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ORIGINAL ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 3  |  Page : 285-290  

The interaction of alpha-mangostin and its derivatives against main protease enzyme in COVID-19 using in silico methods


Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia

Date of Submission09-Dec-2020
Date of Decision16-Feb-2021
Date of Acceptance03-Mar-2021
Date of Web Publication16-Jul-2021

Correspondence Address:
Prof. Muchtaridi Muchtaridi
Jl. Bandung-Sumedang KM 21, Jatinangor, Sumedang 45363
Indonesia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/japtr.JAPTR_299_20

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  Abstract 


More than 111 million people worldwide have been affected by the COVID-19 outbreak caused by SARS-CoV-2. The main therapeutic target of COVID-19 is main protease (Mpro). It plays a key role as an enzyme in the SARS-CoV-2 replication and transcription. In this case, the alpha-mangostin potentially has antiviral activity against Mpro by inhibiting this enzyme. Nevertheless, the alpha-mangostin has low solubility and a lack of information about alpha-mangostin activity against the SARS-CoV-2. The aim of this study is to describe the molecular interactions and identify the pharmacokinetics profile between alpha-mangostin and its derivatives. in silico study was conducted by pharmacokinetics and toxicity prediction, molecular docking simulation, and Lipinski's rule of five. FKS9 has a Gibbs free energy value of-10.5 kcal/mol with an inhibition constant of 36.45 μM and an interaction with amino acid His41 residue. Its human intestinal absorption and Caco-2 values were 95.13% and 47.71% while the plasma protein binding and blood-brain barrier values were 96.66% and 6.99%. FKS9 also has no mutagenic and carcinogenic potential. FKS9 as an alpha-mangostin derivative had the best interaction with the Mpro enzyme and its pharmacokinetic profiles was identified.

Keywords: Alpha-mangostin, COVID-19, in silico, main protease


How to cite this article:
Hidayat S, Ibrahim FM, Pratama KF, Muchtaridi M. The interaction of alpha-mangostin and its derivatives against main protease enzyme in COVID-19 using in silico methods. J Adv Pharm Technol Res 2021;12:285-90

How to cite this URL:
Hidayat S, Ibrahim FM, Pratama KF, Muchtaridi M. The interaction of alpha-mangostin and its derivatives against main protease enzyme in COVID-19 using in silico methods. J Adv Pharm Technol Res [serial online] 2021 [cited 2021 Dec 2];12:285-90. Available from: https://www.japtr.org/text.asp?2021/12/3/285/321516




  Introduction Top


According to the World Health Organization (WHO), more than 111 million confirmed COVID-19 cases have been reported in 216 countries with 2.46 million confirmed deaths.[1] This pandemic is a major and recurrent global public health concern. This outbreak is an emerging infection and rapidly spreading globally.[2] The COVID-19 was caused by the SARS-CoV-2, which is an RNA virus that can spread widely to cause respiratory diseases.[3]

Currently, the primary care for COVID-19 patients is symptomatic therapy. Based on the clinical trials conducted by WHO from around the world, hydroxychloroquine, lopinavir, remdesivir, and interferon have proven ineffective in COVID-19 treatment.[4] These drugs have a tendency to develop acute toxicity and show poor therapeutic results in overcoming COVID-19.[5] As a result, further research related to the discovery of the COVID-19 drug is required to find active compounds that effectively reduce the spread of COVID-19.

The search for active compounds through natural ingredients is accomplished in the search for parent compounds for COVID-19 therapy. Alpha-mangostin, originated from mangosteen pericarp has the potential to be an alternative agent for COVID-19 therapy. This is supported by previous studies which show that alpha-angostin has activity on the protease enzymes of the HIV.[6],[7],[8],[9] HIV protease has a genomic similarity level of 67.5% compared to SARS-CoV-2's main protease (Mpro).[10] Therefore, alpha-mangostin is also thought to be able to have the same activity against the Mpro.

Mpro is referred to as an ideal drug target because of its specific presence that is only owned by viruses.[11] This specific existence is able to suppress the side effects that will be accepted by humans because the compounds will only affect the virus.

