid envenomation and role of nitric oxide in their treatment. Res Square (Research Square). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-4512510/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-4512510/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarıg\u0026uuml;n A, Uyar B, Franke V, Akalin A (2025) PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning. \u003cem\u003eMDC Repository (Max-Delbrueck-Center for Molecular Medicine)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arxiv.2506.20043\u003c/span\u003e\u003cspan address=\"10.48550/arxiv.2506.20043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Nastou K, Koutrouli M, Kirsch R, Mehryary F, Hachilif R, Hu D, Peluso ME, Huang Q, Fang T, Doncheva NT, Pyysalo S, Bork P, Jensen LJ, von Mering C (2024) The STRING database in 2025: protein networks with directionality of regulation. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae1113\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae1113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTighe S, Akhtar D, Iqbal U, Ahmed A (2020) Chronic Liver Disease and Silymarin: A Biochemical and Clinical Review [Review of \u003cem\u003eChronic Liver Disease and Silymarin: A Biochemical and Clinical Review\u003c/em\u003e]. J Clin Translational Hepatol 8(4):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14218/jcth.2020.00012\u003c/span\u003e\u003cspan address=\"10.14218/jcth.2020.00012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimalsina D, Devkota HP (2021) Eclipta prostrata (L.) L. (Asteraceae): Ethnomedicinal Uses, Chemical Constituents, and Biological Activities [Review of \u003cem\u003eEclipta prostrata (L.) L. (Asteraceae): Ethnomedicinal Uses, Chemical Constituents, and Biological Activities\u003c/em\u003e]. Biomolecules 11(11):1738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biom11111738\u003c/span\u003e\u003cspan address=\"10.3390/biom11111738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Multidisciplinary Digital Publishing Institute\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTvrd\u0026yacute; V, Pourov\u0026aacute; J, Jirkovsk\u0026yacute; E, Křen V, Valentov\u0026aacute; K, Mladěnka P (2021) Systematic review of pharmacokinetics and potential pharmacokinetic interactions of flavonolignans from silymarin [Review of \u003cem\u003eSystematic review of pharmacokinetics and potential pharmacokinetic interactions of flavonolignans from silymarin\u003c/em\u003e]. Med Res Rev 41(4):2195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/med.21791\u003c/span\u003e\u003cspan address=\"10.1002/med.21791\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Wiley\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeschke R, Xuan TD (2022) Heavy Metals, Halogenated Hydrocarbons, Phthalates, Glyphosate, Cordycepin, Alcohol, Drugs, and Herbs, Assessed for Liver Injury and Mechanistic Steps [Review of \u003cem\u003eHeavy Metals, Halogenated Hydrocarbons, Phthalates, Glyphosate, Cordycepin, Alcohol, Drugs, and Herbs, Assessed for Liver Injury and Mechanistic Steps\u003c/em\u003e]. \u003cem\u003eFrontiers in Bioscience-Landmark\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(11), 314. IMR Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31083/j.fbl2711314\u003c/span\u003e\u003cspan address=\"10.31083/j.fbl2711314\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrivedi K, Rathaur P, Parmar N, Pancholi S, Gelat B, Chettiar SS, Patel A, Jhala D (2023) Exploring Mechanism of Actions for Eugenol and Beta- Caryophyllene to Combat Colorectal Cancer Chemotherapy Using Network Pharmacology. Res Square (Research Square). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-2767165/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-2767165/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsypin A, Ugadiarov L, Khrabrov K, Avetisian M, Telepov A, Rumiantsev E, Skrynnik A, Panov AI, Vetrov D, Tutubalina E, Kadurin A (2023) Gradual Optimization Learning for Conformational Energy Minimization. \u003cem\u003earXiv (Cornell University)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arxiv.2311.06295\u003c/span\u003e\u003cspan address=\"10.48550/arxiv.2311.06295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTran-Nguyen V, Camproux A (2025) Computational modeling of protein\u0026ndash;ligand interactions: From binding site identification to pose prediction and beyond [Review of \u003cem\u003eComputational modeling of protein\u0026ndash;ligand interactions: From binding site identification to pose prediction and beyond\u003c/em\u003e]. Curr Opin Struct Biol 95:103152 Elsevier BV. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sbi.2025.103152\u003c/span\u003e\u003cspan address=\"10.1016/j.sbi.2025.103152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVinyagam R, Kumar P, Lee KE, Xu B, Matin MN, Kang SG (2020) Biological and Functional Properties of Wedelolactone in Human Chronic Diseases. Phyton 90(1):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32604/phyton.2020.013388\u003c/span\u003e\u003cspan address=\"10.32604/phyton.2020.013388\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerma VV, Vimal S, Mishra MK, Sharma VK (2025) A comprehensive review on structural insights through molecular visualization: tools, applications, and limitations [Review of \u003cem\u003eA comprehensive review on structural insights through molecular visualization: tools, applications, and limitations\u003c/em\u003e]. \u003cem\u003eJournal of Molecular Modeling\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(6), 173. Springer Science\u0026thinsp;+\u0026thinsp;Business Media. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00894-025-06402-y\u003c/span\u003e\u003cspan address=\"10.1007/s00894-025-06402-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Feng X, Li W, Chen L, Wang X, Lan Y-T, Tang R, Jiang T, Zheng L, Liu G (2024) Apigenin as an emerging hepatoprotective agent: current status and future perspectives [Review of \u003cem\u003eApigenin as an emerging hepatoprotective agent: current status and future perspectives\u003c/em\u003e]. Front Pharmacol 15:1508060 Frontiers Media. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2024.1508060\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2024.1508060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen X, Cheng M, Song Z, Hu J, Liang X, Lang W, Yang M, Zhou R, Hao Y (2024) Molecular mechanism of honeysuckle\u0026thinsp;+\u0026thinsp;forsythia in treatment of acute lung injury based on network pharmacology. Biomedical Rep 20(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/br.2024.1720\u003c/span\u003e\u003cspan address=\"10.3892/br.2024.1720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu N, Zhang R, Peng X, Fang L, Chen K, Jestil\u0026auml; JS (2024) Elucidation of protein\u0026ndash;ligand interactions by multiple trajectory analysis methods. Phys Chem Chem Phys 26(8):6903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d3cp03492e\u003c/span\u003e\u003cspan address=\"10.1039/d3cp03492e\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWankowicz SA (2025) Ten rules for a structural bioinformatic analysis. PLoS Comput Biol 21(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pcbi.1013094\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1013094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao T, Liu Y, Hu K, Guo K, Zhang M, Wang T, Lei W, Wang W, Zhou S, Hu Y, Su R (2025) Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics. PLoS Comput Biol 21(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pcbi.1013423\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1013423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Wang C-X, Wang J, Wu S, Yong Q, Li K, Yang J (2023) In silico evidence implicating novel mechanisms of Prunella vulgaris L. as a potential botanical drug against COVID-19-associated acute kidney injury. \u003cem\u003eFrontiers in Pharmacology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2023.1188086\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2023.1188086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao C, Dai S, Wang C, Fu K, Wu R, Zhao X, Yao Y, Li Y (2023) Luteolin as a potential hepatoprotective drug: Molecular mechanisms and treatment strategies [Review of \u003cem\u003eLuteolin as a potential hepatoprotective drug: Molecular mechanisms and treatment strategies\u003c/em\u003e]. Biomed Pharmacother 167:115464 Elsevier BV. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biopha.2023.115464\u003c/span\u003e\u003cspan address=\"10.1016/j.biopha.2023.115464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y-D, Guan X, Chen J, Peng S, Finel M, Zhao Y-Y, Wang R, Bi H, Lei M, Wang D, Ge G (2021) Neobavaisoflavone Induces Bilirubin Metabolizing Enzyme UGT1A1 via PPARα and PPARγ. Front Pharmacol 11:628314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2020.628314\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2020.628314\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou H, Xu M, Hu P, Li Y, Ren C, Li M, Yang P, Wang S, Liu X (2023) Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms. Front Immunol 14:1172724. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2023.1172724\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1172724\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Luo M, Ming Y (2025) Global burden of cirrhosis and other chronic liver diseases caused by specific etiologies from 1990 to 2021. BMC Gastroenterol 25(1):641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12876-025-04264-5\u003c/span\u003e\u003cspan address=\"10.1186/s12876-025-04264-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Q, Xie X, Fang L, Huang C, Li J (2025) Chronic alcohol intake disrupts cytochrome P450 enzyme activity in alcoholic fatty liver disease: insights into metabolic alterations and therapeutic targets. Front Chem 13:1509785. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fchem.2025.1509785\u003c/span\u003e\u003cspan address=\"10.3389/fchem.2025.1509785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"School of Pharmacy, SBV Karaikal Campus, Sri Balaji Vidyapeeth (Deemed to be University), Pondicherry ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Eclipta prostrata., Apigenin, Luteolin, Hepatoprotective, ABC transporter, Network Pharmacology, ADME-toxicity","lastPublishedDoi":"10.21203/rs.3.rs-8453253/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8453253/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLiver disease poses a significant global health challenge, necessitation the discover of safer, multi-target natural hepatoprotective agents. In this work, the hepatoprotective attributes of Apigenin and Luteolin, major flavonoids from \u003cem\u003eEclipta prostrata\u003c/em\u003e L., using an integrated in silico framework. A combination of network-based target exploration, pharmacokinetic and toxicological prediction, and structure-based molecular docking were employed to elucidate their mechanisms. ADME-toxicity predictions indicated favourable drug likeness, good oral absorption, and low toxicity for both compounds, suggesting a promising safety profile for therapeutic application. Network and Venn analysis identified 12 common overlapping targets, including XDH, MAOA, ALOX5, GSK3B, PARP1, ABCG2, TOP1, ESR1, ACHE, PTGS2, ABCC1, AND CETR linking these phytochemicals to lover-associated pathological mechanisms. Functional enrichment analysis using gene ontology and KEGG pathway revealed involvement in xenobiotic detoxification, oxidative stress management, ABC transporter activity, bile secretion and metabolic balance. Molecular docking demonstrated stable and strong interaction of Apigenin and Luteolin with key ABC transporters (CFTR, ABCC1 and ABCG2), mediated by hydrogen bonding, pi stacking, and hydrophobic forces. Apigenin exhibited the highest affinity for CFTR Luteolin showed comparable binding and strong interaction with ABCG2, suggesting the role in enhancing toxic efflux and effector hepatoprotective. These finding indicate the Apigenin and Luteolin may act as promising natural hepatoprotective molecules through transporter modulation, antioxidant and anti-inflammatory pathway. The study provides molecular insight supporting the development of natural multi target apoptotic agent, with experimental validation recommended for clinical translation.\u003c/p\u003e","manuscriptTitle":"Integrated in silico dissection of ABC transporter- mediated hepatoprotective by apigenin and luteolin from Eclipta prostrata using network pharmacology, molecular docking and ADME-toxicity profiling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 11:41:12","doi":"10.21203/rs.3.rs-8453253/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1211c7f9-f936-4dec-b6bb-a918c58c8ce3","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60233247,"name":"Pharmacokinetics"},{"id":60233248,"name":"Toxicology"},{"id":60233249,"name":"Drug Discovery, Design, \u0026 Development"}],"tags":[],"updatedAt":"2025-12-29T11:41:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 11:41:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8453253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8453253","identity":"rs-8453253","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}