Comparative Computational Evaluation of Flavonoid-Based Inhibitors Targeting Human TOP2A: Insights from Molecular Docking, Molecular Dynamics Simulation, and Binding Free Energy Analysis

Comparative Computational Evaluation of Flavonoid-Based Inhibitors Targeting Human TOP2A: Insights from Molecular Docking, Molecular Dynamics Simulation, and Binding Free Energy Analysis | 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 Systematic Review Comparative Computational Evaluation of Flavonoid-Based Inhibitors Targeting Human TOP2A: Insights from Molecular Docking, Molecular Dynamics Simulation, and Binding Free Energy Analysis Bidu Pal, Sajal Roy, Musfiqur Rahman Sakib, Shahlaa Zernaz Ahmed, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9616322/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Prostate cancer (PCa) remains a major global health burden, particularly in its aggressive and treatment-resistant forms. Human topoisomerase IIα (TOP2A), a key regulator of DNA replication and cell proliferation, is significantly overexpressed in advanced prostate cancer and represents an important therapeutic target. In this study, a comprehensive computational pipeline was employed to evaluate phytocompounds derived from Dimocarpus longan as potential TOP2A inhibitors. A focused library of plant-derived compounds was screened using molecular docking, followed by pharmacokinetic and toxicity profiling via SwissADME and pkCSM. The top-ranked compounds were further subjected to 100 ns molecular dynamics (MD) simulations, along with MM/GBSA binding free energy calculations and principal component analysis (PCA), to assess their dynamic stability and interaction behavior. Among the screened compounds, luteolin, quercetin, and rutin demonstrated favorable binding affinities (− 10.4 to − 9.8 kcal/mol), outperforming the reference drug mitoxantrone. MD simulation results revealed stable protein–ligand interactions, with RMSD values below 3 Å and consistent structural compactness. Binding free energy analysis supported strong interaction profiles, particularly for luteolin and quercetin. ADMET predictions indicated acceptable pharmacokinetic properties and low toxicity risks. Overall, this study provides a detailed comparative analysis of flavonoid–TOP2A interactions, offering insights into their binding stability and dynamic behavior. These findings highlight luteolin and quercetin as promising candidates for further experimental validation in prostate cancer therapeutics. TOP2A Molecular docking Molecular dynamics simulation Flavonoids Binding free energy Prostate cancer ADMET analysis Phytochemicals Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cancer is the second leading cause of mortality after cardiovascular disease, and according to recent scientific research, approximately 19.3 million new cancer cases and 10 million deaths will be diagnosed in 2020 1 . In a similar vein, researchers predict that by 2030, there will be approximately 12 million cancer-related fatalities and 24 million new cancer patients. Numerous varieties of cancer are responsible for the deaths of a large number of people, but Prostate Cancer, PCa, is the second most prevalent cancerous tumor and the fifth leading cause of death among men worldwide. In 2020, PCa was detected in approximately 1,414,259 people worldwide and caused the deaths of approximately 375,304 people. According to the American Cancer Society (ACS), approximately 268,490 men will be newly diagnosed with prostate cancer, and approximately 34,500 men will pass away 2 , 3 . Men are the only ones with an organ called the prostate, and men aged 60 or older are more likely to develop prostate cancer. In addition, men under the age of 40 and non-Hispanic Black males are less likely to acquire it. It is more prevalent in both developed and developing nations, yet the rate in developing nations is progressively rising 4 . Identifying the precise molecular target/s of Prostate malignancies makes it difficult for scientists to reduce this mortality rate. However, Topoisomerase II alpha (TOP2A) is a potential prognostic biomarker for PCa, specifically a proliferation marker for aggressive prostate tumors. In fact, the Sullivan research group was the first to identify elevated levels of TOP2A in prostate cancer 5 . Willman et al. (2000) reported that the higher Gleason Score/GS (GS scoring is the more prevalent grading system for the diagnosis of PCa) for TOP2A indicates a correlation between TOP2A and Prostate carcinoma 6 . Specifically, this enzyme is a ubiquitously expressed enzyme that controls DNA replication, repair, transcription, and recombination before cell division. Furthermore, it primarily regulates the fundamental cellular processes by altering the chromosomal DNA topology through the creation of transient double-stranded DNA breaks that allow either strand to pass through the other, which is essential for the survival of the cells 7 . Notably, the expression of TOP2A is linked to the replication fork, which remains tightly bound to the chromosomes during mitosis and is essential for the development of proliferating cells. TOP2A abundance is regulated by the cell cycle to compensate for significant chromosomal copy number changes during mitotic DNA replication 8 . Loss of these checkpoints can result in prostate tumorigenesis and prostate cancer. Consequently, it is now evident that TOP2A is a proliferation marker and can facilitate the rapid proliferation of prostate cancer cells 9 . Perhaps as a result, inhibition of TOP2A is clinically beneficial for the management of PCa malignancies, and scientists are attempting to identify natural compounds because numerous therapeutic compounds have been used for the treatment of this major health concern, but most of these compounds have a variety of side effects. Despite this, plant-based bioactive compounds are safer for PCa disease. Since the turn of the twenty-first century, plant-derived compounds have played a fascinating role in the reduction of numerous life-threatening health problems, such as cardiovascular disease, cancers, diabetes, asthma, obesity, neurodegenerative diseases, infectious diseases, and many others 10 – 14 . Existing secondary metabolites include terpenoids, phenolics, flavonoids, alkaloids, glycosides, etc., and some selective compounds have potential functions in cancer treatment 15 – 17 . In particular, only a small number of bioactive molecules exist in nature that can combat the prostate tumors and cancer significantly 18 . Ursolic acid, atraric acid, oleanolic acid, -amyrin, N-butylbenzene-sulfonamide, ferulic acid, lauric acid, -sitosterol-3-O-glucoside, and -sitosterol are novel PCa-treating compounds isolated from the P. Africana plant. Also, it has been used as a treatment for benign prostatic hyperplasia for many years 19 . Two significant soy isoflavones, indole-3-carbinol and Genistein, are applied as major therapeutic potential compounds for prostate cancer 20 . These two isoflavones work toward the molecular understanding of the BRCA1/2 gene. Marketed chemo-preventive drugs, such as Doxorubicin, Etoposide, and Mitoxantrone, target the TOP2A, which disrupts the normal function of transient DNA- TOP2A, and as a result, they increase the concentration of DNA double-strand break 21 , 22 . Anyway, substantial antiproliferative, anti-oxidant, and anti-cancer roles of Dimocarpus longan have already been published, as have the effects of the compounds Gallic acid, corilagin, and ellagic acid on HepG2, SGC 7901, and A549 cancer cell lines 23 . Importantly, the polyphenol-rich seed extract of D. longan exhibited anti-proliferative activity against the colo 320DM, HT-29, and SW480 cell lines 24 . Obviously, the research indicated that D. longan will be utilized as a potential cancer therapy treatment. However, less frequent research on prostate cancer was conducted. Now, it is imperative to treat Prostate tumors in addition to Cancer, so we designed our current in silico workflow to address this health concern. We chose botanical compounds derived from the D. longan plant based on their antiproliferative and anticancer properties as reported in the published scientific literature. Therefore, we designed an in silico workflow to identify novel drugs. Natural compounds of Longan Lour plant-based were used, a virtual screening technique was applied, molecular target interactions were studied, and then, with the assistance of Way2Drug, the top three virtual candidates were evaluated for their cell-line toxicity activity. Their pharmacokinetic properties supported the drug-likeness activity. The "protein-ligand complexes" stability was further validated by the dynamic simulator, and after the study of the dynamics, MM-GBSA and PCA analyses were conducted. Research Methodology Ligand library building, and Optimization of ligands We obtained phytocompounds for the current research project from various D. Longan plant parts 23 , but we built a library of 30 compounds predominantly using a controlled drug as Mitoxantrone. The 3D structures of these bioactive compounds were retrieved from the PubChem database in Structure Data File (SDF) format. Ligands were optimized using UCSF Chimera version v1.14, applying the Gasteiger method to neutralize the net charge. Finally, both the test ligands and the control drug were converted to mol2 format for subsequent molecular docking analyses 25 . Preparation of Macromolecules The crystal structure of the unmutated human TOP2A protein (PDB ID: 1ZXM) was obtained from the Protein Data Bank. Active site residues were predicted using the COACH-D server ( https://yanglab.nankai.edu.cn/COACH-D/ ), which identifies key amino acids within potential binding pockets 26 . Before docking, the protein structure was refined through energy minimization to enhance stability and eliminate non-essential elements such as non-standard residues, metal ions, ligands, heteroatoms, and water molecules, which could introduce artifacts during interaction analysis. Hydrogen atoms were added to ensure proper protonation states and optimal hydrogen bonding during docking simulations. All preparation steps were executed using UCSF Chimera v1.14, employing the Gasteiger charge method for assigning atomic charges. 27,28 . Site-specific molecular docking The binding sites of the target protein were determined using the COACH-D algorithm, enabling accurate prediction of active site residues. Docking simulations were carried out through PyRx 0.8, after converting the ligands and protein into PDBQT format. The docking process produced binding energy scores and RMSD values, which were recorded in a CSV file, highlighting several ligands with strong binding potential 29 . To complement this, site-specific docking was also performed using the Maestro interface (Schrödinger v2020-3). A site map was created to define the docking region, ensuring precision in ligand-receptor interactions 30 . Interpretation of Cell Line Cytotoxicity Following the docking studies, the biological relevance of the selected botanical compounds was further evaluated for their potential anticancer properties, including activity against prostate cancer. Using the Way2Drug online server, the compounds were analyzed for cytotoxic effects across various cancer cell lines. The predictions, based on Pa (probability of activity) and Pi (probability of inactivity) values, were obtained through the CLP-Cell-line cytotoxicity hub 31 . These results provided additional insight into their therapeutic potential, warranting further validation of their activity specifically against prostate cancer. PK Profiling of Top Hits and Molecular Interaction To further assess the drug-likeness and pharmacokinetic properties of the lead compounds, the SwissADME web tool ( http://www.swissadme.ch/ ) was utilized. This server provided key physicochemical parameters, including molecular weight, hydrogen bond donors/acceptors, topological polar surface area (TPSA), log P, bioavailability score, and others. Additionally, compliance with Lipinski’s Rule of Five (L5) was evaluated to predict oral bioavailability 32 . In parallel, ADMET profiling covering absorption, distribution, metabolism, excretion, and toxicity was performed using the pKCSM platform, providing critical insights into the pharmacological safety of the compounds 33 . To better understand the binding interactions, LigPlot+ v2.2.8 and BIOVIA Discovery Studio were employed to visualize and analyze protein–ligand complexes, particularly focusing on hydrogen bonds and hydrophobic interactions. Additionally, structural visualization and merged output files were obtained using the PyMOL visualizer 34 , supporting detailed interaction mapping. Molecular Dynamics (MD) Simulation The 100 ns MD