Behind these potentials, there are several limitations of alpha-mangostin, such as a low pharmacokinetic profile and a lack of information regarding alpha-mangostin activity against SARS-CoV-2.[12],[13] Therefore, structural modification of alpha-mangostin is required to obtain alpha-mangostin derivatives with better pharmacokinetic profiles and pharmacological activities.


  Materials and Methods Top


Hardware and software

Hardware: A personal computer with Intel® Core ™ i5-6600 CPU, CPU 3.90 GHz, and 8 GB RAM. Software: The ChemOffice 2010 and ChemDraw Ultra 12.0 programs (PerkinElmer Inc., downloaded at http://www.cambridgesoft.com/) for drawing two-dimensional structures and expressing three-dimensional (3D) structures of the ligands. The AutoDock 4.2.6 and AutoDockTools 1.5.6 programs (The Scripps Research Institute, USA) to conduct molecular docking simulations. Pre-ADMET 2.0 program to predict absoprtion, distribution, and toxicity profile. The BIOVIA Discovery Studio 2017 R2 Client (Dassault Systems, downloaded from http://www.accelrys.com/) to visualize 3D structures.

Structure acquisition

The 3D structure of Mpro was downloaded from Protein Data Bank (PDB) (http://www.rscb.org/) with ID code 6 LU7. Mpro was complexed with N3 inhibitor molecule. Separation was performed using BIOVIA Discovery Studio 2017 R2 Client. The 3D structure of the ligands (alpha-mangostin and its derivatives) was drawn and optimized utilizing ChemOffice 2010 and ChemDraw Ultra 12.0 (PerkinElmer Inc.). Nelfinavir was chosen as comparison compound and the structure was downloaded from Pubchem (http://pubchem.ncbi.nlm.nih.gov/).

Pharmacokinetics and toxicity prediction

The pharmacokinetics (absorption and distribution) and toxicity prediction include human intestinal absorption (HIA), Caco2, plasma protein binding (PPB), blood–brain barrier (BBB), Mutagenicity, and Carcinogenicity.

Molecular docking simulation

Ligands and enzyme are prepared using AutodockTools 1.5.6. Polar hydrogen and Kollman charges are added to protein and saved as PDBQT. Gasteiger charges were calculated. The box size is set at 26 × 52 × 32 at the coordinate x = −9,732; y = 11.403; and z = 68.925 with a distance of 0.375 Å. The genetic algorithm is set at 100x runs and other parameters are set by default. Autodock 4.2.6 is used to simulate the molecular docking process. The binding affinities of the compounds were studied using Discovery Studio Visualizer.

Lipinski's rule of five

According to Lipinski's rule of five (RO5), a reasonable compound for use as an orally active candidate must have no more than one violation of the following criteria: ≤5 hydrogen bond donors, ≤10 hydrogen bond acceptors, molecular weight ≤500, and logP ≤5.[14]


  Results Top


The alpha-mangostin structure modification was focused on C1 and C6 atoms which are more reactive than others to improve physicochemical properties, bioavailability, and pharmacological activity of alpha-mangostin. Alpha-mangostin modified compounds shown in the following [Table 1].
Table 1: Alpha-mangostin and its derivative structures

Click here to view


Pharmacokinetics and toxicity prediction

[Table 2] shows the pharmacokinetic profile of these ligands. It shows the absorption profile of the compound represented by the HIA and Caco-2 values, while the distribution profile is shown by the PPB and BBB values, and the toxicity is represented by mutagenicity and carcinogenicity.
Table 2: Pharmacokinetics and toxicity prediction of ligands

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Molecular docking simulation

The molecular docking research control was used to validate the molecular docking parameters. The N3 compound from the 6 LU7 protein complex is reattached to the Mpro. The measured value is in the form of root mean square deviation (RMSD) which shows the deviation of the binding pose occurring in the test ligand compared to the reference binding pose. The lower the RMSD value, the better the model docked to the target structure.[15] The simulation results show that the RMSD value of molecular docking validation is 2.15 Å which indicates that the docking method used is qualified because the value obtained is ≤3 Å.[16] Molecular docking simulation of the alpha-mangostin derivatives that passed the pharmacokinetic profiles selection compared with nelfinavir are shown in [Table 3].
Table 3: Molecular docking parameters of nelfinavir, alpha-mangostin, and its derivative structure

