Virtual Screening of Representative Natural Products Library for...

[Full title: Virtual Screening of Representative Natural Products Library for TGF-β-Mediated Liver Cirrhosis: An in silico and in vitro Multi-Target Study]

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OPEN ACCESS International Journal of Pharmacology ISSN 1811-7775 DOI: 10.3923/ijp.2025.521.540 Research Article Virtual Screening of Representative Natural Products Library for TGF- β -Mediated Liver Cirrhosis: An in silico and in vitro Multi-Target Study 1,2 Haytham Mohamedelfatih Mohamed Makki, 1 Ki-Hoon Park, 1,2,3 Youngbuhm Huh, 2,3,4 Ja-Eun Kim, 2,3,5 Ji Hyun Lee, 2,3,6 Hwajin Lee, 7 Sunyoung Kim, 8,9 Hiroyuki Konishi, 10 Na Young Jeong, 1,2,3 Junyang Jung and 11 Hyung-Joo Chung 1 Department of Anatomy and Neurobiology, College of Medicine, Kyung Hee University, Dongdaemun-gu, Seoul 02447, South Korea 2 Department of Biomedical Science, Graduate School of Medicine, Kyung Hee University, Dongdaemun-gu, Seoul 02447, South Korea 3 Department of Precision Medicine, College of Medicine, Kyung Hee University, Dongdaemun-gu, Seoul 02447, South Korea 4 Department of Pharmacology, College of Medicine, Kyung Hee University, Dongdaemun-gu, Seoul 02447, South Korea 5 Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, Dongdaemun-gu, Seoul 02447, South Korea 6 Department of Biochemistry and Molecular Biology, College of Medicine, Kyung Hee University, Dongdaemun-gu, Seoul 02447, South Korea 7 Department of Family Medicine, College of Medicine, Kyung Hee University Medical Center, Kyung Hee University, Dongdaemun-gu, Seoul 02447, South Korea 8 Division of Neuroanatomy, Department of Neuroscience, Yamaguchi University, Graduate School of Medicine, 1-1-1 Minamikogushi, Ube, Yamaguchi 755-8505, Japan 9 Department of Functional Anatomy and Neuroscience, Nagoya University, Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi 466-8550, Japan 10 Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Seo-gu, Busan 49201, South Korea 11 Department of Anesthesiology and Pain Medicine, College of Medicine, Kosin University, Seo-gu, Busan 49267, South Korea Abstract Background and Objective: Transforming Growth Factor Beta (TGF- $ ) significantly contributes to liver cirrhosis pathogenesis by promoting hepatic fibrosis. Drug discovery using molecular docking (MD) offers valuable insights into potential therapeutic candidates. This study investigated the early-stage discovery of potential natural drug candidates targeting the non-canonical TGF- $ signaling pathway in liver cirrhosis pathogenesis. Materials and Methods: A virtual screening of the Korea Chemical Bank (KCB) natural compounds library was performed against key proteins, including TGF- $ Receptor Type-1 (TGF- $ R 1), Focal Adhesion Kinase (FAK) and Phosphoinositide 3-Kinase (PI 3 K), using MD Bioinformatics analysis identified additional targets such as Matrix Metallopeptidase 13 (MMP 13) and explored pathway enrichments. The predicted Absorption, Distribution, Metabolism and Excretion (ADME) properties of promising compounds were evaluated. Experimental validation on HepG 2 cells using RT-qPCR was conducted for the selected compounds. Results: TGF- $ R 1 binders from the KCB library exhibited higher binding affinities (-11.2 to -10.4 kcal/mol) than the reference inhibitor galunisertib (-10.0 kcal/mol). Bioinformatics identified MMP 13 as a potential target for alcoholic liver cirrhosis, with enriched pathways related to cancer, p 53 and PI 3 K-Akt signaling. Notably, dihydrosanguinarine (DHS) and eriocitrin showed promising inhibitory interactions with fibrogenic kinases. The ADMET analysis indicated DHS, trisindoline and " -Naphthoflavone ( " -NF) as viable oral candidates. The RT-qPCR results highlighted luteolinʼs inhibitory effects, whereas diosmetin and " -NF upregulated target gene expressions. Conclusion: In silico findings underscore the potential of promising natural compounds for liver cirrhosis therapy. However, further in vitro and in vivo studies are needed to confirm their antifibrotic efficacy and therapeutic value Key words: Liver cirrhosis, TGF- $ signaling pathway, molecular docking, natural compounds, bioinformatics, pharmacokinetics profile (ADMET) Citation: Makki, H.M.M., K.H. Park, Y. Huh, J.E. Kim and J.H. Lee et al ., 2025. Virtual screening of representative natural products library for TGF- $ -mediated liver cirrhosis: An in silico and in vitro multi-target study. Int. J. Pharmacol., 21: 521-540 Corresponding Author: Na Young Jeong, Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Seo-gu, Busan 49201, South Korea Junyang Jung, Department of Anatomy and Neurobiology, College of Medicine, Kyung Hee University, Dongdaemun-gu, Seoul, 02447, South Korea Hyung-Joo Chung, Department of Anesthesiology and Pain Medicine, College of Medicine, Kosin University, Seo-gu, Busan 49267, South Korea Copyright: © 2025 Haytham Mohamedelfatih Mohamed Makki et al . This is an open access article distributed under the terms of the creative commons attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. Competing Interest: The authors have declared that no competing interest exists Data Availability: All relevant data are within the paper and its supporting information files.

