In silico evaluation of broccoli and kale leaf extracts as xanthine oxidase inhibitors for gout therapy

In silico evaluation of broccoli and kale leaf extracts as xanthine oxidase inhibitors for gout therapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article In silico evaluation of broccoli and kale leaf extracts as xanthine oxidase inhibitors for gout therapy Daniel Boison, Josephine Anoa Barnor, Stephen Asare Asamoah, Ekow Sekyi Etwire, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9271389/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Xanthine oxidase (XO), a key enzyme in purine metabolism, catalyzes the oxidation of hypoxanthine to xanthine and subsequently, xanthine to uric acid, the final product of purine catabolism in humans. XO enzyme plays a critical role in controlling uric acid levels thus, targeting it, is essential in managing conditions like gout. Objectives: This study is aimed at exploring the bioactive compounds in Brassica oleracea var. italica (broccoli) and Brassica oleracea var. acephala (kale) for their potential anti-gout properties and ability to inhibit XO through an in-silico approach. Methodology: Broccoli and kale leaves were subjected to solvent extraction. Phytochemicals from the extracts were identified using GC-MS analysis and subsequently docked against the XO receptor. ADMET and medicinal chemistry analyses were conducted on selected compounds to assess their pharmacological and safety profiles, and molecular interactions with XO were evaluated. Results: Out of the 28 compounds docked, seven showed favorable binding affinities, with binding energies below –7.0 kcal/mol. Among these, butylated hydroxytoluene and benzyl benzoate emerged as lead compounds, exhibiting favorable pharmacodynamic properties and minimal predicted toxicity. They interacted hydrophobically with key residues of the target protein and showed a markedly inhibitory potential against XO. Conclusion: Butylated hydroxytoluene and benzyl benzoate emerged as lead compounds and exhibited inhibitory effects against XO, suggestive of a therapeutic source for gout therapy. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 INTRODUCTION Gout has emerged as a notable public health issue worldwide, with a marked increase in both its prevalence and incidence in recent decades [1, 2]. It is classified as an inflammatory arthritis condition linked to elevated uric acid levels in the blood, a state known as hyperuricemia. The hyperuricemic condition leads to the deposition of urate crystals in the joints of various body parts such as the wrist, fingers, ankle, toe, and knee [3, 4], resulting in symptoms such as redness, tenderness, warmth, and swelling during acute inflammatory episodes [5]. The synthesis of uric acid occurs through the oxidation of purine metabolites, specifically hypoxanthine and xanthine, facilitated by the enzyme xanthine oxidase (XO) [6]. This molybdenum-dependent enzyme plays a crucial role in the degradation of purine nucleotides, catalyzing two key rate-limiting steps in this metabolic pathway [7, 8]. The enzymatic process involves several intricate steps, including the activation of the hydroxyl group of the molybdenum cofactor, nucleophilic attack on xanthine, and the formation of uric acid, all while managing electron flow through associated cofactors. The conversion of xanthine to uric acid involves a series of four reaction steps facilitated by specific cofactors and amino acids. Initially, the proton from the hydroxyl group of the molybdenum cofactor (Moco) is transferred to Glu1261, which activates the hydroxyl group for a nucleophilic attack on xanthine. In this step, Glu802 plays a crucial role in stabilizing the negative charge of the intermediate formed. The second step involves the transfer of a hydride ion from the tetrahedral intermediate to the sulfur atom of Moco, resulting in the reduction of molybdenum from Mo (VI) to Mo (IV). Subsequently, in the third step, uric acid is produced through protonation by Arg880, with the assistance of a water molecule. The final step involves the reduction of FAD to FADH₂, which re-oxidizes Mo (IV) back to its original oxidation state, Mo (VI). This enzymatic process is supported by two [2Fe-2S] clusters that connect the Moco and FAD cofactors, facilitating electron transfer. After the reaction cycle, the enzyme is reset by the interaction of a water molecule with Moco [9, 10]. Notably, the enzyme exhibits a decreased affinity for NAD + at the FAD site while showing an increased affinity for oxygen. This change promotes the transfer of one or two electrons to O₂, resulting in the formation of superoxide (O₂⁻) and hydrogen peroxide (H₂O₂) [11]. It is clear that XO's activity is characterized by a reduced affinity for NAD + at the FAD site and an increased affinity for oxygen, which leads to the production of reactive oxygen species such as superoxide and hydrogen peroxide. This complex biochemical process underscores the importance of XO in the pathophysiology of gout and highlights it as a potential therapeutic target for managing this condition. Several studies have identified XO as a potential therapeutic target for controlling uric acid accumulation. Inhibitors of XO could be utilized as drugs to prevent the excessive production of uric acid by hindering the enzyme's over activity [12]. Management options for gout include the use of analgesics, non-steroidal anti-inflammatory drugs (NSAIDs), and XO inhibitors like allopurinol and febuxostat [13] used to lower serum uric acid levels [14]. However, none of the existing management options is without adverse effects, as they may cause gastrointestinal side effects such as diarrhea, nausea, and vomiting, especially at high doses, and have been associated with renal, cardiovascular, and hypersensitivity reactions [15]. Given the challenges associated with conventional drug discovery approaches, which are often slow and costly, there is a pressing need for alternative sources with minimal or no adverse effects for inflammatory disorders like gout. In silico investigations present a viable alternative for identifying potential small molecules that may be more effective in managing gout. Additionally, exploring medicinal plants with ethnopharmacological relevance could enhance the likelihood of discovering potential drug candidates. Cruciferous vegetables, including broccoli, cabbage, cauliflower, kale, and brussels sprouts, are highly nutritious and contain various bioactive compounds [16]. Their regular consumption is linked to a reduced risk of several chronic diseases, such as diabetes mellitus, asthma, and Alzheimer’s disease. The health benefits attributed to these vegetables primarily stem from their rich content of phytonutrients, which include glucosinolates, polyphenols, carotenoids, and terpenoids (17). Notably, broccoli and kale are both part of the Brassicaceae family, which is one of the most extensively cultivated plant families globally [18]. Broccoli, scientifically known as Brassica oleracea var. italica , is a globally consumed vegetable known for its nutritional richness, particularly in vitamins such as C, E, and K, as well as several B vitamins. It also provides essential minerals and bioactive compounds, including various glucosinolates [19, 20], which are linked to health benefits such as antibacterial, cardioprotective, antioxidant activities and antifungal properties, as well as potential cancer growth inhibition [19–22]. Kale, also called Brassica oleracea var. acephala , is another important member of the Brassica family, recognized for its resilience and low production costs [23]. It is valued for its nutritional profile, which includes calcium, vitamin C, and various phytochemicals that contribute to its antioxidant and anti-cancer properties. Kale is often referred to as a superfood due to its high antioxidant capacity and the extensive research supporting its health benefits. The leaves and stems are valued for their medicinal and nutritional qualities [24]. Kale is recognized for its numerous health advantages, including antioxidant, anticancer, and protective effects on the cardiovascular and gastrointestinal systems [23, 25]. Its nutritional profile is rich, containing essential nutrients such as calcium, vitamin C, folate, riboflavin, anthocyanins, phenolic acid, flavonoids, and vitamin A [23, 25, 26]. Additionally, kale is noted for its high levels of glucosinolates, carotenoids, and phenolic compounds, which contribute to its pharmacological effects and antioxidant properties [23]. Ethnopharmacologically, Brassica oleracea var. acephala DC has been widely utilized for the treatment of gastric ulcers [27], and its phytoconstituents have proven to impede the activity of the spike glycoprotein trimer associated with SARS-CoV-2, suggesting their possible role in antiviral strategies against this virus [28]. Furthermore, it has been reported that secondary metabolites present in Brassica oleracea var. italica reduce the risk of several serious health conditions, including diabetes, cancer, and neurological disorder [29]. In silico pharmacological screening of the isolated compounds from Brassica oleracea var. italica and Brassica oleracea var. acephala could contribute to identifying new XO inhibitors derived from natural sources [30]. Therefore, this study employed an in silico approach, including molecular docking, to screen the bioactive compounds in Brassica oleracea var. italica and Brassica oleracea var. acephala against the protein receptor XO, assessing their potential anti-gout properties and ability to inhibit XO. MATERIALS AND METHODS Preparation of Plant Material Fresh kale leaves and broccoli were purchased from the Abura market in Cape Coast, Ghana, on February 29, 2024. Both samples were washed under running tap water, air-dried at 50°C for fourteen (14) days, and pulverized using an electric blender. Each powdered sample was macerated with acetonitrile and diethyl ether in a 1:10 w/v ratio for seven (7) days. The macerate was filtered and concentrated using a rotary evaporator at 65°C for acetonitrile and 35°C for diethyl ether. The extraction of kale yielded 0.131g (0.873%) of extract and 0.466g (3.107%) of diethyl ether extract. In contrast, the extraction of broccoli resulted in 0.499g (3.327%) of acetonitrile extract and 0.854g (5.693%) of diethyl ether extract. GC-MS identification of phytochemicals in Extracts The bioactive compounds present in Brassica oleracea extracts were assessed using gas chromatography-mass spectrometry (GC-MS). The GC-MS analysis was conducted using a 7000C GC-MS triple quadrupole system equipped with a PAL autosampler for precise sample injection. An aliquot of 1.5 µL from each extract was dissolved in acetonitrile and diethyl ether before being injected into the GC-MS/MS system (Agilent Technologies, CA, USA). The GC oven temperature program began at 60°C, where it was held for 2 minutes. It then ramped up to 180°C at a rate of 20°C/min, with a 1-minute hold, before increasing further to 280°C at 5°C/min, eventually reaching a peak temperature of 325°C. The system utilized a splitless front inlet with helium as the carrier gas at a pressure of 17.729 psi and a total flow rate of 35 mL/min. An Agilent CP8944 VF-5ms column (30 m x 250 µm x 0.25 µm) was employed, starting with an initial flow rate of 2 mL/min, which decreased to 1.5 mL/min after the run. The mass spectrometer operated in electron ionization mode at 230°C, scanning from m/z 60 to 700, while the collision cell pressure was set to 3 psi, reducing to zero post-analysis. Helium and nitrogen were used as quench and collision gases, respectively, with flow rates of 2.25 mL/min and 1.5 mL/min. The bioactive compounds were identified by comparing their retention times and mass fragmentation patterns with those of known standards and a mass spectral library (MassHunter software). Quantification of the compounds was performed by measuring the peak areas. In silico analysis Retrieval and Preparation of the 3D Structure of Protein Receptor The 3D crystal structure of XO, with a resolution of 2.5 Å, was obtained from the Protein Data Bank ( www.rcsb.org , PDB ID: 1FIQ) in pdb format. The water molecules, heteroatoms, and co-crystallized ligands attached to the XO structure were removed using BIOVIA Discovery Studio 2024. Subsequently, polar hydrogen atoms were incorporated into the PDB file using the same software [31]. The missing residues were replaced on the protein structure, which was then subjected to energy minimization using SwissPDB viewer 4.1.0 ( http://spdbv.unil.ch ) [32]. Retrieval of Identified Ligands The 2D conformer of the identified compounds in the extracts from the GC-MS/MS, as well as the reference drug allopurinol (PubChem ID 135401907), was downloaded from the PubChem Database ( http://pubchem.ncbi.nlm.nih.gov ) in the SDF (Structured Data Files) format [30]. Molecular Docking PyRx 0.8.0.0 ( https://pyrx.sourceforge.io ), which integrates various open-source software tools like AutoDock, AutoDock Vina, and Open Babel, was utilized for the docking process. Prior to their conversion into AutoDock-compatible pdbqt format, the ligands were subjected to energy minimization using the universal force field through Open Babel [33]. The docking procedure was conducted with AutoDock Vina [31] to assess the binding affinity between XO and the compounds identified in Brassica oleracea extracts, utilizing the XYZ coordinates of the grid box that corresponds to the active site of the target protein, centered at X: 21.1884 Å, Y: 16.3671 Å, and Z: 103.7037 Å, and with dimensions of 29.8485 × 24.8610 × 30.8403 Å [32]. The ligands were then ranked based on their binding affinity relative to the reference drugs. Characterization of Binding Mechanism Ligplot+ version 2.2.9 ( www.ebi.ac.uk ) was used to analyze the binding mechanisms between the target protein structure and the selected compounds. This program generates 2D schematic representations of protein-ligand complexes using structural data from the Protein Data Bank. It offers a clear and informative depiction of intermolecular interactions, including hydrogen bonds, hydrophobic interactions, and atom accessibility, along with an indication of interaction strength [34]. Validation of Docking Protocol Validation of Docking Protocol Validating the docking protocol is essential to ensure the accuracy and reliability of the docking results [35]. DockRMSD was utilized for this purpose (DockRMSD: Docking Pose Distance Calculation [zhanggroup.org]). The co-crystallized ligand was extracted from the protein receptor and re-docked into the energy-minimized protein. Both the co-crystallized and re-docked ligands were then converted to MOL2 file formats and input into DockRMSD to calculate the root mean square deviation (RMSD) between the two poses [36]. Using Ligplot+ version 2.2.9 ( www.ebi.ac.uk ), the co-crystallized and re-docked ligands were superimposed to identify the shared residues involved in ligand interactions [33]. Prediction of ADMET properties The ADMET properties of the compounds with the top scores from the molecular docking studies were evaluated using SwissADME ( www.swissadme.ch ) and ADMETlab 3.0 ( http://admetlab3.scbdd.com ). Both SwissADME and ADMETlab 3.0 are valuable tools for drug discovery, providing comprehensive ADMET predictions and supporting the evaluation of potential drug candidates [37, 38]. They enable the computation of physicochemical properties and the prediction of pharmacokinetics, drug-likeness, and other therapeutic attributes [28]. The compounds were filtered using parameters such as Lipinski’s rule of 5, bioavailability score, human intestinal absorption, nephrotoxicity, blood-brain barrier (BBB), drug-induced liver toxicity, Caco-2 permeability, pan-assay interference compounds (PAINS), half-life, and plasma clearance. Biological Activity Prediction PASS Online predicts over 3500 types of biological activities, such as pharmacological effects, toxic effects, interactions with enzymes, and changes in gene expression ( https://www.way2drug.com/passonline/info.php# ). It was employed to predict the biological activity of the selected compounds using the SMILES of the compounds as input. The software assesses compound activity by generating two probability values: probable activity (Pa) and probable inactivity (Pi). These values help predict the compound's activity profile, showing its potential to be either active or inactive [39]. Molecular Dynamic Simulations and MM-PBSA Calculations of the Protein-ligand complexes Two of the hit compounds with the least binding energies with PubChem IDs, 31404 and 2345, as well as the known drug, allopurinol (PubChem ID: 135401907), serving as the control, were subjected to 100ns molecular dynamic simulations using GROMACS 2018 [40, 41]. The OPLS/All Atom force field [42] was first used to generate the topology and position restraint files for the target protein. Similarly, the topology and position restraint files of the three ligands were obtained using LigParGen [43]. To ensure that the surface atoms are not affected, which is normally caused by random particle movements, the protein-ligand complexes were restricted within a cubic simulation box, allowing a 1 nm gap from the edges. A single point charge was used to solvate the system, after which Na⁺ or Cl⁻ ions were added for neutralization. Employing the steepest descent method with 50,000 steps, each system was energy minimized, followed by a 100ps equilibration. Ensembles of the number of substances, volume, and temperature, as well as the number of substances, pressure, and temperature, were applied to ensure a well-equilibrated system was reached at 300 K and 1 bar of pressure. The output files obtained after the simulation, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (Rg) trajectories, were plotted using Qtgrace [44]. MM-PBSA computations of the free binding energy and the per-residue energy contribution were calculated using MM/PBSA [45]. RESULTS GC-MS Profiling of Phytochemicals in Brassica Extracts A total of 16 compounds were detected in the acetonitrile and diethyl ether extracts of broccoli, while 12 compounds were identified in both extracts of kale. The analysis revealed 9 compounds in the diethyl ether extract (Table 1 ) and 7 compounds in the acetonitrile extract (Table 2 ) of broccoli, whereas 6 compounds were identified in each extract of kale (Tables 3 and 4 ). Arachidonic acid was present in both the acetonitrile and diethyl ether extracts of broccoli, while 1,4-benzenedicarboxylic acid, bis(2-ethylhexyl) ester, rhodopin, butylated hydroxytoluene, and fumaric acid, 1-cyclopentylethyl nonyl ester, were detected in both broccoli and kale extracts. Table 1 Bioactive compounds identified in diethyl ether extract of broccoli. Compound RT (minutes) Compound name Area % Molecular formula Molecular weight(g/mol) 1 8.43 Butylated Hydroxytoluene 2.51 C 15 H 24 O 220.35 2 10.813 Benzyl Benzoate 0.91 C 14 H 12 O 2 212.24 3 15.806 Arachidonic acid 3.04 C 20 H 32 O 2 304.5 4 20.132 Hexadecamethyl-heptasiloxane, 0.74 C 16 H 48 O 6 Si 7 533.1 5 22.267 Tetracosamethyl-cyclododecasiloxane 0.85 C 24 H 72 O 12 Si 12 889.8 6 24.877 1,4- Benzenedicarboxylic acid, bis(2-ethylhexyl) ester 100 C 24 H 38 O 4 390.6 7 26.8 Pentyl octadecyl ether 6.93 C 23 H 48 O 340.6 8 29.157 Fumaric acid, 1- cyclopentylethyl nonyl ester 3.37 C 20 H 34 O 4 338.5 9 29.772 Eicosamethyl-cyclodecasiloxane, 1.15 C 20 H 60 O 10 Si 10 741.5 Retention times (RT), peak areas, molecular formulas, and molecular weights are included . Table 2 Bioactive compounds identified in acetonitrile extract of broccoli. Compound RT (minutes) Compound name Area % Molecular formula Molecular weight(g/mol) 1 4.181 Hematoporphyrin 36.09 C 34 H 38 N 4 O 6 598.7 2 12.818 Lycoxanthin 18.6 C 40 H 56 O 552.9 3 13.118 Rhodopin 52.49 C 40 H 58 O 554.9 4 15.749 Arachidonic acid 100 C 20 H 32 O 2 304.5 5 16.049 4-Androsten-9.alpha.- fluoro-17.alpha.-methyl-3.alpha.,6.beta.,11.beta.,17.b eta.-tetra-ol, tetra-trimethylsily 18.5 C 32 H 63 FO 4 Si 4 643.2 6 24.681 1,4- Benzenedicarboxylic acid, bis(2-ethylhexyl) ester 94.13 C 24 H 38 O 4 390.6 7 29.792 Vitamin E 31.24 C 29 H 50 O 2 430.7 Retention times (RT), peak areas, molecular formulas, and molecular weights are included. Table 3 Bioactive compounds identified in diethyl ether extract of kale leaves. Compound RT (minutes) Compound name Area % Molecular formula Molecular weight(g/mol) 1 8.43 Butylated Hydroxytoluene 6.23 C 15 H 24 O 220.35 2 24.815 1,4-Benzenedicarboxylic acid, bis(2-ethylhexyl) ester 100 C 24 H 38 O 4 390.6 3 26.805 1-Decanol, 2-hexyl- 19 C 16 H 34 O 242.44 4 29.162 Fumaric acid, 1-cyclopentylethyl nonyl ester 10.39 C 20 H 34 O 4 338.5 5 29.42 Dasycarpidan-1-methanol, acetate (ester) 4.38 C 20 H 26 N 2 O 2 326.4 6 15.739 Rhodopin 2.67 C 40 H 58 O 554.9 Retention times (RT), peak areas, molecular formulas, and molecular weights are included. Table 4 Bioactive compounds identified in acetonitrile extract of kale leaves. Compound RT (minutes) Compound name Area % Molecular formula Molecular weight(g/mol) 1 5.194 Malic Acid 100 C 4 H 6 O 5 134.09 2 5.272 Butanoic acid, 2,3-dihydroxy propyl ester 13.78 C 7 H 14 O 4 162.18 3 11.304 Paraquat dichloride 8.83 C 12 H 14 C l2 N 2 257.16 4 13.154 Palmitic anhydride 12.03 C 32 H 62 O 3 494.8 5 15.837 Gorlic acid 16.12 C 18 H 30 O 2 278.4 6 24.706 1,4-Benzenedicarboxylic acid, bis(2-ethylhexyl) ester 32.32 C 24 H 38 O 4 390.6 Retention times (RT), peak areas, molecular formulas, and molecular weights are included. Molecular Docking Analysis To identify potential candidates for gout management, molecular docking studies were performed on 28 phytochemicals isolated from two Brassica species, with XO (PDB ID: 1FIQ) as the target. These compounds, along with the reference XO inhibitor allopurinol, were docked against the enzyme. Based on a binding energy threshold of − 7.0 kcal/mol [32, 46], seven (7) compounds demonstrated favorable binding affinities and were selected for further analysis. The binding energies of these ligands, in comparison with the reference drug, are summarized in Table 5 . These top-performing compounds were subsequently subjected to downstream evaluations. Table 5 Molecular docking results of the 28 phytochemicals Broccoli Kale Compound name PubChem ID Binding Energy(kcal/mol) Compound name PubChem ID Binding Energy(kcal/mol) Hematoporphyrin 11103 -9.7 Butylated Hydroxytoluene 31404 -10.4 Butylated Hydroxytoluene 31404 -10.4 1,4-Benzenedicarboxylic acid, bis(2-ethylhexyl) ester 22932 -7.9 Benzyl Benzoate 2345 -7.7 1-Decanol, 2-hexyl- 95337 -5.9 Arachidonic acid 444899 -6.6 Fumaric acid, 1-cyclopentylethyl nonyl ester 91736023 -6.9 Hexadecamethyl- heptasiloxane 10912 *** Dasycarpidan-1-methanol, acetate (ester) 550072 -7.9 Tetracosamethyl-cyclododecasiloxane 167767 *** Rhodopin 5365880 -6.0 1,4- Benzenedicarboxylic acid, bis(2-ethylhexyl) ester 22932 -7.9 Malic Acid 525 -5.2 Pentyl octadecyl ether 87077485 -6.3 Butanoic acid, 2,3-dihydroxy propyl ester 11188 -5.6 Fumaric acid, 1- cyclopentylethyl nonyl ester 91736023 -6.9 Paraquat dichloride 15938 -7.2 Eicosamethyl cyclodecasiloxane 519601 *** Palmitic anhydride 69339 -5.1 Lycoxanthin 5281245 -5.7 Gorlic acid 5282855 -5.7 Rhodopin 5365880 -6.0 4-Androsten-9.alpha.- fluoro-17.alpha.