simulations analyzed the protein-ligand complex structures to determine the binding consistency of the selected 3 candidate ligand compounds with the control drug to the targeted protein (PDB ID- 1ZXM). The molecular dynamic simulation of the protein-ligand complex structures was carried out by using the YASARA v21.6.17 to analyze the thermodynamic stability of receptor-ligand complexes 35 . The cell border in MDS was defined using the AMBER14 force field, and indeed, the density of the solvent utilized in the simulation was kept at 0.997 g/mL. The pH was kept constant at 7.4, and NaCl concentrations were added to keep the pH neutral. To keep the geometry, energy reduction was used. After that, the modeling trajectory was run for 100 ns. The pressure was held at 1 bar, and the temperature was fixed at 298 K When an isobaric atmosphere was necessary, a Berendsen barostat was used to maintain pressure. The ease with which the metaphase of the macros tool was used to execute the full simulation procedure in the YASARA framework aided the current investigation. All Isothermal-Isobaric ensemble (NPT) assemblies that made use of the temperature combination of Nose-Hoover and the isotropic approach were maintained at 300 K and one-atmosphere pressure (1,01325 bar) and were accompanied by 50 PS capturing intervals with an efficiency of 1.2 kcal/mol 36 . The stability of the protein-ligand complex system was determined using root-mean-square deviation (RMSD), the root-mean-square fluctuation (RMSF), hydrogen bonds, solvent accessible surface area (SASA) value, radius of gyration (Rg) value, and MolSa. MM/GBSA Calculation Molecular Machine Generalized Born Surface Area, simply called MM/GBSA, has been calibrated to allow the determination of ligand binding free energies together with strain energies of tested compounds, and where many ligand molecules show their activities in opposite directions to a single target receptor. To conduct our research, the Schrodinger Suite Version 2020-3, Maestro application was fully supported on the Linux platform to reach out top super-active molecules 36 . Principal component analysis (PCA) PCA was utilized to analyze the MDS trajectory, simplifying complex atomic movements into principal components while retaining key structural dynamics. The 3N × 3N covariance matrix in Cartesian space captured essential fluctuations 37 . Visualization with the ‘factoextra’ R package highlighted dominant motion patterns and compact clustering, indicating stable and coordinated behavior of the TOP2A-phytocompound complexes 38 . These results further support the potential of the compounds as effective therapeutic agents. Results Virtual Screening The docking analysis against the TOP2A identified Luteolin, Quercetin, and Rutin as top binders, demonstrating strong binding affinities. Luteolin showed binding energies of -10.4 and − 10.2 kcal/mol, Quercetin scored − 10.1 and − 10.0 kcal/mol, and Rutin exhibited − 10.0 and − 9.8 kcal/mol across two docking platforms. These values notably surpass the control drug Mitoxantrone, which displayed a binding affinity of -8.5 kcal/mol. Comprehensive docking results are summarized in Table S1 (PyRx and Maestro). Interpretation of Cell Line Cytotoxicity The study identified Luteolin, Quercetin, and Rutin as lead bioactive compounds with promising anticancer activity, supported by molecular docking and computational predictions. Using the Way2Drug server, these compounds showed significant potential against various cancers, particularly prostate cancer. Luteolin demonstrated predicted activity against multiple cancer types, including prostate carcinoma epithelial cell line (CWR22R), with Pa = 0.318 and Pi = 0.025. Quercetin shows enhanced prostate cancer relevance with Pa = 0.429 and Pi = 0.004, supporting its potential as a therapeutic candidate. Similarly, Rutin also showed predicted activity (Pa > 0.3) against several cancers, including strong potential against the CWR22R prostate cancer cell line, suggesting its functional relevance in prostate cancer therapy ( Table 1 ) . Table 1 Prediction of Cell line anti-cancer as well as anti-prostate cancer activity of top Docking scored compound. Here, Pa (Prediction of Activity) and Pi (Prediction of Inhibition). Ligand Anti-cancer Activity (Pa > 0.3) Anti-prostate cancer Activity (Pa > Pi) Pa Value Pi value Cell line Tissue Tumor Type Luteolin Oligodendroglioma, Small and Non-small cell lung carcinoma, Colon adenocarcinoma, Gastric Carcinoma, Hepatoblastoma, Prostate Carcinoma epithelial cell line, Breast Carcinoma, Adult B acute lymphoblastic Leukemia, Promyeloblast Leukemia, Leukemic T cells, Adrenal Cortex Carcinoma, Squamous cell lung carcinoma, Breast adenocarcinoma, Metastatic Melanoma 0.318 0.025 CWR22R Prostate Carcinoma Quercetin Prostate Carcinoma epithelial, Oligodendroglioma, Small and Non-small cell lung carcinoma, Promyeloblast Leukemia, Metastatic Melanoma, Adult B acute lymphoblastic Leukemia. 0.429 0.004 Rutin Colon adenocarcinoma, Metastatic Melanoma, Promyeloblast Leukemia, Non-small cell lung cancer 3 stage, Breast Ductal Carcinoma, Fibrosarcoma, Melanoma, Small cell lung carcinoma, Breast carcinoma with adenocarcinoma, Adult B acute lymphoblastic Leukemia 0.291 0.057 PK Profiling of Top Hits The pharmacokinetic behavior and drug-likeness properties of the top-ranked compounds luteolin, quercetin, and rutin were systematically evaluated using established in silico ADMET prediction tools. The analysis demonstrated that luteolin and quercetin exhibited physicochemical properties well within the acceptable range of Lipinski’s Rule of Five, indicating favorable oral bioavailability. In contrast, rutin showed deviations, primarily due to its higher molecular weight and elevated topological polar surface area (TPSA), which may limit its permeability. Both luteolin and quercetin displayed optimal lipophilicity (Log P < 2), low numbers of hydrogen bond donors and acceptors, and minimal structural flexibility, all of which are indicative of good membrane permeability and drug-like behavior. Although rutin demonstrated comparatively lower lipophilicity and higher polarity, its bioavailability score remained within an acceptable range, suggesting partial suitability as a therapeutic candidate. Absorption analysis revealed that luteolin and quercetin possess high intestinal absorption rates (~ 81% and ~ 77%, respectively), whereas rutin exhibited significantly lower absorption (~ 23%), likely due to its larger molecular structure and polarity. All compounds showed moderate water solubility and no inhibitory effects on P-glycoprotein, indicating a reduced risk of efflux-mediated drug resistance. In terms of distribution, all candidates demonstrated acceptable volume of distribution (VDss) and moderate plasma protein binding, suggesting efficient systemic circulation. However, none of the compounds were predicted to effectively penetrate the blood–brain barrier, indicating limited central nervous system activity. Metabolic profiling further indicated that the compounds are not substrates or inhibitors of major cytochrome P450 enzymes (CYP2D6, CYP3A4, CYP2C9, and CYP2C19), suggesting a low likelihood of metabolic drug–drug interactions. Excretion parameters showed that luteolin and quercetin possess moderate clearance rates, whereas rutin exhibited comparatively lower clearance. Importantly, toxicity predictions confirmed that all three compounds are non-mutagenic (AMES negative), non-hepatotoxic, and free from skin sensitization effects. Additionally, none of the compounds inhibited hERG channels, indicating a low risk of cardiotoxicity. Acute toxicity (LD₅₀) values further supported their safety profiles, placing them within a relatively non-toxic range Table 2 37 . Table 2 In silico Physiochemical and Pk Activity of Lead Drug-like Leads. Properties Action Luteolin Quercetin Rutin Control Drug Physiochemical Details Molecular Weight (g/mol) 286.24 302.24 610.52 543.52 Num. Rotatable Bonds 1 1 6 5 Hydrogen Bond Acceptors 6 7 16 12 Hydrogen Bond Donors 4 5 10 6 Molar Refractivity 76.01 78.03 141.38 132.66 TPSA (A 2 ) 111.13 131.36 269.43 206.07 Consensus Log P (o/w) 1.73 1.23 -1.29 0.44 Lipinski Violation 0 0 3 3 Bioavailability Score 0.55 0.55 0.55 0.17 Absorption Water Solubility (log mol/L) -3.094 -2.925 -2.892 -2.915 CaCo2 Permeability (log Papp in 10 − 6 cm/s) 0.096 -0.229 -0.949 0.457 Intestinal Absorption (Human) (% Absorbed) 81.13 77.207 23.446 62.372 Skin Permeability (log Kp) -2.735 -2.735 -2.735 -2.735 P-Glycoprotein I, II inhibitor No No No No Distribution VDss (Human) (log L/kg) 1.153 1.559 1.663 1.647 Fraction Unbound (Human) (Fu) 0.168 0.206 0.187 0.215 BBB Permeability (log BB) -0.907 -1.098 -1.899 -1.379 CNS Permeability (log PS) -2.251 -3.065 -5.178 -4.307 Metabolism CYP2D6 Substrate No No No No CYP3A4 Substrate No No No No CYP2C19 Inhibitor No No No No CYP2C9 Inhibitor No No No No CYP2D6 Inhibitor No No No No CYP3A4 Inhibitor No No No No Excretion Total Clearance (log ml/min/kg) 0.495 0.407 -0.369 0.987 Renal OCT2 Substrate No No No No Toxicity AMES Toxicity No No No No Max. Tolerated Dose (Hum) (log mg/kg/day) 0.499 0.499 0.452 0.081 hERG I, II inhibitor No No No No, Yes Oral Rat Acute Toxicity (LD 50 ) (mol/kg) 2.455 2.471 2.491 2.408 Oral Rat Chronic Toxicity (LOAEL) (log mg/kg_bw/day) 2.409 2.612 3.673 3.339 Hepatoxicity No No No Yes Skin Sensitisation No No No No T. Pyriformis Toxicity (log µg/L) 0.326 0.288 0.285 0.285 Minnow Toxicity (log mM) 3.169 3.721 7.677 4.412 Visualization of Protein-Ligand Complex Interactions Non-covalent interactions (Hydrogen and hydrophobic bonds) were analyzed when we employed the Ligplot + V 2.2.8 Luteolin exhibited the interaction with Arg 98 (2.85), Asn 120 (2.76), Lys 123 (2.82), Ile 141(2.70), Thr 147 (3.14), Ser 148 (2.77), Ser 149 (2.94), Asn 150 (2.79, 3.19), Lys 168 (2.80) residue of TOP2A protein with the hydrogen bonds, although it can be fitted by the hydrophobic bond with Asn 91, Asn 95, Ile 125, Gly 124, Thr 215 residues. Besides, Quercetin showed superior hydrogen bond interaction, and Asn 91, Ser 149, Gly 164, Tyr 165, Gly 166, Ala 167, and Lys 168 participated in hydrogen bond formation. The TOPO II receptor formed a hydrophobic interaction with it by the assessment of the residues of Tyr 34, Asn 95, Ile 125, Ser 148, and Gly 161. Now, the Rutin stands with the aid of hydrophobic bonds (Gln 59, Met 61, Phe 77, Asp 245, Tyr 274, Gln 309, Gln 310, Ile 311, Phe 313, Ala 318, Ser 320), where nine hydrogen bonds interact with the protein, too. The hydrogen bond-forming residues are Trp 62, Tyr 72, Tyr 82, Arg 241, Lys 321, and Glu 379. However, the control drug (Mitoxantrone) fitted with eleven hydrogen bonds with the 1ZXM, the active site residues, Tyr 34, Ile 118, Asn 120, Ser 148, Ser 149, Asn 150, Thr 215, additionally, it follows the Ile 33, Ile 88, Asn 91, Ala 92, Asn 95, Arg 98, Ile 125, Phe 142, Lys 157, Ala 167, Lys 168, Ile 217 hydrophobic interaction (Fig. 1 , and Table 3 ). Table 3 Exploring the Molecular Interaction of Small Molecules and Targeted TOP2A Receptor. Compounds Hydrogen Bonds residues Hydrogen Bond Length (Å) Number of H2 Bonds Other Bond residues Luteolin Arg 98 2.85 10 Asn 91, Asn 95, Ile 125, Gly 124, Thr 215 Asn 120 2.76 Lys 123 2.82 Ile 141 2.70 Thr 147 3.14 Ser 148 2.77 Ser 149 2.94 Asn 150 2.79, 3.19 Lys 168 2.80 Quercetin Asn 91 3.17 7 Tyr 34, Asn 95, Ile 125, Ser 148, Gly 161 Ser 149 3.17 Gly 164 3.17 Tyr 165 2.89 Gly 166 2.88 Ala 167 3.01 Lys 168 3.16 Rutin Trp 62 2.78, 3.27, 3.32 9 Gln 59, Met 61, Phe 77, Asp 245, Tyr 274, Gln 309, Gln 310, Ile 311, Phe 313, Ala 318, Ser 320 Tyr 72 3.04 Tyr 82 2.96 Arg 241 3.15, 3.23 Lys 321 3.02 Glu 379 3.04 Mitoxantrone (Control) Tyr 34 3.14 11 Ile 33, Ile 88, Asn 91, Ala 92, Asn 95, Arg 98, Ile 125, Phe 142, Lys 157, Ala 167, Lys 168, Ile 217 Ile 118 2.82 Asn 120 2.96, 3.18 Ser 148 2.75 Ser 149 2.87, 3.06, 3.10 Asn 150 2.80, 2.90 Thr 215 3.17 Molecular Dynamics Simulation Study To gain a detailed understanding of the conformational behavior, binding stability, and dynamic interaction profile of the selected phytocompounds with the human topoisomerase II alpha (TOP2A) protein, a 100-nanosecond molecular dynamics simulation (MDS) was conducted for each protein-ligand complex, including the control drug Mitoxantrone (CID-4212). The Root Mean Square Deviation (RMSD) analysis, a critical metric for evaluating the overall stability of the protein backbone during the simulation, revealed that all complexes remained stable over the full 100 ns trajectory. Notably, the phytocompounds exhibited RMSD values within the acceptable fluctuation range (< 3.0 Å), with Rutin showing the lowest RMSD at 2.166 Å, followed by Quercetin at 2.249 Å, the control drug at 2.229 Å, and Luteolin at 