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Abstract

The potential targeting of xanthine oxidase (XO) for the treatment of gout has been explored through animal and human experiments. To identify potential XO inhibitors, a virtual screening method was utilized to examine fatty acids from Hedyotis pilulifera. Molecular docking results demonstrated that all possible configurations of the fatty acid 9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid exhibited a strong binding affinity to XO. In vitro experiment also showed that this compound inhibited XO activity compared to the control compound. Molecular dynamic simulation was used to further investigate the interaction between the ligand and protein. Additionally, ADMET studies revealed that this compound has highly favorable drug-like properties and pharmacokinetics.

Keywords

xanthine oxidase fatty acids molecular modeling ADMET

Introduction

Computational biological development has opened up many new avenues in research to find active ingredients with potential biological effects to contribute to developing medicines for the health sciences. Application techniques such as in silico methods are increasingly proving outstanding in research, exploring the biological effects of potential active ingredients on the human body.¹ The application of in silico procedures supports identifying drug targets, evaluating compounds and protein interactions, pharmacokinetic analysis (ADME), and improving the drug properties of molecules.^2–4 These techniques will be a great precursor to new drug development works.

Gout is a common disease characterized by episodes of acute arthritis with symptoms such as swelling, heat, redness, and severe and sudden pain in one or more joints, most commonly in the joint of the big toe.⁵ This is a metabolic pathology caused by the deposition of monosodium urate crystals in the joints and tissues around the joints. Elevated blood uric acid levels form these crystals.⁶ Many causes, such as increased uric acid production or diminished urinary uric acid elimination, can result in hyperuricemia.⁷ Previous studies proved that xanthine oxidase (XO), in particular, is associated with increased uric acid production. XO is an enzyme that catalyzes the hydroxylation of purines, converts hypoxanthine into xanthin, and thereby synthesizes uric acid, which is released into the bloodstream and is the antioxidant capacity of the blood.⁸ Chronic gout causes a lot of prolonged pain and nuisance and limits the mobility of the person; over time, this can lead to complications of muscle atrophy, stiffness, and joint deformity. Also, gout and hyperuricemia are associated with metabolic syndromes, kidney and cardiovascular diseases, hypertension, and diabetes mellitus.⁹ Therefore, gout treatment is necessary and is being cared for by both patients and doctors. Today, under the development of the medicine and pharmaceutical industry, based on the mechanisms that cause hyperuric acidemia, many drugs have been released and contribute to the important regimen for gout treatment. Typical medications used for treating acute gout attacks include oral glucocorticoid anti-inflammatory drugs such as prednisone, and prednisolone; nonsteroid anti-inflammatory drugs: Indomethacin, ibuprofen, naproxen, and colchicine.¹⁰ However, besides the effectiveness of treatment, it is worrying that the above drugs all have severe side effects. For glucocorticoids, side effects with short-term use, medium dose, and high dose include mood swings, hyperglycemia, fluid retention and hypertension, cushingoid features, weight gain, and gastrointestinal effects such as gastritis, gastrointestinal bleeding, ulcer formation, and bone and muscle effects.¹¹ Thus, the aim of this study is to find new molecules as hit compounds targeting XO for safety and effectiveness for patient treatment.

Medicines derived from medicinal herbs tend to be friendly and less toxic to the human body. In Vietnamese traditional medicine, Hedyotis pilulifera (Pit.) Tran Ngoc Ninh. (family Rubiaceae) is often used to treat gastrointestinal diseases and osteoarthritis pain, pharyngitis, and tonsillitis.¹² The main chemical components of this plant are glycosides and triterpene compounds, a group of substances with many good biological activities such as antibacterial, antitumor, and anti-inflammatory.¹³ We isolated 3 fatty acids from Hedyotis pilulifera in a previous publication.¹⁴ Interestingly, many studies have shown that certain fatty acids, such as oleic acid, arachidonic acid, and eicosapentaenoic acid, can inhibit XO activity.¹⁵ Based on this evidence, we applied in silico methods combined with an in vitro inhibition study to analyze the mechanical interactions of fatty acids from Hedyotis pilulifera against XO activity.