-methyl-3.alpha.,6.beta.,11.beta.,17.b eta.-tetra-ol, tetra-trimethylsily 91696616 *** Vitamin E 2116 -10 Reference drug Allopurinol 135401907 -6.4 Reference drug (used as a control), along with their binding energies. *** represents the silicon- containing phytochemicals. Validation of Docking Protocol The structures of the re-docked and co-crystallized ligands were superimposed, and a root mean square deviation (RMSD) of 2.085 Å was computed. Docking poses with RMSD values ranging from 2.0 to 3.0 Å exhibit positional deviations from the reference structure; however, they retain the correct binding orientation [47]. This shows the accuracy and reliability of the docking results. When the co-crystallized and re-docked ligands were superimposed in Ligplot+, the 2D interaction analysis revealed that 7 residues were common to both complexes. There was an overlap of two hydrogen bonds (with Phe798 and Met1038) and five hydrophobic interactions (with Arg912, Ser1080, Gln1040, Gly1039, and Gly1260). The observed overlaps demonstrate AutoDock Vina’s effectiveness in reproducing the experimental binding conformation [33]. Drug Likeness . Lipinski’s rule assesses whether a biologically active compound possesses suitable chemical and physical properties for oral bioavailability [33]. The criteria include molecular weight ≤ 500 Da, LogP ≤ 5, hydrogen bond donors ≤ 5, hydrogen bond acceptors ≤ 10, and 40 ≤ molar refractivity ≤ 140 [32]. Among the seven compounds exhibiting high binding affinity, six (PubChem IDs: 2116, 2345, 22932, 550072, 31404, 15938) satisfied Lipinski’s rule (Table 6 ). All six compounds demonstrated a bioavailability score of 0.55, further confirming their compliance with the rule. Additionally, none of the compounds triggered alerts for Pan-Assay Interference Compounds (PAINS), indicating the absence of substructures known to cause false-positive results in biological assays [48]. These six compounds were subsequently selected for downstream ADMET analyses. The reference drug also satisfied Lipinski’s rule and showed no PAINS alerts. Table 6 Medicinal chemistry properties of the 7 selected compounds in Brassica oleracea extracts and reference drug using Lipinski rule, bioavailability score and PAINS. PubChem ID Lipinski rule (Number of violations) Bioavailability score PAINS 2116 1 0.55 0 alerts 2345 0 0.55 0 alerts 22932 1 0.55 0 alerts 550072 0 0.55 0 alerts 31404 1 0.55 0 alerts 15938 0 0.55 0 alerts 11103 2 0.11 0 alerts 135401907 0 0.55 0 alerts ADMET profiling The ADMET evaluation of the investigated compounds provides valuable insights into their pharmacokinetic characteristics and safety profiles. The predicted ADMET parameters for the selected compounds are summarized in Table 7 . Among the compounds assessed, Caco-2 permeability values indicated excellent absorption for PubChem IDs 2116, 2345, 22932, 550072, and 31404, with values of − 5.011, − 4.670, − 4.916, − 5.053, and − 4.987, respectively. PubChem ID 15938 exhibited poor Caco-2 permeability and was therefore excluded from further analysis. The reference drug also showed excellent permeability. Assessment of human intestinal absorption (HIA) corroborated these findings, with PubChem IDs 2116, 2345, 22932, 550072, and the reference drug all displaying excellent absorption profiles, while PubChem IDs 15938 and 31404 showed intermediate absorption levels. Evaluation of blood-brain barrier (BBB) permeability showed that PubChem IDs 22932, 2116, and 550072 were BBB-permeant, whereas PubChem IDs 31404, 2345, and the reference drug were BBB-impermeant. Due to the significance of BBB selectivity in drug design [49], the BBB-permeant compounds were excluded from further development. Pharmacokinetic profiling indicated moderate plasma clearance for PubChem IDs 31404, 2345, and the reference drug, with a medium half-life for PubChem IDs 31404 and 2345, and a poor half-life for the reference drug. For the toxicity parameters, which included nephrotoxicity and drug-induced liver injury (DILI), both PubChem ID 31404 and 2345 were non-nephrotoxic and exhibited no signs of hepatotoxicity. In contrast, the reference drug demonstrated a poor safety profile in both toxicity parameters. Following completion of the ADMET evaluation, PubChem IDs 31404 and 2345 emerged as the most promising lead candidates, demonstrating favorable pharmacokinetic and toxicity profiles. Table 7 ADMET Prediction of selected compounds and the reference drug. ADMET properties PubChem ID 2116 PubChem ID 2345 PubChem ID 22932 PubChem ID 550072 PubChem ID 31404 PubChem ID 15938 PubChem ID 135401907 Caco-2 permeability Excellent Excellent Excellent Excellent Excellent Poor Excellent Human Intestinal Absorption High High High High Moderate Moderate High BBB Poor Medium Poor poor Excellent Excellent Excellent Plasma clearance Moderate Moderate Moderate Moderate Moderate Moderate Moderate Half-life Excellent Medium Medium Poor Medium Poor Poor Nephrotoxicity Excellent Excellent Excellent Excellent Excellent Poor Poor Drug induced liver toxicity Excellent Excellent Excellent Excellent Excellent Poor Poor Molecular Interaction of Selected Ligand The protein, together with the selected ligands, formed a complex and was run in Ligplot+ version 2.2.8 to assess the molecular interaction. The binding interactions between the ligands and protein were studied to determine the significant intermolecular bonds within the complexes. PubChem ID 31404 exhibited hydrophobic interactions with Lys1045, Ser1082, Thr1083, Gln1040, Gln767, Thr1077, Met1038, Arg912, Phe798, Gln1261, Gly1260, and Ser1080. It also formed a hydrogen bond with Gln1194 (2.85 Å) PubChem ID 2345 also displayed hydrophobic interactions with Glu802, Phe798, Gly799, Glu1261, Arg912, Gly1260, Gln1040, Gly1039, Ala1079, Ser1080, Gln1194, and Met1038. The reference drug formed two hydrogen bonds with Gln767 (bond length: 2.90 Å and 3.00 Å), one hydrogen bond with Glu802 (2.94 Å), and one with Thr1077 (3.06 Å). Additionally, it engaged in hydrophobic interactions with Phe914, Glu1261, Gly1260, Phe798, Gly799, Ser1080, Ala1078, and Met1038. These interactions collectively suggest a stable and favorable binding conformation within the protein’s active site. A comparison of the molecular interactions revealed that the residues Phe798, Met1038, Gly1260, Ser1080, and Glu1261 were commonly involved in the binding of both lead compounds and the reference drug. These shared amino acids suggest conserved interaction sites within the protein's active pocket. XO Inhibitory and Anti-Gout Predictions Using PASS Using PASS (Prediction of Activity Spectra for Substances), the potential biological activities of the compounds were assessed, along with their respective probabilities of activity (Pa) and inactivity (Pi). For PubChem ID 31404, notable predicted activities included oxidoreductase inhibition, antioxidant effects, anti-inflammatory properties, kidney function stimulation, oxidizing action, anti-uremic activity, free radical scavenging, antiarthritic potential, xanthine dehydrogenase inhibition, gout treatment, uric acid excretion stimulation, and non-steroidal anti-inflammatory effects, with Pa scores spanning 0.118 to 0.806. Similarly, PubChem ID 2345 was associated with oxidoreductase inhibition, antioxidant behavior, anti-inflammatory action, kidney function stimulation, oxidizing effects, anti-uremic properties, free radical scavenging, antiarthritic activity, xanthine dehydrogenase inhibition, gout treatment, non-steroidal anti-inflammatory action, hypoxanthine phosphoribosyl transferase inhibition, renal failure treatment, and xanthine oxidase inhibition, displaying Pa values from 0.15 to 0.655. Table 8 PASS-predicted biological activities of lead compounds, with probable activity (Pa) and inactivity (Pi) scores. Compound Biological activity Probable activity (Pa) Probable inactivity (Pi) Butylated Hydroxytoluene Anti-Inflammatory 0.806 0.006 Kidney Function Stimulant 0.727 0.006 Oxygen Scavenger 0.669 0.01 Antioxidant 0.58 0.005 Oxidoreductase Inhibitor 0.576 0.046 Anti-uremic 0.502 0.005 Free Radical Scavenger 0.484 0.011 Non-Steroidal Anti-inflammatory Agent 0.444 0.017 Oxidizing Agent 0.363 0.028 Antiarthritic 0.324 0.118 Uric Acid Excretion Stimulant 0.224 0.053 Gout Treatment 0.195 0.062 Xanthine Dehydrogenase Inhibitor 0.118 0.038 Benzyl Benzoate Kidney Function Stimulant 0.655 0.020 Oxidoreductase Inhibitor 0.618 0.031 Oxygen Scavenger 0.548 0.036 Oxidizing Agent 0.49 0.009 Anti-uremic 0.457 0.008 Anti-Inflammatory 0.459 0.07 Non-Steroidal Anti-inflammatory Agent 0.311 0.04 Hypoxanthine Phosphoribosyl transferase Inhibitor 0.215 0.018 Xanthine Oxidase Inhibitor 0.217 0.023 Free Radical Scavenger 0.224 0.059 Antioxidant 0.175 0.074 Renal Failure Treatment 0.135 0.043 Xanthine Dehydrogenase Inhibitor 0.174 0.014 Gout Treatment 0.15 0.11 MD Simulations Molecular docking studies have mostly allowed high ligand flexibility, while the protein is maintained with limited or no flexibility to amino acids within the active site or residues close to the binding site pockets [50]. In addition, cryptic pockets of target proteins which can only be revealed by conformational changes, are difficult to explore during molecular docking [51]. Molecular dynamic (MD) simulations are therefore employed to complement results from molecular docking studies by mitigating these challenges. MD simulations provide, at the atomic level, the dynamic interactions of proteins in a complex with ligands, offering insights into binding energy, stability, and conformational changes [52]. Integration of this concept has become critical, as understanding of how proteins interact with drugs offers an opportunity in designing new and effective therapeutic agents. Based on the aforementioned, a 100ns MD simulation was executed, and the outcomes, the RMSD, RMSF, Rg, and hydrogen bond graphs, were plotted. The RMSD, which measures the deviations of the protein backbone from the initial pose over time, was computed to examine the stability of the various complexes [53]. Complexes with RMSD lower than 3 Å are said to be stable [54], while those greater with high fluctuations are said to be unstable. The ligands, 31404, 2345, and the reference drug, 135401907, recorded an initial surge in RMSD (to a maximum of 0.24 Å) in the first 40s (Fig. 10 a) before levelling off. A careful look at the RMSD graph shows that both 2345 and 135401907 showed an average RMSD of 0.2 Å, while 31404 had the highest 0.21 Å (Fig. 10 a). Overall, all the selected hit compounds and the reference drug recorded an average RMSD lower than the threshold and therefore suggested that the hit compounds possess the potential of forming stable complexes with the target protein. Rg was computed to evaluate the compactness of the protein-ligand complexes [55]. Notably, a high Rg connotes a decrease in compactness upon ligand binding, possibly resulting in protein unfolding, while a low Rg is an indication of an increased folding of the target protein [54, 55]. Both identified hit compounds, 31404 and 2345, recorded an average Rg of 2.8 nm, comparable to the reference, 315401907 [0.2785 nm] (Fig. 10 b), suggesting the potential of the ligands enhancing protein folding upon binding. RMSF, a metric used to quantify the flexibility of amino acids within a protein, was computed to identify which residues are critical for ligand binding [55]. Amino acid residues within the binding pockets of the target protein are mostly flexible, which, upon interacting with ligands, show minimum fluctuations, recording low RMSF (54). From the RMSF graph, residues within the ranges 610–990, 1100–1200, and 1150–1220 (Fig. 10 c) recorded low RMSF, indicating their involvement in ligand binding and hence suggested to contribute to complex stability. The number of hydrogen bonding interactions formed during the entire simulation period shows the reference compound, 315401907, formed four hydrogen bonds with the target protein, while 31404 formed one hydrogen bond with 2345, not recording any hydrogen bond formation (Fig. 10 d). Overall, results from the RMSD, RMSF, Rg, and number of hydrogen bond formations suggest the selected hit compounds and reference drug formed stable complexes with the target protein. Molecular Mechanics Poisson-Boltzmann Surface Area Computations of Free Binding Energy MM-PBSA has become essential in computational drug development for predicting and ranking hit compounds, as it computes an accurate free binding energy (ΔG bind ) using van der Waals interactions energy (ΔG vdW ), electrostatic energy (ΔG ele ), polar solvation energy (ΔG pol, sol ), and non-polar solvation energy (ΔG SASA ) [56]. Complementing molecular docking results with MM-PBSA calculations ensures that the identified hit compounds possess the potential of inhibiting the target protein in the absence of the experimental evaluation [57]. The hit compound, 31404, recorded the least ΔG bind of − 12.183 kJ/mol (Table 9 ), lower than the reference compound, 135401907 (− 4.384 kJ/mol), suggesting that it forms a feasible and stable complex with the target protein. While ΔG pol, sol is the worst contributor, the ΔG vdW , ΔG ele , and ΔG SASA contributed favorable energies to the observed ΔG bind values. Interestingly, the hit compound, 2345, recorded a ΔG bind of + 3.323 kJ/mol (Table 9 ), suggesting it does not possess the potential of inhibiting XO. Results from the molecular docking and MM-PBSA computations suggest the potential of 135401907 for modulating XO worthy of further experimental evaluation. Table 9 Computed free binding energies of the complexes of selected hit compounds and the reference drug Complex ΔG vdW (kJ/mol) ΔG ele (kJ/mol) ΔG pol,sol (kJ/mol) ΔG SASA (kJ/mol) ΔG bind (kJ/mol) 31404 −21.240 ± 2.631 −0.700 ± 1.001 13.407 ± 3.814 −3.650 ± 5.030 −12.183 ± 4.997 2345 −0.005 ± 0.014 −0.017 ± 0.332 3.242 ± 5.279 0.103 ± 2.894 + 3.323 ± 5.312 135401907 −12.827 ± 4.923 −0.772 ± 2.013 9.490 ± 5.604 −0.275 ± 1.126 −4.384 ± 2.702 The energy contribution of each amino acid to the ΔG bind was evaluated using per-residue decomposition analysis. The per-residue decomposition studies identify which amino acid is critical for ligand binding, as those involved contribute the most to the ΔG bind . Amino acid residues that contribute energies greater than + 5 kJ/mol or lower than − 5 kJ/mol are considered necessary for complex stability [58]. Interestingly, while all the amino acids present in the target protein contributed useful energies (Fig. 11 ) to ΔG bind , none was able to meet the threshold. This notwithstanding, exploring the residues in the binding pocket still provides a means of developing anti-gout chemotypes targeting XO. DISCUSSION The present study was conducted to screen for XO inhibitors from Brassica oleracea extracts for the possible treatment of gout. Plants generate diverse secondary metabolites as defense compounds, serving as a rich source of bioactive small molecules with potential therapeutic applications [59]. Among the members of the family Brassiceae, the genus Brassica stands out as the most important, comprising several globally significant crop species such as Brassica oleracea L., Brassica napus L., and Brassica rapa L. Notably, B. oleracea is the primary vegetable species and includes widely consumed vegetables like kale, broccoli, cauliflower, Brussels sprouts, and both red and white cabbage [60]. They are recognized for their wide-ranging health-promoting effects [61]. The in silico analysis performed in this study offers important insights into the potential inhibitory effects of bioactive compounds found in Brassica oleracea extracts on XO. Molecular docking is a widely used approach in drug discovery that facilitates the identification of optimal ligand orientations within a protein's binding site and enables the prediction of their binding affinities [59]. Seven (7) compounds, hematoporphyrin, butylated hydroxytoluene, 1,4-benzenedicarboxylic acid bis(2-ethylhexyl) ester, benzyl benzoate, dasycarpidan-1-methanol acetate (ester), paraquat dichloride, and vitamin E, exhibited binding affinities surpassing the − 7.0 kcal/mol threshold, indicating stronger molecular interactions with the target enzyme compared to the reference drug, allopurinol, which displayed a binding affinity of − 6.4 kcal/mol. In docking studies, if a compound exhibits lower binding energy compared to the standard, it indicates that the compound has the potential to exhibit greater activity [62]. A lower binding energy value suggests a more favorable and stable interaction between the ligand and the protein receptor. The inability of silicon-containing compounds to dock successfully in AutoDock Vina may stem from several factors inherent to the software’s limitations in handling such molecules. Our docking experiments revealed an important limitation when working with silicon-containing compounds. This technical constraint reflects the underlying force field design of AutoDock Vina, which was specifically parameterized for common biological atoms, including carbon, nitrogen, oxygen, sulfur, and hydrogen [63, 64]. Since AutoDock’s atom typing is also case-sensitive, the recognition of unsupported elements such as “Si” fails entirely. While this design choice enables efficient docking of typical drug-like molecules, it necessarily excludes certain organometallic and organosilicon compounds from analysis. Such limitations are not unique to AutoDock Vina, as many widely used docking tools exhibit similar constraints when confronted with non-standard atomic types [65]. Data on the physicochemical properties of the selected ligands were gathered through pharmacological analysis, along with safety profiling to assess their potential risks and benefits. Lipinski's rule determines and defines the drug-likeness and druggability of a compound. The Rule of 5 (Ro5) guidelines help predict whether a compound is likely to be effective as an oral drug [66]. Due to the widespread acceptance of these guidelines in drug development, there is a reduced pursuit of compounds that violate two or more of the Ro5 criteria [67]. Hematoporphyrin (PubChem ID: 11103) presents two violations of Lipinski's Rule of Five: its molecular weight exceeds the 500 Da threshold, and it possesses more than five hydrogen bond donors (NH or OH); therefore, it was excluded from downstream analysis due to poor drug-likeness. With only one violation of Lipinski’s Rule observed in 1,4-benzenedicarboxylic acid bis(2-ethylhexyl) ester (PubChem ID: 22932), vitamin E (PubChem ID: 2116), and butylated hydroxytoluene (PubChem ID: 31404), and full compliance seen in dasycarpidan-1-methanol acetate (PubChem ID: 550072), benzyl benzoate (PubChem ID: 2345), and paraquat dichloride (PubChem ID: 15938), these compounds demonstrated favorable drug-like properties and warrant further biological investigation. The bioavailability score (ABS) is assessed based on a compound's compliance with or deviation from Lipinski’s Rule of Five. At biological pH, a compound is expected to achieve more than 10% bioavailability (F) in rats if it adheres to Lipinski’s criteria, resulting in an ABS of 0.55, meaning there's a 55% probability that F will exceed 10% in rats. On the other hand, if the compound does not meet these criteria, the ABS decreases to 0.17, indicating only a 17% chance of the compound having bioavailability greater than 10% in rats [68]. The selected compounds had an ABS of 0.55, making them effective via the oral route. PAINS are groups of chemical compounds with similar structural features, which increases the likelihood of these compounds exhibiting activity (or being identified as "hits") in various tests [69]. PAINS compounds can lead to misleading results in drug discovery assays due to their tendency to exhibit nonspecific interactions or cause other undesirable effects unrelated to the intended target [70]. This can lead to the inefficient use of time and resources by focusing on compounds that are unlikely to develop into viable drug candidates. Hence, compounds with 0 PAINS alerts are reliable for further development into therapeutic agents. The properties of absorption, distribution, metabolism, excretion, and toxicity (ADMET) are crucial to pharmaceutical drug design. It is frequently reported that the inability of drug candidates to meet the necessary ADMET standards is a common reason for their high failure rates during the development process [71]. In silico ADMET studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds [72]. First, the Caco-2 (Colon Adenocarcinoma 2) monolayer cell culture model, which is regarded as the "gold standard" for evaluating drug permeability, is widely used in drug discovery to predict human intestinal absorption [73]. Five out of six selected compounds exhibited Caco-2 permeability values greater than − 5.15 log cm/s, suggesting favorable absorption profiles in the human body and eventually could possess better ADMET profiles. The blood-brain barrier (BBB) is a highly selective semipermeable barrier that tightly regulates the movement of ions, molecules, and cells between the blood and the central nervous system (CNS). It protects the neural tissues from toxins and pathogens [74]. One of the key challenges in drug design is determining whether a compound can cross the BBB. Drugs that act on the nervous system must be able to pass through the BBB to work effectively. In contrast, medications aimed at other body parts should ideally not cross the BBB to avoid potential psychotropic side effects [49]. Butylated hydroxytoluene and benzyl benzoate were predicted to be impermeable to the BBB, indicating limited central nervous system penetration and a potentially favorable safety profile. Clearance is a key pharmacokinetic (PK) parameter, as it influences both the half-life of a drug (in conjunction with the volume of distribution) and its bioavailability (alongside oral absorption). It therefore plays a critical role in determining the dosing regimen, both the frequency of administration and the appropriate dose required for effective treatment [75]. The reference drug, allopurinol (PubChem ID: 134018), exhibited lower plasma clearance compared to butylated hydroxytoluene and benzyl benzoate. Efficient plasma clearance is essential for drug elimination and helps prevent drug accumulation in the body. The half-life of a drug impacts its duration of effect, the time it takes to achieve stable concentrations, and the period necessary for the drug to be eliminated from the body [76]. In this regard, allopurinol also demonstrated a shorter and less favorable half-life compared to butylated hydroxytoluene and benzyl benzoate, potentially reducing its therapeutic efficacy. Nephrotoxicity arises when the kidneys fail to perform their detoxification and excretion functions effectively, resulting from damage or impairment caused by external or internal toxic substances [77]. Drug-induced liver Injury (DILI) is a harmful liver response triggered by exposure to pharmaceutical agents or other xenobiotics. It may occur as a predictable reaction to high doses of toxic substances or as an unpredictable, idiosyncratic event even at therapeutic doses. DILI poses a significant challenge in drug development and clinical therapy, as genetic and environmental factors can alter drug metabolism