2.913 Å. These findings indicate minimal structural drift from the initial conformations, suggesting that the phytocompounds bind stably within the active site of TOP2A ( Fig. 2 ) . The Root Mean Square Fluctuation (RMSF) analysis provided insights into the flexibility of individual amino acid residues throughout the simulation. Fluctuations were primarily localized at the N- and C-terminal regions, which are typically more disordered, while the core regions consisting of alpha-helices and beta-strands remained highly stable. Luteolin and Rutin demonstrated the lowest RMSF values of 2.041 Å, indicating reduced atomic-level fluctuations and enhanced stability upon binding, while Quercetin recorded a slightly higher RMSF of 2.74 Å. The Radius of Gyration (Rg), an important measure of the overall compactness and folding stability of the protein-ligand complex, remained consistent throughout the simulation period. Luteolin displayed the highest Rg value of 27.793 Å, followed by Rutin (27.673 Å), Quercetin (27.473 Å), and the reference drug (27.494 Å), suggesting minor conformational rearrangements upon ligand binding but no major structural unfolding, confirming the stable folding state of the complexes. Additionally, the Solvent Accessible Surface Area (SASA) was evaluated to determine the extent of protein surface exposure to the solvent environment. Luteolin (32,574.259 Ų), Quercetin (32,150.518 Ų), and Rutin (32,214.021 Ų) showed higher SASA values compared to Mitoxantrone (31,918.158 Ų), indicating that the selected phytocompounds promoted more extensive exposure of surface residues, potentially improving interactions with solvent molecules and enhancing solubility. Molecular Surface Area (MolSA) analysis, which approximates the van der Waals contact surface, revealed that all phytocompound complexes exhibited broader surface coverage than the control drug. Specifically, Luteolin demonstrated the largest MolSA at 36,320.325 Ų, followed by Rutin (35,548.998 Ų), Quercetin (35,098.012 Ų), and Mitoxantrone (35,171.871 Ų), supporting their enhanced molecular interaction profiles ( Fig. 3 ) . Furthermore, the intramolecular hydrogen bond analysis provided a quantitative measure of ligand stability within the active binding pocket. Luteolin formed the highest number of hydrogen bonds (1349), followed by Rutin (1316), Quercetin (1292), and Mitoxantrone (1336). A higher number of hydrogen bonds indicates a more robust and stable ligand-protein interaction, which is crucial for inhibitory efficiency (Table 4 ). These dynamic results suggest that Luteolin, in particular, forms a highly stable and conformationally favorable complex with TOP2A, showing not only strong binding but also the ability to maintain structural integrity throughout the simulation. Quercetin and Rutin also performed remarkably well across all parameters, reinforcing their potential as effective natural inhibitors. Collectively, the MDS data, including structural stability (RMSD), residue flexibility (RMSF), compactness (Rg), surface exposure (SASA and MolSA), and intermolecular bonding, strongly support the conclusion that Luteolin and Quercetin are promising bioactive compounds with significant potential to inhibit TOP2A activity in prostate cancer, thereby offering valuable insights for the development of targeted phytochemical-based therapeutics. Table 4 Numerous MD Simulation Features Organized in a Tabular Format. Ligands RMSD RMSF Rg SASA MolSA H2 Bond Luteolin 2.913 2.041 27.793 32574.259 36320.325 1349 Quercetin 2.249 2.74 27.473 32150.518 35098.012 1292 Rutin 2.166 2.041 27.673 32214.021 35548.998 1316 Mitoxantrone (Control) 2.229 2.449 27.494 31918.158 35171.871 1336 Post Simulation Trajectory analysis (MM/GBSA and PCA) Binding free energy estimation of the top MDS outputs, including Luteolin, Quercetin, Rutin, and control drug, produces different values, including G_Bind scores of -70.8835589 for luteolin, quercetin, and rutin, whereas the value for the control drug is -72.7597367. Moreover, the examination of other binding free energy values to understand the stability of better-performed dynamics outputs, and significantly measured the Coulomb Energy (ΔG_Bind_Coulomb), Covalent Energy (ΔG_Bind_Covalent), Hydrogen bond energy (ΔG_Bind_Hbond), Lipophilicity energy (ΔG_Bind_Lipo), ΔG_Bind_Packing, ΔG_Bind_Solv_GB, and Van der Waals interaction energy (ΔG_Bind_vdW). All the results of this evaluation indicate a robust binding affinity between Luteolin and Quercetin and the targeted TOPO II (Fig. 4 ). Besides, the principal component analysis, or simply PCA, determines the structural variation in different ligand-targeted protein complexes, and we also plotted the PCA graphs in Fig. 5 . The eigenmodes serially decrease, which suggests that the local instability in the structure of the target gained stability; this value is represented by a percentage. The tested bioactive compounds' highest value is close to 90%, but the lowest value is Rutin CID-5280445 (11.87%), and Quercetin CID-5280343 (8.34%). After completing the study of eigenvalues, the cumulative value has been shown to be fully cumulative. Table 5 shows three eigenvectors for the TOP2A enzyme-ligand based on the MDS trajectory and exposed in clusters. The TOP2A complexes formed clusters 0.70711 on PC1, whereas − 0.70711 on PC2. Table 5 Calculating the PCA Value Eigenvalue Percentage Of variance Cumulative Extracted Eigenvectors Co-efficient of PC1 Co-efficient of PC2 Luteolin 1.76256 88.13% 88.13% 0.70711 -0.70711 0.23744 11.87% 100% Quercetin 1.83328 91.66% 91.66% 0.70711 -0.70711 0.16672 8.34% 100% Rutin 1.84834 92.42% 92.42% 0.70711 -0.70711 0.15166 7.58% 100% Mitoxantrone (Control) 1.67401 83.70% 83.70% 0.70711 -0.70711 0.32599 16.30% 100% Discussion Prostate cancer remains a major therapeutic challenge, particularly in advanced stages where resistance to standard treatments frequently develops. Targeting essential enzymes such as topoisomerase IIα (TOP2A), which plays a critical role in DNA replication and cell cycle regulation, has therefore become a well-established strategy in anticancer drug development. In the present study, an integrated computational pipeline was applied to identify potential natural inhibitors of TOP2A, combining molecular docking, pharmacokinetic evaluation, molecular dynamics simulation, and free energy analysis. The adoption of multi-step in silico approaches has significantly advanced modern drug discovery by enabling efficient screening and mechanistic understanding of ligand–protein interactions. Similar integrated strategies have been successfully employed in recent studies targeting diverse biological systems, including viral proteins, neurological targets, and cancer-related enzymes, demonstrating their effectiveness in accelerating lead identification and optimization 37 , 41 , 44 . In this study, luteolin, quercetin, and rutin were identified as the top-performing compounds based on their strong binding interactions with the TOP2A active site. Their docking scores exceeded that of the reference drug, suggesting a higher binding potential (Table 1 ). Comparable findings have been reported in previous computational investigations, where natural compounds, particularly flavonoids, exhibited strong inhibitory potential against key therapeutic targets 39 , 45 . However, docking alone cannot fully capture the complexity of drug behavior, necessitating further pharmacokinetic and dynamic validation. Pharmacokinetic profiling revealed that luteolin and quercetin possess favorable drug-like characteristics, including high intestinal absorption, optimal lipophilicity, and low predicted toxicity, whereas rutin showed some limitations due to its physicochemical properties (Table 2 ). These results are consistent with earlier studies emphasizing the importance of ADMET analysis in improving the success rate of drug candidates during development 42 , 43 . Additionally, the lack of significant interaction with major cytochrome P450 enzymes suggests a lower risk of metabolic complications, which is a desirable property for drug candidates. Detailed interaction analysis further demonstrated that the selected compounds formed stable hydrogen bonds and hydrophobic interactions with key residues in the TOP2A active site, supporting their strong binding affinity (Fig. 1 and Table 3 ). Such interaction patterns are critical for ensuring binding specificity and inhibitory efficiency, as reported in similar structure-based drug discovery and molecular interaction studies 44 , 48 . To further validate the stability of the protein–ligand complexes, molecular dynamics simulations were conducted. The results demonstrated that all selected compounds maintained stable interactions within the TOP2A binding pocket over the simulation period. Parameters such as RMSD and RMSF indicated minimal structural fluctuations, while stable radius of gyration values confirmed the preservation of protein compactness (Fig. 2 , Fig. 3 , and Table 4 ). These observations are in agreement with previous studies where stable dynamic behavior is considered indicative of effective ligand binding and functional inhibition 46 , 47 . Moreover, solvent-accessible surface area (SASA), molecular surface area (MolSA), and hydrogen bond analyses suggested consistent interaction patterns throughout the simulation, reinforcing the stability of the complexes. Binding free energy calculations using the MM/GBSA approach further confirmed strong ligand–protein interactions, providing a more reliable estimation of binding affinity beyond docking alone 44 . Principal component analysis (PCA) revealed coordinated molecular motions and reduced structural instability within the complexes, indicating stable conformational behavior over time (Table 5 and Fig. 5 ). Similar applications of PCA in computational studies have been shown to effectively capture essential dynamic features of biomolecular systems 37 , 38 . Overall, luteolin and quercetin emerged as the most promising candidates, exhibiting a balanced combination of strong binding affinity, favorable pharmacokinetic properties, and stable dynamic behavior. Although rutin also demonstrated notable interactions, its comparatively lower pharmacokinetic performance may limit its therapeutic potential without further optimization. These findings are consistent with previous reports highlighting the potential of plant-derived compounds as effective therapeutic agents 39 , 41 . Despite the promising outcomes, it is important to recognize that computational predictions alone are not sufficient to confirm biological efficacy. Experimental validation through in vitro and in vivo studies is necessary to verify these findings and assess their clinical relevance. Future work should therefore focus on biochemical assays, cellular studies, and animal models to further explore the therapeutic potential of these compounds. Summary and Future Perspectives It is now clear that TOP2A is the major prognostic biomarker as well as a proliferation marker in PCa. This protein represents the primary modulator of replicating DNA, the transcription process, as well as cellular cycling checkpoints. Because of this, it is the crucial target subject of the treatment of PCa sufferers; nevertheless, there are currently fewer viable prospective marketing items. Additionally, individuals are resorting to herbal substances made using medicinal herbs to cure it. The D. Longan is a plentiful source of botanical compounds. We obtained the top ten highest docked compounds against the TOP2A, and the top three are: Luteolin (-10.4/-10.2 Kcal/mol), Quercetin (-10.1/-10.0 Kcal/mol), and Rutin (-10.0/-9.8 Kcal/mol). Such a type of lead molecule is selected for the cell-line cytotoxic experiment. In addition, the context reveals that all of them play a variety of cancer-fighting activities in different types of cell lines; most precisely, their anti-prostate cancer action is found in the CWR22R prostate cell line. Alternatively, these substances' ADMET characteristics are very satisfactory; they function as drugs. However, in its biochemical interactions, all substances are associated with the designated proteins with greater effectiveness. They displayed an extensive amount of bonds of hydrogen bonds, which further indicates a superior match to the pocket residues. After we finished the post-docking representation, individuals were chosen to participate in the MD simulations study. Experimental chemicals Luteolin, Quercetin, and Rutin demonstrated experimental greatest MD simulation outcomes towards the desired protein compared to the control drug. Indeed, Luteolin and Quercetin followed all positive results in the simulation study, and these two substances underwent MM/GBSA and PCA verification