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Lipinski's rule of five

The Lipinski's RO5 also considered regarding the active compound to be administered orally. This is due to the fact that 90% of the active compounds administered by the oral route have passed phase II clinical trials. The RO5 relates to the acceptance of solubility and permeability of compounds in the gastrointestinal tract and this is the initial stage in determining the oral bioavailability of active substances.[17] [Table 4] shows the Lipinski's RO5 parameters of alpha-mangostin and its derivatives.
Table 4: Physicochemical properties of alpha-mangostin and its derivative structures

Click here to view



  Discussion Top


The structural modification of alpha-mangostin is focused on modifying the substituted dihydroxy group on the aromatic ring (C1 and C6 atoms) to increase its affinity for the catalytic site, Cys145 and His41.[18]

Pharmacokinetics and toxicity prediction

The HIA value shows the degree of absorption of the active substance in the human intestine. There are three categories, namely HIA 0%–20% (low), 20%–70% (moderate), and 70%–100% (high).[19] The HIA value of alpha-mangostin and its derivatives is in the range of 90%–99% which indicates that it can be properly absorbed by the intestine.

In addition, Caco-2 cell modeling is recommended as a good in vitro model for predicting the absorption of orally administered active substances. The quality of absorption of Caco-2 cells was categorized into three groups, namely <4 (low), 4–70 (moderate), >70 (high).[20] The Caco-2 value of alpha-mangostin and its derivatives shows that the ability of these compounds to penetrate the cell membrane is in the medium category.

The degree of binding of the drug to plasma proteins affects the pharmacokinetic and pharmacodynamic properties of the drug. PPB values <90% indicate the drug is strongly bound to protein, whereas PPB values below 90% indicate the drug is weakly bound to plasma proteins so that the drug can be easily partitioned for distribution to cells. PPB values of alpha-mangostin, FKS4, FKS5, FKS7, FKS8, FKS9, FKS11, and FKS12 have PPB values above 90% which indicates that these compounds are strongly bound to plasma proteins so that only a small portion of the drug is in its free form which can then reach the Mpro. Meanwhile, the PPB of FKS1, FKS2, FKS6, FKS7, and FKS10 values were below 90% so that it could be partitioned much more easily in the blood and drugs would be much easier to distribute to cells.

The BBB value shows the drug concentration in the brain and blood. There are three categories, namely value >2.0 indicates that the compound has the ability to penetrate the central nervous system (CNS) so that it is thought to affect CNS activity. A BBB value between 2.0 and 1.0 indicates a moderate degree of absorption in the brain, and a BBB value below 1 indicates a low absorption in the brain.[21] The design of medicinal compounds for anti-COVID-19 is not targeted at the CNS, but at the Mpro enzyme, which is likely to be in the nasopharyngeal and lung cells. Therefore, drug penetration against the CNS needs to be avoided so that drugs do not have CNS side effects.[22] FKS1, FKS3, FKS5, FKS6, FKS7, and FKS10 have low penetration of SSP while other compounds are in moderate penetration. This shows that the alpha-mangistin and its derivatives are thought to have a mild CNS effect.

Meanwhile, the overall level of toxicity, nelfinavir, alpha-mangostin, and its derivatives have no potential for mutagens or carcinogens. However, FKS11 and FKS8 have the potential to be mutagenic or can cause mutation effects on the surrounding cells so that it is excluded from the anti-COVID-19 drug candidate.

Molecular docking simulation

The alpha-mangostin derivatives that passed the pharmacokinetics profile selection (FKS9) [Table 2] were subjected to molecular docking simulations. The results of molecular docking of the FKS9 compound [Table 4] were compared with nelfinavir, one of the COVID-19 drugs.[9]

There are four parameters considered to determine the affinity of the compounds, including △ G, inhibition constant, hydrogen bond, and Van der Waals interactions. Based on [Table 4], it can be seen that FKS9 (−10.15 kcal/mol) has a higher △ G value than nelfinavir (−9.74 kcal/mol) and alpha-mangostin (−8.58 kcal/mol). This shows that the interactions that occur between FKS9 and Mpro are at a higher level compared to alpha-mangostin and also the comparison compound (nelfinavir). The more negative the ΔG value, the more stable the bonds are. As a result, the ligand-protein affinity is getting better which leads to better activity.[23]

The next parameter is the inhibition constant (Ki). The smaller the Ki, the smaller the doses required to demonstrate pharmacological abilities. The FKS9 has a Ki value of 36.45 μM. This was much smaller than that of nelfinavir (72.09 μM) and alpha-mangostin (511.49 μM).