and excretion, leading to cellular stress, immune activation, and potentially severe liver damage. Its occurrence highlights the importance of evaluating liver toxicity during early stages of drug screening to ensure patient safety and regulatory compliance [78]. Butylated hydroxytoluene and benzyl benzoate were found to be neither hepatotoxic nor nephrotoxic, suggesting a favorable safety profile in contrast to the reference drug. Following ADMET profiling, butylated hydroxytoluene and benzyl benzoate emerged as promising lead candidates, underscoring their promise for further development, although experimental validation through in vitro and in vivo studies remains essential. The biological activity spectrum was introduced to define the characteristics of substances that exhibit biological activity [79]. The predictive accuracy of the software, PASS, differs across various biological activities. Biological activities with Pa > Pi are considered worthy of pharmacological evaluation [32]. If the value of Pa exceeds 0.7, the substance is likely to demonstrate activity in experiments and has a high probability of being similar to an existing pharmaceutical agent. When Pa is between 0.5 and 0.7, the substance may still show activity, but with a lower likelihood, and it is less likely to resemble known pharmaceutical agents. If Pa falls below 0.5, the substance is unlikely to exhibit activity in experiments; however, if an activity is observed, it could indicate the discovery of a new chemical entity [79]. Butylated hydroxytoluene exhibited anti-inflammatory activity with a Pa value of 0.806, indicating its potential relevance for treating gout, an inflammatory form of arthritis [3]. Additionally, it showed kidney function stimulant activity with a Pa value of 0.727, suggesting a high likelihood of experimental validation, as values above 0.7 are generally considered predictive of true biological activity. A kidney function stimulant enhances the activity of the kidneys so it can effectively filter the blood and excrete waste substances like uric acid, which is primarily excreted through urine [80]. Benzyl benzoate exhibited oxidoreductase inhibitor activity with a Pa value of 0.618, suggesting a likelihood of inhibiting xanthine oxidoreductase in an experimental setting, though with a lower probability. Research has demonstrated a close association between the development of hyperuricemia or gouty arthritis and the production of reactive oxygen species (ROS) [81]. Antioxidants help reduce reactive oxygen species (ROS) levels and decrease oxidative stress [82]. Providing antioxidant support to patients with active gout may be a viable treatment strategy [83]. It is therefore not surprising that butylated hydroxytoluene and benzyl benzoate could prevent redox imbalance, suggesting potential antioxidant activity crucial for gout therapy. Several activities, including XO inhibition, gout treatment, anti-uremic, antiarthritic, and others, showed Pa values below 0.5, suggesting a low probability of being confirmed in experimental settings. However, if these activities are validated in future studies, butylated hydroxytoluene and benzyl benzoate could represent novel therapeutic candidates. XO inhibitors are not solely restricted to the treatment of hyperuricemia and gout; evidence suggests that they can also be effective in addressing cardiovascular diseases [84]. The interaction between the protein and the lead compounds (i.e., butylated hydroxytoluene and benzyl benzoate) was predominantly governed by hydrophobic forces. Hydrophobic interactions are crucial contributors to the binding strength between drug candidates and their molecular targets. Studies indicate that prioritizing these interactions, even over traditional hydrogen bonding, can markedly influence the biological performance of lead compounds [85]. They are widely recognized as the principal thermodynamic driving force governing the association of small molecules with their protein receptors [86]. Moreover, increasing the number of hydrophobic atoms within the binding region of the drug-target complex has been associated with enhanced biological activity and therapeutic potential [85]. It was shown that butylated hydroxytoluene and benzyl benzoate portrayed higher hydrophobic interactions with their target molecules, indicating stronger biological activity with marked therapeutic effects. Glu802 and Glu1261 are recognized as key catalytic residues within the active site of XO, playing central roles in substrate binding and the enzymatic conversion of xanthine to uric acid [9]. In this study, butylated hydroxytoluene and benzyl benzoate were found to interact with these critical residues, indicating their potential to modulate enzyme activity. Given the beneficial properties of butylated hydroxytoluene and benzyl benzoate, it is essential and worthwhile to explore their potential therapeutic effects, as they could be highly significant for the treatment of gout. CONCLUSION Overall, butylated hydroxytoluene and benzyl benzoate emerged as promising lead candidates to inhibit XO and serve as potential anti-gout agents. Following molecular docking, medicinal chemistry evaluation, and ADMET analyses, these compounds, identified as the most potent inhibitors from Brassica oleracea var. italica and Brassica oleracea var. acephala , demonstrated good binding affinities, high hydrophobic interactions, XO inhibitory activities, as well as a relative safety profile, and could possess a better ADMET profile. Inhibitors like butylated hydroxytoluene and benzyl benzoate present promising potential as alternatives or complementary agents to synthetic drugs such as allopurinol, offering the added benefit of potentially reduced side effects. These findings underscore the therapeutic potential of Brassica oleracea as a valuable source of natural xanthine oxidase inhibitors and highlight the need for further in vitro and in vivo studies to validate their clinical efficacy. Abbreviations ADMET Absorption, Distribution, Metabolism, Excretion, and Toxicity BBB Blood-Brain Barrier H-bond Hydrogen bond MD Molecular Dynamics PDB Protein Data Bank SDF Structure Data File SMILES Simplified Molecular Input Line Entry System MM-PBSA Molecular Mechanics Poisson–Boltzmann Surface Area XO Xanthine oxidase ABS Bioavailability score PASS Prediction of Activity Spectra for Substances GC-MS Gas chromatography-mass spectrometry PAINS Pan-Assay Interference Compounds RMSD Root mean square deviation RMSF Root mean square fluctuation CNS Central Nervous System Declarations Ethics approval and consent to participate Not Applicable. Competing interests The authors declare no conflicts of interest. Data availability Authors have provided all the data required in the manuscript. Any additional data are available upon request from the corresponding author. Consent for publication Not applicable. Clinical Trial Number Not applicable Funding This research received no external funding. Acknowledgement The West African Centre for Cell Biology of Infectious Pathogens, University of Ghana provided the high-performance computing platform (Zuputo) for running the molecular dynamic simulations and the MM-PBSA computations. Authors’ contributions DB : conceptualization, supervision, methodology, data curation, validation, investigation, data analysis, Writing – original draft, review & editing, approved manuscript. JAB : Data curation, investigation, methodology, data analysis, Writing - review & editing, approved manuscript. SAA : Data analysis, investigation, Writing - review & editing, approved manuscript. ESA : Data curation, investigation, data analysis, Writing - review & editing, approved manuscript. RA : Data analysis, Writing - review & editing, approved manuscript. MAA : methodology, investigation, validation, data analysis, Writing - original draft, review & editing, approved manuscript. FAA : Data analysis, Writing - review & editing, approved manuscript. SKK: Investigation, data analysis, Writing - review & editing, approved manuscript. MBA : conceptualization, supervision, data analysis, methodology, investigation, Writing - original draft, review & editing, approved manuscript. POS : conceptualization, supervision, methodology, investigation, validation, data analysis, Writing - original draft, review & editing, approved manuscript. References Han, T., Chen, W., Qiu, X., & Wang, W. (2024). Epidemiology of gout–Global burden of disease research from 1990 to 2019 and future trend predictions. Therapeutic advances in endocrinology and metabolism , 15, 20420188241227295. https://doi.org/10.1177/20420188241227295 Jin, Z., Wang, Z., Wang, R., Xiang, S., Zhang, W., Tang, Y. ... & Lv, S. (2024). Global burden and epidemic trends of gout attributable to high body mass index from 1990 to 2019. Archives of Medical Science: AMS , 20(1), 71. doi:10.5114/aoms/175469 Kuo, C. F., Grainge, M. J., Zhang, W., & Doherty, M. (2015). Global epidemiology of gout: prevalence, incidence and risk factors. Nature reviews rheumatology , 11(11), 649-662. doi:10.1038/nrrheum.2015.91 Yip, K., & Berman, J. (2021). What is gout?. Jama, 326(24), 2541-2541.doi:10.1001/jama.2021.19770 Jin, M., Yang, F., Yang, I., Yin, Y., Luo, J. J., Wang, H., & Yang, X. F. (2012). Uric acid, hyperuricemia and vascular diseases. Frontiers in bioscience: a journal and virtual library , 17, 656. doi: 10.2741/3950 Sekizuka, H. (2022). Uric acid, xanthine oxidase, and vascular damage: potential of xanthine oxidoreductase inhibitors to prevent cardiovascular diseases. Hypertension Research , 45(5), 772-774. doi:10.1038/s41440-022-00891-7 Furuhashi, M. (2020). New insights into purine metabolism in metabolic diseases: role of xanthine oxidoreductase activity. American Journal of Physiology-Endocrinology and Metabolism , 319(5), E827-E834. doi:10.1152/ajpendo.00378.2020 Harrison, R. (2002). Structure and function of xanthine oxidoreductase: where are we now?. Free Radical Biology and Medicine , 33(6), 774-797. doi:10.1016/S0891-5849(02)00956-5 Cao, H., Pauff, J. M., & Hille, R. (2010). Substrate orientation and catalytic specificity in the action of xanthine oxidase: the sequential hydroxylation of hypoxanthine to uric acid. Journal of Biological Chemistry , 285(36), 28044-28053. doi:10.1074/jbc.M110.128561 Ribeiro, P. M., Fernandes, H. S., Maia, L. B., Sousa, S. F., Moura, J. J., & Cerqueira, N. M. (2021). The complete catalytic mechanism of xanthine oxidase: a computational study. Inorganic Chemistry Frontiers , 8(2), 405-416. doi:10.1039/D0QI01029D Cantu-Medellin, N., & Kelley, E. E. (2013). Xanthine oxidoreductase-catalyzed reactive species generation: a process in critical need of reevaluation. Redox biology , 1(1), 353-358. doi:10.1016/j.redox.2013.05.002 Nawaz, M. Z., Ain, Q. U., Zahid, S., Zulfiqar, T., Attique, S. A., Bilal, M. ... & Iqbal, H. M. (2020). Physicochemical features and structural analysis of xanthine oxidase as a potential therapeutic target to prevent gout. Journal of Radiation Research and Applied Sciences , 13(1), 616-628. doi:10.1080/16878507.2020.1812807 White, W. B. (2018). Gout, xanthine oxidase inhibition, and cardiovascular outcomes. Circulation , 138(11), 1127-1129. doi:10.1161/CIRCULATIONAHA.118.036148 Centers for Disease Control and Prevention. Gout [Internet]. 2023 [cited 2024 Mar 14]. Report No. Available from: https://www.cdc.gov/arthritis/gout/index.html Suresh, E., & Das, P. (2012). Recent advances in management of gout. QJM: An International Journal of Medicine , 105(5), 407-417. doi:10.1093/qjmed/hcr242 Canbey, İ., Çınar, A. Y., Yıldız, E., & Gürbüz, O. (2022). Functional properties and health benefits of cruciferous vegetables. Food Health and Technology Innovations , 5(11), 452-462. https://izlik.org/JA49MH32KB Ağagündüz, D., Şahin, T. Ö., Yılmaz, B., Ekenci, K. D., Duyar Özer, Ş., & Capasso, R. (2022). Cruciferous vegetables and their bioactive metabolites: from prevention to novel therapies of colorectal cancer. Evidence‐Based Complementary and Alternative Medicine , 2022(1), 1534083. doi:10.1155/2022/1534083 Olsen, H., Grimmer, S., Aaby, K., Saha, S., & Borge, G. I. A. (2012). Antiproliferative effects of fresh and thermal processed green and red cultivars of curly kale ( Brassica oleracea L. convar. acephala var. sabellica ). Journal of agricultural and food chemistry , 60(30), 7375-7383. doi:10.1021/jf300875f Nagraj, G. S., Chouksey, A., Jaiswal, S., & Jaiswal, A. K. (2020). Broccoli. In Nutritional composition and antioxidant properties of fruits and vegetables (pp. 5-17). Academic Press. doi:10.1016/B978-0-12-812780-3.00001-5 Vasanthi, H. R., Mukherjee, S., & Das, D. K. (2009). Potential health benefits of broccoli-a chemico-biological overview. Mini reviews in medicinal chemistry , 9(6), 749-759. doi:10.2174/138955709788452685 Latté, K. P., Appel, K. E., & Lampen, A. (2011). Health benefits and possible risks of broccoli–an overview. Food and Chemical Toxicology , 49(12), 3287-3309. doi:10.1016/j.fct.2011.08.019 Moreno, D. A., Carvajal, M., López-Berenguer, C., & García-Viguera, C. (2006). Chemical and biological characterization of nutraceutical compounds of broccoli. Journal of pharmaceutical and biomedical analysis , 41(5), 1508-1522. doi:10.1016/j.jpba.2006.04.003 Ortega-Hernández, E., Antunes-Ricardo, M., & Jacobo-Velázquez, D. A. (2021). Improving the health-benefits of kales ( Brassica oleracea L. var. acephala DC) through the application of controlled abiotic stresses: A review. Plants , 10(12), 2629. doi:10.3390/plants10122629 Khalid, W., Iqra, Afzal, F., Rahim, M. A., Abdul Rehman, A., Faiz ul Rasul, H. ... & Refai, M. (2023). Industrial applications of kale ( Brassica oleracea var. sabellica ) as a functional ingredient: A review. International Journal of Food Properties , 26(1), 489-501. doi:10.1080/10942912.2023.2168011 Ayaz, F. A., Hayırlıoglu-Ayaz, S., Alpay-Karaoglu, S., Grúz, J., Valentová, K., Ulrichová, J., & Strnad, M. (2008). Phenolic acid contents of kale ( Brassica oleraceae L. var. acephala DC.) extracts and their antioxidant and antibacterial activities. Food Chemistry , 107(1), 19-25. doi:10.1016/j.foodchem.2007.07.003 Šamec, D., Urlić, B., & Salopek-Sondi, B. (2019). Kale ( Brassica oleracea var. acephala ) as a superfood: Review of the scientific evidence behind the statement. Critical reviews in food science and nutrition , 59(15), 2411-2422. doi:10.1080/10408398.2018.1454400 Lemos, M., Santin, J. R., Júnior, L. C. K., Niero, R., & De Andrade, S. F. (2011). Gastroprotective activity of hydroalcoholic extract obtained from the leaves of Brassica oleracea var. acephala DC in different animal models. Journal of ethnopharmacology , 138(2), 503-507. doi:10.1016/j.jep.2011.09.046 Jose, S., Gupta, M., Sharma, U., Quintero-Saumeth, J., & Dwivedi, M. (2022). Potential of phytocompounds from Brassica oleracea targeting S2-domain of SARS-CoV-2 spike glycoproteins: Structural and molecular insights. Journal of Molecular Structure , 1254, 132369. doi:10.1016/j.molstruc.2022.132369 Kamboj, A., Sharma, S., Singh, V. P., Sinha, A., Yadav, K. S., Lal, B. ... & Devi, L. (2023). Phytochemical and therapeutic potential of broccoli ( Brassica oleracea ): A review. Pharma Innov. J , 12, 633-638. Ghallab, D. S., Shawky, E., Metwally, A. M., Celik, I., Ibrahim, R. S., & Mohyeldin, M. M. (2022). Integrated in silico–in vitro strategy for the discovery of potential xanthine oxidase inhibitors from Egyptian propolis and their synergistic effect with allopurinol and febuxostat. RSC advances , 12(5), 2843-2872. doi:10.1039/D1RA08011C Baskaran, K. P., Arumugam, A., Kandasamy, R., & Alagarsamy, S. (2020). In silico method for prediction of maximum binding affinity and ligand-protein interaction studies on Alzheimer’s disease. Int. J. Res. Granthaalayah , 8(11), 362-370. doi:10.29121/granthaalayah.v8.i11.2020.2472 Kwofie, S. K., Annan, D. G., Adinortey, C. A., Boison, D., Kwarko, G. B., Abban, R. A., & Adinortey, M. B. (2022). Identification of novel potential inhibitors of varicella-zoster virus thymidine kinase from ethnopharmacologic relevant plants through an in-silico approach. Journal of Biomolecular Structure and Dynamics , 40(23), 12932-12947. doi:10.1080/07391102.2021.1977700 Adams, L., Afiadenyo, M., Kwofie, S. K., Wilson, M. D., Kusi, K. A., Obiri-Yeboah, D. ... & McKeon-Bennett, M. (2023). In silico screening of phytochemicals from Dissotis rotundifolia against Plasmodium falciparum Dihydrofolate Reductase. Phytomedicine Plus , 3(2), 100447. doi:10.1016/j.phyplu.2023.100447 Wallace, A. C., Laskowski, R. A., & Thornton, J. M. (1995). LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein engineering, design and selection , 8(2), 127-134. doi:10.1093/protein/8.2.127 Umar, H. I., Josiah, S. S., Saliu, T. P., Jimoh, T. O., Ajayi, A., & Danjuma, J. B. (2021). In-silico analysis of the inhibition of the SARS-CoV-2 main protease by some active compounds from selected African plants. Journal of Taibah University Medical Sciences , 16(2), 162-176. doi:10.1016/j.jtumed.2020.12.005 Mateev, E., Valkova, I., Angelov, B., Georgieva, M., & Zlatkov, A. (2022). Validation through re-docking, cross-docking and ligand enrichment in various well-resoluted MAO-B receptors. Int. J. Pharm. Sci. Res , 13(3), 1000-1007. doi.org/10.13040/IJPSR.0975-8232 Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific reports , 7(1), 42717. doi:10.1038/srep42717 Fu, L., Shi, S., Yi, J., Wang, N., He, Y., Wu, Z. ... & Cao, D. (2024). ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic acids research , 52(W1), W422-W431. doi:10.1093/nar/gkae236 Chy, M. N. U., Chakrabarty, N., Roy, A., Paul, A., Emu, K. A., Dutta, T. ... & Tasnim, S. M. (2019). Antibacterial, anthelmintic, and analgesic activities of Piper sylvaticum (Roxb.) leaves and in silico molecular docking and PASS prediction studies of its isolated compounds. Journal of Complementary and Integrative Medicine , 16(4), 20180176. doi:10.1515/jcim-2018-0176 Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX , 1, 19-25. doi:10.1016/J.SOFTX.2015.06.001 Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. (2005). GROMACS: fast, flexible, and free. Journal of computational chemistry , 26(16), 1701-1718. doi:10.1002/JCC.20291 Siu, S. W., Pluhackova, K., & Böckmann, R. A. (2012). Optimization of the OPLS-AA force field for long hydrocarbons. Journal of Chemical theory and Computation , 8(4), 1459-1470. doi:10.1021/ct200908r Jorgensen, W. L., & Tirado-Rives, J. (2005). Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. Proceedings of the National Academy of Sciences , 102(19), 6665-6670. doi:10.1073/pnas.0408037102 Dahiya, R., Mohammad, T., Gupta, P., Haque, A., Alajmi, M. F., Hussain, A., & Hassan, M. I. (2019). Molecular interaction studies on ellagic acid for its anticancer potential targeting pyruvate dehydrogenase kinase 3. RSC advances , 9(40), 23302-23315. doi:10.1039/C9RA02864A Kumari, R., Kumar, R., Open Source Drug Discovery Consortium, & Lynn, A. (2014). g_mmpbsa A GROMACS tool for high-throughput MM-PBSA calculations. J ournal of chemical information and modeling , 54(7), 1951-1962. doi:10.1021/ci500020m Prasanth, D. S. N. B. K., Murahari, M., Chandramohan, V., Panda, S. P., Atmakuri, L. R., & Guntupalli, C. (2021). In silico identification of potential inhibitors from Cinnamon against main protease and spike glycoprotein of SARS CoV-2. Journal of Biomolecular Structure and Dynamics , 39(13), 4618-4632. doi:10.1080/07391102.2020.1779129 Ramírez, D., & Caballero, J. (2018). Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data?. Molecules , 23(5), 1038. doi:10.3390/molecules23051038 Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C. ... & Cao, D. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic acids research , 49(W1), W5-W14. doi:10.1093/nar/gkab255 Carpenter, T. S., Kirshner, D. A., Lau, E. Y., Wong, S. E., Nilmeier, J. P., & Lightstone, F. C. (2014). A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations. Biophysical journal , 107(3), 630-641. doi:10.1016/j.bpj.2014.06.024 Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2014). Receptor–ligand molecular docking. Biophysical reviews , 6(1), 75-87. doi:10.1007/s12551-013-0130-2 Cimermancic, P., Weinkam, P., Rettenmaier, T. J., Bichmann, L., Keedy, D. A., Woldeyes, R. A. ... & Sali, A. (2016). CryptoSite: expanding the druggable proteome by characterization and prediction of cryptic binding sites. J ournal of molecular biology , 428(4), 709-719. doi:10.1016/J.JMB.2016.01.029 Sasidharan, S., Gosu, V., Tripathi, T., & Saudagar, P. (2023). Molecular dynamics simulation to study protein conformation and ligand interaction. In Protein folding dynamics and stability: experimental and computational methods (pp. 107-127). Singapore: Springer Nature Singapore. doi:10.1007/978-981-99-2079-2_6 Prabhakaran, P., Hebbani, A. V., Menon, S. V., Paital, B., Murmu, S., Kumar, S., ... & Desai, P. P. D. (2023). In silico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning. Frontiers in microbiology , 14, 1194794. doi:10.3389/fmicb.2023.1194794 Ashley, C. N., Broni, E., Pena-Martinez, M., Wood, C. M., Kwofie, S. K., & Miller III, W. A. (2025). Computer-Aided Discovery of Natural Compounds Targeting the ADAR2 dsRBD2-RNA Interface and Computational Modeling of Full-Length ADAR2 Protein Structure. International Journal of Molecular Sciences , 26(9), 4075. doi:10.3390/IJMS26094075/S1 Sakyi, P. O., Kwofie, S. K., Tuekpe, J. K., Gwira, T. M., Broni, E., Miller III, W. A., ... & Amewu, R. K. (2023). Inhibiting Leishmania donovani sterol methyltransferase to identify lead compounds using molecular modelling. Pharmaceuticals , 16(3), 330. doi:10.3390/ph16030330 Huang, K., Luo, S., Cong, Y., Zhong, S., Zhang, J. Z., & Duan, L. (2020). An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein–ligand binding affinity. Nanoscale , 12(19), 10737-10750. doi:10.1039/C9NR10638C Ganesan, A., Coote, M. L., & Barakat, K. (2017). Molecular dynamics-driven drug discovery: leaping forward with confidence. Drug discovery today , 22(2), 249-269. doi:10.1016/J.DRUDIS.2016.11.001 Kwofie, S. K., Dolling, N. N., Donkoh, E., Laryea, G. M., Mosi, L., Miller III, W. A. ... & Wilson, M. D. (2021). Pharmacophore-guided identification of natural products as potential inhibitors of Mycobacterium ulcerans cystathionine γ-synthase MetB. Computation , 9(3), 32. doi:10.3390/computation9030032 Adetutu, A., Olaniyi, T. D., & Owoade, O. A. (2021). GC-MS analysis and in silico assessment of constituents of Psidium guajava leaf extract against DNA gyrase of Salmonella enterica serovar Typhi. Informatics in Medicine Unlocked , 26, 100722. doi:10.1016/j.imu.2021.100722 Kapusta-Duch, J., Kopec, A., Piatkowska, E., Borczak, B., & Leszczynska, T. (2012). The beneficial effects of Brassica vegetables on human health. Roczniki Państwowego Zakładu Higieny , 63(4). Shankar, S., Segaran, G., Sundar, R. D. V., Settu, S., & Sathiavelu, M. (2019). Brassicaceae-A classical review on its pharmacological activities. Int. J. Pharm. Sci. Rev. Res , 55(1), 107-113. Madeswaran, A., Umamaheswari, M., Asokkumar, K., Sivashanmugam, T., Subhadradevi, V., & Jagannath, P. (2011). Docking studies: In silico lipoxygenase inhibitory activity of some commercially available flavonoids. Bangladesh Journal of Pharmacology , 6(2), 133-138. doi:10.3329/bjp.v6i2.9408 Huey, R., Morris, G. M., Olson, A. J., & Goodsell, D. S. (2007). A semiempirical free energy force field with charge‐based desolvation. J ournal of computational chemistry , 28(6), 1145-1152. doi:10.1002/jcc.20634 Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry , 31(2), 455-461. doi:10.1002/jcc.21334 Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson, A. J. (2016). Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nature protocols , 11(5), 905-919. doi:10.1038/nprot.2016.051 Morak-Młodawska, B., Jeleń, M., Martula, E., & Korlacki, R. (2023). Study of lipophilicity and ADME properties of 1, 9-diazaphenothiazines with anticancer action. International journal of molecular sciences , 24(8), 6970. doi:10.3390/ijms24086970 Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016). BDDCS, the Rule of 5 and drugability. Advanced drug delivery reviews , 101, 89-98. doi:10.1016/j.addr.2016.05.007 Sharma, P., & Shanavas, A. (2021). Natural derivatives with dual binding potential against SARS-CoV-2 main protease and human ACE2 possess low oral bioavailability: a brief computational analysis. Journal of Biomolecular Structure and Dynamics , 39(15), 5819-5830. doi:10.1080/07391102.2020.1794970 Baell, J. B., & Nissink, J. W. M. (2018). Seven-Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017- Utility and Limitations. ACS chemical biology , 13(1), 36-44. doi:10.1021/acschembio.7b00903 Sun, J., Zhong, H., Wang, K., Li, N., & Chen, L. (2021). Gains from no real PAINS: Where ‘Fair Trial Strategy’stands in the development of multi-target ligands. Acta Pharmaceutica Sinica B , 11(11), 3417-3432. doi:10.1016/j.apsb.2021.02.023 Venkatraman, V. (2021). FP-ADMET: a compendium of fingerprint-based ADMET prediction models. Journal of cheminformatics , 13(1), 75. doi:10.1186/s13321-021-00557-5 Chakravarthi, P. V., Karthikeyan, M., Lakshmanan, L., Murugesan, S., Arivuchelvan, A., Sukumar, K. ... & Jagadeeswaran, A. (2023). Computational study of Piper betle L. phytocompounds by in silico and ADMET analysis for prediction of potential xanthine oxidase inhibitory activity. BioRxiv , 2023-01. doi:10.1101/2023.01.13.523909 Falcón-Cano, G., Molina, C., & Cabrera-Pérez, M. Á. (2022). Reliable prediction of Caco-2 permeability by supervised recursive machine learning approaches. Pharmaceutics , 14(10), 1998. doi:10.3390/pharmaceutics14101998 Daneman, R., & Prat, A. (2015). The blood–brain barrier. Cold Spring Harbor perspectives in biology , 7(1), a020412. doi:10.1101/cshperspect.a020412 Berellini, G., Waters, N. J., & Lombardo, F. (2012). In silico prediction of total human plasma clearance. Journal of chemical information and modeling , 52(8), 2069-2078. doi:10.1021/ci300155y Andrade, C. (2022). The practical importance of half-life in psychopharmacology. The Journal of Clinical Psychiatry , 83(4), 22f14584-22f14584. doi:10.4088/JCP.22f14584 Kim, S. Y., & Moon, A. (2012). Drug-induced nephrotoxicity and its biomarkers. Biomolecules & therapeutics , 20(3), 268. doi:10.4062/biomolther.2012.20.3.268 Andrade, R. J., Chalasani, N., Björnsson, E. S., Suzuki, A., Kullak-Ublick, G. A., Watkins, P. B., ... & Aithal, G. P. (2019). Drug-induced liver injury. Nature reviews Disease primers , 5(1), 58. doi:10.1038/s41572-019-0105-0 Lagunin, A., Stepanchikova, A., Filimonov, D., & Poroikov, V. (2000). PASS: prediction of activity spectra for biologically active substances. Bioinformatics , 16(8), 747-748. doi:10.1093/bioinformatics/16.8.747 Bobulescu, I. A., & Moe, O. W. (2012). Renal transport of uric acid: evolving concepts and uncertainties. Advances in chronic kidney disease , 19(6), 358-371.doi:10.1053/j.ackd.2012.07.009 Zhang, S., Li, D., Fan, M., Yuan, J., Xie, C., Yuan, H. ... & Gao, H. (2024). Mechanism of reactive oxygen species-guided immune responses in gouty arthritis and potential therapeutic targets. Biomolecules , 14(8), 978. doi:10.3390/biom14080978 Kumar, S., Saxena, J., Srivastava, V. K., Kaushik, S., Singh, H., Abo-EL-Sooud, K. ... & Saluja, R. (2022). The interplay of oxidative stress and ROS scavenging: antioxidants as a therapeutic potential in sepsis. Vaccines , 10(10), 1575. doi:10.3390/vaccines10101575 Balik, A. R., Omma, A., & Sandikci, S. C. (2022). Evaluation of oxidative stress in gout disease; thiol-disulfide homeostasis and ischemia-modified albumin levels. Int J Med Biochem , 2022;5(2), 109-115 doi:10.14744/ijmb.2022.52714 Ali, H. I., Fujita, T., Akaho, E., & Nagamatsu, T. (2010). A comparative study of AutoDock and PMF scoring performances, and SAR of 2-substituted pyrazolotriazolopyrimidines and 4-substituted pyrazolopyrimidines as potent xanthine oxidase inhibitors. J ournal of computer-aided molecular design , 24(1), 57-75. doi:10.1007/s10822-009-9314-z Patil, R., Das, S., Stanley, A., Yadav, L., Sudhakar, A., & Varma, A. K. (2010). Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug designing. PloS one , 5(8), e12029. doi:10.1371/journal.pone.0012029 Young, T., Abel, R., Kim, B., Berne, B. J., & Friesner, R. A. (2007). Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding. Proceedings of the National Academy of Sciences , 104(3), 808-813. https://doi.org/10.1073/pnas.0610202104 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor invited by journal 21 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 07 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9271389","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629041027,"identity":"2cc1012a-5d2a-4aa6-88f8-8f28b3875665","order_by":0,"name":"Daniel Boison","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Boison","suffix":""},{"id":629041029,"identity":"6e1fecc9-1554-4d17-8439-16ed79e44d2f","order_by":1,"name":"Josephine Anoa Barnor","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Josephine","middleName":"Anoa","lastName":"Barnor","suffix":""},{"id":629041031,"identity":"f9553a1c-b167-404b-8718-30f2c481dd52","order_by":2,"name":"Stephen Asare Asamoah","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"Asare","lastName":"Asamoah","suffix":""},{"id":629041034,"identity":"de48efd1-acc8-40eb-b2b9-1c4ec3225e98","order_by":3,"name":"Ekow Sekyi Etwire","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Ekow","middleName":"Sekyi","lastName":"Etwire","suffix":""},{"id":629041038,"identity":"a1670bd8-b9a5-4dfd-a818-8ae368e4ad27","order_by":4,"name":"Robertson Akrofi","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Robertson","middleName":"","lastName":"Akrofi","suffix":""},{"id":629041039,"identity":"92af6f6d-390d-4ce4-9773-01fabe9a7626","order_by":5,"name":"Meshack Antwi-Adjei","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Meshack","middleName":"","lastName":"Antwi-Adjei","suffix":""},{"id":629041040,"identity":"406d5d2f-a856-429a-9a38-afead9d54cb4","order_by":6,"name":"Francis Ackah Armah","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"Ackah","lastName":"Armah","suffix":""},{"id":629041042,"identity":"ccfa2059-d5ad-4daf-84ba-ddda47eaa849","order_by":7,"name":"Samuel Kojo Kwofie","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"Kojo","lastName":"Kwofie","suffix":""},{"id":629041045,"identity":"f5b8b3bc-6e0f-49e1-8d8c-b1ffbf528b70","order_by":8,"name":"Michael Buenor Adinortey","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"Buenor","lastName":"Adinortey","suffix":""},{"id":629041046,"identity":"453c3924-f191-4906-a4ef-d8fedf3f3410","order_by":9,"name":"Patrick Opare Sakyi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYLCCBCBkYGADMiqgIjxAbIBTPTOyljPEamGAaWFsI0KLOfv5Yx8e1KQxyLcfS5N4OO+wnLn0AcYHb9sY7Lbj0GLZk8w8I+FYDoPBmbRjEonbDhtb9iUwG85tY0je2YBdi8GBZGaGxIYKoDPS20BaEjecYWCT5gVqMTiAQ8v5xxAt8v3PgVrmHK4HamH/jVfLDbAtOQwMN0AOazicYAC0hRmoxQ6XFssZj40ZEo6l8RjceJZskXAs3XDDGcZmyTnnJBJwaTHnT3zM+KMmWU6+P83w5o8aa3mDM8wHP7wps7HH6TAoDYoIFgkIm7EBSAAdiV0HSnwxf0CWscehYxSMglEwCkYeAAAs5lpDUHmDnAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Energy and Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Patrick","middleName":"Opare","lastName":"Sakyi","suffix":""}],"badges":[],"createdAt":"2026-03-30 19:08:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9271389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9271389/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108203881,"identity":"84590243-4746-4972-9b2c-9f5915aeb3b2","added_by":"auto","created_at":"2026-04-30 12:30:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31762,"visible":true,"origin":"","legend":"\u003cp\u003eAn illustration of the chemical structure of Moco and Xanthine molecules.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/ab3694d9f0bcdb5bfbd268b7.jpg"},{"id":108491095,"identity":"cd8f139c-ff48-4a71-b2c3-2d08543e1451","added_by":"auto","created_at":"2026-05-05 09:52:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110643,"visible":true,"origin":"","legend":"\u003cp\u003e3D crystal structure of XO retrieved from Protein Data Bank https://cdn.rcsb.org/images/structures/1fiq_assembly-1.jpeg\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/658a4bd727fc5be43cc47836.jpg"},{"id":108203883,"identity":"2e001012-56a8-420f-9ddd-08fa6bb493e5","added_by":"auto","created_at":"2026-04-30 12:30:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76220,"visible":true,"origin":"","legend":"\u003cp\u003eA chromatogram of the retention times of the compounds present in the diethyl ether extract of broccoli.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/28c0f3cdb8a789edc4551c74.jpg"},{"id":108491518,"identity":"77b88052-5718-4bce-96e9-7f9be6de1f9d","added_by":"auto","created_at":"2026-05-05 09:54:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85477,"visible":true,"origin":"","legend":"\u003cp\u003eA chromatogram of the retention times of the compounds present in the acetonitrile extract of broccoli.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/9ec5858a0f84ebcfdde8e95e.jpg"},{"id":108491180,"identity":"a46ba021-e54b-4b62-aafa-77b0d3ca1997","added_by":"auto","created_at":"2026-05-05 09:52:44","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78231,"visible":true,"origin":"","legend":"\u003cp\u003eA chromatogram of the retention times of the compounds present in the diethyl ether extract of kale.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/59b9ecaeaf86eed71d6ec018.jpg"},{"id":108203884,"identity":"f6966c21-8b99-4606-b5aa-391486c31839","added_by":"auto","created_at":"2026-04-30 12:30:34","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84049,"visible":true,"origin":"","legend":"\u003cp\u003eA chromatogram of the retention times of the compounds present in the acetonitrile extract of kale.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/f96c0df87923f69aeceb9d23.jpg"},{"id":108491309,"identity":"70a0f40e-0ae2-4b4b-8fe6-aca71c02075d","added_by":"auto","created_at":"2026-05-05 09:53:13","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":46162,"visible":true,"origin":"","legend":"\u003cp\u003eSuperimposition of the Ligands of the co-crystallized structure (yellow) and the redocked structure (green) to determine the RMSD.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/96aab57a3bcf988c86d4b593.jpg"},{"id":108491571,"identity":"89d1cc4c-f01d-4381-b05f-de60ea1d0437","added_by":"auto","created_at":"2026-05-05 09:54:38","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":129541,"visible":true,"origin":"","legend":"\u003cp\u003ePyMol representation of 1FIQ depicting the co-crystallized ligand (yellow) and the re-docked ligand (green) within the binding pocket (A) and 2D superimposed LigPlot+ diagram (B) showing the overlapping interactions between the co-crystallized and re-docked ligands. Residues involved in both binding modes are highlighted with red circles.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/0c38586f41f442d95e86f845.jpg"},{"id":108203892,"identity":"e9c04d98-e409-413c-a9d3-8ceed1404d90","added_by":"auto","created_at":"2026-04-30 12:30:35","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":112307,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-ligand interaction of (A) 1FIQ- PubChem ID 31404, (B) 1FIQ- PubChem ID 2345, and (C) 1FIQ- reference drug complexes.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/cd11d39458abe5e685adaf89.jpg"},{"id":108203889,"identity":"64897eae-224c-4dc9-9d59-3c34702d1a70","added_by":"auto","created_at":"2026-04-30 12:30:34","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":159135,"visible":true,"origin":"","legend":"\u003cp\u003eResults from the molecular dynamics simulations studies compute (a) RMSD, (b) Rg, (c) RMSF and (d) HB for the protein-ligand complexes\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/07ca8e3b1a0c1dea54f676da.jpg"},{"id":108491517,"identity":"69468bcf-f16d-47e1-82a8-a4c4a1155318","added_by":"auto","created_at":"2026-05-05 09:54:22","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":55098,"visible":true,"origin":"","legend":"\u003cp\u003eResults from the per-residue decomposition studies for the complexes of (a) 31404 and (b) 135401907\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/687e2fa5af133b7f4153d568.jpg"},{"id":108804588,"identity":"4ee22f60-7b3a-4899-813d-fc82493ce076","added_by":"auto","created_at":"2026-05-08 15:21:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1754503,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9271389/v1/9df185bc-facf-4d3b-9d6a-56c754244a42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico evaluation of broccoli and kale leaf extracts as xanthine oxidase inhibitors for gout therapy","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGout has emerged as a notable public health issue worldwide, with a marked increase in both its prevalence and incidence in recent decades [1, 2]. It is classified as an inflammatory arthritis condition linked to elevated uric acid levels in the blood, a state known as hyperuricemia. The hyperuricemic condition leads to the deposition of urate crystals in the joints of various body parts such as the wrist, fingers, ankle, toe, and knee [3, 4], resulting in symptoms such as redness, tenderness, warmth, and swelling during acute inflammatory episodes [5].\u003c/p\u003e \u003cp\u003eThe synthesis of uric acid occurs through the oxidation of purine metabolites, specifically hypoxanthine and xanthine, facilitated by the enzyme xanthine oxidase (XO) [6]. This molybdenum-dependent enzyme plays a crucial role in the degradation of purine nucleotides, catalyzing two key rate-limiting steps in this metabolic pathway [7, 8]. The enzymatic process involves several intricate steps, including the activation of the hydroxyl group of the molybdenum cofactor, nucleophilic attack on xanthine, and the formation of uric acid, all while managing electron flow through associated cofactors.\u003c/p\u003e \u003cp\u003eThe conversion of xanthine to uric acid involves a series of four reaction steps facilitated by specific cofactors and amino acids. Initially, the proton from the hydroxyl group of the molybdenum cofactor (Moco) is transferred to Glu1261, which activates the hydroxyl group for a nucleophilic attack on xanthine. In this step, Glu802 plays a crucial role in stabilizing the negative charge of the intermediate formed. The second step involves the transfer of a hydride ion from the tetrahedral intermediate to the sulfur atom of Moco, resulting in the reduction of molybdenum from Mo (VI) to Mo (IV). Subsequently, in the third step, uric acid is produced through protonation by Arg880, with the assistance of a water molecule. The final step involves the reduction of FAD to FADH₂, which re-oxidizes Mo (IV) back to its original oxidation state, Mo (VI).\u003c/p\u003e \u003cp\u003eThis enzymatic process is supported by two [2Fe-2S] clusters that connect the Moco and FAD cofactors, facilitating electron transfer. After the reaction cycle, the enzyme is reset by the interaction of a water molecule with Moco [9, 10]. Notably, the enzyme exhibits a decreased affinity for NAD\u0026thinsp;+\u0026thinsp;at the FAD site while showing an increased affinity for oxygen. This change promotes the transfer of one or two electrons to O₂, resulting in the formation of superoxide (O₂⁻) and hydrogen peroxide (H₂O₂) [11].\u003c/p\u003e \u003cp\u003eIt is clear that XO's activity is characterized by a reduced affinity for NAD\u003csup\u003e+\u003c/sup\u003e at the FAD site and an increased affinity for oxygen, which leads to the production of reactive oxygen species such as superoxide and hydrogen peroxide. This complex biochemical process underscores the importance of XO in the pathophysiology of gout and highlights it as a potential therapeutic target for managing this condition. Several studies have identified XO as a potential therapeutic target for controlling uric acid accumulation. Inhibitors of XO could be utilized as drugs to prevent the excessive production of uric acid by hindering the enzyme's over activity [12]. Management options for gout include the use of analgesics, non-steroidal anti-inflammatory drugs (NSAIDs), and XO inhibitors like allopurinol and febuxostat [13] used to lower serum uric acid levels [14]. However, none of the existing management options is without adverse effects, as they may cause gastrointestinal side effects such as diarrhea, nausea, and vomiting, especially at high doses, and have been associated with renal, cardiovascular, and hypersensitivity reactions [15].\u003c/p\u003e \u003cp\u003eGiven the challenges associated with conventional drug discovery approaches, which are often slow and costly, there is a pressing need for alternative sources with minimal or no adverse effects for inflammatory disorders like gout. \u003cem\u003eIn silico\u003c/em\u003e investigations present a viable alternative for identifying potential small molecules that may be more effective in managing gout. Additionally, exploring medicinal plants with ethnopharmacological relevance could enhance the likelihood of discovering potential drug candidates.\u003c/p\u003e \u003cp\u003eCruciferous vegetables, including broccoli, cabbage, cauliflower, kale, and brussels sprouts, are highly nutritious and contain various bioactive compounds [16]. Their regular consumption is linked to a reduced risk of several chronic diseases, such as diabetes mellitus, asthma, and Alzheimer\u0026rsquo;s disease. The health benefits attributed to these vegetables primarily stem from their rich content of phytonutrients, which include glucosinolates, polyphenols, carotenoids, and terpenoids (17). Notably, broccoli and kale are both part of the Brassicaceae family, which is one of the most extensively cultivated plant families globally [18].\u003c/p\u003e \u003cp\u003eBroccoli, scientifically known as \u003cem\u003eBrassica oleracea var. italica\u003c/em\u003e, is a globally consumed vegetable known for its nutritional richness, particularly in vitamins such as C, E, and K, as well as several B vitamins. It also provides essential minerals and bioactive compounds, including various glucosinolates [19, 20], which are linked to health benefits such as antibacterial, cardioprotective, antioxidant activities and antifungal properties, as well as potential cancer growth inhibition [19\u0026ndash;22].\u003c/p\u003e \u003cp\u003eKale, also called \u003cem\u003eBrassica oleracea var. acephala\u003c/em\u003e, is another important member of the Brassica family, recognized for its resilience and low production costs [23]. It is valued for its nutritional profile, which includes calcium, vitamin C, and various phytochemicals that contribute to its antioxidant and anti-cancer properties. Kale is often referred to as a superfood due to its high antioxidant capacity and the extensive research supporting its health benefits. The leaves and stems are valued for their medicinal and nutritional qualities [24]. Kale is recognized for its numerous health advantages, including antioxidant, anticancer, and protective effects on the cardiovascular and gastrointestinal systems [23, 25]. Its nutritional profile is rich, containing essential nutrients such as calcium, vitamin C, folate, riboflavin, anthocyanins, phenolic acid, flavonoids, and vitamin A [23, 25, 26]. Additionally, kale is noted for its high levels of glucosinolates, carotenoids, and phenolic compounds, which contribute to its pharmacological effects and antioxidant properties [23]. Ethnopharmacologically, \u003cem\u003eBrassica oleracea var. acephala\u003c/em\u003e DC has been widely utilized for the treatment of gastric ulcers [27], and its phytoconstituents have proven to impede the activity of the spike glycoprotein trimer associated with SARS-CoV-2, suggesting their possible role in antiviral strategies against this virus [28]. Furthermore, it has been reported that secondary metabolites present in \u003cem\u003eBrassica oleracea var. italica\u003c/em\u003e reduce the risk of several serious health conditions, including diabetes, cancer, and neurological disorder [29].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eIn silico\u003c/em\u003e pharmacological screening of the isolated compounds from \u003cem\u003eBrassica oleracea var. italica\u003c/em\u003e and \u003cem\u003eBrassica oleracea var. acephala\u003c/em\u003e could contribute to identifying new XO inhibitors derived from natural sources [30]. Therefore, this study employed an \u003cem\u003ein silico\u003c/em\u003e approach, including molecular docking, to screen the bioactive compounds in \u003cem\u003eBrassica oleracea var. italica\u003c/em\u003e and \u003cem\u003eBrassica oleracea\u003c/em\u003e var. \u003cem\u003eacephala\u003c/em\u003e against the protein receptor XO, assessing their potential anti-gout properties and ability to inhibit XO.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of Plant Material\u003c/h2\u003e \u003cp\u003eFresh kale leaves and broccoli were purchased from the Abura market in Cape Coast, Ghana, on February 29, 2024. Both samples were washed under running tap water, air-dried at 50\u0026deg;C for fourteen (14) days, and pulverized using an electric blender. Each powdered sample was macerated with acetonitrile and diethyl ether in a 1:10 w/v ratio for seven (7) days. The macerate was filtered and concentrated using a rotary evaporator at 65\u0026deg;C for acetonitrile and 35\u0026deg;C for diethyl ether. The extraction of kale yielded 0.131g (0.873%) of extract and 0.466g (3.107%) of diethyl ether extract. In contrast, the extraction of broccoli resulted in 0.499g (3.327%) of acetonitrile extract and 0.854g (5.693%) of diethyl ether extract.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGC-MS identification of phytochemicals in Extracts\u003c/h3\u003e\n\u003cp\u003eThe bioactive compounds present in \u003cem\u003eBrassica oleracea\u003c/em\u003e extracts were assessed using gas chromatography-mass spectrometry (GC-MS). The GC-MS analysis was conducted using a 7000C GC-MS triple quadrupole system equipped with a PAL autosampler for precise sample injection. An aliquot of 1.5 \u0026micro;L from each extract was dissolved in acetonitrile and diethyl ether before being injected into the GC-MS/MS system (Agilent Technologies, CA, USA). The GC oven temperature program began at 60\u0026deg;C, where it was held for 2 minutes. It then ramped up to 180\u0026deg;C at a rate of 20\u0026deg;C/min, with a 1-minute hold, before increasing further to 280\u0026deg;C at 5\u0026deg;C/min, eventually reaching a peak temperature of 325\u0026deg;C. The system utilized a splitless front inlet with helium as the carrier gas at a pressure of 17.729 psi and a total flow rate of 35 mL/min. An Agilent CP8944 VF-5ms column (30 m x 250 \u0026micro;m x 0.25 \u0026micro;m) was employed, starting with an initial flow rate of 2 mL/min, which decreased to 1.5 mL/min after the run. The mass spectrometer operated in electron ionization mode at 230\u0026deg;C, scanning from m/z 60 to 700, while the collision cell pressure was set to 3 psi, reducing to zero post-analysis. Helium and nitrogen were used as quench and collision gases, respectively, with flow rates of 2.25 mL/min and 1.5 mL/min. The bioactive compounds were identified by comparing their retention times and mass fragmentation patterns with those of known standards and a mass spectral library (MassHunter software). Quantification of the compounds was performed by measuring the peak areas.