research. This is essential to clarify how molecules work in vivo research, including cell types or animal models, to serve as an intriguing prospective substance-like alternative towards prostate malignancy. Contemporary chemotherapy of Prostate carcinoma is progressing fast, going between highly handled lingual followed by injected therapeutic regimens through all-oral, easily tolerated medication pairings, having rates of recovery approaching 90%. The next wave of treatments will see improvements in the management of Prostate carcinoma. Because these represent only a small sample of the numerous current developmental endeavors as well as revolutionary methods to accomplish therefore, the next section will examine a few distinct methods related to treating it. Prospective anti-cancer drugs are going to face a variety of pragmatic issues that now preclude application in particular situations, including medication-drug relationships, adverse reactions, recurring safety issues, dosage restrictions, and effectiveness problems. According to the consequence, our study's conclusions would tackle all main issues and offer considerable improvements over present medical practices. Authors Contribution Conceptualization : D.D. Formal analysis and validation : B.P., S.R., M.R.S., P.P., P.B., and D.D. Writing original draft : B.P., S.R., P.P., D.D., S.Z.A., P.B., M.M., M.N.H. Writing_Editing_Reviewing : P.B., M.N.H., M.M. Project administration and Supervision : B.P., D.D. All authors read and agreed to submit the final version of this manuscript for publication. Abbreviations Topoisomerase II alpha (TOPO IIα), Pharmacokinetics (PK), ADMET (Absorption, Distribution, Metabolism, Elimination, and Toxicity), Molecular Dynamics (MD) simulation, Isothermal-Isobaric ensemble (NPT), Root-mean-square deviation (RMSD), Root-mean-square fluctuation (RMSF), Solvent accessible surface area (SASA) value, radius of gyration (Rg) value, and MolSa, Molecular Machine Generalized Born Surface Area (MM-GBSA), PCA (Principal Component Analysis), American Cancer Society (ACS). Declarations Ethical statement Not applicable to this study. Conflict of interest All authors declare that there is no conflict of interest regarding this study. Funding This research did not receive any financial support from any donor agency. Author Contribution Conceptualization: D.D. Formal analysis and validation: B.P., S.R., M.R.S., P.P., P.B., and D.D. Writing original draft: B.P., S.R., P.P., D.D., S.Z.A., P.B., M.M., M.N.H. Writing. Editing, Reviewing: P.B., M.N.H., M.M. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9616322","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":636951898,"identity":"fcfedf32-0147-46bd-a958-ec475eff95ab","order_by":0,"name":"Bidu Pal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACA2YGAyBlwczG3nwAyJCQIVaLBDsfz7EEEIOHsBYGiBZ+OQkfEIOBsBZzduZt0gU1EtJsEjyfX92oseBhYD98dAM+LZbNbGXSM45JGLNJ926zzjkGdBhPWtoNvA47zGMmzcMmkcwmc3abcQ7QLqB3zIjQ8k+ivk0i55lxzj9itfC2STCzSeQwP85tI0IL0C/F1jP7gFp4jpkx5/ZJ8LAR8os5/+GNtwu+2TDLtzc//pzzrU6On/3wMbxaQIAZSrNJgElCypG1MH8gRvUoGAWjYBSMPAAAe2g7aXmXaWsAAAAASUVORK5CYII=","orcid":"","institution":"Gopalganj Science and Technology University , Gopalganj","correspondingAuthor":true,"prefix":"","firstName":"Bidu","middleName":"","lastName":"Pal","suffix":""},{"id":636951899,"identity":"9fae58fe-a213-45f1-87f3-c7a8b25b79b4","order_by":1,"name":"Sajal Roy","email":"","orcid":"","institution":"Gopalganj Science and Technology University , Gopalganj","correspondingAuthor":false,"prefix":"","firstName":"Sajal","middleName":"","lastName":"Roy","suffix":""},{"id":636951900,"identity":"42cf22c5-72d4-45e8-98cc-76418510d8f2","order_by":2,"name":"Musfiqur Rahman Sakib","email":"","orcid":"","institution":"Gopalganj Science and Technology University , Gopalganj","correspondingAuthor":false,"prefix":"","firstName":"Musfiqur","middleName":"Rahman","lastName":"Sakib","suffix":""},{"id":636951901,"identity":"0b002ab6-5a91-456b-8b6b-64b43aeb191c","order_by":3,"name":"Shahlaa Zernaz Ahmed","email":"","orcid":"","institution":"North South University","correspondingAuthor":false,"prefix":"","firstName":"Shahlaa","middleName":"Zernaz","lastName":"Ahmed","suffix":""},{"id":636951902,"identity":"b88ac07f-1f48-4797-8f7a-6dae550de51c","order_by":4,"name":"Dipta Dey","email":"","orcid":"","institution":"Gopalganj Science and Technology University , Gopalganj","correspondingAuthor":false,"prefix":"","firstName":"Dipta","middleName":"","lastName":"Dey","suffix":""},{"id":636951903,"identity":"5a283bd1-05f1-47d1-bbd4-e6a03249a238","order_by":5,"name":"Priyank Paul","email":"","orcid":"","institution":"Gopalganj Science and Technology University , Gopalganj","correspondingAuthor":false,"prefix":"","firstName":"Priyank","middleName":"","lastName":"Paul","suffix":""},{"id":636951904,"identity":"4ce33534-774b-4124-b223-703137fa9e9e","order_by":6,"name":"Partha Biswas","email":"","orcid":"","institution":"Jashore University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Partha","middleName":"","lastName":"Biswas","suffix":""},{"id":636951905,"identity":"21e4f2e9-1637-4e3d-87f1-193ac38e8722","order_by":7,"name":"Md. Nazmul Hasan","email":"","orcid":"","institution":"Jashore University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Nazmul","lastName":"Hasan","suffix":""},{"id":636951906,"identity":"8b186749-3be2-47b8-946e-475227fc66d1","order_by":8,"name":"Manoj Mandal","email":"","orcid":"","institution":"Gopalganj Science and Technology University , Gopalganj","correspondingAuthor":false,"prefix":"","firstName":"Manoj","middleName":"","lastName":"Mandal","suffix":""}],"badges":[],"createdAt":"2026-05-05 09:09:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9616322/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9616322/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108942831,"identity":"d37be804-e581-4eab-b2ee-23e1979b4a5c","added_by":"auto","created_at":"2026-05-11 05:42:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":250055,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical illustration of post docking interactions between the TOP2A and compounds isolated from \u003cem\u003eD. longan.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9616322/v1/84771fec9b8f4f74382e5139.png"},{"id":108942837,"identity":"b9661041-935c-42b0-bed4-f56268ec5ad3","added_by":"auto","created_at":"2026-05-11 05:42:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145289,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of MD simulation study, where A) RMSD Calculation in 100ns time period, B) Interpretation of RMSF for TOP2A protein, C) Radius of Gyration value of the protein-ligand complex. Luteolin (CID-5280445), Quercetin (CID-5280343), Rutin (CID-5280805) and Control (CID 4212).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9616322/v1/e07bab48464217aa68162337.png"},{"id":108942847,"identity":"64c4c757-1e01-40a0-83b5-fa70daf0b9db","added_by":"auto","created_at":"2026-05-11 05:42:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197888,"visible":true,"origin":"","legend":"\u003cp\u003eA graph depicts the solvent-accessible surface area (SASA) analysis (D), MolSA (E), and investigating the amount of Hydrogen Bonds (F). Luteolin (CID-5280445), Quercetin (CID-5280343), Rutin (CID-5280805) and Control (CID 4212).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9616322/v1/3ed1b4ef1485df240db28d75.png"},{"id":108942830,"identity":"d5d6155e-fa05-477a-aead-f91361f54b82","added_by":"auto","created_at":"2026-05-11 05:42:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137168,"visible":true,"origin":"","legend":"\u003cp\u003e3D view of numerous MM-GBSA parameters for Selected ligands.\u003cstrong\u003e (A)\u003c/strong\u003e Luteolin (CID-5280445), \u003cstrong\u003e(B) \u003c/strong\u003eQuercetin (CID-5280343), \u003cstrong\u003e(C) \u003c/strong\u003eRutin (CID-5280805) and \u003cstrong\u003e(D) \u003c/strong\u003eControl (CID 4212).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9616322/v1/dd44304e18c0637d54326e4a.png"},{"id":108942839,"identity":"7b5e3030-1165-48ad-a235-8f5e9ceca883","added_by":"auto","created_at":"2026-05-11 05:42:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":103354,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D illustration of PCA\u003c/strong\u003e Eigenvalue and Eigenvectors-PC1, PC2. \u003cstrong\u003e(A)\u003c/strong\u003e Luteolin (CID-5280445), \u003cstrong\u003e(B) \u003c/strong\u003eQuercetin (CID-5280343), \u003cstrong\u003e(C) \u003c/strong\u003eRutin (CID-5280805) and \u003cstrong\u003e(D) \u003c/strong\u003eControl (CID 4212).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9616322/v1/2ca62fb544e701d201b94d6a.png"},{"id":108978229,"identity":"75e3785b-8d9d-4934-8201-2ea294a80d0e","added_by":"auto","created_at":"2026-05-11 11:35:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1267624,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9616322/v1/8b54c934-5edf-4d68-a40b-549351f3bc85.pdf"},{"id":108942848,"identity":"9ec9b5c1-d39f-4dff-b980-84a011caa104","added_by":"auto","created_at":"2026-05-11 05:42:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":168598,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementfileProstateCancer11.docx","url":"https://assets-eu.researchsquare.com/files/rs-9616322/v1/dba05168c276cfe80d04b43e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Computational Evaluation of Flavonoid-Based Inhibitors Targeting Human TOP2A: Insights from Molecular Docking, Molecular Dynamics Simulation, and Binding Free Energy Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is the second leading cause of mortality after cardiovascular disease, and according to recent scientific research, approximately 19.3\u0026nbsp;million new cancer cases and 10\u0026nbsp;million deaths will be diagnosed in 2020 \u003csup\u003e1\u003c/sup\u003e. In a similar vein, researchers predict that by 2030, there will be approximately 12\u0026nbsp;million cancer-related fatalities and 24\u0026nbsp;million new cancer patients. Numerous varieties of cancer are responsible for the deaths of a large number of people, but Prostate Cancer, PCa, is the second most prevalent cancerous tumor and the fifth leading cause of death among men worldwide. In 2020, PCa was detected in approximately 1,414,259 people worldwide and caused the deaths of approximately 375,304 people. According to the American Cancer Society (ACS), approximately 268,490 men will be newly diagnosed with prostate cancer, and approximately 34,500 men will pass away \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Men are the only ones with an organ called the prostate, and men aged 60 or older are more likely to develop prostate cancer. In addition, men under the age of 40 and non-Hispanic Black males are less likely to acquire it. It is more prevalent in both developed and developing nations, yet the rate in developing nations is progressively rising \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Identifying the precise molecular target/s of Prostate malignancies makes it difficult for scientists to reduce this mortality rate. However, Topoisomerase II alpha (TOP2A) is a potential prognostic biomarker for PCa, specifically a proliferation marker for aggressive prostate tumors. In fact, the Sullivan research group was the first to identify elevated levels of TOP2A in prostate cancer \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Willman et al. (2000) reported that the higher Gleason Score/GS (GS scoring is the more prevalent grading system for the diagnosis of PCa) for TOP2A indicates a correlation between TOP2A and Prostate carcinoma \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Specifically, this enzyme is a ubiquitously expressed enzyme that controls DNA replication, repair, transcription, and recombination before cell division. Furthermore, it primarily regulates the fundamental cellular processes by altering the chromosomal DNA topology through the creation of transient double-stranded DNA breaks that allow either strand to pass through the other, which is essential for the survival of the cells \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Notably, the expression of TOP2A is linked to the replication fork, which remains tightly bound to the chromosomes during mitosis and is essential for the development of proliferating cells. TOP2A abundance is regulated by the cell cycle to compensate for significant chromosomal copy number changes during mitotic DNA replication \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Loss of these checkpoints can result in prostate tumorigenesis and prostate cancer. Consequently, it is now evident that TOP2A is a proliferation marker and can facilitate the rapid proliferation of prostate cancer cells \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Perhaps as a result, inhibition of TOP2A is clinically beneficial for the management of PCa malignancies, and scientists are attempting to identify natural compounds because numerous therapeutic compounds have been used for the treatment of this major health concern, but most of these compounds have a variety of side effects. Despite this, plant-based bioactive compounds are safer for PCa disease. Since the turn of the twenty-first century, plant-derived compounds have played a fascinating role in the reduction of numerous life-threatening health problems, such as cardiovascular disease, cancers, diabetes, asthma, obesity, neurodegenerative diseases, infectious diseases, and many others \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Existing secondary metabolites include terpenoids, phenolics, flavonoids, alkaloids, glycosides, etc., and some selective compounds have potential functions in cancer treatment \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In particular, only a small number of bioactive molecules exist in nature that can combat the prostate tumors and cancer significantly \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Ursolic acid, atraric acid, oleanolic acid, -amyrin, N-butylbenzene-sulfonamide, ferulic acid, lauric acid, -sitosterol-3-O-glucoside, and -sitosterol are novel PCa-treating compounds isolated from the \u003cem\u003eP. Africana\u003c/em\u003e plant. Also, it has been used as a treatment for benign prostatic hyperplasia for many years \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Two significant soy isoflavones, indole-3-carbinol and Genistein, are applied as major therapeutic potential compounds for prostate cancer \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These two isoflavones work toward the molecular understanding of the BRCA1/2 gene. Marketed chemo-preventive drugs, such as Doxorubicin, Etoposide, and Mitoxantrone, target the TOP2A, which disrupts the normal function of transient DNA- TOP2A, and as a result, they increase the concentration of DNA double-strand break \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Anyway, substantial antiproliferative, anti-oxidant, and anti-cancer roles of \u003cem\u003eDimocarpus longan\u003c/em\u003e have already been published, as have the effects of the compounds Gallic acid, corilagin, and ellagic acid on HepG2, SGC 7901, and A549 cancer cell lines \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Importantly, the polyphenol-rich seed extract of \u003cem\u003eD. longan\u003c/em\u003e exhibited anti-proliferative activity against the colo 320DM, HT-29, and SW480 cell lines \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Obviously, the research indicated that \u003cem\u003eD. longan\u003c/em\u003e will be utilized as a potential cancer therapy treatment. However, less frequent research on prostate cancer was conducted. Now, it is imperative to treat Prostate tumors in addition to Cancer, so we designed our current in silico workflow to address this health concern. We chose botanical compounds derived from the \u003cem\u003eD. longan\u003c/em\u003e plant based on their antiproliferative and anticancer properties as reported in the published scientific literature. Therefore, we designed an \u003cem\u003ein silico\u003c/em\u003e workflow to identify novel drugs. Natural compounds of Longan Lour plant-based were used, a virtual screening technique was applied, molecular target interactions were studied, and then, with the assistance of Way2Drug, the top three virtual candidates were evaluated for their cell-line toxicity activity. Their pharmacokinetic properties supported the drug-likeness activity. The \"protein-ligand complexes\" stability was further validated by the dynamic simulator, and after the study of the dynamics, MM-GBSA and PCA analyses were conducted.\u003c/p\u003e"},{"header":"Research Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLigand library building, and Optimization of ligands\u003c/h2\u003e \u003cp\u003eWe obtained phytocompounds for the current research project from various \u003cem\u003eD. Longan\u003c/em\u003e plant parts \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, but we built a library of 30 compounds predominantly using a controlled drug as Mitoxantrone. The 3D structures of these bioactive compounds were retrieved from the PubChem database in Structure Data File (SDF) format. Ligands were optimized using UCSF Chimera version v1.14, applying the Gasteiger method to neutralize the net charge. Finally, both the test ligands and the control drug were converted to mol2 format for subsequent molecular docking analyses \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePreparation of Macromolecules\u003c/h3\u003e\n\u003cp\u003eThe crystal structure of the unmutated human TOP2A protein (PDB ID: 1ZXM) was obtained from the Protein Data Bank. Active site residues were predicted using the COACH-D server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yanglab.nankai.edu.cn/COACH-D/\u003c/span\u003e\u003cspan address=\"https://yanglab.nankai.edu.cn/COACH-D/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which identifies key amino acids within potential binding pockets \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Before docking, the protein structure was refined through energy minimization to enhance stability and eliminate non-essential elements such as non-standard residues, metal ions, ligands, heteroatoms, and water molecules, which could introduce artifacts during interaction analysis. Hydrogen atoms were added to ensure proper protonation states and optimal hydrogen bonding during docking simulations. All preparation steps were executed using UCSF Chimera v1.14, employing the Gasteiger charge method for assigning atomic charges. \u003csup\u003e27,28\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eSite-specific molecular docking\u003c/h3\u003e\n\u003cp\u003eThe binding sites of the target protein were determined using the COACH-D algorithm, enabling accurate prediction of active site residues. Docking simulations were carried out through PyRx 0.8, after converting the ligands and protein into PDBQT format. The docking process produced binding energy scores and RMSD values, which were recorded in a CSV file, highlighting several ligands with strong binding potential \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. To complement this, site-specific docking was also performed using the Maestro interface (Schr\u0026ouml;dinger v2020-3). A site map was created to define the docking region, ensuring precision in ligand-receptor interactions \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eInterpretation of Cell Line Cytotoxicity\u003c/h3\u003e\n\u003cp\u003eFollowing the docking studies, the biological relevance of the selected botanical compounds was further evaluated for their potential anticancer properties, including activity against prostate cancer. Using the Way2Drug online server, the compounds were analyzed for cytotoxic effects across various cancer cell lines. The predictions, based on Pa (probability of activity) and Pi (probability of inactivity) values, were obtained through the CLP-Cell-line cytotoxicity hub \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These results provided additional insight into their therapeutic potential, warranting further validation of their activity specifically against prostate cancer.\u003c/p\u003e\n\u003ch3\u003ePK Profiling of Top Hits and Molecular Interaction\u003c/h3\u003e\n\u003cp\u003eTo further assess the drug-likeness and pharmacokinetic properties of the lead compounds, the SwissADME web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized. This server provided key physicochemical parameters, including molecular weight, hydrogen bond donors/acceptors, topological polar surface area (TPSA), log P, bioavailability score, and others. Additionally, compliance with Lipinski\u0026rsquo;s Rule of Five (L5) was evaluated to predict oral bioavailability \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In parallel, ADMET profiling covering absorption, distribution, metabolism, excretion, and toxicity was performed using the pKCSM platform, providing critical insights into the pharmacological safety of the compounds \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. To better understand the binding interactions, LigPlot+ v2.2.8 and BIOVIA Discovery Studio were employed to visualize and analyze protein\u0026ndash;ligand complexes, particularly focusing on hydrogen bonds and hydrophobic interactions. Additionally, structural visualization and merged output files were obtained using the PyMOL visualizer \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, supporting detailed interaction mapping.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics (MD) Simulation\u003c/h2\u003e \u003cp\u003eThe 100 ns MD simulations analyzed the protein-ligand complex structures to determine the binding consistency of the selected 3 candidate ligand compounds with the control drug to the targeted protein (PDB ID- 1ZXM). The molecular dynamic simulation of the protein-ligand complex structures was carried out by using the YASARA v21.6.17 to analyze the thermodynamic stability of receptor-ligand complexes \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The cell border in MDS was defined using the AMBER14 force field, and indeed, the density of the solvent utilized in the simulation was kept at 0.997 g/mL. The pH was kept constant at 7.4, and NaCl concentrations were added to keep the pH neutral. To keep the geometry, energy reduction was used. After that, the modeling trajectory was run for 100 ns. The pressure was held at 1 bar, and the temperature was fixed at 298 K When an isobaric atmosphere was necessary, a Berendsen barostat was used to maintain pressure. The ease with which the metaphase of the macros tool was used to execute the full simulation procedure in the YASARA framework aided the current investigation. All Isothermal-Isobaric ensemble (NPT) assemblies that made use of the temperature combination of Nose-Hoover and the isotropic approach were maintained at 300 K and one-atmosphere pressure (1,01325 bar) and were accompanied by 50 PS capturing intervals with an efficiency of 1.2 kcal/mol \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The stability of the protein-ligand complex system was determined using root-mean-square deviation (RMSD), the root-mean-square fluctuation (RMSF), hydrogen bonds, solvent accessible surface area (SASA) value, radius of gyration (Rg) value, and MolSa.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMM/GBSA Calculation\u003c/h3\u003e\n\u003cp\u003eMolecular Machine Generalized Born Surface Area, simply called MM/GBSA, has been calibrated to allow the determination of ligand binding free energies together with strain energies of tested compounds, and where many ligand molecules show their activities in opposite directions to a single target receptor. To conduct our research, the Schrodinger Suite Version 2020-3, Maestro application was fully supported on the Linux platform to reach out top super-active molecules \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003ePrincipal component analysis (PCA)\u003c/h3\u003e\n\u003cp\u003ePCA was utilized to analyze the MDS trajectory, simplifying complex atomic movements into principal components while retaining key structural dynamics. The 3N \u0026times; 3N covariance matrix in Cartesian space captured essential fluctuations \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Visualization with the \u0026lsquo;factoextra\u0026rsquo; R package highlighted dominant motion patterns and compact clustering, indicating stable and coordinated behavior of the TOP2A-phytocompound complexes \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These results further support the potential of the compounds as effective therapeutic agents.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVirtual Screening\u003c/h2\u003e \u003cp\u003eThe docking analysis against the TOP2A identified Luteolin, Quercetin, and Rutin as top binders, demonstrating strong binding affinities. Luteolin showed binding energies of -10.4 and \u0026minus;\u0026thinsp;10.2 kcal/mol, Quercetin scored\u0026thinsp;\u0026minus;\u0026thinsp;10.1 and \u0026minus;\u0026thinsp;10.0 kcal/mol, and Rutin exhibited\u0026thinsp;\u0026minus;\u0026thinsp;10.0 and \u0026minus;\u0026thinsp;9.8 kcal/mol across two docking platforms. These values notably surpass the control drug Mitoxantrone, which displayed a binding affinity of -8.5 kcal/mol. Comprehensive docking results are summarized in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e (PyRx and Maestro).