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Int. J. Pharmacol., 21 (3): 521-540, 2025 INTRODUCTION Liver cirrhosis (LC) is a chronic liver disease that results from various etiologies, including viral hepatitis, alcohol abuse and non-alcoholic fatty liver disease. It is associated with significant morbidity and mortality worldwide. Often, liver cirrhotic patients end with the emergence of Hepatocellular Carcinoma (HCC) 1,2 One of the key molecular pathways involved in the pathogenesis of liver cirrhosis is the Transforming Growth Factor Beta (TGF- $ ) signaling pathway. The TGF- $ pathway plays a crucial role in the regulation of cell growth, differentiation and tissue homeostasis. TGF- $ ligands bind to TGF- $ receptors, leading to the activation of downstream signaling cascades, including the canonical Smad-dependent pathway and non-Smad signaling pathways such as MAPK and PI 3 K/Akt. Dysregulation of the TGF- $ pathway has been implicated in the development and progression of liver cirrhosis 3,4 . The TGF- $ plays a pivotal role in hepatic fibrosis, the hallmark of liver cirrhosis. It promotes the activation of Hepatic Stellate Cells (HSCs) into myofibroblasts, which are responsible for excessive production and deposition of Extracellular Matrix (ECM) components, such as collagen. The TGF- $ induces the expression of ECM proteins and inhibits their degradation, leading to ECM accumulation and fibrotic scar formation 5,6 Given the significant role of the TGF- $ in LC, targeting this pathway has emerged as a potential therapeutic strategy Several preclinical and clinical studies have explored the efficacy of TGF- $ pathway inhibitors, such as TGF- $ receptor type-1 (TGF- $ R 1) inhibitors: galunisertib and vactosertib 7,8 In silico is computational analysis and simulation performed on a computer, using algorithms, mathematical models and molecular dynamics to study biological systems without time-consuming experiments. In drug discovery, in silico methods are important for identifying potential drug candidates through virtual screening of chemical libraries against target proteins, enabling rapid identification of compounds with high binding affinity, specificity and low toxicity. Molecular Docking (MD) is a specific in silico technique used to predict binding mode and affinity between a small molecule and a protein receptor. This information is useful for understanding drug mechanisms, optimizing lead compounds and designing new molecules with improved pharmacological properties 9-11 Natural compounds have long been a valuable source of therapeutic candidates in drug discovery and development Many of these compounds have shown promising biological activities and therapeutic potential in treating various diseases. Itʼs important to note that while natural compounds show promise, their development into therapeutic drugs often involves further research, clinical trials and safety assessments Additionally, the effectiveness of these compounds may vary depending on factors such as dosage, bioavailability and interaction with other medications 12 . Drugs with a single target might be ineffective in preventing or curing diseases that induce pathogenesis via multiple target pathways 13 Recently, several research studies were conducted by virtual screening of natural compounds against multiple targets in LC/HCC 14-17 . Therefore, this multi-target approach study aimed to investigate promising candidates from the KCB representative natural compounds library targeting TGF- $ R 1, FAK and PI 3 K through the MD technique. Another approach was to predict and identify potential protein targets related to LC through bioinformatics. MATERIALS AND METHODS Study area: This study was conducted for 9 months (April to December, 2023) at the Department of Anatomy and Neurobiology, College of Medicine, Kyung Hee University (Seoul Campus) Software: Discovery Studio Client (BIOVIA, Dassault Systèmes, v 21.1.0, San Diego, 2021), AutoDockTools(v 1.5.7) 18 , OpenBabelGUI (v 2.4.1) 19 , AutoDock Vina 20 and Padre, the Perl-integrated Development Environment (IDE) were used in a Samsung notebook with specifications: Intel(R) Celeron(R) 6305 @1.80 GHz, Windows 10 Education, 64-bit OS and 12.0 GB RAM Receptor and ligand preparations: The three-dimensional (3 D) structures of the target proteins TGF $ R 1, FAK and PI 3 K with PDB IDs: 5 E 8 S 21 , 3 BZ 3 22 and 5 T 23 23 , respectively (Table S 1), were downloaded in PDB file format from the Research Collaboratory for Structural Bioinformatics (RCSB) database 24 . Using the Discovery Studio Client software, each receptor was modified by deleting water molecules, obtaining XYZ values of the active binding sites for docking based on the original ligand in the crystal complex, followed by deleting the contaminant ligands. Then, the free-ligand receptor was opened in the AutoDockTools software for adding polar hydrogens and Kollman charges and finally saved in PDBQT format 522

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Int. J. Pharmacol., 21 (3): 521-540, 2025 A total of 1278 ligands in PDB file format were obtained from the representative natural compounds library in KCB and then converted to 3 D structures in PDBQT format using OpenBabelGUI software. Galunisertib (CID: 10090485) and vactosertib (CID: 54766013) were downloaded from PubChem in SDF format and then converted to AutoDock Structure File (PDBQT) format Molecular docking (MD): After collecting all required PDBQT files in one folder, the docking of up to 100 ligands into each targeted receptor per session was executed in AutoDock Vina and Padre, the Perl IDE software using the command prompt “perl Vina̲windows.pl” Prediction and identification of potential targets related to alcoholic liver cirrhosis (ALC) and KCB natural compounds: The genes associated with ALC were obtained from DisGeNET 25 . However, the promising targets related to the best TGF $ R 1 binders among KCB natural compounds were predicted using SwissTargetPrediction 26 . Overlapped genes were identified using an online VENNY (v 2.1.0) diagram tool Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis: The identified overlapped genes were uploaded into the Database for Annotation, Visualization and Integrated Discovery (DAVID) 27 bioinformatics database for the gene functional annotation, including GO and KEGG analyses Protein-protein interaction (PPI) network analysis: The overlapped gene targets were uploaded into the STRING database 28 (v 12.0), which provides evaluation and integration of both physical and functional protein-protein interactions Then, the PPI information was visualized with Cytoscape 29 (v 3.10.0), which is an open-source software platform that pathways while also combining annotations, gene expression profiles and other state data In silico prediction of the ADMET profile and bioactivity score: The SMILES structures of the top ten KCB hit compounds and the reference inhibitors were uploaded to online web tools, ADMETlab 30 (v 2.0) and SwissADME 31 . These web tools provide estimations for the pharmacokinetic profile (absorption, distribution, metabolism, excretion and toxicity, ADMET) in addition to the physicochemical and oral druggable properties of the small molecules. The SDF files of small molecules were uploaded to Molinspiration cheminformatics (Molinspiration Cheminformatics free web services, Slovensky Grob, Slovakia), which is a free web tool used to predict bioactivity scores for important drug targets such as kinases and nuclear receptors Experimental validation using cell viability assay and RT-qPCR: To validate the protein-ligand affinities the effects of three compounds, Luteolin (Cat. No. 2874, Tocris Bioscience), Diosmetin (Cat. No. D 7321, Sigma-Aldrich) and Alpha-naphthoflavone ( " -NF; Cat. No. N 5757, Sigma-Aldrich) were tested on human hepatoma (HepG 2) cells, which were obtained from the Korean cell line bank (KCLB, Cat. No. 88065, Seoul, South Korea) and maintained at 37 E C with 5% CO 2 in minimum essential medium (MEM, Cat. No. 11095080, Gibco™, Billings, Montana, USA) supplemented with 25 mM HEPES, 25 mM NaHCO 3 and 10% FBS. Drugs were diluted in MEM to the required working concentrations and then incubated with 3000 cells/well in a 96-well plate for 48 hrs to detect cell viability using MTS assay (CellTiter 96® AQ ueous One Solution Cell Proliferation Assay, Cat. No. G 3581, Promega) according to the manufacturerʼs protocol. Moreover, examined the mRNA expressions of the target proteins using RT-qPCR as previously described by Park et al 32 . Briefly, cells were cultured in a 6-well plate for 24 hrs and then the media was replaced with a drug-containing media for another 24 hrs. used Trizol® Reagent (Cat. No. 15596-026, Invitrogen, Carlsbad, California, USA) for total RNA extraction from cells of each group. To make cDNA from RNA templates, used oligo-dT primers and the Superscript III First-Strand kit (Cat. No. 18080-044, Invitrogen). Finally, the RT-qPCR reaction on the Takara Thermal Cycler Dice Real Time System Lite (Takara Bio CO., Otsu, Japan) with 10 ng of cDNA was conducted, 4 pmoles of each gene-specific primer and TB Green Premix Ex Taq (Cat. No. RR 420 A, Takara Bio Co., Otsu, Japan) following the manufacturerʼs protocol of 20 µL reaction. The sequences of forward and reverse primers were listed in Table S 2 Statistical analysis: The data from in vitro analysis were analyzed using GraphPad Prism 8.0.2 software (GraphPad Software, Inc., CA, USA). Two-tailed unpaired Studentʼs t-test or one-way ANOVA analyses followed by Dunnettʼs post hoc tests for multiple comparisons were carried out. Data were expressed as Mean±SEM. Significant differences were symbolled with *p<0.05 523