Result

Virtual Screening

Our previous study isolated 3 fatty acids from Hedyotis pilulifera.¹⁴ However, the bioactivity of these compounds has not been studied. Herein, we applied molecular docking to screen the fatty acids’ inhibitory activity on XO. Following the previous publication, the protocol was validated by redocking the cocrystal ligand to the 3D protein structure.¹⁶ This docking protocol can be applied in this study as, according to the findings, the docking pose has a similar conformation to the crystal pose with an RMSD of 1.327 Å. Allopurinol is commonly used in gout treatment due to the competitive inhibitory activity of XO.¹⁷ Therefore, we selected allopurinol as a reference inhibitor. The structures of the 3 compounds are shown in Figure 1. In the previous study, the stereoisomers of the 3 fatty acids were not determined; therefore, we created all of the stereoisomers of these compounds to validate their binding poses to XO. In particular, 9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid (1) and 9,12,13-trihydroxyoctadeca-10E-enoic acid (2) each had 8 stereoisomers, whereas octadeca-9Z,12Z-dienoic acid (3) had only one.¹⁴ All configurations were simulated interacting with XO at the active site of the enzyme (Figure 2A). The screening process showed that most of the stereoisomers of the 3 fatty acids had docking scores better than −7.111 kcal/mol, the docking score of allopurinol (Table 1). For further experiments, all 3 compounds, 9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid (1), 9,12,13-trihydroxyoctadeca-10E-enoic acid (2), and octadeca-9Z,12Z-dienoic acid (3) were selected for in vitro testing.

Figure 1.

Chemical structures of fatty acids.

Figure 2.

(A) The docking site of xanthine oxidase (XO) (B) interaction between compound 1 (9R,12R,13S) and XO.

Table 1.

Docking Results of the Stereoisomers of the 3 Fatty Acids Selected for Virtual Screening.

No.	Compound	Chiral centers	Docking score
1	9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid	9S,12R,13R	−8.398
		9R,12S,13S	−7.084
		9R,12R,13R	−7.991
		9S,12S,13R	−8.232
		9S,12R,13S	−8.170
		9R,12S,13R	−8.421
		9R,12R,13S	−8.425
		9S,12S,13S	−8.369
2	9,12,13-trihydroxyoctadeca-10E-enoic acid	9S,12R,13R	−7.424
		9R,12S,13S	−8.033
		9R,12R,13R	−7.834
		9S,12S,13R	−7.866
		9S,12R,13S	−6.629
		9R,12S,13R	−7.711
		9R,12R,13S	−7.215
		9S,12S,13S	−8.036
3	Octadeca-9Z,12Z-dienoic acid	–	−7.267
4	Allopurinol	–	−7.111

Xanthine Oxidase Inhibitory Activity

Accordingly, in vitro experimental investigation followed the virtual screening to identify the potential hit compound(s) against XO. A range of different concentrations of compounds was used to assess their inhibitory activity, and allopurinol was utilized as the positive control. Compound 1 displayed inhibitory activity against XO, with an IC₅₀ value of 91.18 ± 8.17 μg/mL, whereas allopurinol had an IC₅₀ value of 1.36 ± 0.04 μg/mL. In contrast, compounds 2 and 3 did not show the ability to inhibit XO activity under the experimental conditions (IC₅₀ >500 μg/mL).

Molecular Docking Analysis

The highest score docking pose was shown and selected for dynamic simulation to further explain the biological interaction mechanism between compound 1 and XO. The stereoisomer of compound 1 with the best docking score (9R, 12R, 13S) was selected to analyze its interaction with XO. Compound 1 was found to interact with XO via 4 hydrogen bonds at Thr1010, Gln767, Gln1040, and Arg880 and 5 hydrophobic interactions at Ala1079, Phe914, Ala1078, Ala910, and Arg912 (Figure 2B). Allopurinol also showed a similar interaction with XO to compound 1 and XO complex. It is stated in bold that allopurinol also interacted with enzymes with 2 hydrogen bonds, including Arg880 and Thr1010, and hydrophobic interaction at Ala1079, Ala1078, Phe1009, Phe914, and Val1011. Allopurinol has a binding affinity of −7.111 kcal/mol; however, its inhibitory activity was explained by its ability to form strong bonds and interact with several key amino acids. This phenomenon can be attributed to the compound's structural characteristics. The previous study also explained why the inhibitory activity of allopurinol was stronger.¹⁶ Due to its spatially compact structure, this compound can readily attach itself to the interaction site located at the center of XO, a small cavity. Compound 1 is characterized by a lengthy hydrocarbon chain in its chemical structure. However, the entire molecule faces steric hindrance in binding to the catalytic site of XO. Nevertheless, the torsion angles’ flexibility allows a portion of the molecule to bind to the enzyme's active site potentially. Additionally, carboxyl and hydroxyl groups enable the molecule to form robust hydrocarbon bonds with amino acids at the docking site.