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of Cell Line Cytotoxicity\u003c/h2\u003e \u003cp\u003eThe study identified Luteolin, Quercetin, and Rutin as lead bioactive compounds with promising anticancer activity, supported by molecular docking and computational predictions. Using the Way2Drug server, these compounds showed significant potential against various cancers, particularly prostate cancer. Luteolin demonstrated predicted activity against multiple cancer types, including prostate carcinoma epithelial cell line (CWR22R), with Pa\u0026thinsp;=\u0026thinsp;0.318 and Pi\u0026thinsp;=\u0026thinsp;0.025. Quercetin shows enhanced prostate cancer relevance with Pa\u0026thinsp;=\u0026thinsp;0.429 and Pi\u0026thinsp;=\u0026thinsp;0.004, supporting its potential as a therapeutic candidate. Similarly, Rutin also showed predicted activity (Pa\u0026thinsp;\u0026gt;\u0026thinsp;0.3) against several cancers, including strong potential against the CWR22R prostate cancer cell line, suggesting its functional relevance in prostate cancer therapy \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction of Cell line anti-cancer as well as anti-prostate cancer activity of top Docking scored compound. Here, Pa (Prediction of Activity) and Pi (Prediction of Inhibition).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLigand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnti-cancer Activity (Pa\u0026thinsp;\u0026gt;\u0026thinsp;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eAnti-prostate cancer Activity (Pa\u0026thinsp;\u0026gt;\u0026thinsp;Pi)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePa Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePi value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCell line\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eTissue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTumor Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligodendroglioma, Small and Non-small cell lung carcinoma, Colon adenocarcinoma, Gastric Carcinoma, Hepatoblastoma, Prostate Carcinoma epithelial cell line, Breast Carcinoma, Adult B acute lymphoblastic Leukemia, Promyeloblast Leukemia, Leukemic T cells, Adrenal Cortex Carcinoma, Squamous cell lung carcinoma, Breast adenocarcinoma,\u003c/p\u003e \u003cp\u003eMetastatic Melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCWR22R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProstate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProstate Carcinoma epithelial, Oligodendroglioma, Small and Non-small cell lung carcinoma, Promyeloblast Leukemia, Metastatic Melanoma, Adult B acute lymphoblastic Leukemia.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRutin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColon adenocarcinoma, Metastatic Melanoma, Promyeloblast Leukemia, Non-small cell lung cancer 3 stage, Breast Ductal Carcinoma, Fibrosarcoma, Melanoma, Small cell lung carcinoma, Breast carcinoma with adenocarcinoma, Adult B acute lymphoblastic Leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePK Profiling of Top Hits\u003c/h2\u003e \u003cp\u003eThe pharmacokinetic behavior and drug-likeness properties of the top-ranked compounds luteolin, quercetin, and rutin were systematically evaluated using established in silico ADMET prediction tools. The analysis demonstrated that luteolin and quercetin exhibited physicochemical properties well within the acceptable range of Lipinski\u0026rsquo;s Rule of Five, indicating favorable oral bioavailability. In contrast, rutin showed deviations, primarily due to its higher molecular weight and elevated topological polar surface area (TPSA), which may limit its permeability. Both luteolin and quercetin displayed optimal lipophilicity (Log P\u0026thinsp;\u0026lt;\u0026thinsp;2), low numbers of hydrogen bond donors and acceptors, and minimal structural flexibility, all of which are indicative of good membrane permeability and drug-like behavior. Although rutin demonstrated comparatively lower lipophilicity and higher polarity, its bioavailability score remained within an acceptable range, suggesting partial suitability as a therapeutic candidate. Absorption analysis revealed that luteolin and quercetin possess high intestinal absorption rates (~\u0026thinsp;81% and ~\u0026thinsp;77%, respectively), whereas rutin exhibited significantly lower absorption (~\u0026thinsp;23%), likely due to its larger molecular structure and polarity. All compounds showed moderate water solubility and no inhibitory effects on P-glycoprotein, indicating a reduced risk of efflux-mediated drug resistance. In terms of distribution, all candidates demonstrated acceptable volume of distribution (VDss) and moderate plasma protein binding, suggesting efficient systemic circulation. However, none of the compounds were predicted to effectively penetrate the blood\u0026ndash;brain barrier, indicating limited central nervous system activity. Metabolic profiling further indicated that the compounds are not substrates or inhibitors of major cytochrome P450 enzymes (CYP2D6, CYP3A4, CYP2C9, and CYP2C19), suggesting a low likelihood of metabolic drug\u0026ndash;drug interactions. Excretion parameters showed that luteolin and quercetin possess moderate clearance rates, whereas rutin exhibited comparatively lower clearance. Importantly, toxicity predictions confirmed that all three compounds are non-mutagenic (AMES negative), non-hepatotoxic, and free from skin sensitization effects. Additionally, none of the compounds inhibited hERG channels, indicating a low risk of cardiotoxicity. Acute toxicity (LD₅₀) values further supported their safety profiles, placing them within a relatively non-toxic range \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e \u003csup\u003e\u003cb\u003e37\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIn silico Physiochemical and Pk Activity of Lead Drug-like Leads.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRutin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl Drug\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003ePhysiochemical Details\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecular Weight (g/mol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e302.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e610.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e543.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNum. Rotatable Bonds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrogen Bond Acceptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrogen Bond Donors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolar Refractivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTPSA (A\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e206.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsensus Log P (o/w)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipinski Violation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBioavailability Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAbsorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Solubility (log mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaCo2 Permeability (log Papp in 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntestinal Absorption (Human) (% Absorbed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkin Permeability (log Kp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-Glycoprotein I, II inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVDss (Human) (log L/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFraction Unbound (Human) (Fu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBBB Permeability (log BB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNS Permeability (log PS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMetabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP2D6 Substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP3A4 Substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP2C19 Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP2C9 Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP2D6 Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP3A4 Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExcretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Clearance (log ml/min/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal OCT2 Substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eToxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAMES Toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax. Tolerated Dose (Hum) (log mg/kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehERG I, II inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo, Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOral Rat Acute Toxicity (LD\u003csub\u003e50\u003c/sub\u003e) (mol/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOral Rat Chronic Toxicity (LOAEL) (log mg/kg_bw/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHepatoxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkin Sensitisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eT. Pyriformis\u003c/em\u003e Toxicity (log \u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinnow Toxicity (log mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eVisualization of Protein-Ligand Complex Interactions\u003c/h2\u003e \u003cp\u003eNon-covalent interactions (Hydrogen and hydrophobic bonds) were analyzed when we employed the Ligplot\u0026thinsp;+\u0026thinsp;V 2.2.8 Luteolin exhibited the interaction with Arg 98 (2.85), Asn 120 (2.76), Lys 123 (2.82), Ile 141(2.70), Thr 147 (3.14), Ser 148 (2.77), Ser 149 (2.94), Asn 150 (2.79, 3.19), Lys 168 (2.80) residue of TOP2A protein with the hydrogen bonds, although it can be fitted by the hydrophobic bond with Asn 91, Asn 95, Ile 125, Gly 124, Thr 215 residues. Besides, Quercetin showed superior hydrogen bond interaction, and Asn 91, Ser 149, Gly 164, Tyr 165, Gly 166, Ala 167, and Lys 168 participated in hydrogen bond formation. The TOPO II receptor formed a hydrophobic interaction with it by the assessment of the residues of Tyr 34, Asn 95, Ile 125, Ser 148, and Gly 161. Now, the Rutin stands with the aid of hydrophobic bonds (Gln 59, Met 61, Phe 77, Asp 245, Tyr 274, Gln 309, Gln 310, Ile 311, Phe 313, Ala 318, Ser 320), where nine hydrogen bonds interact with the protein, too. The hydrogen bond-forming residues are Trp 62, Tyr 72, Tyr 82, Arg 241, Lys 321, and Glu 379. However, the control drug (Mitoxantrone) fitted with eleven hydrogen bonds with the 1ZXM, the active site residues, Tyr 34, Ile 118, Asn 120, Ser 148, Ser 149, Asn 150, Thr 215, additionally, it follows the Ile 33, Ile 88, Asn 91, Ala 92, Asn 95, Arg 98, Ile 125, Phe 142, Lys 157, Ala 167, Lys 168, Ile 217 hydrophobic interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploring the Molecular Interaction of Small Molecules and Targeted TOP2A Receptor.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrogen Bonds residues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydrogen Bond Length (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of H2 Bonds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther Bond residues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArg 98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eAsn 91, Asn 95, Ile 125, Gly 124, Thr 215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn 120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys 123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIle 141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThr 147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer 148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer 149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn 150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.79, 3.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys 168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn 91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eTyr 34, Asn 95, Ile 125, Ser 148, Gly 161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer 149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGly 164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr 165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGly 166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAla 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys 168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eRutin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrp 62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.78, 3.27, 3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eGln 59, Met 61, Phe 77, Asp 245, Tyr 274, Gln 309, Gln 310, Ile 311, Phe 313, Ala 318, Ser 320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr 72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr 82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArg 241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.15, 3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys 321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu 379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eMitoxantrone (Control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr 34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eIle 33, Ile 88, Asn 91, Ala 92, Asn 95, Arg 98, Ile 125, Phe 142, Lys 157, Ala 167, Lys 168, Ile 217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIle 118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn 120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.96, 3.