The analysis of molecular docking results is also important to review the ability of the test ligand to interact with the ligand-binding domain (LBD) of the target protein. Cys145 and His41 are catalytic amino acid residues in LBD so that antagonistic compounds that are able to inhibit LBD by binding with these residues through hydrogen bond interactions cause virus replication to not occur.[18],[24] In this case, The interactions of alpha-mangostin [Figure 1], nelfinavir [Figure 2], and FKS9 [Figure 3] were compared to see compounds with better interactions. FKS9 [Figure 3] is able to interact with Mpro catalytic residue, His41, via hydrogen bond, which indicates that FKS9 has a good potential as an Mpro antagonist in inhibiting SARS-CoV-2 replication compared to alpha-mangostin and nelfinavir.
Figure 1: Interaction between alpha-mangostin and main protease

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Figure 2: Interaction between nelfinavir and main protease

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Figure 3: Interaction between FKS9 and main protease

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Lipinski's rule of five

Based on [Table 3], the FKS9 achieves the RO5 with only one violation, namely the logP value that exceeds the requirements (logP <5). Thus, FKS9 can be further investigated in vitro and it is predicted that it can be administered orally with a determinable oral bioavailability.


  Conclusion Top


In conclusion, FKS9 as an alpha-mangostin derivative had the best interaction with the Mpro enzyme as indicated by △ G and Ki values of −10.5 kcal/mol and 36.45 μM and also an interaction with His41 residue. The pharmacokinetic profile of FKS9 has been known as shown by the HIA and Caco-2 values of 95.13% and 47.71%, these two values indicate that FKS9 can be well absorbed in the intestine and has the ability to penetrate the membrane. The PPB and BBB values of 96.66% and 6.99% indicating the distribution profile of FKS9 in terms of binding to plasma proteins and the ability to penetrate the BBB. FKS9 also has no mutagenic and carcinogenic potential.

Acknowledgment

This study was supported by The Directorate General of Higher Education of The Ministry of Research and Technology of Indonesia through Seeds Basic Research of Higher Education (PDUPT) Grants no. 1123ak/UN6.O/LT/2019.

Financial support and sponsorship

The Directorate General of Higher Education of The Ministry of Research and Technology of Indonesia through Seeds Basic Research of Higher Education (PDUPT) Grants no. 1123ak/UN6.O/LT/2019.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
WHO. Coronavirus Disease (COVID-19) Pandemic; 2020. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019. [Last accessed on 2020 Jul 21].  Back to cited text no. 1
    
2.
Bogoch II, Watts A, Thomas-Bachli A, Huber C, Kraemer MU, Khan K. Pneumonia of unknown aetiology in Wuhan, China: Potential for international spread via commercial air travel. J Travel Med 2020;27:1-7.  Back to cited text no. 2
    
3.
Weiss SR, Leibowitz JL. Coronavirus pathogenesis. Adv Virus Res 2011;81:85-164.  Back to cited text no. 3
    
4.
WHO Solidarity Trial Consortium, Pan H, Peto R, Henao-Restrepo AM, Preziosi MP, Sathiyamoorthy V, et al. Repurposed Antiviral Drugs for Covid-19-Interim WHO Solidarity Trial Results. N Engl J Med 2021;384:497-511.  Back to cited text no. 4
    
5.
Sanders JM, Monogue ML, Jodlowski TZ, Cutrell JB. Pharmacologic treatments for coronavirus disease 2019 (COVID-19): A review. JAMA 2020;323:1824-36.  Back to cited text no. 5
    