Bovine XO's overall sequence resembles that of human XO by approximately 90%. Most amino acids that interact with ursolic acid are conserved in both human and bovine.¹⁸ The binding pose's lipophilic character and Van der Waals interaction are preserved because both enzymes’ amino acids at the catalytic region are the same. Hence, the molecular docking simulations suggested binding is applicable to the human enzyme.

Molecular Dynamic Simulation

Binding site stability

The molecular dynamic simulation selected the ligand's optimal docking pose, demonstrating general stability over 100 ns. Analysis of the RMSD of the protein carbon backbone in Figure 3A indicated that the protein rapidly attained a steady state after the 10th ns with an average RMSD value of approximately 0.5 nm. A similar result can also be seen for the complex, where the RMSD value changed slightly. Meanwhile, compound 1 had a change in conformation from the 60th ns. This was explained because there are many rotatable bonds in compound 1. However, this result was also displayed when compound 1 was bound to XO. The complex was able to remain stable over the simulation period. The RMSF results also showed that the ligand stabilized several interaction-related residues at the active site (Figure 3B). The radius of gyration (Rg) describes how atoms are distributed along the axis of a protein. The value minimally changed, reaching 0.6 Å (Figure 3C).

Figure 3.

Molecular dynamic simulation interaction between compound 1 and xanthine oxidase (XO) (A) RMSD, (B) RMSF, (C) number of hydrogen bond, and (D) chemical contributions.

Hydrogen bond analysis

During the 100 ns simulation, the ligand consistently formed hydrogen bonds with the protein, as shown in Figure 3D. Among the initial 10 ns, the protein and ligand formed 4 to 8 hydrogen bonds. However, from the 10th ns onward, the ligand only formed 2 to 4 hydrogen bonds with the protein. Nevertheless, the ligand-protein complex remained stable until the end of the simulation.

Binding free energy

At the final stage of simulation, the binding free energy of compound 1 further explained the potency of this compound. The different energy components contributing to binding energy are shown in Table 2. These confirmed the molecular docking results and demonstrated that compound 1 could bind strongly with XO.

Table 2.

Components of Binding Free Energy Calculation.

Compound	Van der Waal energy (kJ/mol)	Electrostatic energy (kJ/mol)	Polar solvation energy (kJ/mol)	SASA energy (kJ/mol)	Binding energy (kJ/mol)
1	−193.71 ± 18.17	−131.78 ± 55.70	251.61 ± 38.55	−22.98 ± 0.95	−96.86 ± 38.52

ADMET Studies

Lipinski's Rule of 5 is a principle used in drug discovery that assesses the pharmacokinetic and pharmacodynamic properties of a potential drug compound. The parameters, such as molecular weight (MW), log of octanol/water partition coefficient, number of hydrogen bond acceptors (nHBA), and number of hydrogen bond donors (nHBD), are considered to determine the oral bioavailability of compounds with optimal membrane permeability and hydrophobicity for therapeutic purposes. Acceptable values of nRotB and MR suggest oral bioavailability of drugs and good intestinal absorption.

According to the results presented in Table 3, compound 1 has favorable drug-like properties and adheres to relevant drug-likeness criteria such as a MW of ≤500 Da, a maximum of 5 nHBD, a Log P value of <5, a maximum of 10 nHBA, and a total polar surface area (TPSA) of <140 Å.¹⁹ These findings suggest that Compound 1 could be a promising candidate for drug development due to its favorable oral bioavailability. Furthermore, the Log S values indicate good water solubility, while the TPSA value of 97.99 suggests potential for good intestinal absorption.²⁰ However, the high value of nRotB, which exceeds 10, indicates that this molecule may not be stable with regard to its stereochemistry.

Table 3.