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Int. J. Pharmacol., 21 (3): 521-540, 2025 RESULTS AND DISCUSSION Visualization of 3 D structural data of the targeted proteins: The three-dimensional (3 D) structural models of TGF $ R 1 (Fig. 1 a), FAK (Fig. 1 b) and PI 3 K (Fig. 1 c) proteins were meticulously visualized employing the Discovery Studio Client software. This tool enabled an in-depth structural analysis and presentation of these proteins. Among KCBʼs natural compounds, a specific selection process was undertaken, focusing on identifying the ten ligands exhibiting the lowest binding affinity values, expressed in kcal/mol, towards the TGF $ R 1 protein. These selected ligands were then subjected to a detailed two-dimensional (2 D) visualization process to highlight their structural features and interactions. The 2 D structures of the reference TGF $ R 1 inhibitors, galunisertib (Fig. 1 d) and vactosertib (Fig. 1 e) were represented with the top-ranked TGF $ R 1 binders, which are Dihydrosanguinarine (Fig. 1 f), Quercetin 7-O-glucoside (Fig. 1 g), Eriocitrin (Fig. 1 h), Diosmetin-7-O-rutinoside (Fig. 1 i), Myricetin (Fig. 1 j), Trisindoline (Fig. 1 k), Luteolin-8-C-glucoside (Fig. 1 l), Luteolin 7-galactoside (Fig. 1 m), 3-Oxolup-20(29)-en-28-oic acid (Fig. 1 n) and Alpha-naphthoflavone (Fig. 1 o) MD with AutoDock Vina: Docking results between the macromolecule (protein) and the small molecules (ligands) were presented in terms of binding affinity, measured in kcal/mol, in correlation with RMSD (Root Mean Square Deviation) values. Binding affinity indicates the strength of the interaction between the protein and ligand, while RMSD values measure the positional deviation of docked ligand poses from a reference pose. In docking studies, the methodʼs success is often defined by achieving RMSD values of less than 2.0 angstroms (Å) 33 , as this indicates high structural similarity and stability. The RMSD values falling within the range of 3.0>RMSD>2.0 Å are still acceptable 34 . From the ten output docking poses generated for each ligand, the pose that exhibited the lowest binding affinity value was selected, as it was deemed to represent the most energetically favorable and thus likely the most accurate binding mode MD of KCB natural compounds with the target protein kinases: The docking analysis of TGFBR 1 showed binding affinity values that varied significantly, ranging from -11.2 to -2.2 kcal/mol (Fig. 2 a). For the FAK target, the binding affinity analysis produced values that ranged from -11.4 to -3.9 kcal/mol, in Fig. 2 b. This range suggests that the ligands have varying degrees of binding strength with FAK. In Fig. 2 c, the binding affinity values for the kinase protein PI 3 K are depicted, with a range spanning from -10.6 to -3.8 kcal/mol. This analysis reveals that some ligands show strong binding potential with PI 3 K, as evidenced by their lower kcal/mol scores. These results highlight the promising binding potential of KCB natural compounds, which outperformed the reference inhibitors in terms of binding affinity, suggesting their potential as effective alternatives to the current standard inhibitors in targeting TGFBR 1, FAK and PI 3 K Specifically, the heatmap illustrated in Fig. 2 d and Table S 3 highlights the top ten ligands that exhibited the highest binding affinity to TGFBR 1, FAK and PI 3 K, comparing these values with those of known TGFBR 1 inhibitors, galunisertib and vactosertib. With TGFBR 1, these reference inhibitors presented binding affinity values of -10 and -10.6 kcal/mol, respectively. However, when interacted with FAK, galunisertib and vactosertib demonstrated binding affinity values of -10.7 and -9.7 kcal/mol, respectively Notably, two natural compounds, eriocitrin (-10.5 kcal/mol) and alpha-naphthoflavone (-10.1 kcal/mol), exhibited binding scores that were lower than those of the reference inhibitors galunisertib (-9.4 kcal/mol) and vactosertib (-9.3 kcal/mol) These reference values provide a benchmark for evaluating the effectiveness of other ligands in binding to TGFBR 1, FAK and PI 3 K and indicate that some tested ligands may offer stronger interactions with these kinases than the reference inhibitors Additionally, the RMSD results provided insights into the docking stability of these ligands. The analyzed ligands demonstrated optimal docking poses, particularly when the RMSD values were at or below 2.0 Å, indicating a close match to the reference inhibitor, vactosertib. This finding suggests that these ligands are highly compatible in terms of binding conformation. However, some ligands showed acceptable docking poses, with RMSD values falling within the range of 3.0 >RMSD >2.0 Å, about the reference inhibitor galunisertib, as illustrated in Fig. 2 e. These values reflect a less precise but still acceptable alignment in binding pose with the galunisertib reference structure Visualization of the receptor-ligand interactions: Biovia Discovery Studio 2021 was used to visualize the two-dimensional (2 D) interactions between the TGF $ R 1 and the KCB natural compounds in comparison with the canonical TGF $ R 1 inhibitors: galunisertib and vactosertib. Figure 3 represented the interacted amino acid residues ofTGF $ R 1 through bonds of conventional hydrogen, pi-sigma and alkyl/pi-alkyl. Particularly, TGF $ R 1 could be interacted through 524

Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 1(a-o): 3 D and 2 D structures of macromolecules and small molecules, 3 D structures of macromolecules (proteins) are: (a) TGFBR 1 (PDB: 5 E 8 S), (b) FAK (PDB: 3 BZ 3) and (c) PI 3 K (PDB: 5 T 23), The 2 D structures of small molecules (ligands) are: (d) Galunisertib: Known TGFBR 1 inhibitor with antifibrotic properties, (e) Vactosertib: Another TGFBR 1 inhibitor targeting fibrosis mechanisms, The top 10 of KCB natural compounds having the best docking scores with TGFBR 1, including: (f) Dihydrosanguinarine, (g) Quercetin 7-O-glucoside, (h) Eriocitrin, (i) Diosmetin-7-O-rutinoside, (j) Myricetin, (k) Trisindoline, (l) Luteolin-8-C-glucoside, (m) Luteolin 7-galactoside, (n) 3-Oxolup-20(29)-en-28-oic acid, (o) Alpha-naphthoflavone 525 HO OH HO O O HO OH O O OH OH OH (g) N N N H C 3 NH 2 O N (d) N N N N NH HN N CH 3 (e) F O O H C 3 N O O (f) OH OH OH OH OH OH OH O O O O O OH HO H C 3 O (h) HO O OH O OH OH OH OH (j) OH O HO HO O O HO OH OH OH (l) OH O HO HO HO OH OH OH O OH O O (m) HO O CH CH 3 H C 3 CH 3 O H C 3 CH 3 H C 2 (n) O O (o) NH O HN HN (k) O OH O HO O CH 3 HO HO O O O O OH HO OH OH CH 3 (i) (a) (b) (c)

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Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 2(a-e): (a) Binding affinity scores of KCB natural compounds with TGFBR 1. The black dashed line indicates the threshold for strong binding (< -6.0 kcal/mol), (b) Binding affinity scores of KCB natural compounds with FAK. The black dashed line shows the strong binding threshold (< -6.0 kcal/mol), (c) Binding affinity scores of KCB natural compounds with PI 3 K. The black dashed line marks the favorable binding threshold (< -6.0 kcal/mol), (d) Heatmap of the best binding scores of KCB compounds compared with reference TGFBR 1 inhibitors, Galunisertib and Vactosertib and (e) RMSD values of docked ligands normalized to reference inhibitors. Galunisertib is shown as grey columns, Vactosertib as a dark red line. The black dashed line represents the acceptable deviation threshold (<3 Å) the conventional hydrogen binding to the amino acids: Isoleucine (ILE 211), glycine (GLY 214) lysine (LYS 232, LYS 337), glutamic acid (GLU 245), tyrosine (TYR 249), leucine (LEU 278), serine (SER 280, SER 287), aspartic acid (ASP 281, ASP 290, ASP 351), histidine (HIS 283) and asparagine (ASN 338) (Fig. 3). The results revealed that FAK could be inhibited by dihydrosanguinarine (Fig. 4 a) and galunisertib (Fig. 4 b) at the binding sites: ASP 564, LEU 567, LEU 553, valine (VAL 436), alanine (ALA 452), VAL 484 and methionine (MET 499). However, PI 3 K interacted through conventional hydrogen bonds with eriocitrin (Fig. 4 c) at LYS 833, VAL 882, ASP 950 and ASP 964 but with vactosertib (Fig. 4 d) at threonine (THR 887) 526 0 -3 -6 -9 -12 (a) Number of compounds 0 300 600 900 1200 Bi ndi ng affin it y (k cal/ mo l) Dihydrosanguinarine Quercetin 7-O-glucoside Eriocitrin Diosmetin 7-O-rutinoside Myricetin Trisindoline Luteolin-8-C-glucoside Luteolin 7-galactoside 3-Oxolup-20(29)-en-28-oic acid Alpha-Naphthoflavone 0 1 2 3 4 RMSD (Å ) (e) -8 -9 -10 -11 Bi ndi ng affin it y (k cal/ mo l) TG FBR 1 FAK PI 3 K Dihydrosanguinarine Quercetin 7-O-glucoside Eriocitrin Diosmetin 7-O-rutinoside Myricetin Trisindoline Luteolin-8-C-glucoside Luteolin 7-galactoside 3-Oxolup-20(29)-en-28-oic acid Alpha-Naphthoflavone Galunisertib Vactosertib (d) 0 300 600 900 1200 Number of compounds 0 -3 -6 -9 -12 (b) Bi ndi ng affin it y (k cal/ mo l) 0 300 600 900 1200 Number of compounds 0 -3 -6 -9 -12 (c) Bi ndi ng affin it y (k cal/ mo l)