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eanalysis\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eRetrieval and Preparation of the 3D Structure of Protein Receptor\u003c/h3\u003e\n\u003cp\u003eThe 3D crystal structure of XO, with a resolution of 2.5 \u0026Aring;, was obtained from the Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.rcsb.org\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, PDB ID: 1FIQ) in pdb format. The water molecules, heteroatoms, and co-crystallized ligands attached to the XO structure were removed using BIOVIA Discovery Studio 2024. Subsequently, polar hydrogen atoms were incorporated into the PDB file using the same software [31]. The missing residues were replaced on the protein structure, which was then subjected to energy minimization using SwissPDB viewer 4.1.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://spdbv.unil.ch\u003c/span\u003e\u003cspan address=\"http://spdbv.unil.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [32].\u003c/p\u003e\n\u003ch3\u003eRetrieval of Identified Ligands\u003c/h3\u003e\n\u003cp\u003eThe 2D conformer of the identified compounds in the extracts from the GC-MS/MS, as well as the reference drug allopurinol (PubChem ID 135401907), was downloaded from the PubChem Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"http://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in the SDF (Structured Data Files) format [30].\u003c/p\u003e\n\u003ch3\u003eMolecular Docking\u003c/h3\u003e\n\u003cp\u003ePyRx 0.8.0.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pyrx.sourceforge.io\u003c/span\u003e\u003cspan address=\"https://pyrx.sourceforge.io\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which integrates various open-source software tools like AutoDock, AutoDock Vina, and Open Babel, was utilized for the docking process. Prior to their conversion into AutoDock-compatible pdbqt format, the ligands were subjected to energy minimization using the universal force field through Open Babel [33]. The docking procedure was conducted with AutoDock Vina [31] to assess the binding affinity between XO and the compounds identified in Brassica oleracea extracts, utilizing the XYZ coordinates of the grid box that corresponds to the active site of the target protein, centered at X: 21.1884 \u0026Aring;, Y: 16.3671 \u0026Aring;, and Z: 103.7037 \u0026Aring;, and with dimensions of 29.8485 \u0026times; 24.8610 \u0026times; 30.8403 \u0026Aring; [32]. The ligands were then ranked based on their binding affinity relative to the reference drugs.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of Binding Mechanism\u003c/h2\u003e \u003cp\u003eLigplot+ version 2.2.9 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.rcsb.org\" target=\"_blank\"\u003ewww.ebi.ac.uk\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to analyze the binding mechanisms between the target protein structure and the selected compounds. This program generates 2D schematic representations of protein-ligand complexes using structural data from the Protein Data Bank. It offers a clear and informative depiction of intermolecular interactions, including hydrogen bonds, hydrophobic interactions, and atom accessibility, along with an indication of interaction strength [34].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidation of Docking Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eValidation of Docking Protocol\u003c/div\u003e \u003cp\u003eValidating the docking protocol is essential to ensure the accuracy and reliability of the docking results [35]. DockRMSD was utilized for this purpose (DockRMSD: Docking Pose Distance Calculation [zhanggroup.org]). The co-crystallized ligand was extracted from the protein receptor and re-docked into the energy-minimized protein. Both the co-crystallized and re-docked ligands were then converted to MOL2 file formats and input into DockRMSD to calculate the root mean square deviation (RMSD) between the two poses [36]. Using Ligplot+ version 2.2.9 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ebi.ac.uk\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the co-crystallized and re-docked ligands were superimposed to identify the shared residues involved in ligand interactions [33].\u003c/p\u003e\n\u003ch3\u003ePrediction of ADMET properties\u003c/h3\u003e\n\u003cp\u003eThe ADMET properties of the compounds with the top scores from the molecular docking studies were evaluated using SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.swissadme.ch\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ADMETlab 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://admetlab3.scbdd.com\u003c/span\u003e\u003cspan address=\"http://admetlab3.scbdd.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Both SwissADME and ADMETlab 3.0 are valuable tools for drug discovery, providing comprehensive ADMET predictions and supporting the evaluation of potential drug candidates [37, 38]. They enable the computation of physicochemical properties and the prediction of pharmacokinetics, drug-likeness, and other therapeutic attributes [28]. The compounds were filtered using parameters such as Lipinski\u0026rsquo;s rule of 5, bioavailability score, human intestinal absorption, nephrotoxicity, blood-brain barrier (BBB), drug-induced liver toxicity, Caco-2 permeability, pan-assay interference compounds (PAINS), half-life, and plasma clearance.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBiological Activity Prediction\u003c/h2\u003e \u003cp\u003ePASS Online predicts over 3500 types of biological activities, such as pharmacological effects, toxic effects, interactions with enzymes, and changes in gene expression (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.way2drug.com/passonline/info.php#\u003c/span\u003e\u003cspan address=\"https://www.way2drug.com/passonline/info.php#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). It was employed to predict the biological activity of the selected compounds using the SMILES of the compounds as input. The software assesses compound activity by generating two probability values: probable activity (Pa) and probable inactivity (Pi). These values help predict the compound's activity profile, showing its potential to be either active or inactive [39].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamic Simulations and MM-PBSA Calculations of the Protein-ligand complexes\u003c/h2\u003e \u003cp\u003eTwo of the hit compounds with the least binding energies with PubChem IDs, 31404 and 2345, as well as the known drug, allopurinol (PubChem ID: 135401907), serving as the control, were subjected to 100ns molecular dynamic simulations using GROMACS 2018 [40, 41]. The OPLS/All Atom force field [42] was first used to generate the topology and position restraint files for the target protein. Similarly, the topology and position restraint files of the three ligands were obtained using LigParGen [43]. To ensure that the surface atoms are not affected, which is normally caused by random particle movements, the protein-ligand complexes were restricted within a cubic simulation box, allowing a 1 nm gap from the edges. A single point charge was used to solvate the system, after which Na⁺ or Cl⁻ ions were added for neutralization. Employing the steepest descent method with 50,000 steps, each system was energy minimized, followed by a 100ps equilibration. Ensembles of the number of substances, volume, and temperature, as well as the number of substances, pressure, and temperature, were applied to ensure a well-equilibrated system was reached at 300 K and 1 bar of pressure. The output files obtained after the simulation, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (Rg) trajectories, were plotted using Qtgrace [44]. MM-PBSA computations of the free binding energy and the per-residue energy contribution were calculated using MM/PBSA [45].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eGC-MS Profiling of Phytochemicals in\u003c/b\u003e \u003cb\u003eBrassica\u003c/b\u003e \u003cb\u003eExtracts\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 16 compounds were detected in the acetonitrile and diethyl ether extracts of broccoli, while 12 compounds were identified in both extracts of kale. The analysis revealed 9 compounds in the diethyl ether extract (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and 7 compounds in the acetonitrile extract (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) of broccoli, whereas 6 compounds were identified in each extract of kale (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Arachidonic acid was present in both the acetonitrile and diethyl ether extracts of broccoli, while 1,4-benzenedicarboxylic acid, bis(2-ethylhexyl) ester, rhodopin, butylated hydroxytoluene, and fumaric acid, 1-cyclopentylethyl nonyl ester, were detected in both broccoli and kale extracts.\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\u003eBioactive compounds identified in diethyl ether extract of broccoli.\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003cp\u003e(minutes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMolecular formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMolecular weight(g/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eButylated Hydroxytoluene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e24\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e220.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenzyl Benzoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e14\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e212.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArachidonic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e304.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHexadecamethyl-heptasiloxane,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e48\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003eSi\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e533.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTetracosamethyl-cyclododecasiloxane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e24\u003c/sub\u003eH\u003csub\u003e72\u003c/sub\u003eO\u003csub\u003e12\u003c/sub\u003eSi\u003csub\u003e12\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e889.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,4- Benzenedicarboxylic acid, bis(2-ethylhexyl) ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e24\u003c/sub\u003eH\u003csub\u003e38\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e390.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePentyl octadecyl ether\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e23\u003c/sub\u003eH\u003csub\u003e48\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e340.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFumaric acid, 1- cyclopentylethyl nonyl ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e34\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e338.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEicosamethyl-cyclodecasiloxane,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e60\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003eSi\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e741.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRetention times (RT), peak areas, molecular formulas, and molecular weights are included\u003c/em\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\u003eBioactive compounds identified in acetonitrile extract of broccoli.\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003cp\u003e(minutes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMolecular formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMolecular weight(g/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHematoporphyrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e34\u003c/sub\u003eH\u003csub\u003e38\u003c/sub\u003eN\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e598.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLycoxanthin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e40\u003c/sub\u003eH\u003csub\u003e56\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e552.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRhodopin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e40\u003c/sub\u003eH\u003csub\u003e58\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e554.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArachidonic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e304.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4-Androsten-9.alpha.- fluoro-17.alpha.-methyl-3.alpha.,6.beta.,11.beta.,17.b eta.-tetra-ol, tetra-trimethylsily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e32\u003c/sub\u003eH\u003csub\u003e63\u003c/sub\u003eFO\u003csub\u003e4\u003c/sub\u003eSi\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e643.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,4- Benzenedicarboxylic acid, bis(2-ethylhexyl) ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e24\u003c/sub\u003eH\u003csub\u003e38\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e390.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVitamin E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e29\u003c/sub\u003eH\u003csub\u003e50\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e430.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRetention times (RT), peak areas, molecular formulas, and molecular weights are included.\u003c/em\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\u003eBioactive compounds identified in diethyl ether extract of kale leaves.\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003cp\u003e(minutes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMolecular formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMolecular weight(g/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eButylated Hydroxytoluene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e24\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e220.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,4-Benzenedicarboxylic acid, bis(2-ethylhexyl) ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e24\u003c/sub\u003eH\u003csub\u003e38\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e390.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-Decanol, 2-hexyl-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e34\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e242.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFumaric acid, 1-cyclopentylethyl nonyl ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e34\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e338.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDasycarpidan-1-methanol, acetate (ester)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e26\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e326.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRhodopin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e40\u003c/sub\u003eH\u003csub\u003e58\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e554.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRetention times (RT), peak areas, molecular formulas, and molecular weights are included.\u003c/em\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\u003eBioactive compounds identified in acetonitrile extract of kale leaves.\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003cp\u003e(minutes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMolecular formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMolecular weight(g/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e6\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eButanoic acid, 2,3-dihydroxy propyl ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003eH\u003csub\u003e14\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e162.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParaquat dichloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e14\u003c/sub\u003eC\u003csub\u003el2\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e257.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePalmitic anhydride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e32\u003c/sub\u003eH\u003csub\u003e62\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e494.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGorlic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e278.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,4-Benzenedicarboxylic acid, bis(2-ethylhexyl) ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003e24\u003c/sub\u003eH\u003csub\u003e38\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e390.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRetention times (RT), peak areas, molecular formulas, and molecular weights are included.\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking Analysis\u003c/h2\u003e \u003cp\u003eTo identify potential candidates for gout management, molecular docking studies were performed on 28 phytochemicals isolated from two \u003cem\u003eBrassica\u003c/em\u003e species, with XO (PDB ID: 1FIQ) as the target. These compounds, along with the reference XO inhibitor allopurinol, were docked against the enzyme. Based on a binding energy threshold of \u0026minus;\u0026thinsp;7.0 kcal/mol [32, 46], seven (7) compounds demonstrated favorable binding affinities and were selected for further analysis. The binding energies of these ligands, in comparison with the reference drug, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. These top-performing compounds were subsequently subjected to downstream evaluations.\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\u003eMolecular docking results of the 28 phytochemicals\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBroccoli\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eKale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePubChem\u003c/p\u003e \u003cp\u003eID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding Energy(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompound name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePubChem ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBinding Energy(kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematoporphyrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eButylated Hydroxytoluene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButylated Hydroxytoluene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,4-Benzenedicarboxylic acid, bis(2-ethylhexyl) ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzyl Benzoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1-Decanol, 2-hexyl-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArachidonic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFumaric acid, 1-cyclopentylethyl nonyl ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91736023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHexadecamethyl-\u003c/p\u003e \u003cp\u003eheptasiloxane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDasycarpidan-1-methanol, acetate (ester)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e550072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTetracosamethyl-cyclododecasiloxane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRhodopin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5365880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1,4- Benzenedicarboxylic acid, bis(2-ethylhexyl) ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePentyl octadecyl ether\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87077485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eButanoic acid, 2,3-dihydroxy propyl ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFumaric acid, 1-\u003c/p\u003e \u003cp\u003ecyclopentylethyl nonyl ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91736023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParaquat dichloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEicosamethyl cyclodecasiloxane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e519601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePalmitic anhydride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLycoxanthin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5281245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGorlic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5282855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhodopin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5365880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4-Androsten-9.alpha.- fluoro-17.alpha.-methyl-3.alpha.,6.beta.,11.beta.,17.b eta.-tetra-ol, tetra-trimethylsily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91696616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReference drug\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllopurinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135401907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eReference drug (used as a control), along with their binding energies. *** represents the silicon- containing phytochemicals.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation of Docking Protocol\u003c/h2\u003e \u003cp\u003eThe structures of the re-docked and co-crystallized ligands were superimposed, and a root mean square deviation (RMSD) of 2.085 \u0026Aring; was computed. Docking poses with RMSD values ranging from 2.0 to 3.0 \u0026Aring; exhibit positional deviations from the reference structure; however, they retain the correct binding orientation [47]. This shows the accuracy and reliability of the docking results. When the co-crystallized and re-docked ligands were superimposed in Ligplot+, the 2D interaction analysis revealed that 7 residues were common to both complexes. There was an overlap of two hydrogen bonds (with Phe798 and Met1038) and five hydrophobic interactions (with Arg912, Ser1080, Gln1040, Gly1039, and Gly1260). The observed overlaps demonstrate AutoDock Vina\u0026rsquo;s effectiveness in reproducing the experimental binding conformation [33].