Drug Properties of Isolated Compounds Analyzed With SwissADME.

Compound	Molecular weight (g/mol)	Log P	nHBD	nHBA	TPSA	MR	Lipinski violation	Log S	nRotB
Compound 1	328.44	2.77	4	5	97.99	92.95	0	soluble	14

Abbreviations: MW, molecular weight; LogP, Log of octanol/water partition coefficient; nHBA, number of hydrogen bond acceptor(s); nHBD, number of hydrogen bond donor(s); MR, molar refractivity; nRotB, number of rotatable bonds; Log S, log of solubility; TPSA, total polar surface area.

Table 4 shows that compound 1 is highly likely to be absorbed in the gastrointestinal tract and is also expected to penetrate the blood–brain barrier. Additionally, several cytochromes (CYPs) play a role in drug metabolism, with CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4 being key for drug biotransformation.²¹ According to the SwissADME in silico prediction, compound 1 did not inhibit all cytochromes. In the current study, the toxicity of compound 1 was estimated through the DL-AOT prediction server. The expected LD₅₀ value of compound 1 was 3.4 mg/kg. The classification of these compounds as “Caution” in the 4 groups—“Danger/Poison,” “Warning,” “Caution,” and “None Required”—indicates that high doses of these compounds may be slightly toxic.

Table 4.

ADME Predictions of Isolated Compounds Computed by SwissADME.

Compound	GI Abs	BBB Per	Inhibitor interaction
Compound	GI Abs	BBB Per	P-gp substrate	CYP1A2 inhibitor	CYP2C19 inhibitor	CYP2C9 inhibitor	CYP2D6 inhibitor	CYP3A4 inhibitor
Compound 1	High	Yes	Yes	No	No	No	No	No

Abbreviations: log Kp, Log of skin permeability; GI Abs, gastrointestinal absorption; BBB Per, blood–brain barrier permeability; P-gp, P-glycoprotein; CYP, cytochrome-P.

Discussion

Xanthine oxidase is an enzyme that plays a key role in the production of uric acid in the body. Uric acid is a waste product formed when purines are broken down. Excess uric acid can lead to several health problems, including gout and kidney stones. Many studies have shown that certain fatty acids can inhibit XO activity. In particular, polyunsaturated fatty acids such as oleic acid, arachidonic acid, eicosapentaenoic acid, and docosahexaenoic acid, found in fish oil, have been shown to have XO inhibitory effects.¹⁵ These fatty acids reduce the production of reactive oxygen species (ROS) in the body, which is known to activate XO. By reducing ROS production, these fatty acids can inhibit XO activity and lower uric acid levels in the body. In addition, Kelley et al also showed that nitro-oleic acid could inhibit XO activity in a concentration-dependent manner.²² In our study, compared with control compounds, we have shown that 9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid has the ability to inhibit XO activity. Although further studies are needed to determine the absolute configuration of this compound, the molecular docking results also indicated no significant difference in binding energies between the possible configurations of 9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid for XO. In addition, by selecting the configuration with the best binding energy, the binding mechanism between 9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid and XO is also clarified by simulation methods. Although further experimental studies are needed to modify the structure of 9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid to increase its biological activity and reduce the risk of toxicity to the body, this result shows the potential role for fatty acids in the treatment of gout targeting XO.

Conclusion

According to our research, the fatty acids tested, 9,12,13-trihydroxyoctadeca-10E,15Z-dienoic acid, had the highest affinity for XO. In vitro studies also demonstrated its inhibitory activity against XO, compared to the control compound, allopurinol. To understand more about its biological properties, ADMET studies and molecular interaction analysis were undertaken. These results suggest that the bioactive chemical we discovered has promise as a gout therapy. However, more study is required to strengthen this molecule's chemical characteristics and inhibitory activity.

Material and Method

Protein and Ligand Preparation

The Research Collaboratory for Structural Bioinformatics Protein Data Bank was applied to acquire the crystal structure of bovine XO complexed with hypoxanthine (PDB ID: 3NRZ). The remaining ligands and water molecules in the XO crystal structures were removed using Discovery Studio 2020. AutoDockTools version 1.5.6²³ was then utilized to add polar hydrogens and Kollman charges to the protein, after which the macromolecule was exported to a dockable pdbqt format for molecular docking. To compute the RMSD between 2 molecules, Pymol 2.4 was employed. The 3-dimensional structures of fatty acids derived from Hedyotis pilulifera were either downloaded from the PubChem database or generated by MarvinSketch 20.17²⁴ (ChemAxon). Eventually, employing Open Babel 3.1.1, all ligands were converted into a dockable pdbqt format.