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Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 3(a-i): 2 D interaction maps of TGFBR 1 with reference inhibitors and KCB natural compounds, (a) Galunisertib with TGFBR 1, showing hydrogen bonds and hydrophobic interactions with key amino acid residues, (b) Vactosertib with TGFBR 1, highlighting stabilizing interactions with specific residues, (c) DHS with TGFBR 1, indicating favorable contacts contributing to ligand affinity, (d) Quercetin 7-O-glucoside with TGFBR 1, displaying bonding patterns that support stable binding, (e) Eriocitrin with TGFBR 1, identifying crucial amino acid contacts and bonding types, (f) Diosmetin-7- O-rutinoside with TGFBR 1, showing hydrogen bonding and hydrophobic forces, (g) Myricetin with TGFBR 1, with labeled residues involved in stabilizing the ligand, (h) Trisindolineʼs 2 D interaction with TGFBR 1, emphasizing contacts that may enhance binding strength, (i) Luteolin-8-C-glucoside interaction map, demonstrating stabilizing bonds with TGFBR 1 residues, (j) Luteolin 7-galactoside with TGFBR 1, outlining key residue engagements, (k) OOA with TGFBR 1, revealing hydrogen bonds and hydrophobic interactions and (l) Alpha-NF with TGFBR 1, showing molecular forces contributing to ligand stabilization These interaction diagrams collectively provide molecular insights into how each compound engages with the TGFBR 1 binding pocket, comparing natural compounds with reference inhibitors 527 (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) Interactions Conventional hydrogen bond Carbon hydrogen bond Unfavorable donor-donor Pi-sigma Alkyl or Pi-alkyl Pi-cation

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Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 4(a-e): Molecular docking visualization of interactions with FAK and PI 3 K, (a) 3 D docking visualization of DHS with FAK, highlighting its spatial orientation and interaction within the active site, (b) 3 D docking pose of Galunisertib with FAK, showing its binding alignment and key contact regions, (c) 3 D interaction of Eriocitrin with PI 3 K in ionized form, illustrating the molecular docking conformation and critical binding interactions, (d) 3 D docking model of Vactosertib with PI 3 K, displaying spatial positioning and interaction points within the binding pocket and (e) Combined 3 D and 2 D representations of docking interactions for the ligands, detailing hydrogen bonds, hydrophobic contacts and interacting amino acid residues involved in stabilizing ligand binding with FAK and PI 3 K Bioinformatics analysis: A total of 126 gene targets associated with ALC were obtained from DisGeNET. However, a total of a hundred gene targets per each KCB natural compounds were predicted using SwissTargetPrediction. Then, a total of 17 overlapped genes were identified including Matrix Metallopeptidase 2 (MMP 2), Cytochrome P 450 Family 1 Subfamily A Member 1 (CYP 1 A 1), Arginase 1 (ARG 1), Vitamin D Receptor (VDR), Angiotensin Converting Enzyme (ACE), Peroxisome Proliferator Activated Receptor Gamma (PPARG), MDM 2 Proto-Oncogene (MDM 2), BCL 2 apoptosis regulator (BCL 2), Tumor Protein p 53 (TP 53), Tumor Necrosis Factor (TNF), Matrix Metallopeptidase 13 (MMP 13), myeloperoxidase (MPO), aldehyde dehydrogenase 2 family member (ALDH 2), interleukin 2 (IL 2), nitric oxide synthase 2 (NOS 2), aldo-keto reductase family 1 member A 1 (AKR 1 A 1) and albumin (ALB) (Fig. 5 a) 528 Interactions Conventional hydrogen bond Carbon hydrogen bond Unfavorable donor-donor Pi-sigma Alkyl or Pi-alkyl Pi-cation (d) (c) (b) (a)