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDrug Likeness\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eLipinski\u0026rsquo;s rule assesses whether a biologically active compound possesses suitable chemical and physical properties for oral bioavailability [33]. The criteria include molecular weight\u0026thinsp;\u0026le;\u0026thinsp;500 Da, LogP\u0026thinsp;\u0026le;\u0026thinsp;5, hydrogen bond donors\u0026thinsp;\u0026le;\u0026thinsp;5, hydrogen bond acceptors\u0026thinsp;\u0026le;\u0026thinsp;10, and 40\u0026thinsp;\u0026le;\u0026thinsp;molar refractivity\u0026thinsp;\u0026le;\u0026thinsp;140 [32]. Among the seven compounds exhibiting high binding affinity, six (PubChem IDs: 2116, 2345, 22932, 550072, 31404, 15938) satisfied Lipinski\u0026rsquo;s rule (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). All six compounds demonstrated a bioavailability score of 0.55, further confirming their compliance with the rule. Additionally, none of the compounds triggered alerts for Pan-Assay Interference Compounds (PAINS), indicating the absence of substructures known to cause false-positive results in biological assays [48]. These six compounds were subsequently selected for downstream ADMET analyses. The reference drug also satisfied Lipinski\u0026rsquo;s rule and showed no PAINS alerts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMedicinal chemistry properties of the 7 selected compounds in Brassica oleracea extracts and reference drug using Lipinski rule, bioavailability score and PAINS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubChem ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipinski rule\u003c/p\u003e \u003cp\u003e(Number of violations)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBioavailability score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePAINS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 alerts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 alerts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 alerts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e550072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 alerts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 alerts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 alerts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 alerts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e135401907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 alerts\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\u003eADMET profiling\u003c/h2\u003e \u003cp\u003eThe ADMET evaluation of the investigated compounds provides valuable insights into their pharmacokinetic characteristics and safety profiles. The predicted ADMET parameters for the selected compounds are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Among the compounds assessed, Caco-2 permeability values indicated excellent absorption for PubChem IDs 2116, 2345, 22932, 550072, and 31404, with values of \u0026minus;\u0026thinsp;5.011, \u0026minus;\u0026thinsp;4.670, \u0026minus;\u0026thinsp;4.916, \u0026minus;\u0026thinsp;5.053, and \u0026minus;\u0026thinsp;4.987, respectively. PubChem ID 15938 exhibited poor Caco-2 permeability and was therefore excluded from further analysis. The reference drug also showed excellent permeability. Assessment of human intestinal absorption (HIA) corroborated these findings, with PubChem IDs 2116, 2345, 22932, 550072, and the reference drug all displaying excellent absorption profiles, while PubChem IDs 15938 and 31404 showed intermediate absorption levels. Evaluation of blood-brain barrier (BBB) permeability showed that PubChem IDs 22932, 2116, and 550072 were BBB-permeant, whereas PubChem IDs 31404, 2345, and the reference drug were BBB-impermeant. Due to the significance of BBB selectivity in drug design [49], the BBB-permeant compounds were excluded from further development. Pharmacokinetic profiling indicated moderate plasma clearance for PubChem IDs 31404, 2345, and the reference drug, with a medium half-life for PubChem IDs 31404 and 2345, and a poor half-life for the reference drug. For the toxicity parameters, which included nephrotoxicity and drug-induced liver injury (DILI), both PubChem ID 31404 and 2345 were non-nephrotoxic and exhibited no signs of hepatotoxicity. In contrast, the reference drug demonstrated a poor safety profile in both toxicity parameters. Following completion of the ADMET evaluation, PubChem IDs 31404 and 2345 emerged as the most promising lead candidates, demonstrating favorable pharmacokinetic and toxicity profiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eADMET Prediction of selected compounds and the reference drug.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADMET properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePubChem\u003c/p\u003e \u003cp\u003eID 2116\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePubChem ID 2345\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePubChem ID 22932\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePubChem\u003c/p\u003e \u003cp\u003eID 550072\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePubChem ID\u003c/p\u003e \u003cp\u003e31404\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePubChem\u003c/p\u003e \u003cp\u003eID 15938\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePubChem ID\u003c/p\u003e \u003cp\u003e135401907\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaco-2 permeability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Intestinal Absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma clearance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHalf-life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNephrotoxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug induced liver toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePoor\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\u003eMolecular Interaction of Selected Ligand\u003c/h2\u003e \u003cp\u003eThe protein, together with the selected ligands, formed a complex and was run in Ligplot+ version 2.2.8 to assess the molecular interaction. The binding interactions between the ligands and protein were studied to determine the significant intermolecular bonds within the complexes. PubChem ID 31404 exhibited hydrophobic interactions with Lys1045, Ser1082, Thr1083, Gln1040, Gln767, Thr1077, Met1038, Arg912, Phe798, Gln1261, Gly1260, and Ser1080. It also formed a hydrogen bond with Gln1194 (2.85 \u0026Aring;) PubChem ID 2345 also displayed hydrophobic interactions with Glu802, Phe798, Gly799, Glu1261, Arg912, Gly1260, Gln1040, Gly1039, Ala1079, Ser1080, Gln1194, and Met1038. The reference drug formed two hydrogen bonds with Gln767 (bond length: 2.90 \u0026Aring; and 3.00 \u0026Aring;), one hydrogen bond with Glu802 (2.94 \u0026Aring;), and one with Thr1077 (3.06 \u0026Aring;). Additionally, it engaged in hydrophobic interactions with Phe914, Glu1261, Gly1260, Phe798, Gly799, Ser1080, Ala1078, and Met1038. These interactions collectively suggest a stable and favorable binding conformation within the protein\u0026rsquo;s active site. A comparison of the molecular interactions revealed that the residues Phe798, Met1038, Gly1260, Ser1080, and Glu1261 were commonly involved in the binding of both lead compounds and the reference drug. These shared amino acids suggest conserved interaction sites within the protein's active pocket.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eXO Inhibitory and Anti-Gout Predictions Using PASS\u003c/h2\u003e \u003cp\u003eUsing PASS (Prediction of Activity Spectra for Substances), the potential biological activities of the compounds were assessed, along with their respective probabilities of activity (Pa) and inactivity (Pi). For PubChem ID 31404, notable predicted activities included oxidoreductase inhibition, antioxidant effects, anti-inflammatory properties, kidney function stimulation, oxidizing action, anti-uremic activity, free radical scavenging, antiarthritic potential, xanthine dehydrogenase inhibition, gout treatment, uric acid excretion stimulation, and non-steroidal anti-inflammatory effects, with Pa scores spanning 0.118 to 0.806. Similarly, PubChem ID 2345 was associated with oxidoreductase inhibition, antioxidant behavior, anti-inflammatory action, kidney function stimulation, oxidizing effects, anti-uremic properties, free radical scavenging, antiarthritic activity, xanthine dehydrogenase inhibition, gout treatment, non-steroidal anti-inflammatory action, hypoxanthine phosphoribosyl transferase inhibition, renal failure treatment, and xanthine oxidase inhibition, displaying Pa values from 0.15 to 0.655.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePASS-predicted biological activities of lead compounds, with probable activity (Pa) and inactivity (Pi) scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBiological activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eProbable activity\u003c/p\u003e \u003cp\u003e(Pa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eProbable inactivity (Pi)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eButylated Hydroxytoluene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAnti-Inflammatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eKidney Function Stimulant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOxygen Scavenger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAntioxidant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOxidoreductase Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAnti-uremic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFree Radical Scavenger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNon-Steroidal Anti-inflammatory Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOxidizing Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAntiarthritic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUric Acid Excretion Stimulant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGout Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eXanthine Dehydrogenase Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBenzyl Benzoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eKidney Function Stimulant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOxidoreductase Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOxygen Scavenger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOxidizing Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAnti-uremic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAnti-Inflammatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNon-Steroidal Anti-inflammatory Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHypoxanthine Phosphoribosyl transferase Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eXanthine Oxidase Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFree Radical Scavenger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAntioxidant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRenal Failure Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eXanthine Dehydrogenase Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGout Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.11\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=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMD Simulations\u003c/h2\u003e \u003cp\u003eMolecular docking studies have mostly allowed high ligand flexibility, while the protein is maintained with limited or no flexibility to amino acids within the active site or residues close to the binding site pockets [50]. In addition, cryptic pockets of target proteins which can only be revealed by conformational changes, are difficult to explore during molecular docking [51]. Molecular dynamic (MD) simulations are therefore employed to complement results from molecular docking studies by mitigating these challenges. MD simulations provide, at the atomic level, the dynamic interactions of proteins in a complex with ligands, offering insights into binding energy, stability, and conformational changes [52]. Integration of this concept has become critical, as understanding of how proteins interact with drugs offers an opportunity in designing new and effective therapeutic agents. Based on the aforementioned, a 100ns MD simulation was executed, and the outcomes, the RMSD, RMSF, Rg, and hydrogen bond graphs, were plotted.\u003c/p\u003e \u003cp\u003eThe RMSD, which measures the deviations of the protein backbone from the initial pose over time, was computed to examine the stability of the various complexes [53]. Complexes with RMSD lower than 3 \u0026Aring; are said to be stable [54], while those greater with high fluctuations are said to be unstable. The ligands, 31404, 2345, and the reference drug, 135401907, recorded an initial surge in RMSD (to a maximum of 0.24 \u0026Aring;) in the first 40s (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea) before levelling off. A careful look at the RMSD graph shows that both 2345 and 135401907 showed an average RMSD of 0.2 \u0026Aring;, while 31404 had the highest 0.21 \u0026Aring; (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea). Overall, all the selected hit compounds and the reference drug recorded an average RMSD lower than the threshold and therefore suggested that the hit compounds possess the potential of forming stable complexes with the target protein.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRg was computed to evaluate the compactness of the protein-ligand complexes [55]. Notably, a high Rg connotes a decrease in compactness upon ligand binding, possibly resulting in protein unfolding, while a low Rg is an indication of an increased folding of the target protein [54, 55]. Both identified hit compounds, 31404 and 2345, recorded an average Rg of 2.8 nm, comparable to the reference, 315401907 [0.2785 nm] (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb), suggesting the potential of the ligands enhancing protein folding upon binding. RMSF, a metric used to quantify the flexibility of amino acids within a protein, was computed to identify which residues are critical for ligand binding [55]. Amino acid residues within the binding pockets of the target protein are mostly flexible, which, upon interacting with ligands, show minimum fluctuations, recording low RMSF (54). From the RMSF graph, residues within the ranges 610\u0026ndash;990, 1100\u0026ndash;1200, and 1150\u0026ndash;1220 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec) recorded low RMSF, indicating their involvement in ligand binding and hence suggested to contribute to complex stability. The number of hydrogen bonding interactions formed during the entire simulation period shows the reference compound, 315401907, formed four hydrogen bonds with the target protein, while 31404 formed one hydrogen bond with 2345, not recording any hydrogen bond formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ed). Overall, results from the RMSD, RMSF, Rg, and number of hydrogen bond formations suggest the selected hit compounds and reference drug formed stable complexes with the target protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Mechanics Poisson-Boltzmann Surface Area Computations of Free Binding Energy\u003c/h2\u003e \u003cp\u003eMM-PBSA has become essential in computational drug development for predicting and ranking hit compounds, as it computes an accurate free binding energy (ΔG\u003csub\u003ebind\u003c/sub\u003e) using \u003cem\u003evan der Waals\u003c/em\u003e interactions energy (ΔG\u003csub\u003evdW\u003c/sub\u003e), electrostatic energy (ΔG\u003csub\u003eele\u003c/sub\u003e), polar solvation energy (ΔG\u003csub\u003epol, sol\u003c/sub\u003e), and non-polar solvation energy (ΔG\u003csub\u003eSASA\u003c/sub\u003e) [56]. Complementing molecular docking results with MM-PBSA calculations ensures that the identified hit compounds possess the potential of inhibiting the target protein in the absence of the experimental evaluation [57]. The hit compound, 31404, recorded the least ΔG\u003csub\u003ebind\u003c/sub\u003e of \u0026minus;\u0026thinsp;12.183 kJ/mol (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), lower than the reference compound, 135401907 (\u0026minus;\u0026thinsp;4.384 kJ/mol), suggesting that it forms a feasible and stable complex with the target protein. While ΔG\u003csub\u003epol, sol\u003c/sub\u003e is the worst contributor, the ΔG\u003csub\u003evdW\u003c/sub\u003e, ΔG\u003csub\u003eele\u003c/sub\u003e, and ΔG\u003csub\u003eSASA\u003c/sub\u003e contributed favorable energies to the observed ΔG\u003csub\u003ebind\u003c/sub\u003e values. Interestingly, the hit compound, 2345, recorded a ΔG\u003csub\u003ebind\u003c/sub\u003e of +\u0026thinsp;3.323 kJ/mol (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), suggesting it does not possess the potential of inhibiting XO. Results from the molecular docking and MM-PBSA computations suggest the potential of 135401907 for modulating XO worthy of further experimental evaluation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComputed free binding energies of the complexes of selected hit compounds and the reference drug\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\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔG\u003csub\u003evdW\u003c/sub\u003e (kJ/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔG\u003csub\u003eele\u003c/sub\u003e (kJ/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔG\u003csub\u003epol,sol\u003c/sub\u003e (kJ/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔG\u003csub\u003eSASA\u003c/sub\u003e (kJ/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔG\u003csub\u003ebind\u003c/sub\u003e (kJ/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;21.240\u0026thinsp;\u0026plusmn;\u0026thinsp;2.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.700\u0026thinsp;\u0026plusmn;\u0026thinsp;1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.407\u0026thinsp;\u0026plusmn;\u0026thinsp;3.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;3.650\u0026thinsp;\u0026plusmn;\u0026thinsp;5.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;12.183\u0026thinsp;\u0026plusmn;\u0026thinsp;4.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.242\u0026thinsp;\u0026plusmn;\u0026thinsp;5.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.103\u0026thinsp;\u0026plusmn;\u0026thinsp;2.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;3.323\u0026thinsp;\u0026plusmn;\u0026thinsp;5.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e135401907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;12.827\u0026thinsp;\u0026plusmn;\u0026thinsp;4.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.772\u0026thinsp;\u0026plusmn;\u0026thinsp;2.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.490\u0026thinsp;\u0026plusmn;\u0026thinsp;5.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.275\u0026thinsp;\u0026plusmn;\u0026thinsp;1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;4.384\u0026thinsp;\u0026plusmn;\u0026thinsp;2.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe energy contribution of each amino acid to the ΔG\u003csub\u003ebind\u003c/sub\u003e was evaluated using per-residue decomposition analysis. The per-residue decomposition studies identify which amino acid is critical for ligand binding, as those involved contribute the most to the ΔG\u003csub\u003ebind\u003c/sub\u003e. Amino acid residues that contribute energies greater than +\u0026thinsp;5 kJ/mol or lower than \u0026minus;\u0026thinsp;5 kJ/mol are considered necessary for complex stability [58]. Interestingly, while all the amino acids present in the target protein contributed useful energies (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e) to ΔG\u003csub\u003ebind\u003c/sub\u003e, none was able to meet the threshold. This notwithstanding, exploring the residues in the binding pocket still provides a means of developing anti-gout chemotypes targeting XO.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study was conducted to screen for XO inhibitors from \u003cem\u003eBrassica oleracea\u003c/em\u003e extracts for the possible treatment of gout. Plants generate diverse secondary metabolites as defense compounds, serving as a rich source of bioactive small molecules with potential therapeutic applications [59]. Among the members of the family Brassiceae, the genus \u003cem\u003eBrassica\u003c/em\u003e stands out as the most important, comprising several globally significant crop species such as \u003cem\u003eBrassica oleracea\u003c/em\u003e L., \u003cem\u003eBrassica napus\u003c/em\u003e L., and \u003cem\u003eBrassica rapa\u003c/em\u003e L. Notably, \u003cem\u003eB. oleracea\u003c/em\u003e is the primary vegetable species and includes widely consumed vegetables like kale, broccoli, cauliflower, Brussels sprouts, and both red and white cabbage [60]. They are recognized for their wide-ranging health-promoting effects [61].\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ein silico\u003c/em\u003e analysis performed in this study offers important insights into the potential inhibitory effects of bioactive compounds found in \u003cem\u003eBrassica oleracea\u003c/em\u003e extracts on XO. Molecular docking is a widely used approach in drug discovery that facilitates the identification of optimal ligand orientations within a protein's binding site and enables the prediction of their binding affinities [59]. Seven (7) compounds, hematoporphyrin, butylated hydroxytoluene, 1,4-benzenedicarboxylic acid bis(2-ethylhexyl) ester, benzyl benzoate, dasycarpidan-1-methanol acetate (ester), paraquat dichloride, and vitamin E, exhibited binding affinities surpassing the \u0026minus;\u0026thinsp;7.0 kcal/mol threshold, indicating stronger molecular interactions with the target enzyme compared to the reference drug, allopurinol, which displayed a binding affinity of \u0026minus;\u0026thinsp;6.4 kcal/mol. In docking studies, if a compound exhibits lower binding energy compared to the standard, it indicates that the compound has the potential to exhibit greater activity [62]. A lower binding energy value suggests a more favorable and stable interaction between the ligand and the protein receptor. The inability of silicon-containing compounds to dock successfully in AutoDock Vina may stem from several factors inherent to the software\u0026rsquo;s limitations in handling such molecules. Our docking experiments revealed an important limitation when working with silicon-containing compounds. This technical constraint reflects the underlying force field design of AutoDock Vina, which was specifically parameterized for common biological atoms, including carbon, nitrogen, oxygen, sulfur, and hydrogen [63, 64]. Since AutoDock\u0026rsquo;s atom typing is also case-sensitive, the recognition of unsupported elements such as \u0026ldquo;Si\u0026rdquo; fails entirely. While this design choice enables efficient docking of typical drug-like molecules, it necessarily excludes certain organometallic and organosilicon compounds from analysis. Such limitations are not unique to AutoDock Vina, as many widely used docking tools exhibit similar constraints when confronted with non-standard atomic types [65].\u003c/p\u003e \u003cp\u003eData on the physicochemical properties of the selected ligands were gathered through pharmacological analysis, along with safety profiling to assess their potential risks and benefits. Lipinski's rule determines and defines the drug-likeness and druggability of a compound. The Rule of 5 (Ro5) guidelines help predict whether a compound is likely to be effective as an oral drug [66]. Due to the widespread acceptance of these guidelines in drug development, there is a reduced pursuit of compounds that violate two or more of the Ro5 criteria [67]. Hematoporphyrin (PubChem ID: 11103) presents two violations of Lipinski's Rule of Five: its molecular weight exceeds the 500 Da threshold, and it possesses more than five hydrogen bond donors (NH or OH); therefore, it was excluded from downstream analysis due to poor drug-likeness. With only one violation of Lipinski\u0026rsquo;s Rule observed in 1,4-benzenedicarboxylic acid bis(2-ethylhexyl) ester (PubChem ID: 22932), vitamin E (PubChem ID: 2116), and butylated hydroxytoluene (PubChem ID: 31404), and full compliance seen in dasycarpidan-1-methanol acetate (PubChem ID: 550072), benzyl benzoate (PubChem ID: 2345), and paraquat dichloride (PubChem ID: 15938), these compounds demonstrated favorable drug-like properties and warrant further biological investigation.