Molecular Docking

AutoDock Vina 1.2.0 was utilized to find potential compounds that react with XO.²⁵ A grid box covering the protein's active site was generated using the parameters center_x, center_y, and center_z = 37.35, 19.85, 17.85, respectively, and the size of the grid box (30 × 30 × 30). Based on their docking scores, the compounds were ordered and reported in kcal/mol. Finally, Discovery Studio Visualizer 2020 was employed to visualize the molecular interactions between the proteins and the selected ligands.

In Vitro Assay

The previously published protocol was slightly modified to assess XO's inhibitory activity in 96-well plates using a spectrophotometric technique.¹⁶ The experiment included 150 nmol/L of xanthine solution dissolved in phosphate buffer (pH 7.5), 70 nmol/L of phosphate buffer (pH 7.5), and 0.01 IU/mL of XO solution. Test compounds were prepared in dimethyl sulfoxide (DMSO) and diluted with phosphate buffer (pH 7.5). The absorbance was measured at 290 nm on a microplate spectrophotometer (BioTek Epoch). The blank was prepared in the same way, but without the XO solution. The negative control was 0.5% DMSO without an inhibitor, and the positive control was allopurinol.

The compounds were tested at 500, 100, 20, and 4 μg/mL doses and determined as the half-maximal inhibitory concentration (IC₅₀) using SigmaPlot 12.5. All experiments were repeated 3 times. The XO inhibitory activity was expressed as the inhibitory percentage (I) calculated using the following formula:

I n h i b i t o r y p e r c e n t a g e (I) = (D_{O D c o n t r o l} - D_{O D s a m p l e}) / D_{O D c o n t r o l} \times 100 (%)

D_{OD control} and D_{OD samples} are the blank-corrected optical density values of the control wells (in the absence of test materials) and test wells (in the presence of test samples).

Molecular Dynamics Simulations and Binding Free Energy Calculations

The simulation technique was implemented following our earlier studies.²⁶ The stability of the ligand in the complex with XO was further investigated using molecular dynamics simulations with GROMACS 2020.4. The complex's topology under the CHARMM36 forcefield was generated using the CHARMM-GUI server,²⁷ and it was then solvated in a truncated octahedral box containing TIP3P water molecules. NaCl (0.15 M) was added to the system. The requisite values of 300 K temperature and 1 bar pressure were attained to provide a completely stable system. Finally, the molecular dynamics simulation for the complex was run for 100 ns, while the trajectory data were saved every 10 ps for analysis.

In Silico Drug-Likeness and Toxicity

The Lipinski rule has been employed to predict a compound's drug-likeness. The SwissADME website, which provides details on pharmacokinetics properties, was utilized to calculate the ADME parameters of the ligands.²⁸ The DL-AOT prediction server was used to predict acute oral toxicity (T).²⁹

Footnotes

Acknowledgments

This research was supported by Hue University under the Core Research Program, Grant No. NCM.DHH.2023.01.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by Hue University under the Core Research Program, Grant No. NCM.DHH.2023.01.

ORCID iDs

Duc Viet Ho

Linh Thuy Thi Tran

Hoai Thi Nguyen

Tan Khanh Nguyen

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Xanthine Oxidase Inhibitory Activity of Fatty Acids From Hedyotis pilulifera : In Vitro and In Silico Studies

Abstract

Keywords

Introduction

Result

Virtual Screening

Xanthine Oxidase Inhibitory Activity

Molecular Docking Analysis

Molecular Dynamic Simulation

Binding site stability

Hydrogen bond analysis

Binding free energy

ADMET Studies

Discussion

Conclusion

Material and Method

Protein and Ligand Preparation

Molecular Docking

In Vitro Assay

Molecular Dynamics Simulations and Binding Free Energy Calculations

In Silico Drug-Likeness and Toxicity

Footnotes

Acknowledgments

Declaration of Conflicting Interests

Funding

ORCID iDs

References