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Int. J. Pharmacol., 21 (3): 521-540, 2025 The PPI network analysis revealed that the network consisted of 17 nodes and 62 edges. Moreover, the interaction enrichment p-value was calculated as 1.89 e-15, indicating that these proteins have more interactions with one another than would be predicted and at the very least, they were loosely physiologically linked (Fig. 5 b) The GO of cell component (GO̲CC) revealed that the identified genes were enriched in the extracellular space, nucleus and mitochondrion (Fig. 5 c). Related to biological processes (GO̲BP), the target genes were enriched in response to xenobiotics-induced oxidative stress, inducing apoptosis or inflammation and ECM organization (Fig. 5 d). In terms of molecular functions (GO̲MF), the target genes were enriched in enzyme binding, metalloendopeptidase (MEP) activity, p 53 binding and chaperone binding (Fig. 5 e) Finally, the KEGG pathway analysis showed off that the promising gene targets were significantly enriched in cancer-inducing pathways, p 53 or PI 3 K-Akt signaling pathways (Fig. 5 f) Another virtual screening was executed using the predicted potential target MMP 13 with PDB ID: 4 A 7 B 35 and the best TGF $ R 1 binders among the KCB hit compounds in addition to the reference ligands. The results revealed that the docked ligands to MMP 13 have lower affinity scores in chain B than chain A as shown in Table S 4 ADMET profile predictions: The results of oral bioavailability were assessed using SwissADME according to six physicochemical properties: lipophilicity, size, polarity, water solubility, flexibility and saturation. The results of the bioavailability radar demonstrated that DHS and trisindoline were closely similar to galunisertib and vactosertib (Fig. 6) Regarding the pharmacokinetic profile, the results of ADMETlab 2.0 revealed that DHS and alpha-Naphthoflavone ( " -NF) had optimal absorption parameters (Caco-2 cell permeability >-5.15 log unit and human intestinal absorption (HIA)³ 30%) as compared to reference compounds (Fig. 7 and Fig. 8 a). The metabolism results represented DHS and trisindoline as potential inhibitors of the cytochrome P 450 (CYP) isoenzymes (Fig. 7). Moreover, the distribution profile represented four compounds with plasma protein binding (PPB) percentage out of the optimal range, however, 3-Oxolup-20(29)-en-28-oic acid (OOA) showed off higher blood-brain barrier (BBB) penetration score (Fig. 8 b). The excretion parameters noted that DHSG had the highest clearance with a shorter half-life time (T 1/2 ) than vactosertib (Fig. 8 c). In toxicity prediction, the results exhibited that KCB hit compounds had no cardiotoxic (hERG) nor human hepatotoxic (H-HT) effects as compared to galunisertib and vactosertib, however, OOA revealed non-mutagenic (Ames) and non-carcinogenic effects (Fig. 7) Furthermore, the oral drug-likeness assessment was obtained from SwissADME according to five different rule-based filters, which are used by major pharmaceutical companies: Lipinski (Pfizer) 36 , Ghose (Amgen) 37 , Veber (GSK) 38 , Egan (Pharmacia) 39 and Muegge (Bayer) 40 filters. As shown in Fig. 8 d, DHS, trisindoline and " -NF exhibited no violations in the drug-likeness filters as compared to galunisertib and vactosertib The predicted bioactivity scores in Molinspiration cheminformatics revealed that DHS and OOA had higher scores as G Protein-Coupled Receptor (GPCR) ligands in compare to galunisertib. Moreover, DHS and myricetin could be bioactive as kinase inhibitors. As a nuclear receptor ligand, OOA exhibited the best score as compared to the reference inhibitors (Table 1) Table 1: Predicted bioactivity scores in Molinspiration cheminformatics Ion channel Nuclear Protease Compound No. Small molecule GPCR ligand modulator Kinase inhibitor receptor ligand inhibitor Enzyme inhibitor 1252 Dihydrosanguinarine 0.19 0.09 0.23 0.16 0.00 0.16 125 Quercetin 7-O-glucoside 0.04 -0.10 0.15 0.23 -0.06 0.42 225 Eriocitrin 0.06 -0.47 -0.28 -0.08 0.05 0.16 298 Diosmetin-7-O-rutinoside -0.05 -0.53 -0.13 -0.23 -0.06 0.09 90 Myricetin -0.06 -0.18 0.28 0.32 -0.20 0.30 119 Trisindoline 0.05 -0.07 0.01 -0.17 -0.11 -0.07 304 Luteolin-8-C-glucoside 0.12 -0.14 0.20 0.20 0.01 0.45 33 Luteolin 7-galactoside 0.09 -0.02 0.15 0.27 -0.01 0.42 164 3-Oxolup-20(29)-en-28-oic acid 0.21 -0.06 -0.69 0.88 0.04 0.47 438 Alpha-naphthoflavone -0.09 -0.31 0.20 0.12 -0.36 0.12 - Galunisertib 0.12 -0.13 1.13 0.59 0.06 0.25 - Vactosertib 0.27 -0.01 1.03 -0.45 0.09 0.30 More positive the value of the score, the greater the chance that the small molecule could be bioactive 529

[Find the meaning and references behind the names: Level, Venn, Light, Common]

Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 5(a-f): Bioinformatics analysis of KCB natural compounds and alcoholic liver cirrhosis (ALC), (a) Venn diagram showing the overlap between KCB hit compound targets and ALC-related targets, identifying shared molecular pathways potentially involved in liver cirrhosis, (b) STRING PPI network illustrating predicted protein-protein interactions among the common targets, highlighting their connectivity and functional relationships, (c) Gene Ontology (GO) enrichment analysis-cellular component: Classification of targets based on their cellular localization and structural context, (d) GO enrichment analysis-biological process: Categorization of targets by their roles in biological activities relevant to ALC, (e) GO enrichment analysis-molecular function: Analysis of functional roles played by target proteins at the molecular level and (f) KEGG pathway enrichment analysis identifying key signaling pathways, such as p 53 and PI 3 K-Akt, associated with the predicted targets, shedding light on possible mechanisms in liver cirrhosis pathogenesis 530 MMP 2 CYP 1 A 1 ARG 1 VDR ACE PPARG MDM 2 BCL 2 TP 53 TNF MMP 13 MPO ALDH 2 IL 2 NOS AKR 1 A 1 ALB 17 ALC tragets n = 119 Predicted targets of 10 KCB natural compounds n = 1000 (a) 60 50 40 30 20 10 0 5 4 3 2 1 0 Gene ratio p-value Extracellular space Nucleus Mitochondrion Gene ration -Log (p-value) (c) (b) NOS 2 MPO ACE ALDH 2 AKR 1 A 1 CYP 1 A 1 CYP 27 B 1 BCL 2 MMP 13 TNF ALB PPARG MMP 2 IL 2 MDM 2 TP 53 ARG 1 Gene ration -Log (p-value) 0 2 4 6 8 10 0 10 20 30 40 50 Xenobiotics response Apoptosis Anflammation Cell growth inhibition ECM stress-induced apoptosis ER organization apoptosis Response to ethanol Oxidative stress response (d) Gene ratio p-value 30 25 20 15 10 5 0 Gene ration -Log (p-value) 3 2 1 0 Enzyme binding M E P activity Protease binding E 3 ligase binding P 53 binding Chaperone binding (e) Gene ratio p-value 40 30 20 10 0 3 2 1 0 Cancer pathways P 13 K-Akt pathway p 53 pathway Apoptosis Hepatitis B Gene ration -Log (p-value) (f) Gene ratio p-value

[Find the meaning and references behind the names: Portal, Dose, Pink, Lipo, Manner, Flex, Good, Property, Flow]

Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 6(a-I): Physicochemical properties of KCB hit compounds, (a) DHS, (b) Quercetin 7-O-glucoside, (c) Eriocitrin, (d) Diosmetin-7- O-rutinoside, (e) Myricetin, (f) Trisindoline, (g) Luteolin-8-C-glucoside, (h) Luteolin 7-galactoside, (i) OOA, (j) " -NF, (k) Galunisertib and (l) Vactosertib The pink area represents the optimal range for each property: lipophilicity (LIPO): XLOGP 3 between ! 0.7 and +5.0, size: MW between 150 and 500 g/mol, polarity (POLAR): TPSA between 20 and 130 Å2, solubility (INSOLU): log S not higher than 6, saturation (INSATU): Fraction of carbons in the sp 3 hybridization not less than 0.25 and flexibility (FLEX): No more than 9 rotatable bonds Experimental validation of the virtual screening results: The MD findings revealed that both luteolin (Fig. 9 a) and diosmetin (Fig. 9 b) had good multitarget binding affinities (Fig. 9 c), which were found to interact with HIS 283 and SER 280 by conventional hydrogen bonds (Fig. 9 d-e) Therefore, these findings were experimentally validated by investigating the effects of three flavonoid compounds: Luteolin, diosmetin and " -NF on HepG 2 cells using cell viability and RT-qPCR analyses. In comparison with the untreated control cells, luteolin and diosmetin inhibited but " -NF induced the cell proliferation dose-dependently; luteolin and diosmetin exhibited calculated Half-Maximal Inhibitory Concentration (IC 50 ) values of 35 and 70 µM However, " -NF revealed a calculated half-maximal effective concentration (EC 50 ) at 867 µM (Fig. 10 a-b). However, RT-qPCR analysis revealed that luteolin inhibited the mRNA expressions of the targeted proteins at the higher concentrations (Fig. 10 c) but both diosmetin and " -NF upregulated the target proteins in a dose-dependent manner (Fig. 10 d-e) Cirrhosis is the end stage of any chronic liver disease, regardless of the etiology. It is characterized by liver parenchyma deformation, fibrous septae, nodules and blood flow changes. Clinically, LC starts as an asymptomatic compensated phase and then proceeds to a decompensated phase, causing complications like ascites, jaundice, portal hypertension and hepatic encephalopathy 41,42 . Treatment of LC depends on the etiology removal such as abstinence from alcohol or using antivirals. However, no direct antifibrotic medicine is currently available, making it critically needed Direct antifibrotic treatment seeks to reduce scar formation or hasten the healing process. Therefore, inhibition of the TGF- $ signaling pathway is considered a potential target to create effective antifibrotic medicines 43-45 531 (a) LIPO INSOLU INSATU SIZE POLAR FLEX LIPO INSOLU INSATU FLEX SIZE POLAR (b) LIPO INSOLU INSATU FLEX SIZE POLAR (c) LIPO INSOLU INSATU FLEX SIZE POLAR (d) LIPO INSOLU INSATU FLEX (h) SIZE POLAR LIPO INSOLU INSATU FLEX SIZE POLAR (g) LIPO INSOLU INSATU FLEX SIZE POLAR (f ) LIPO INSOLU INSATU FLEX SIZE POLAR (e) LIPO INSOLU INSATU FLEX SIZE POLAR (i) LIPO INSOLU INSATU FLEX SIZE POLAR (j) LIPO INSOLU INSATU FLEX SIZE POLAR (k) LIPO INSOLU INSATU FLEX SIZE POLAR (l)