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer 148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSer 149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87, 3.06, 3.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn 150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.80, 2.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThr 215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics Simulation Study\u003c/h2\u003e \u003cp\u003eTo gain a detailed understanding of the conformational behavior, binding stability, and dynamic interaction profile of the selected phytocompounds with the human topoisomerase II alpha (TOP2A) protein, a 100-nanosecond molecular dynamics simulation (MDS) was conducted for each protein-ligand complex, including the control drug Mitoxantrone (CID-4212). The Root Mean Square Deviation (RMSD) analysis, a critical metric for evaluating the overall stability of the protein backbone during the simulation, revealed that all complexes remained stable over the full 100 ns trajectory. Notably, the phytocompounds exhibited RMSD values within the acceptable fluctuation range (\u0026lt;\u0026thinsp;3.0 \u0026Aring;), with Rutin showing the lowest RMSD at 2.166 \u0026Aring;, followed by Quercetin at 2.249 \u0026Aring;, the control drug at 2.229 \u0026Aring;, and Luteolin at 2.913 \u0026Aring;. These findings indicate minimal structural drift from the initial conformations, suggesting that the phytocompounds bind stably within the active site of TOP2A \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The Root Mean Square Fluctuation (RMSF) analysis provided insights into the flexibility of individual amino acid residues throughout the simulation. Fluctuations were primarily localized at the N- and C-terminal regions, which are typically more disordered, while the core regions consisting of alpha-helices and beta-strands remained highly stable. Luteolin and Rutin demonstrated the lowest RMSF values of 2.041 \u0026Aring;, indicating reduced atomic-level fluctuations and enhanced stability upon binding, while Quercetin recorded a slightly higher RMSF of 2.74 \u0026Aring;. The Radius of Gyration (Rg), an important measure of the overall compactness and folding stability of the protein-ligand complex, remained consistent throughout the simulation period. Luteolin displayed the highest Rg value of 27.793 \u0026Aring;, followed by Rutin (27.673 \u0026Aring;), Quercetin (27.473 \u0026Aring;), and the reference drug (27.494 \u0026Aring;), suggesting minor conformational rearrangements upon ligand binding but no major structural unfolding, confirming the stable folding state of the complexes. Additionally, the Solvent Accessible Surface Area (SASA) was evaluated to determine the extent of protein surface exposure to the solvent environment. Luteolin (32,574.259 \u0026Aring;\u0026sup2;), Quercetin (32,150.518 \u0026Aring;\u0026sup2;), and Rutin (32,214.021 \u0026Aring;\u0026sup2;) showed higher SASA values compared to Mitoxantrone (31,918.158 \u0026Aring;\u0026sup2;), indicating that the selected phytocompounds promoted more extensive exposure of surface residues, potentially improving interactions with solvent molecules and enhancing solubility. Molecular Surface Area (MolSA) analysis, which approximates the van der Waals contact surface, revealed that all phytocompound complexes exhibited broader surface coverage than the control drug. Specifically, Luteolin demonstrated the largest MolSA at 36,320.325 \u0026Aring;\u0026sup2;, followed by Rutin (35,548.998 \u0026Aring;\u0026sup2;), Quercetin (35,098.012 \u0026Aring;\u0026sup2;), and Mitoxantrone (35,171.871 \u0026Aring;\u0026sup2;), supporting their enhanced molecular interaction profiles \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Furthermore, the intramolecular hydrogen bond analysis provided a quantitative measure of ligand stability within the active binding pocket. Luteolin formed the highest number of hydrogen bonds (1349), followed by Rutin (1316), Quercetin (1292), and Mitoxantrone (1336). A higher number of hydrogen bonds indicates a more robust and stable ligand-protein interaction, which is crucial for inhibitory efficiency (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These dynamic results suggest that Luteolin, in particular, forms a highly stable and conformationally favorable complex with TOP2A, showing not only strong binding but also the ability to maintain structural integrity throughout the simulation. Quercetin and Rutin also performed remarkably well across all parameters, reinforcing their potential as effective natural inhibitors. Collectively, the MDS data, including structural stability (RMSD), residue flexibility (RMSF), compactness (Rg), surface exposure (SASA and MolSA), and intermolecular bonding, strongly support the conclusion that Luteolin and Quercetin are promising bioactive compounds with significant potential to inhibit TOP2A activity in prostate cancer, thereby offering valuable insights for the development of targeted phytochemical-based therapeutics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumerous MD Simulation Features Organized in a Tabular Format.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLigands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSASA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMolSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2 Bond\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32574.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36320.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32150.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35098.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRutin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32214.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35548.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitoxantrone (Control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31918.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35171.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePost Simulation Trajectory analysis (MM/GBSA and PCA)\u003c/h2\u003e \u003cp\u003eBinding free energy estimation of the top MDS outputs, including Luteolin, Quercetin, Rutin, and control drug, produces different values, including G_Bind scores of -70.8835589 for luteolin, quercetin, and rutin, whereas the value for the control drug is -72.7597367. Moreover, the examination of other binding free energy values to understand the stability of better-performed dynamics outputs, and significantly measured the Coulomb Energy (ΔG_Bind_Coulomb), Covalent Energy (ΔG_Bind_Covalent), Hydrogen bond energy (ΔG_Bind_Hbond), Lipophilicity energy (ΔG_Bind_Lipo), ΔG_Bind_Packing, ΔG_Bind_Solv_GB, and Van der Waals interaction energy (ΔG_Bind_vdW). All the results of this evaluation indicate a robust binding affinity between Luteolin and Quercetin and the targeted TOPO II (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Besides, the principal component analysis, or simply PCA, determines the structural variation in different ligand-targeted protein complexes, and we also plotted the PCA graphs in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The eigenmodes serially decrease, which suggests that the local instability in the structure of the target gained stability; this value is represented by a percentage. The tested bioactive compounds' highest value is close to 90%, but the lowest value is Rutin CID-5280445 (11.87%), and Quercetin CID-5280343 (8.34%). After completing the study of eigenvalues, the cumulative value has been shown to be fully cumulative. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows three eigenvectors for the TOP2A enzyme-ligand based on the MDS trajectory and exposed in clusters. The TOP2A complexes formed clusters 0.70711 on PC1, whereas \u0026minus;\u0026thinsp;0.70711 on PC2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCalculating the PCA Value\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePercentage Of variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCumulative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eExtracted Eigenvectors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCo-efficient of PC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCo-efficient of PC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.70711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.70711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.70711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.70711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRutin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.70711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.70711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMitoxantrone (Control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.70711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.70711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eProstate cancer remains a major therapeutic challenge, particularly in advanced stages where resistance to standard treatments frequently develops. Targeting essential enzymes such as topoisomerase IIα (TOP2A), which plays a critical role in DNA replication and cell cycle regulation, has therefore become a well-established strategy in anticancer drug development. In the present study, an integrated computational pipeline was applied to identify potential natural inhibitors of TOP2A, combining molecular docking, pharmacokinetic evaluation, molecular dynamics simulation, and free energy analysis. The adoption of multi-step in silico approaches has significantly advanced modern drug discovery by enabling efficient screening and mechanistic understanding of ligand\u0026ndash;protein interactions. Similar integrated strategies have been successfully employed in recent studies targeting diverse biological systems, including viral proteins, neurological targets, and cancer-related enzymes, demonstrating their effectiveness in accelerating lead identification and optimization \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In this study, luteolin, quercetin, and rutin were identified as the top-performing compounds based on their strong binding interactions with the TOP2A active site. Their docking scores exceeded that of the reference drug, suggesting a higher binding potential (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Comparable findings have been reported in previous computational investigations, where natural compounds, particularly flavonoids, exhibited strong inhibitory potential against key therapeutic targets \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. However, docking alone cannot fully capture the complexity of drug behavior, necessitating further pharmacokinetic and dynamic validation. Pharmacokinetic profiling revealed that luteolin and quercetin possess favorable drug-like characteristics, including high intestinal absorption, optimal lipophilicity, and low predicted toxicity, whereas rutin showed some limitations due to its physicochemical properties (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results are consistent with earlier studies emphasizing the importance of ADMET analysis in improving the success rate of drug candidates during development \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Additionally, the lack of significant interaction with major cytochrome P450 enzymes suggests a lower risk of metabolic complications, which is a desirable property for drug candidates. Detailed interaction analysis further demonstrated that the selected compounds formed stable hydrogen bonds and hydrophobic interactions with key residues in the TOP2A active site, supporting their strong binding affinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Such interaction patterns are critical for ensuring binding specificity and inhibitory efficiency, as reported in similar structure-based drug discovery and molecular interaction studies \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. To further validate the stability of the protein\u0026ndash;ligand complexes, molecular dynamics simulations