\u003c/p\u003e \u003cp\u003eThe bioavailability score (ABS) is assessed based on a compound's compliance with or deviation from Lipinski\u0026rsquo;s Rule of Five. At biological pH, a compound is expected to achieve more than 10% bioavailability (F) in rats if it adheres to Lipinski\u0026rsquo;s criteria, resulting in an ABS of 0.55, meaning there's a 55% probability that F will exceed 10% in rats. On the other hand, if the compound does not meet these criteria, the ABS decreases to 0.17, indicating only a 17% chance of the compound having bioavailability greater than 10% in rats [68]. The selected compounds had an ABS of 0.55, making them effective via the oral route. PAINS are groups of chemical compounds with similar structural features, which increases the likelihood of these compounds exhibiting activity (or being identified as \"hits\") in various tests [69]. PAINS compounds can lead to misleading results in drug discovery assays due to their tendency to exhibit nonspecific interactions or cause other undesirable effects unrelated to the intended target [70]. This can lead to the inefficient use of time and resources by focusing on compounds that are unlikely to develop into viable drug candidates. Hence, compounds with 0 PAINS alerts are reliable for further development into therapeutic agents.\u003c/p\u003e \u003cp\u003eThe properties of absorption, distribution, metabolism, excretion, and toxicity (ADMET) are crucial to pharmaceutical drug design. It is frequently reported that the inability of drug candidates to meet the necessary ADMET standards is a common reason for their high failure rates during the development process [71]. \u003cem\u003eIn silico\u003c/em\u003e ADMET studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds [72]. First, the Caco-2 (Colon Adenocarcinoma 2) monolayer cell culture model, which is regarded as the \"gold standard\" for evaluating drug permeability, is widely used in drug discovery to predict human intestinal absorption [73]. Five out of six selected compounds exhibited Caco-2 permeability values greater than \u0026minus;\u0026thinsp;5.15 log cm/s, suggesting favorable absorption profiles in the human body and eventually could possess better ADMET profiles.\u003c/p\u003e \u003cp\u003eThe blood-brain barrier (BBB) is a highly selective semipermeable barrier that tightly regulates the movement of ions, molecules, and cells between the blood and the central nervous system (CNS). It protects the neural tissues from toxins and pathogens [74]. One of the key challenges in drug design is determining whether a compound can cross the BBB. Drugs that act on the nervous system must be able to pass through the BBB to work effectively. In contrast, medications aimed at other body parts should ideally not cross the BBB to avoid potential psychotropic side effects [49]. Butylated hydroxytoluene and benzyl benzoate were predicted to be impermeable to the BBB, indicating limited central nervous system penetration and a potentially favorable safety profile.\u003c/p\u003e \u003cp\u003eClearance is a key pharmacokinetic (PK) parameter, as it influences both the half-life of a drug (in conjunction with the volume of distribution) and its bioavailability (alongside oral absorption). It therefore plays a critical role in determining the dosing regimen, both the frequency of administration and the appropriate dose required for effective treatment [75]. The reference drug, allopurinol (PubChem ID: 134018), exhibited lower plasma clearance compared to butylated hydroxytoluene and benzyl benzoate. Efficient plasma clearance is essential for drug elimination and helps prevent drug accumulation in the body. The half-life of a drug impacts its duration of effect, the time it takes to achieve stable concentrations, and the period necessary for the drug to be eliminated from the body [76]. In this regard, allopurinol also demonstrated a shorter and less favorable half-life compared to butylated hydroxytoluene and benzyl benzoate, potentially reducing its therapeutic efficacy.\u003c/p\u003e \u003cp\u003eNephrotoxicity arises when the kidneys fail to perform their detoxification and excretion functions effectively, resulting from damage or impairment caused by external or internal toxic substances [77]. Drug-induced liver Injury (DILI) is a harmful liver response triggered by exposure to pharmaceutical agents or other xenobiotics. It may occur as a predictable reaction to high doses of toxic substances or as an unpredictable, idiosyncratic event even at therapeutic doses. DILI poses a significant challenge in drug development and clinical therapy, as genetic and environmental factors can alter drug metabolism and excretion, leading to cellular stress, immune activation, and potentially severe liver damage. Its occurrence highlights the importance of evaluating liver toxicity during early stages of drug screening to ensure patient safety and regulatory compliance [78]. Butylated hydroxytoluene and benzyl benzoate were found to be neither hepatotoxic nor nephrotoxic, suggesting a favorable safety profile in contrast to the reference drug. Following ADMET profiling, butylated hydroxytoluene and benzyl benzoate emerged as promising lead candidates, underscoring their promise for further development, although experimental validation through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies remains essential.\u003c/p\u003e \u003cp\u003eThe biological activity spectrum was introduced to define the characteristics of substances that exhibit biological activity [79]. The predictive accuracy of the software, PASS, differs across various biological activities. Biological activities with Pa\u0026thinsp;\u0026gt;\u0026thinsp;Pi are considered worthy of pharmacological evaluation [32]. If the value of Pa exceeds 0.7, the substance is likely to demonstrate activity in experiments and has a high probability of being similar to an existing pharmaceutical agent. When Pa is between 0.5 and 0.7, the substance may still show activity, but with a lower likelihood, and it is less likely to resemble known pharmaceutical agents. If Pa falls below 0.5, the substance is unlikely to exhibit activity in experiments; however, if an activity is observed, it could indicate the discovery of a new chemical entity [79]. Butylated hydroxytoluene exhibited anti-inflammatory activity with a Pa value of 0.806, indicating its potential relevance for treating gout, an inflammatory form of arthritis [3]. Additionally, it showed kidney function stimulant activity with a Pa value of 0.727, suggesting a high likelihood of experimental validation, as values above 0.7 are generally considered predictive of true biological activity. A kidney function stimulant enhances the activity of the kidneys so it can effectively filter the blood and excrete waste substances like uric acid, which is primarily excreted through urine [80]. Benzyl benzoate exhibited oxidoreductase inhibitor activity with a Pa value of 0.618, suggesting a likelihood of inhibiting xanthine oxidoreductase in an experimental setting, though with a lower probability. Research has demonstrated a close association between the development of hyperuricemia or gouty arthritis and the production of reactive oxygen species (ROS) [81]. Antioxidants help reduce reactive oxygen species (ROS) levels and decrease oxidative stress [82]. Providing antioxidant support to patients with active gout may be a viable treatment strategy [83]. It is therefore not surprising that butylated hydroxytoluene and benzyl benzoate could prevent redox imbalance, suggesting potential antioxidant activity crucial for gout therapy.\u003c/p\u003e \u003cp\u003eSeveral activities, including XO inhibition, gout treatment, anti-uremic, antiarthritic, and others, showed Pa values below 0.5, suggesting a low probability of being confirmed in experimental settings. However, if these activities are validated in future studies, butylated hydroxytoluene and benzyl benzoate could represent novel therapeutic candidates. XO inhibitors are not solely restricted to the treatment of hyperuricemia and gout; evidence suggests that they can also be effective in addressing cardiovascular diseases [84].\u003c/p\u003e \u003cp\u003eThe interaction between the protein and the lead compounds (i.e., butylated hydroxytoluene and benzyl benzoate) was predominantly governed by hydrophobic forces. Hydrophobic interactions are crucial contributors to the binding strength between drug candidates and their molecular targets. Studies indicate that prioritizing these interactions, even over traditional hydrogen bonding, can markedly influence the biological performance of lead compounds [85]. They are widely recognized as the principal thermodynamic driving force governing the association of small molecules with their protein receptors [86]. Moreover, increasing the number of hydrophobic atoms within the binding region of the drug-target complex has been associated with enhanced biological activity and therapeutic potential [85]. It was shown that butylated hydroxytoluene and benzyl benzoate portrayed higher hydrophobic interactions with their target molecules, indicating stronger biological activity with marked therapeutic effects.\u003c/p\u003e \u003cp\u003eGlu802 and Glu1261 are recognized as key catalytic residues within the active site of XO, playing central roles in substrate binding and the enzymatic conversion of xanthine to uric acid [9]. In this study, butylated hydroxytoluene and benzyl benzoate were found to interact with these critical residues, indicating their potential to modulate enzyme activity. Given the beneficial properties of butylated hydroxytoluene and benzyl benzoate, it is essential and worthwhile to explore their potential therapeutic effects, as they could be highly significant for the treatment of gout.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOverall, butylated hydroxytoluene and benzyl benzoate emerged as promising lead candidates to inhibit XO and serve as potential anti-gout agents. Following molecular docking, medicinal chemistry evaluation, and ADMET analyses, these compounds, identified as the most potent inhibitors from \u003cem\u003eBrassica oleracea var. italica\u003c/em\u003e and \u003cem\u003eBrassica oleracea var. acephala\u003c/em\u003e, demonstrated good binding affinities, high hydrophobic interactions, XO inhibitory activities, as well as a relative safety profile, and could possess a better ADMET profile. Inhibitors like butylated hydroxytoluene and benzyl benzoate present promising potential as alternatives or complementary agents to synthetic drugs such as allopurinol, offering the added benefit of potentially reduced side effects. These findings underscore the therapeutic potential of \u003cem\u003eBrassica oleracea\u003c/em\u003e as a valuable source of natural xanthine oxidase inhibitors and highlight the need for further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies to validate their clinical efficacy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eADMET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eAbsorption, Distribution, Metabolism, Excretion, and Toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eBBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eBlood-Brain Barrier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eH-bond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eHydrogen bond\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eMolecular Dynamics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003ePDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eProtein Data Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eSDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eStructure Data File\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eSMILES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eSimplified Molecular Input Line Entry System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eMM-PBSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eMolecular Mechanics Poisson\u0026ndash;Boltzmann Surface Area\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eXO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eXanthine oxidase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eABS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eBioavailability score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003ePASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003ePrediction of Activity Spectra for Substances\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eGC-MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eGas chromatography-mass spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003ePAINS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003ePan-Assay Interference Compounds\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eRMSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eRoot mean square deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eRMSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eRoot mean square fluctuation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75.9615%;\"\u003e\n \u003cp\u003eCentral Nervous System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have provided all the data required in the manuscript. Any additional data are available upon request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe West African Centre for Cell Biology of Infectious Pathogens, University of Ghana provided the high-performance computing platform (Zuputo) for running the molecular dynamic simulations and the MM-PBSA computations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDB\u003c/strong\u003e: conceptualization, supervision, methodology, data curation, validation, investigation, data analysis, Writing – original draft, review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJAB\u003c/strong\u003e: Data curation, investigation, methodology, data analysis, Writing - review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSAA\u003c/strong\u003e: Data analysis, investigation, Writing - review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eESA\u003c/strong\u003e: Data curation, investigation, data analysis, Writing - review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRA\u003c/strong\u003e: Data analysis, Writing - review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAA\u003c/strong\u003e: methodology, investigation, validation, data analysis, Writing - original draft, review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFAA\u003c/strong\u003e: Data analysis, Writing - review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSKK:\u003c/strong\u003e Investigation, data analysis, Writing - review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMBA\u003c/strong\u003e: conceptualization, supervision, data analysis, methodology, investigation, Writing - original draft, review \u0026amp; editing, approved manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePOS\u003c/strong\u003e: conceptualization, supervision, methodology, investigation, validation, data analysis, Writing - original draft, review \u0026amp; editing, approved manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHan, T., Chen, W., Qiu, X., \u0026amp; Wang, W. (2024). Epidemiology of gout\u0026ndash;Global burden of disease research from 1990 to 2019 and future trend predictions. \u003cem\u003eTherapeutic advances in endocrinology and metabolism\u003c/em\u003e, 15, 20420188241227295. https://doi.org/10.1177/20420188241227295\u003c/li\u003e\n\u003cli\u003eJin, Z., Wang, Z., Wang, R., Xiang, S., Zhang, W., Tang, Y. ... \u0026amp; Lv, S. (2024). Global burden and epidemic trends of gout attributable to high body mass index from 1990 to 2019. \u003cem\u003eArchives of Medical Science: AMS\u003c/em\u003e, 20(1), 71. doi:10.5114/aoms/175469\u003c/li\u003e\n\u003cli\u003eKuo, C. F., Grainge, M. J., Zhang, W., \u0026amp; Doherty, M. (2015). Global epidemiology of gout: prevalence, incidence and risk factors. \u003cem\u003eNature reviews rheumatology\u003c/em\u003e, 11(11), 649-662. doi:10.1038/nrrheum.2015.91\u003c/li\u003e\n\u003cli\u003eYip, K., \u0026amp; Berman, J. (2021). What is gout?. \u003cem\u003eJama,\u003c/em\u003e 326(24), 2541-2541.doi:10.1001/jama.2021.19770\u003c/li\u003e\n\u003cli\u003eJin, M., Yang, F., Yang, I., Yin, Y., Luo, J. J., Wang, H., \u0026amp; Yang, X. F. (2012). Uric acid, hyperuricemia and vascular diseases. \u003cem\u003eFrontiers in bioscience: a journal and virtual library\u003c/em\u003e, 17, 656. doi: 10.2741/3950\u003c/li\u003e\n\u003cli\u003eSekizuka, H. (2022). Uric acid, xanthine oxidase, and vascular damage: potential of xanthine oxidoreductase inhibitors to prevent cardiovascular diseases. \u003cem\u003eHypertension Research\u003c/em\u003e, 45(5), 772-774. doi:10.1038/s41440-022-00891-7\u003c/li\u003e\n\u003cli\u003eFuruhashi, M. (2020). New insights into purine metabolism in metabolic diseases: role of xanthine oxidoreductase activity. \u003cem\u003eAmerican Journal of Physiology-Endocrinology and Metabolism\u003c/em\u003e, 319(5), E827-E834. doi:10.1152/ajpendo.00378.2020\u003c/li\u003e\n\u003cli\u003eHarrison, R. (2002). Structure and function of xanthine oxidoreductase: where are we now?. \u003cem\u003eFree Radical Biology and Medicine\u003c/em\u003e, 33(6), 774-797. doi:10.1016/S0891-5849(02)00956-5\u003c/li\u003e\n\u003cli\u003eCao, H., Pauff, J. M., \u0026amp; Hille, R. (2010). Substrate orientation and catalytic specificity in the action of xanthine oxidase: the sequential hydroxylation of hypoxanthine to uric acid. \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e, 285(36), 28044-28053. doi:10.1074/jbc.M110.128561\u003c/li\u003e\n\u003cli\u003eRibeiro, P. M., Fernandes, H. S., Maia, L. B., Sousa, S. F., Moura, J. J., \u0026amp; Cerqueira, N. M. (2021). The complete catalytic mechanism of xanthine oxidase: a computational study. \u003cem\u003eInorganic Chemistry Frontiers\u003c/em\u003e, 8(2), 405-416. doi:10.1039/D0QI01029D\u003c/li\u003e\n\u003cli\u003eCantu-Medellin, N., \u0026amp; Kelley, E. E. (2013). Xanthine oxidoreductase-catalyzed reactive species generation: a process in critical need of reevaluation. \u003cem\u003eRedox biology\u003c/em\u003e, 1(1), 353-358. doi:10.1016/j.redox.2013.05.002\u003c/li\u003e\n\u003cli\u003eNawaz, M. Z., Ain, Q. U., Zahid, S., Zulfiqar, T., Attique, S. A., Bilal, M. ... \u0026amp; Iqbal, H. M. (2020). Physicochemical features and structural analysis of xanthine oxidase as a potential therapeutic target to prevent gout. \u003cem\u003eJournal of Radiation Research and Applied Sciences\u003c/em\u003e, 13(1), 616-628. doi:10.1080/16878507.2020.1812807\u003c/li\u003e\n\u003cli\u003eWhite, W. B. (2018). Gout, xanthine oxidase inhibition, and cardiovascular outcomes. \u003cem\u003eCirculation\u003c/em\u003e, 138(11), 1127-1129. doi:10.1161/CIRCULATIONAHA.118.036148\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Gout [Internet]. 2023 [cited 2024 Mar 14]. Report No. Available from: https://www.cdc.gov/arthritis/gout/index.html\u003c/li\u003e\n\u003cli\u003eSuresh, E., \u0026amp; Das, P. (2012). Recent advances in management of gout. QJM: \u003cem\u003eAn International Journal of Medicine\u003c/em\u003e, 105(5), 407-417. doi:10.1093/qjmed/hcr242\u003c/li\u003e\n\u003cli\u003eCanbey, İ., \u0026Ccedil;ınar, A. Y., Yıldız, E., \u0026amp; G\u0026uuml;rb\u0026uuml;z, O. (2022). Functional properties and health benefits of cruciferous vegetables. \u003cem\u003eFood Health and Technology Innovations\u003c/em\u003e, 5(11), 452-462. https://izlik.org/JA49MH32KB\u003c/li\u003e\n\u003cli\u003eAğag\u0026uuml;nd\u0026uuml;z, D., Şahin, T. \u0026Ouml;., Yılmaz, B., Ekenci, K. D., Duyar \u0026Ouml;zer, Ş., \u0026amp; Capasso, R. (2022). Cruciferous vegetables and their bioactive metabolites: from prevention to novel therapies of colorectal cancer. \u003cem\u003eEvidence‐Based Complementary and Alternative Medicine\u003c/em\u003e, 2022(1), 1534083. doi:10.1155/2022/1534083\u003c/li\u003e\n\u003cli\u003eOlsen, H., Grimmer, S., Aaby, K., Saha, S., \u0026amp; Borge, G. I. A. (2012). Antiproliferative effects of fresh and thermal processed green and red cultivars of curly kale (\u003cem\u003eBrassica oleracea L. convar. acephala var. sabellica\u003c/em\u003e). \u003cem\u003eJournal of agricultural and food chemistry\u003c/em\u003e, 60(30), 7375-7383. doi:10.1021/jf300875f\u003c/li\u003e\n\u003cli\u003eNagraj, G. S., Chouksey, A., Jaiswal, S., \u0026amp; Jaiswal, A. K. (2020). Broccoli. In Nutritional composition and antioxidant properties of fruits and vegetables (pp. 5-17). Academic Press. doi:10.1016/B978-0-12-812780-3.00001-5\u003c/li\u003e\n\u003cli\u003eVasanthi, H. R., Mukherjee, S., \u0026amp; Das, D. K. (2009). Potential health benefits of broccoli-a chemico-biological overview. \u003cem\u003eMini reviews in medicinal chemistry\u003c/em\u003e, 9(6), 749-759. doi:10.2174/138955709788452685\u003c/li\u003e\n\u003cli\u003eLatt\u0026eacute;, K. P., Appel, K. E., \u0026amp; Lampen, A. (2011). Health benefits and possible risks of broccoli\u0026ndash;an overview. \u003cem\u003eFood and Chemical Toxicology\u003c/em\u003e, 49(12), 3287-3309. doi:10.1016/j.fct.2011.08.019\u003c/li\u003e\n\u003cli\u003eMoreno, D. A., Carvajal, M., L\u0026oacute;pez-Berenguer, C., \u0026amp; Garc\u0026iacute;a-Viguera, C. (2006). Chemical and biological characterization of nutraceutical compounds of broccoli. \u003cem\u003eJournal of pharmaceutical and biomedical analysis\u003c/em\u003e, 41(5), 1508-1522. doi:10.1016/j.jpba.2006.04.003\u003c/li\u003e\n\u003cli\u003eOrtega-Hern\u0026aacute;ndez, E., Antunes-Ricardo, M., \u0026amp; Jacobo-Vel\u0026aacute;zquez, D. A. (2021). Improving the health-benefits of kales (\u003cem\u003eBrassica oleracea L. var. acephala\u003c/em\u003e DC) through the application of controlled abiotic stresses: \u003cem\u003eA review. Plants\u003c/em\u003e, 10(12), 2629. doi:10.3390/plants10122629\u003c/li\u003e\n\u003cli\u003eKhalid, W., Iqra, Afzal, F., Rahim, M. A., Abdul Rehman, A., Faiz ul Rasul, H. ... \u0026amp; Refai, M. (2023). Industrial applications of kale (\u003cem\u003eBrassica oleracea var. sabellica\u003c/em\u003e) as a functional ingredient: A review. \u003cem\u003eInternational Journal of Food Properties\u003c/em\u003e, 26(1), 489-501. doi:10.1080/10942912.2023.2168011\u003c/li\u003e\n\u003cli\u003eAyaz, F. A., Hayırlıoglu-Ayaz, S., Alpay-Karaoglu, S., Gr\u0026uacute;z, J., Valentov\u0026aacute;, K., Ulrichov\u0026aacute;, J., \u0026amp; Strnad, M. (2008). Phenolic acid contents of kale (\u003cem\u003eBrassica oleraceae L. var. acephala\u003c/em\u003e DC.) extracts and their antioxidant and antibacterial activities. \u003cem\u003eFood Chemistry\u003c/em\u003e, 107(1), 