[Find the meaning and references behind the names: Roa, Evidence, Dili, Poor, Excellent, Play]

Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 7: In silico pharmacokinetics (PK) profile evaluation of KCB hit compounds Heatmap of absorption, metabolism and toxicity; Category 0: non-inhibitor, non-substrate, or non-toxic; Category 1: Inhibitor, substrate or toxic. The output value is the probability of being inhibitor, substrate, or toxic, within the range of 0 to 1. HIA; Category 0: HIA >30%; Category 1: HIA <30%. Toxicity: Category 0: Negative (-); Category 1: Positive (+). The output value is the probability of being toxic within the range of 0 to 1. Empirical decision: 0-0.3: Excellent (green); 0.3-0.7: Medium (black) and 0.7-1.0: Poor (red) This study, performed a virtual screening of the KCB natural compounds library in order to screen promising compounds with potential inhibitory effects on TGF-mediated LC. In MD, the binding affinity score explains the interaction stability and the small moleculeʼs potency to either induce or inhibit the macromolecule as indicated with the lowest value of binding affinity 46,47 , preferably <-6 kcal/mol 48 . The results revealed potent TGF $ R 1 binders with lower binding scores compared to the reference inhibitors (Fig. 2). Moreover, it has been reported that MD with AutoDock Vina predicted the inhibitory interaction of 5 E 8 S, which was suppressed via hydrogen bonds with SER 288, LYS 337 and ASP 351 49 , these findings correlated with current results suggesting that these residues are essential for TGF $ R 1 interactions (Fig. 3) Both Focal Adhesion Kinase (FAK) and Phosphoinositide 3-Kinase (PI 3 K) play significant roles in the pathogenesis of liver cirrhosis. Their activation contributes to HSCs activation, ECM production and cell survival, ultimately leading to the development and progression of liver fibrosis and cirrhosis Targeting FAK and PI 3 K signaling pathways may offer potential therapeutic strategies to mitigate liver fibrosis and its associated complications 50,51 . Current results showed off hit compounds with lower affinity scores than the reference compounds and thus indicating potential inhibitory effects against FAK and PI 3 K (Fig. 2). Moreover, current results in Fig. 4 exhibited the same binding sites of interactions with FAK but less similarity with PI 3 K as reported by other studies in the literature 52-54 . Thus, the affinity scores with visualization of the interactions could provide clear evidence that these macromolecules could be inhibited through binding to the aforementioned amino acids 532 Absorption 1.0 0.8 0.6 0.4 0.2 0.0 Metabolism Toxicity Pgp-inh Pgp-sub HIA F (20%) F (30%) CYP 1 A 2-inh CYP 1 A 2-sub CYP 2 C 19-inh CYP 2 C 19-sub CYP 2 C 9-inh CYP 2 C 9-sub CYP 2 D 6-inh CYP 2 D 6-sub CYP 3 A 4-inh CYP 3 A 4-sub Herg H-HT DILI Ames ROA FDAMDD SkinSen Carcinogenicity

[Find the meaning and references behind the names: Normal, Mind, Vital, Hold, Keep, Organ, Novel, Lines, Sec, Purple, Blue, Min]

Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 8(a-d): ADME and drug-likeness profiles estimation, (a) Absorption: Caco-2 and MDCK cell permeability; the blue dashed line defines the lower limit for Caco-2 permeability, whereas the red dotted line margins the upper limit of MDCK permeability, (b) Distribution: The black dashed line defines the upper limit of PPB, whereas the score of BBB is interpreted as: 0-0.3: Excellent; 0.3-0.7: Medium; 0.7-1.0: Poor, (c) Excretion: The purple dashed lines indicate the moderate clearance range; however, the half-life time (T 1/2 ) is estimated at <3 hrs if the score is 0 and >3 hrs at the score of 1 and (d) Heatmap of the drug-likeness rule filters The MMP 2 (gelatinase A) and MMP 13 (collagenase 3) are matrix metalloproteinase enzymes. These enzymes are vital in tissue remodeling, wound healing and extracellular matrix (ECM) degradation. They are involved in the pathogenesis of liver cirrhosis. Under normal conditions, ECM homeostasis is maintained by MMPs and the Tissue Inhibitors of Metalloproteinases (TIMPs). Even though that MMPs have inhibitory functions in early stages of liver fibrosis but excessive and prolonged activity may promote the progression of cirrhosis by allowing the invasion of inflammatory cells and promoting angiogenesis 55,56 . Moreover, higher serum levels of MMP 2 and MMP 13 is a characteristic diagnostic features of ALC 57,58 . The understanding of MMP 2 and MMP 13 in liver cirrhosis is still an active area of research and therapeutic targeting of these enzymes may hold promise in the development of novel treatment strategies for liver cirrhosis. However, itʼs important to keep in mind that the liver is a complex organ with multiple interacting pathways and 533 Dihydrosanguinarine Quercetin 7-O-glucoside Eriocitrin Diosmetin-7-O-rutinoside Myricetin Trisindoline Luteolin-8-C-glucoside Luteolin 7-galactoside OOA Alpha-naphthoflavone Galunisertib Vactosertib 4 3 2 1 0 N umber o f viol at io ns (d) Caco-2 permeabilit (log unit) Dihydrosanguinarine Quercetin 7-O-glucoside Eriocitrin Disometin-7-O-rutinoside Myricetin Trisindoline Luteolin-8-C-glucoside Luteolin 7-galactoside OOA Alpha-Naphthoflavone Galunisertib Vactosertib -5.15 -8 -6 -4 0 MDCK permeability (cm/sec) 0 2 10 H G 5 4 10 H G 5 6 10 H G 5 8 10 H G 5 1 10 H G 4 (a) Caco-2 MDCK Dihydrosanguinarine Quercetin 7-O-glucoside Eriocitrin Disometin-7-O-rutinoside Myricetin Trisindoline Luteolin-8-C-glucoside Luteolin 7-galactoside OOA Alpha-naphthoflavone Galunisertib Vactosertib 50 60 70 80 90 100 PPB (%) (b) 0.0 0.2 0.4 0.6 0.8 1.0 BBB penetration BBB PPB Dihydrosanguinarine Quercetin 7-O-glucoside Eriocitrin Disometin-7-O-rutinoside Myricetin Trisindoline Luteolin-8-C-glucoside Luteolin 7-galactoside OOA Alpha-naphthoflavone Galunisertib Vactosertib 0.0 0.2 0.4 0.6 0.8 1.0 T 1/2 0 5 10 15 20 (c) CL (mL/min/kg) CL T 1/2