6.
Ibrahim MY, Hashim NM, Mariod AA, Mohan S, Abdulla MA, Abdelwahab SI, et al. α-Mangostin from Garcinia mangostana Linn: An updated review of its pharmacological properties. Arab J Chem 2016;9:317-29. [doi.org/10.1016/j.arabjc. 2014.02.011].  Back to cited text no. 6
    
7.
Muchtaridi M, Suryani D, Qosim W, Saptarini NM. Quantitative analysis of A-mangostin in mangosteen (Garcinia mangostana L.) pericarp extract from four district of West Java by HPLC method. Int J Pharm Pharm Sci 2016;8:232-6.  Back to cited text no. 7
    
8.
Muchtaridi M, Puteri NA, Milanda T, Musfiroh I. Validation analysis methods of α-mangostin, γ-mangostin and gartanin mixture in Mangosteen (Garcinia mangostana L.) fruit rind extract from west java with HPLC. J Appl Pharm Sci 2017;7:125-30.  Back to cited text no. 8
    
9.
Xu Z, Peng C, Shi Y, Zu Z, Mu K, Wang X, et al. Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation. BioRxiv 2020:1-20. [doi: 10.1101/2020.01.27.921627].  Back to cited text no. 9
    
10.
Eleftheriou P, Amanatidou D, Petrou A, Geronikaki A. In silico Evaluation of the effectivity of approved protease inhibitors against the main protease of the novel SARS-CoV-2 Virus. Molecules 2020;25:25-9.  Back to cited text no. 10
    
11.
Ullrich S, Nitsche C. The SARS-CoV-2 main protease as drug target. Bioorg Med Chem Lett 2020;30:127377.  Back to cited text no. 11
    
12.
Li L, Brunner I, Han A, Hamburger M, Kinghorn AD, Frye R, et al. Pharmacokinetics of αlpha-mangostin in rats after intravenous and oral application. Mol Nutr Food Res 2011;55:67-74.  Back to cited text no. 12
    
13.
Gutierrez-Orozco F, Failla ML. Biological activities and bioavailability of mangosteen xanthones: A critical review of the current evidence. Nutrients 2013;5:3163-83.  Back to cited text no. 13
    
14.
Lipinski CA. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov Today Technol 2004;1:337-41.  Back to cited text no. 14
    
15.
Sherman W, Beard HS, Farid R. Use of an induced fit receptor structure in virtual screening. Chem Biol Drug Des 2006;67:83-4.  Back to cited text no. 15
    
16.
Jain AN, Nicholls A. Recommendations for evaluation of computational methods. J Comput Aided Mol Des 2008;22:133-9.  Back to cited text no. 16
    
17.
Ramachandran B, Kesavan S, Rajkumar T. Molecular modeling and docking of small molecule inhibitors against NEK2. Bioinformation 2016;12:62-8.  Back to cited text no. 17
    
18.
Tahir Ul Qamar M, Alqahtani SM, Alamri MA, Chen LL. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. J Pharm Anal 2020;10:313-9.  Back to cited text no. 18
    
19.
Cheng F, Li W, Liu G, Tang Y. In silico ADMET prediction: Recent advances, current challenges and future trends. Curr Top Med Chem 2013;13:1273-89.  Back to cited text no. 19
    
20.
Yazdanian M, Glynn SL, Wright JL, Hawi A. Correlating partitioning and caco-2 cell permeability of structurally diverse small molecular weight compounds. Pharm Res 1998;15:1490-4.  Back to cited text no. 20
    
21.
Ma XL, Chen C, Yang J. Predictive model of blood-brain barrier penetration of organic compounds. Acta Pharmacol Sin 2005;26:500-12.  Back to cited text no. 21
    
22.
Upadhyay RK. Drug delivery systems, CNS protection, and the blood brain barrier. Biomed Res Int 2014;2014:1-38.  Back to cited text no. 22
    
23.
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev Drug Discov 2004;3:935-49.  Back to cited text no. 23
    
24.
Tripathi MK, Singh P, Sharma S, Singh TP, Ethayathulla AS, Kaur P. Identification of bioactive molecule from Withania somnifera (Ashwagandha) as SARS-CoV-2 main protease inhibitor. J Biomol Struct Dyn. July 2020:1-14. [doi: 10.1080/07391102.2020.1790425].  Back to cited text no. 24
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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