were conducted. The results demonstrated that all selected compounds maintained stable interactions within the TOP2A binding pocket over the simulation period. Parameters such as RMSD and RMSF indicated minimal structural fluctuations, while stable radius of gyration values confirmed the preservation of protein compactness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These observations are in agreement with previous studies where stable dynamic behavior is considered indicative of effective ligand binding and functional inhibition \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Moreover, solvent-accessible surface area (SASA), molecular surface area (MolSA), and hydrogen bond analyses suggested consistent interaction patterns throughout the simulation, reinforcing the stability of the complexes. Binding free energy calculations using the MM/GBSA approach further confirmed strong ligand\u0026ndash;protein interactions, providing a more reliable estimation of binding affinity beyond docking alone \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Principal component analysis (PCA) revealed coordinated molecular motions and reduced structural instability within the complexes, indicating stable conformational behavior over time (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similar applications of PCA in computational studies have been shown to effectively capture essential dynamic features of biomolecular systems \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Overall, luteolin and quercetin emerged as the most promising candidates, exhibiting a balanced combination of strong binding affinity, favorable pharmacokinetic properties, and stable dynamic behavior. Although rutin also demonstrated notable interactions, its comparatively lower pharmacokinetic performance may limit its therapeutic potential without further optimization. These findings are consistent with previous reports highlighting the potential of plant-derived compounds as effective therapeutic agents \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Despite the promising outcomes, it is important to recognize that computational predictions alone are not sufficient to confirm biological efficacy. Experimental validation through in vitro and in vivo studies is necessary to verify these findings and assess their clinical relevance. Future work should therefore focus on biochemical assays, cellular studies, and animal models to further explore the therapeutic potential of these compounds.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSummary and Future Perspectives\u003c/h2\u003e \u003cp\u003eIt is now clear that TOP2A is the major prognostic biomarker as well as a proliferation marker in PCa. This protein represents the primary modulator of replicating DNA, the transcription process, as well as cellular cycling checkpoints. Because of this, it is the crucial target subject of the treatment of PCa sufferers; nevertheless, there are currently fewer viable prospective marketing items. Additionally, individuals are resorting to herbal substances made using medicinal herbs to cure it. The \u003cem\u003eD. Longan\u003c/em\u003e is a plentiful source of botanical compounds. We obtained the top ten highest docked compounds against the TOP2A, and the top three are: Luteolin (-10.4/-10.2 Kcal/mol), Quercetin (-10.1/-10.0 Kcal/mol), and Rutin (-10.0/-9.8 Kcal/mol). Such a type of lead molecule is selected for the cell-line cytotoxic experiment. In addition, the context reveals that all of them play a variety of cancer-fighting activities in different types of cell lines; most precisely, their anti-prostate cancer action is found in the CWR22R prostate cell line. Alternatively, these substances' ADMET characteristics are very satisfactory; they function as drugs. However, in its biochemical interactions, all substances are associated with the designated proteins with greater effectiveness. They displayed an extensive amount of bonds of hydrogen bonds, which further indicates a superior match to the pocket residues. After we finished the post-docking representation, individuals were chosen to participate in the MD simulations study. Experimental chemicals Luteolin, Quercetin, and Rutin demonstrated experimental greatest MD simulation outcomes towards the desired protein compared to the control drug. Indeed, Luteolin and Quercetin followed all positive results in the simulation study, and these two substances underwent MM/GBSA and PCA verification research. This is essential to clarify how molecules work in vivo research, including cell types or animal models, to serve as an intriguing prospective substance-like alternative towards prostate malignancy. Contemporary chemotherapy of Prostate carcinoma is progressing fast, going between highly handled lingual followed by injected therapeutic regimens through all-oral, easily tolerated medication pairings, having rates of recovery approaching 90%. The next wave of treatments will see improvements in the management of Prostate carcinoma. Because these represent only a small sample of the numerous current developmental endeavors as well as revolutionary methods to accomplish therefore, the next section will examine a few distinct methods related to treating it. Prospective anti-cancer drugs are going to face a variety of pragmatic issues that now preclude application in particular situations, including medication-drug relationships, adverse reactions, recurring safety issues, dosage restrictions, and effectiveness problems. According to the consequence, our study's conclusions would tackle all main issues and offer considerable improvements over present medical practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAuthors Contribution\u003c/h2\u003e \u003cp\u003e \u003cb\u003eConceptualization\u003c/b\u003e: D.D. \u003cb\u003eFormal analysis and validation\u003c/b\u003e: B.P., S.R., M.R.S., P.P., P.B., and D.D. \u003cb\u003eWriting original draft\u003c/b\u003e: B.P., S.R., P.P., D.D., S.Z.A., P.B., M.M., M.N.H. \u003cb\u003eWriting_Editing_Reviewing\u003c/b\u003e: P.B., M.N.H., M.M. \u003cb\u003eProject administration and Supervision\u003c/b\u003e: B.P., D.D. All authors read and agreed to submit the final version of this manuscript for publication.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTopoisomerase II alpha (TOPO IIα), Pharmacokinetics (PK), ADMET (Absorption, Distribution, Metabolism, Elimination, and Toxicity), Molecular Dynamics (MD) simulation, Isothermal-Isobaric ensemble (NPT), Root-mean-square deviation (RMSD), Root-mean-square fluctuation (RMSF), Solvent accessible surface area (SASA) value, radius of gyration (Rg) value, and MolSa, Molecular Machine Generalized Born Surface Area (MM-GBSA), PCA (Principal Component Analysis), American Cancer Society (ACS).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical statement\u003c/h2\u003e \u003cp\u003eNot applicable to this study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eAll authors declare that there is no conflict of interest regarding this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any financial support from any donor agency.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: D.D. Formal analysis and validation: B.P., S.R., M.R.S., P.P., P.B., and D.D. Writing original draft: B.P., S.R., P.P., D.D., S.Z.A., P.B., M.M., M.N.H. Writing. Editing, Reviewing: P.B., M.N.H., M.M. Project administration and Supervision: B.P., D.D. All authors read and agreed to submit the final version of this manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Int J Mol Sci May 28(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms23116073\u003c/span\u003e\u003cspan address=\"10.3390/ijms23116073\" 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":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"TOP2A, Molecular docking, Molecular dynamics simulation, Flavonoids, Binding free energy, Prostate cancer, ADMET analysis, Phytochemicals","lastPublishedDoi":"10.21203/rs.3.rs-9616322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9616322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProstate cancer (PCa) remains a major global health burden, particularly in its aggressive and treatment-resistant forms. Human topoisomerase IIα (TOP2A), a key regulator of DNA replication and cell proliferation, is significantly overexpressed in advanced prostate cancer and represents an important therapeutic target. In this study, a comprehensive computational pipeline was employed to evaluate phytocompounds derived from \u003cem\u003eDimocarpus longan\u003c/em\u003e as potential TOP2A inhibitors. A focused library of plant-derived compounds was screened using molecular docking, followed by pharmacokinetic and toxicity profiling via SwissADME and pkCSM. The top-ranked compounds were further subjected to 100 ns molecular dynamics (MD) simulations, along with MM/GBSA binding free energy calculations and principal component analysis (PCA), to assess their dynamic stability and interaction behavior. Among the screened compounds, luteolin, quercetin, and rutin demonstrated favorable binding affinities (\u0026minus;\u0026thinsp;10.4 to \u0026minus;\u0026thinsp;9.8 kcal/mol), outperforming the reference drug mitoxantrone. MD simulation results revealed stable protein\u0026ndash;ligand interactions, with RMSD values below 3 \u0026Aring; and consistent structural compactness. Binding free energy analysis supported strong interaction profiles, particularly for luteolin and quercetin. ADMET predictions indicated acceptable pharmacokinetic properties and low toxicity risks. Overall, this study provides a detailed comparative analysis of flavonoid\u0026ndash;TOP2A interactions, offering insights into their binding stability and dynamic behavior. These findings highlight luteolin and quercetin as promising candidates for further experimental validation in prostate cancer therapeutics.\u003c/p\u003e","manuscriptTitle":"Comparative Computational Evaluation of Flavonoid-Based Inhibitors Targeting Human TOP2A: Insights from Molecular Docking, Molecular Dynamics Simulation, and Binding Free Energy Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:42:08","doi":"10.21203/rs.3.rs-9616322/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T14:35:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T13:27:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329581494102287422735500104908632421004","date":"2026-05-13T13:18:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18007472496843858271904832750696811827","date":"2026-05-13T11:28:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305513799439465680043355751403280153939","date":"2026-05-12T13:13:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82281949973929429102120797518355698875","date":"2026-05-11T13:54:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242912074994736684673344802427938391879","date":"2026-05-11T13:51:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-11T12:54:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-08T01:47:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-08T01:46:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"In Silico Pharmacology","date":"2026-05-05T09:00:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3bb07db2-3cc6-42af-ba53-fd82d672e5ee","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T14:35:46+00:00","index":27,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T13:27:15+00:00","index":26,"fulltext":""},{"type":"reviewerAgreed","content":"329581494102287422735500104908632421004","date":"2026-05-13T13:18:49+00:00","index":25,"fulltext":""},{"type":"reviewerAgreed","content":"18007472496843858271904832750696811827","date":"2026-05-13T11:28:53+00:00","index":24,"fulltext":""},{"type":"reviewerAgreed","content":"305513799439465680043355751403280153939","date":"2026-05-12T13:13:55+00:00","index":22,"fulltext":""},{"type":"reviewerAgreed","content":"82281949973929429102120797518355698875","date":"2026-05-11T13:54:58+00:00","index":20,"fulltext":""},{"type":"reviewerAgreed","content":"242912074994736684673344802427938391879","date":"2026-05-11T13:51:12+00:00","index":19,"fulltext":""},{"type":"reviewersInvited","content":"11","date":"2026-05-11T12:54:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-08T01:47:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-08T01:46:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"In Silico Pharmacology","date":"2026-05-05T09:00:32+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T13:12:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:42:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9616322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9616322","identity":"rs-9616322","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}