[Find the meaning and references behind the names: Fruits, Plant, Lemons, Due]

Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 9(a-e): Molecular docking analysis of experimentally validated compounds, (a) 2 D chemical structure of Luteolin, (b) 2 D chemical structure of Diosmetin, (c) Docking scores of Luteolin and Diosmetin with TGFBR 1, illustrating their relative binding affinities and potential inhibitory strength, (d) 3 D visualization of the co-crystallized structure of TGFBR 1 with Luteolin, highlighting the binding pocket and specific interacting amino acid residues and (e) Structural representation of TGFBR 1 complexed with Diosmetin, showing detailed interactions and residues contributing to ligand stabilization within the active site cirrhosis is a multifactorial disease with various etiologies. Therefore, the role of MMP 2 and MMP 13 in liver cirrhosis may vary depending on the specific etiology and stage of the disease. Current bioinformatics findings revealed the prediction of MMP 2 and MMP 13 as potential targets for the selected KCB hit compounds. Furthermore, MD results showed off lower affinity scores than the threshold score (-6.0 kcal/mol), indicating the good binding interactions with MMP 13 (Table S 4) Dihydrosanguinarine (DHS) is a derivative of the alkaloid sanguinarine, which is found in various plant species, including Papaveraceae. Sanguinarine and its derivatives have been of interest to researchers due to their potential biological activities, including antimicrobial, anticancer, anti-inflammatory and antioxidant properties 59,60 . Among the best selected hit compounds, DHS had the lowest binding affinities to TGF $ R 1 and FAK. However, eriocitrin had the best affinity to PI 3 K and MMP 13 (Fig. 2 and Table S 3). Eriocitrin (Eriodictyol 7-O-rutinoside) is a flavonoid glycoside found in various plants, particularly in the peels of citrus fruits such as lemons, oranges and grapefruits. It is a derivative of the flavonoid eriodictyol and is known for its potential health benefits due to its antioxidant and anti-inflammatory properties 61 . According to the aforementioned, both DHS and eriocitrin could be novel therapeutic candidates with antifibrogenic effects against LC 534 (d) (e) HO OH O OH OH O (a) Luteolin Diosmetin 0 -2 -4 -6 -8 -10 Binding a ffinity (kcal/mol) TGFBR 1 FAK P 13 K (c) HO O OH OH O (b) CH 3

[Find the meaning and references behind the names: Change, Bile, Central, Dios, Lut, Bar, Seven]

Int. J. Pharmacol., 21 (3): 521-540, 2025 Fig. 10(a-e): In vitro experimental validation of flavonoidsʼ effects, (a-b) Cell viability of HepG 2 cells and (c-e) Relative mRNA expression (a-b) Graphs showing the impact of flavonoid treatments-Luteolin, Diosmetin and " -NF-on the viability of HepG 2 liver cancer cells after 48 hrs of exposure. Statistically significant differences compared to the untreated control group (0) are denoted by symbols (*p<0.05, **p<0.01 and ***p<0.001), analyzed using a two-tailed unpaired t-test and (c-e) Bar graphs representing changes in mRNA expression levels of target proteins in HepG 2 cells treated with the flavonoids for 48 hrs. Statistical differences from the control group are marked (*p<0.05, **p<0.01 and ***p<0.001), assessed via one-way ANOVA The liver plays a central role in drug metabolism and elimination and in cirrhosis, these processes are significantly altered. The reduced hepatic blood flow, decreased functional liver tissue and altered enzyme activity compromise drug metabolism, leading to prolonged drug half-lives and potential drug accumulation. Additionally, the disrupted bile excretion impairs drug absorption into the intestines, further contributing to altered pharmacokinetics 62 . Typically, any drug candidate should have a long half-life time (T 1/2 ) to allow for dosage reduction and thus minimal toxic effects 63 . Accordingly, the ADME and drug-likeness profiles demonstrated that DHS, trisindoline, OOA and " -NF could be acceptable drug candidates for LC as they exhibited excellent oral bioavailability results (Fig. 8) According to Molinspiration bioactivity scores, the investigated small molecules could be classified as active (> 0), moderately active (-0.5 to 0) and inactive (<-0.5) 64 . Therefore and since targeted multi-kinase receptors of LC, the prediction results exhibited a total of seven compounds as active kinase inhibitors (Table 1) 535 200 175 150 125 100 Cell viability (%) 0 125 250 500 750 867 1000 Alpha napthophlavone (μM) EC 50 (b) 2000 1.6 1.4 1.2 1.0 0.8 m R N A fold change TGFBR 1 FAK PI 3 K *** *** ** *** ** ** *** *** Control -NF 125 a -NF 250 a -NF 500 a (e) 125 100 75 50 25 0 Cell viability (%) Luteolin Diosmetin 0 35 12.5 25 50 70 100 200 400 IC 50 IC 50 (a) Concentration (μM) 3 2 1 0 m R N A fold change TGFBR 1 FAK PI 3 K * ** *** * *** *** *** *** Control Dios 25 Dios 50 Diso 100 (d) 1.5 1.0 0.5 0.0 Control Lut 25 Lut 50 Lut 100 m R N A fold change *** *** *** *** *** *** * (c) TGFBR 1 FAK PI 3 K

[Find the meaning and references behind the names: Stauber, Wang, Dual, Soukupova, Dooley, Fabregat, Basic, Xia, Driscoll, Rep, Kitchen, Ther, Fit, Burden, Furr, Zhou, Dev, Sawyer, Dewidar, Friedman, Knowledge, Nat, Afdhal, Shi, Focus]