19-25. doi:10.1016/j.foodchem.2007.07.003\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;amec, D., Urlić, B., \u0026amp; Salopek-Sondi, B. (2019). Kale (\u003cem\u003eBrassica oleracea var. acephala\u003c/em\u003e) as a superfood: Review of the scientific evidence behind the statement. \u003cem\u003eCritical reviews in food science and nutrition\u003c/em\u003e, 59(15), 2411-2422. doi:10.1080/10408398.2018.1454400\u003c/li\u003e\n\u003cli\u003eLemos, M., Santin, J. R., J\u0026uacute;nior, L. C. K., Niero, R., \u0026amp; De Andrade, S. F. (2011). Gastroprotective activity of hydroalcoholic extract obtained from the leaves of \u003cem\u003eBrassica oleracea var. acephala\u003c/em\u003e DC in different animal models. \u003cem\u003eJournal of ethnopharmacology\u003c/em\u003e, 138(2), 503-507. doi:10.1016/j.jep.2011.09.046\u003c/li\u003e\n\u003cli\u003eJose, S., Gupta, M., Sharma, U., Quintero-Saumeth, J., \u0026amp; Dwivedi, M. (2022). Potential of phytocompounds from \u003cem\u003eBrassica oleracea\u003c/em\u003e targeting S2-domain of SARS-CoV-2 spike glycoproteins: Structural and molecular insights. \u003cem\u003eJournal of Molecular Structure\u003c/em\u003e, 1254, 132369. doi:10.1016/j.molstruc.2022.132369\u003c/li\u003e\n\u003cli\u003eKamboj, A., Sharma, S., Singh, V. P., Sinha, A., Yadav, K. S., Lal, B. ... \u0026amp; Devi, L. (2023). Phytochemical and therapeutic potential of broccoli (\u003cem\u003eBrassica oleracea\u003c/em\u003e): A review. \u003cem\u003ePharma Innov. J\u003c/em\u003e, 12, 633-638. \u003c/li\u003e\n\u003cli\u003eGhallab, D. S., Shawky, E., Metwally, A. M., Celik, I., Ibrahim, R. S., \u0026amp; Mohyeldin, M. M. (2022). Integrated in silico\u0026ndash;in vitro strategy for the discovery of potential xanthine oxidase inhibitors from Egyptian propolis and their synergistic effect with allopurinol and febuxostat. \u003cem\u003eRSC advances\u003c/em\u003e, 12(5), 2843-2872. doi:10.1039/D1RA08011C\u003c/li\u003e\n\u003cli\u003eBaskaran, K. P., Arumugam, A., Kandasamy, R., \u0026amp; Alagarsamy, S. (2020). \u003cem\u003eIn silico\u003c/em\u003e method for prediction of maximum binding affinity and ligand-protein interaction studies on Alzheimer\u0026rsquo;s disease. \u003cem\u003eInt. J. Res. Granthaalayah\u003c/em\u003e, 8(11), 362-370. doi:10.29121/granthaalayah.v8.i11.2020.2472\u003c/li\u003e\n\u003cli\u003eKwofie, S. K., Annan, D. G., Adinortey, C. A., Boison, D., Kwarko, G. B., Abban, R. A., \u0026amp; Adinortey, M. B. (2022). Identification of novel potential inhibitors of varicella-zoster virus thymidine kinase from ethnopharmacologic relevant plants through an \u003cem\u003ein-silico\u003c/em\u003e approach. \u003cem\u003eJournal of Biomolecular Structure and Dynamics\u003c/em\u003e, 40(23), 12932-12947. doi:10.1080/07391102.2021.1977700\u003c/li\u003e\n\u003cli\u003eAdams, L., Afiadenyo, M., Kwofie, S. K., Wilson, M. D., Kusi, K. A., Obiri-Yeboah, D. ... \u0026amp; McKeon-Bennett, M. (2023). \u003cem\u003eIn silico\u003c/em\u003e screening of phytochemicals from \u003cem\u003eDissotis rotundifolia\u003c/em\u003e against Plasmodium falciparum Dihydrofolate Reductase. \u003cem\u003ePhytomedicine Plus\u003c/em\u003e, 3(2), 100447. doi:10.1016/j.phyplu.2023.100447\u003c/li\u003e\n\u003cli\u003eWallace, A. C., Laskowski, R. A., \u0026amp; Thornton, J. M. (1995). LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. \u003cem\u003eProtein engineering, design and selection\u003c/em\u003e, 8(2), 127-134. doi:10.1093/protein/8.2.127\u003c/li\u003e\n\u003cli\u003eUmar, H. I., Josiah, S. S., Saliu, T. P., Jimoh, T. O., Ajayi, A., \u0026amp; Danjuma, J. B. (2021). \u003cem\u003eIn-silico\u003c/em\u003e analysis of the inhibition of the SARS-CoV-2 main protease by some active compounds from selected African plants. \u003cem\u003eJournal of Taibah University Medical Sciences\u003c/em\u003e, 16(2), 162-176. doi:10.1016/j.jtumed.2020.12.005\u003c/li\u003e\n\u003cli\u003eMateev, E., Valkova, I., Angelov, B., Georgieva, M., \u0026amp; Zlatkov, A. (2022). Validation through re-docking, cross-docking and ligand enrichment in various well-resoluted MAO-B receptors. \u003cem\u003eInt. J. Pharm. Sci. Res\u003c/em\u003e, 13(3), 1000-1007. doi.org/10.13040/IJPSR.0975-8232\u003c/li\u003e\n\u003cli\u003eDaina, A., Michielin, O., \u0026amp; Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. \u003cem\u003eScientific reports\u003c/em\u003e, 7(1), 42717. doi:10.1038/srep42717\u003c/li\u003e\n\u003cli\u003eFu, L., Shi, S., Yi, J., Wang, N., He, Y., Wu, Z. ... \u0026amp; Cao, D. (2024). ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. \u003cem\u003eNucleic acids research\u003c/em\u003e, 52(W1), W422-W431. doi:10.1093/nar/gkae236\u003c/li\u003e\n\u003cli\u003eChy, M. N. U., Chakrabarty, N., Roy, A., Paul, A., Emu, K. A., Dutta, T. ... \u0026amp; Tasnim, S. M. (2019). Antibacterial, anthelmintic, and analgesic activities of Piper sylvaticum (Roxb.) leaves and in silico molecular docking and PASS prediction studies of its isolated compounds. \u003cem\u003eJournal of Complementary and Integrative Medicine\u003c/em\u003e, 16(4), 20180176. doi:10.1515/jcim-2018-0176\u003c/li\u003e\n\u003cli\u003eAbraham, M. J., Murtola, T., Schulz, R., P\u0026aacute;ll, S., Smith, J. C., Hess, B., \u0026amp; Lindahl, E. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. \u003cem\u003eSoftwareX\u003c/em\u003e, 1, 19-25. doi:10.1016/J.SOFTX.2015.06.001\u003c/li\u003e\n\u003cli\u003eVan Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., \u0026amp; Berendsen, H. J. (2005). GROMACS: fast, flexible, and free. \u003cem\u003eJournal of computational chemistry\u003c/em\u003e, 26(16), 1701-1718. doi:10.1002/JCC.20291\u003c/li\u003e\n\u003cli\u003eSiu, S. W., Pluhackova, K., \u0026amp; Böckmann, R. A. (2012). Optimization of the OPLS-AA force field for long hydrocarbons. \u003cem\u003eJournal of Chemical theory and Computation\u003c/em\u003e, 8(4), 1459-1470. doi:10.1021/ct200908r\u003c/li\u003e\n\u003cli\u003eJorgensen, W. L., \u0026amp; Tirado-Rives, J. (2005). Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, 102(19), 6665-6670. doi:10.1073/pnas.0408037102\u003c/li\u003e\n\u003cli\u003eDahiya, R., Mohammad, T., Gupta, P., Haque, A., Alajmi, M. F., Hussain, A., \u0026amp; Hassan, M. I. (2019). Molecular interaction studies on ellagic acid for its anticancer potential targeting pyruvate dehydrogenase kinase 3. \u003cem\u003eRSC advances\u003c/em\u003e, 9(40), 23302-23315. doi:10.1039/C9RA02864A\u003c/li\u003e\n\u003cli\u003eKumari, R., Kumar, R., Open Source Drug Discovery Consortium, \u0026amp; Lynn, A. (2014). g_mmpbsa A GROMACS tool for high-throughput MM-PBSA calculations. J\u003cem\u003eournal of chemical information and modeling\u003c/em\u003e, 54(7), 1951-1962. doi:10.1021/ci500020m\u003c/li\u003e\n\u003cli\u003ePrasanth, D. S. N. B. K., Murahari, M., Chandramohan, V., Panda, S. P., Atmakuri, L. R., \u0026amp; Guntupalli, C. (2021). \u003cem\u003eIn silico\u003c/em\u003e identification of potential inhibitors from Cinnamon against main protease and spike glycoprotein of SARS CoV-2. \u003cem\u003eJournal of Biomolecular Structure and Dynamics\u003c/em\u003e, 39(13), 4618-4632. doi:10.1080/07391102.2020.1779129\u003c/li\u003e\n\u003cli\u003eRam\u0026iacute;rez, D., \u0026amp; Caballero, J. (2018). Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data?. \u003cem\u003eMolecules\u003c/em\u003e, 23(5), 1038. doi:10.3390/molecules23051038\u003c/li\u003e\n\u003cli\u003eXiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C. ... \u0026amp; Cao, D. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. \u003cem\u003eNucleic acids research\u003c/em\u003e, 49(W1), W5-W14. doi:10.1093/nar/gkab255\u003c/li\u003e\n\u003cli\u003eCarpenter, T. S., Kirshner, D. A., Lau, E. Y., Wong, S. E., Nilmeier, J. P., \u0026amp; Lightstone, F. C. (2014). A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations. \u003cem\u003eBiophysical journal\u003c/em\u003e, 107(3), 630-641. doi:10.1016/j.bpj.2014.06.024\u003c/li\u003e\n\u003cli\u003eGuedes, I. A., de Magalh\u0026atilde;es, C. S., \u0026amp; Dardenne, L. E. (2014). Receptor\u0026ndash;ligand molecular docking. \u003cem\u003eBiophysical reviews\u003c/em\u003e, 6(1), 75-87. doi:10.1007/s12551-013-0130-2\u003c/li\u003e\n\u003cli\u003eCimermancic, P., Weinkam, P., Rettenmaier, T. J., Bichmann, L., Keedy, D. A., Woldeyes, R. A. ... \u0026amp; Sali, A. (2016). CryptoSite: expanding the druggable proteome by characterization and prediction of cryptic binding sites. J\u003cem\u003eournal of molecular biology\u003c/em\u003e, 428(4), 709-719. doi:10.1016/J.JMB.2016.01.029\u003c/li\u003e\n\u003cli\u003eSasidharan, S., Gosu, V., Tripathi, T., \u0026amp; Saudagar, P. (2023). Molecular dynamics simulation to study protein conformation and ligand interaction. In Protein folding dynamics and stability: experimental and computational methods (pp. 107-127). \u003cem\u003eSingapore: Springer Nature\u003c/em\u003e Singapore. doi:10.1007/978-981-99-2079-2_6\u003c/li\u003e\n\u003cli\u003ePrabhakaran, P., Hebbani, A. V., Menon, S. V., Paital, B., Murmu, S., Kumar, S., ... \u0026amp; Desai, P. P. D. (2023). \u003cem\u003eIn silico\u003c/em\u003e generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning. \u003cem\u003eFrontiers in microbiology\u003c/em\u003e, 14, 1194794. doi:10.3389/fmicb.2023.1194794\u003c/li\u003e\n\u003cli\u003eAshley, C. N., Broni, E., Pena-Martinez, M., Wood, C. M., Kwofie, S. K., \u0026amp; Miller III, W. A. (2025). Computer-Aided Discovery of Natural Compounds Targeting the ADAR2 dsRBD2-RNA Interface and Computational Modeling of Full-Length ADAR2 Protein Structure. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e, 26(9), 4075. doi:10.3390/IJMS26094075/S1\u003c/li\u003e\n\u003cli\u003eSakyi, P. O., Kwofie, S. K., Tuekpe, J. K., Gwira, T. M., Broni, E., Miller III, W. A., ... \u0026amp; Amewu, R. K. (2023). Inhibiting Leishmania donovani sterol methyltransferase to identify lead compounds using molecular modelling. \u003cem\u003ePharmaceuticals\u003c/em\u003e, 16(3), 330. doi:10.3390/ph16030330\u003c/li\u003e\n\u003cli\u003eHuang, K., Luo, S., Cong, Y., Zhong, S., Zhang, J. Z., \u0026amp; Duan, L. (2020). An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein\u0026ndash;ligand binding affinity. \u003cem\u003eNanoscale\u003c/em\u003e, 12(19), 10737-10750. doi:10.1039/C9NR10638C \u003c/li\u003e\n\u003cli\u003eGanesan, A., Coote, M. L., \u0026amp; Barakat, K. (2017). Molecular dynamics-driven drug discovery: leaping forward with confidence. \u003cem\u003eDrug discovery today\u003c/em\u003e, 22(2), 249-269. doi:10.1016/J.DRUDIS.2016.11.001 \u003c/li\u003e\n\u003cli\u003eKwofie, S. K., Dolling, N. N., Donkoh, E., Laryea, G. M., Mosi, L., Miller III, W. A. ... \u0026amp; Wilson, M. D. (2021). Pharmacophore-guided identification of natural products as potential inhibitors of \u003cem\u003eMycobacterium ulcerans\u003c/em\u003e cystathionine \u0026gamma;-synthase MetB. \u003cem\u003eComputation\u003c/em\u003e, 9(3), 32. doi:10.3390/computation9030032\u003c/li\u003e\n\u003cli\u003eAdetutu, A., Olaniyi, T. D., \u0026amp; Owoade, O. A. (2021). GC-MS analysis and in silico assessment of constituents of Psidium guajava leaf extract against DNA gyrase of \u003cem\u003eSalmonella enterica \u003c/em\u003eserovar Typhi. \u003cem\u003eInformatics in Medicine Unlocked\u003c/em\u003e, 26, 100722. doi:10.1016/j.imu.2021.100722\u003c/li\u003e\n\u003cli\u003eKapusta-Duch, J., Kopec, A., Piatkowska, E., Borczak, B., \u0026amp; Leszczynska, T. (2012). The beneficial effects of Brassica vegetables on human health. \u003cem\u003eRoczniki Państwowego Zakładu Higieny\u003c/em\u003e, 63(4).\u003c/li\u003e\n\u003cli\u003eShankar, S., Segaran, G., Sundar, R. D. V., Settu, S., \u0026amp; Sathiavelu, M. (2019). Brassicaceae-A classical review on its pharmacological activities. \u003cem\u003eInt. J. Pharm. Sci. Rev. Res\u003c/em\u003e, 55(1), 107-113.\u003c/li\u003e\n\u003cli\u003eMadeswaran, A., Umamaheswari, M., Asokkumar, K., Sivashanmugam, T., Subhadradevi, V., \u0026amp; Jagannath, P. (2011). Docking studies: \u003cem\u003eIn silico\u003c/em\u003e lipoxygenase inhibitory activity of some commercially available flavonoids. \u003cem\u003eBangladesh Journal of Pharmacology\u003c/em\u003e, 6(2), 133-138. doi:10.3329/bjp.v6i2.9408\u003c/li\u003e\n\u003cli\u003eHuey, R., Morris, G. M., Olson, A. J., \u0026amp; Goodsell, D. S. (2007). A semiempirical free energy force field with charge‐based desolvation. J\u003cem\u003eournal of computational chemistry\u003c/em\u003e, 28(6), 1145-1152. doi:10.1002/jcc.20634\u003c/li\u003e\n\u003cli\u003eTrott, O., \u0026amp; Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. \u003cem\u003eJournal of computational chemistry\u003c/em\u003e, 31(2), 455-461. doi:10.1002/jcc.21334\u003c/li\u003e\n\u003cli\u003eForli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., \u0026amp; Olson, A. J. (2016). Computational protein\u0026ndash;ligand docking and virtual drug screening with the AutoDock suite. \u003cem\u003eNature protocols\u003c/em\u003e, 11(5), 905-919. doi:10.1038/nprot.2016.051\u003c/li\u003e\n\u003cli\u003eMorak-Młodawska, B., Jeleń, M., Martula, E., \u0026amp; Korlacki, R. (2023). Study of lipophilicity and ADME properties of 1, 9-diazaphenothiazines with anticancer action. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e, 24(8), 6970. doi:10.3390/ijms24086970\u003c/li\u003e\n\u003cli\u003eBenet, L. Z., Hosey, C. M., Ursu, O., \u0026amp; Oprea, T. I. (2016). BDDCS, the Rule of 5 and drugability. \u003cem\u003eAdvanced drug delivery reviews\u003c/em\u003e, 101, 89-98. doi:10.1016/j.addr.2016.05.007\u003c/li\u003e\n\u003cli\u003eSharma, P., \u0026amp; Shanavas, A. (2021). Natural derivatives with dual binding potential against SARS-CoV-2 main protease and human ACE2 possess low oral bioavailability: a brief computational analysis. \u003cem\u003eJournal of Biomolecular Structure and Dynamics\u003c/em\u003e, 39(15), 5819-5830. doi:10.1080/07391102.2020.1794970\u003c/li\u003e\n\u003cli\u003eBaell, J. B., \u0026amp; Nissink, J. W. M. (2018). Seven-Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017- Utility and Limitations.\u003cem\u003e ACS chemical biology\u003c/em\u003e, 13(1), 36-44. doi:10.1021/acschembio.7b00903\u003c/li\u003e\n\u003cli\u003eSun, J., Zhong, H., Wang, K., Li, N., \u0026amp; Chen, L. (2021). Gains from no real PAINS: Where \u0026lsquo;Fair Trial Strategy\u0026rsquo;stands in the development of multi-target ligands. \u003cem\u003eActa Pharmaceutica Sinica B\u003c/em\u003e, 11(11), 3417-3432. doi:10.1016/j.apsb.2021.02.023\u003c/li\u003e\n\u003cli\u003eVenkatraman, V. (2021). FP-ADMET: a compendium of fingerprint-based ADMET prediction models. \u003cem\u003eJournal of cheminformatics\u003c/em\u003e, 13(1), 75. doi:10.1186/s13321-021-00557-5\u003c/li\u003e\n\u003cli\u003eChakravarthi, P. V., Karthikeyan, M., Lakshmanan, L., Murugesan, S., Arivuchelvan, A., Sukumar, K. ... \u0026amp; Jagadeeswaran, A. (2023). Computational study of \u003cem\u003ePiper betle\u003c/em\u003e L. phytocompounds by\u003cem\u003e in silico\u003c/em\u003e and ADMET analysis for prediction of potential xanthine oxidase inhibitory activity. \u003cem\u003eBioRxiv\u003c/em\u003e, 2023-01. doi:10.1101/2023.01.13.523909\u003c/li\u003e\n\u003cli\u003eFalc\u0026oacute;n-Cano, G., Molina, C., \u0026amp; Cabrera-P\u0026eacute;rez, M. \u0026Aacute;. (2022). Reliable prediction of Caco-2 permeability by supervised recursive machine learning approaches. \u003cem\u003ePharmaceutics\u003c/em\u003e, 14(10), 1998. doi:10.3390/pharmaceutics14101998\u003c/li\u003e\n\u003cli\u003eDaneman, R., \u0026amp; Prat, A. (2015). The blood\u0026ndash;brain barrier. \u003cem\u003eCold Spring Harbor perspectives in biology\u003c/em\u003e, 7(1), a020412. doi:10.1101/cshperspect.a020412\u003c/li\u003e\n\u003cli\u003eBerellini, G., Waters, N. J., \u0026amp; Lombardo, F. (2012). \u003cem\u003eIn silico\u003c/em\u003e prediction of total human plasma clearance. \u003cem\u003eJournal of chemical information and modeling\u003c/em\u003e, 52(8), 2069-2078. doi:10.1021/ci300155y\u003c/li\u003e\n\u003cli\u003eAndrade, C. (2022). The practical importance of half-life in psychopharmacology. \u003cem\u003eThe Journal of Clinical Psychiatry\u003c/em\u003e, 83(4), 22f14584-22f14584. doi:10.4088/JCP.22f14584\u003c/li\u003e\n\u003cli\u003eKim, S. Y., \u0026amp; Moon, A. (2012). Drug-induced nephrotoxicity and its biomarkers. \u003cem\u003eBiomolecules \u0026amp; therapeutics\u003c/em\u003e, 20(3), 268. doi:10.4062/biomolther.2012.20.3.268\u003c/li\u003e\n\u003cli\u003eAndrade, R. J., Chalasani, N., Bj\u0026ouml;rnsson, E. S., Suzuki, A., Kullak-Ublick, G. A., Watkins, P. B., ... \u0026amp; Aithal, G. P. (2019). Drug-induced liver injury. \u003cem\u003eNature reviews Disease primers\u003c/em\u003e, 5(1), 58. doi:10.1038/s41572-019-0105-0\u003c/li\u003e\n\u003cli\u003eLagunin, A., Stepanchikova, A., Filimonov, D., \u0026amp; Poroikov, V. (2000). PASS: prediction of activity spectra for biologically active substances. \u003cem\u003eBioinformatics\u003c/em\u003e, 16(8), 747-748. doi:10.1093/bioinformatics/16.8.747\u003c/li\u003e\n\u003cli\u003eBobulescu, I. A., \u0026amp; Moe, O. W. (2012). Renal transport of uric acid: evolving concepts and uncertainties. \u003cem\u003eAdvances in chronic kidney disease\u003c/em\u003e, 19(6), 358-371.doi:10.1053/j.ackd.2012.07.009\u003c/li\u003e\n\u003cli\u003eZhang, S., Li, D., Fan, M., Yuan, J., Xie, C., Yuan, H. ... \u0026amp; Gao, H. (2024). Mechanism of reactive oxygen species-guided immune responses in gouty arthritis and potential therapeutic targets. \u003cem\u003eBiomolecules\u003c/em\u003e, 14(8), 978. doi:10.3390/biom14080978\u003c/li\u003e\n\u003cli\u003eKumar, S., Saxena, J., Srivastava, V. K., Kaushik, S., Singh, H., Abo-EL-Sooud, K. ... \u0026amp; Saluja, R. (2022). The interplay of oxidative stress and ROS scavenging: antioxidants as a therapeutic potential in sepsis. \u003cem\u003eVaccines\u003c/em\u003e, 10(10), 1575. doi:10.3390/vaccines10101575\u003c/li\u003e\n\u003cli\u003eBalik, A. R., Omma, A., \u0026amp; Sandikci, S. C. (2022). Evaluation of oxidative stress in gout disease; thiol-disulfide homeostasis and ischemia-modified albumin levels. \u003cem\u003eInt J Med Biochem\u003c/em\u003e, 2022;5(2), 109-115 doi:10.14744/ijmb.2022.52714\u003c/li\u003e\n\u003cli\u003eAli, H. I., Fujita, T., Akaho, E., \u0026amp; Nagamatsu, T. (2010). A comparative study of AutoDock and PMF scoring performances, and SAR of 2-substituted pyrazolotriazolopyrimidines and 4-substituted pyrazolopyrimidines as potent xanthine oxidase inhibitors. J\u003cem\u003eournal of computer-aided molecular design\u003c/em\u003e, 24(1), 57-75. doi:10.1007/s10822-009-9314-z\u003c/li\u003e\n\u003cli\u003ePatil, R., Das, S., Stanley, A., Yadav, L., Sudhakar, A., \u0026amp; Varma, A. K. (2010). Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug designing. \u003cem\u003ePloS one\u003c/em\u003e, 5(8), e12029. doi:10.1371/journal.pone.0012029\u003c/li\u003e\n\u003cli\u003eYoung, T., Abel, R., Kim, B., Berne, B. J., \u0026amp; Friesner, R. A. (2007). Motifs for molecular recognition exploiting hydrophobic enclosure in protein\u0026ndash;ligand binding. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, 104(3), 808-813. https://doi.org/10.1073/pnas.0610202104\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9271389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9271389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXanthine oxidase (XO), a key enzyme in purine metabolism, catalyzes the oxidation of hypoxanthine to xanthine and subsequently, xanthine to uric acid, the final product of purine catabolism in humans. XO enzyme plays a critical role in controlling uric acid levels thus, targeting it, is essential in managing conditions like gout.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003eThis study is aimed at exploring the bioactive compounds in \u003cem\u003eBrassica oleracea\u003c/em\u003e var. italica (broccoli) and \u003cem\u003eBrassica oleracea\u003c/em\u003e var. acephala (kale) for their potential anti-gout properties and ability to inhibit XO through an \u003cem\u003ein-silico\u003c/em\u003e approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003eBroccoli and kale leaves were subjected to solvent extraction. Phytochemicals from the extracts were identified using GC-MS analysis and subsequently docked against the XO receptor. ADMET and medicinal chemistry analyses were conducted on selected compounds to assess their pharmacological and safety profiles, and molecular interactions with XO were evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOut of the 28 compounds docked, seven showed favorable binding affinities, with binding energies below –7.0 kcal/mol. Among these, butylated hydroxytoluene and benzyl benzoate emerged as lead compounds, exhibiting favorable pharmacodynamic properties and minimal predicted toxicity. They interacted hydrophobically with key residues of the target protein and\u003cdel\u003e \u003c/del\u003eshowed a markedly inhibitory potential against XO.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eButylated hydroxytoluene and benzyl benzoate emerged as lead compounds and exhibited inhibitory effects against XO, suggestive of a therapeutic source for gout therapy.\u003c/p\u003e","manuscriptTitle":"In silico evaluation of broccoli and kale leaf extracts as xanthine oxidase inhibitors for gout therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 12:30:29","doi":"10.21203/rs.3.rs-9271389/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T07:26:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66233088638479922781956516601547303440","date":"2026-05-07T10:24:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279242283489449025627543031829631192470","date":"2026-05-01T23:18:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37765681762378023605597420710224904902","date":"2026-05-01T03:58:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216068593036765967050695652627237164177","date":"2026-05-01T01:43:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T08:56:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-21T13:08:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T11:46:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T10:33:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2026-04-07T08:35:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9508f4c5-e73f-4d94-8fd9-023b751da38c","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T07:26:47+00:00","index":39,"fulltext":""},{"type":"reviewerAgreed","content":"66233088638479922781956516601547303440","date":"2026-05-07T10:24:50+00:00","index":38,"fulltext":""},{"type":"reviewerAgreed","content":"279242283489449025627543031829631192470","date":"2026-05-01T23:18:23+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"37765681762378023605597420710224904902","date":"2026-05-01T03:58:41+00:00","index":36,"fulltext":""},{"type":"reviewerAgreed","content":"216068593036765967050695652627237164177","date":"2026-05-01T01:43:51+00:00","index":35,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T12:30:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 12:30:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9271389","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9271389","identity":"rs-9271389","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}