Int. J. Pharmacol., 21 (3): 521-540, 2025 The binding interaction between a ligand and a target protein can lead to either activation or inhibition of the receptor. The in vitro results suggested that luteolin had inhibitory effects on the target proteins, leading to cell death. However, the activatory effects of diosmetin and " -NF on the target proteins represented that TGF- $ R 1 could play dual roles in apoptosis and cell survival leading to HCC progression (Fig. 10). Moreover, it has been reported that luteolin and diosmetin had anticancer effects on HepG 2 cells IC 50 values of 9 µg/mL (31.4 µM) and 12 µg/mL (39 µM), respectively targeting TGF- $ signaling pathway 65,66 . Furthermore, Xia et al 67 reported hepatoprotective effects of " -NF against both in vitro and in vivo models of Non-Alcoholic Fatty Liver Disease (NAFLD) 67 . These findings were partially correlated with current in vitro analysis results (Fig. 10), which supported the anticancer effects of both luteolin and diosmetin and also suggested the potential protective effects of " -NF against chronic liver disease models CONCLUSION This study has conducted a virtual screening of the KCB natural compounds library with multiple kinases related to LC pathogenesis. Using MD, promising multi-kinase ligands were identified targeting the non-canonical TGF- $ signaling pathway. However, through bioinformatics, an essential enzyme receptor, MMP 13, was predicted as a potential target by the hit compounds. Moreover, the predicted ADMET profile results exhibited three compounds (DHS, trisindoline and " -NF) that ideally fit the standard drug-likeness parameters as oral drug candidates. However, strongly recommended to conduct further preclinical studies to investigate the potential effects of these natural compounds, particularly DHS, trisindoline and " -NF on in vitro and in vivo models of chronic liver injury for more validation and confirmation of the docking findings and the efficacy of the targeted mechanism of action SIGNIFICANCE STATEMENT Liver cirrhosis is a major global health burden driven by TGF- $ -mediated fibrosis. This study employed molecular docking and bioinformatics to screen the Korea Chemical Bank (KCB) natural compounds library, identifying multi-target inhibitors for TGF- $ R 1, FAK and PI 3 K. Promising candidates, including dihydrosanguinarine (DHS) and eriocitrin, showed high binding affinities, while ADMET analysis confirmed their oral bioavailability and drug-likeness. The RT-qPCR validation revealed luteolinʼs inhibitory effects on fibrogenic genes, suggesting its therapeutic relevance. These findings highlight the potential of natural compounds for antifibrotic therapy. Future studies should focus on in vivo validation, mechanistic analyses and drug formulation to optimize their therapeutic efficacy and clinical applicability for liver cirrhosis treatment. This research expands current knowledge on multi-target natural drug discovery for liver cirrhosis ACKNOWLEDGMENT Special acknowledgments to Korea Research Institute of Chemical Technology (KRICT) and KCB for providing PDB files of the natural product library. Also, our acknowledge all contributors to in silico techniques used in this study, particularly the web designers and software developers. The Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT and Future Planning, supported this study with grants No. 2021 R 1 A 2 C 1004184 (to H-J.C.) No. 2021 R 1 A 2 C 1004133 (to N.Y.J.) and No. 2023 R 1 A 2 C 1003763 (to J.J.) REFERENCES 1 Schuppan, D. and N.H. Afdhal, 2008. Liver cirrhosis. Lancet, 371: 838-851 2 Dooley, S. and P. ten Dijke, 2012. TGF- $ in progression of liver disease. Cell Tissue Res., 347: 245-256 3 Dewidar, B., J. Soukupova, I. Fabregat and S. Dooley, 2015. TGF- $ in hepatic stellate cell activation and liver fibrogenesis: updated. Curr. Pathobiol. Rep., 3: 291-305 4 Shi, X., C.D. Young, H. Zhou and X.J. Wang, 2020. Transforming growth factor- $ signaling in fibrotic diseases and cancer-associated fibroblasts. Biomolecules, Vol. 10. 10.3390/biom 10121666 5 Friedman, S.L., 2008. Hepatic stellate cells: Protean, multifunctional, and enigmatic cells of the liver. Physiol. Rev., 88: 125-172 6 Kisseleva, T., 2017. The origin of fibrogenic myofibroblasts in fibrotic liver. Hepatology, 65: 1039-1043 7 Herbertz, S., J.S. Sawyer, A.J. Stauber, I. Gueorguieva and K.E. Driscoll et al ., 2015. Clinical development of galunisertib (LY 2157299 monohydrate), a small molecule inhibitor of transforming growth factor-beta signaling pathway. Drug Des. Dev. Ther., 9: 4479-4499 8 Lee, H.J., 2020. Recent advances in the development of TGF- $ signaling inhibitors for anticancer therapy. J. Cancer Prev., 25: 213-222 9 Kitchen, D.B., H. Decornez, J.R. Furr and J. Bajorath, 2004. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discovery, 3: 935-949 536

[Find the meaning and references behind the names: Zhang, Martins, Santos, Xiong, Hutchison, Force, Wen, Anguita, Elsaman, Babel, Berman, Xie, Halliday, Abdulla, Zeng, Savi, Zhao, Gao, Jiang, Olson, Gerhardt, Kiefer, Morris, Westbrook, Sci, Jiao, Hao, Chem, Trott, Inf, Field, Fight, Alzain, Nash, Petersen, Michielin, Speed, Chin, Sar, Furlong, Part, Morley, Perry, Mukhtar, Forslund, Centeno, Hart, Bhat, Cooper, Apo, Gilliland, Lindstrom, Sanz, Zheng, Daina, Energy, Eberhardt, Ting, Sherman, Caballero, Server, Lett, Feng, Ung, Heller, Svensson, Med, Guo, Roberts, Take, Chen, Huey, Shannon, Tree, James, Boyle, Yang, Qiu, Franceschini, Whalen]

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Int. J. Pharmacol., 21 (3): 521-540, 2025 Table S 4: Docking scores with MMP 13 Binding affinity (kcal/mol) ----------------------------------------------------------------- Compound No Small molecule (Ligand) Chain A Chain B 1252 Dihydrosanguinarine -8.3 -7.6 125 Quercetin 7-O-glucoside -8.7 -9.1 225 Eriocitrin -9.5 -9.8 298 Diosmetin-7-O-rutinoside -8.9 -9.1 90 Myricetin -8.9 -8.7 119 Trisindoline -6.1 -9.0 304 Luteolin-8-C-glucoside -7.2 -8.5 33 Luteolin 7-galactoside -9.3 -9.3 164 3-Oxolup-20(29)-en-28-oic acid -5.8 -7.0 438 alpha-Naphthoflavone -9.3 -9.7 - Galunisertib -8.8 -9.0 - Vactosertib -9.6 -9.7 540

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Go, Dh, Inflammation, Jaundice, Ascites, Wound healing, Drug Metabolism, Preclinical studies, Extracellular matrix, One-way ANOVA, Non Alcoholic Fatty Liver Disease, Liver Cirrhosis, Portal Hypertension, Hepatic Encephalopathy, Hepatoprotective effect, Molecular docking, Binding affinity, Binding site, Docking Score, Lipinski's Rule of Five, Experimental Validation, ADME, Physicochemical properties, Multifactorial disease, Drug formulation, Cell viability, Inhibitory effect, Tissue inhibitors of metalloproteinases, Cell proliferation, Oral bioavailability, Biological activities, MRNA expression, Cell viability assay, Alcohol abuse, Therapeutic strategies, Viral hepatitis, Bile excretion, Luteolin, Binding Affinities, Bioinformatics analysis, Tissue remodeling, SwissTargetPrediction, Hepatocellular carcinoma (HCC), Liver fibrosis, Hepatic blood flow, Virtual screening, In Silico, Binding pocket, Hydrogen bond, Hydrophobic interaction, Target Protein, Molecular docking analysis, Pharmacokinetic profile, Flexibility, Drug accumulation, Anticancer effect, KEGG, Blood Brain Barrier (BBB), Myricetin, Natural compound, Solubility, Potential biological activities, Cell survival, HepG2 cell, Cell death, Focal adhesion kinase, Vitamin D receptor (VDR), Myeloperoxidase (MPO), Focal Adhesion Kinase (FAK), ADMET profile, Lipophilicity, Caco-2 cell permeability, Potential health benefits, Gene Ontology, Pharmacokinetic, Half Maximal Inhibitory Concentration (IC50), Polarity, Hepatic fibrosis, Disgenet, STRING database, Extracellular matrix (ECM), Therapeutic targeting, Tumor necrosis factor (TNF), Cytoscape, AutoDock Vina, Altered pharmacokinetics, Nucleus, Bioactivity score, Drug candidate, Inducing apoptosis, RMSD, PDBQT format, Molinspiration Cheminformatics, Pharmacokinetics profile, Phosphoinositide 3-kinase, Pi-Sigma interactions, Dosage reduction, Chronic liver injury, Half maximal effective concentration (EC50), Flavonoid glycoside, RT-qPCR, Myofibroblasts, Small molecule, David, Caco-2 permeability, Extracellular space, MAPK, MMP, AutoDockTools, GraphPad Prism, Inflammatory cell, HSC, Mitochondrion, MD, Saturation, RMSD value, Conventional hydrogen bond, MRNA expression level, Diosmetin, SwissADME, PI3K, Basic Science Research Program, KEGG pathway, Gene Ontology (GO), ECM, Kinase inhibitor, KEGG pathway analysis, Nuclear receptor ligand, PPI network, Hepatic stellate cell, TIMP, LC, BBB, GO enrichment analysis, Minimal toxic effects, Half-life time, PPB, 3D visualization, Fibrogenic genes.