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Research Detail

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Abdirahman ELMI*
Medicinal Research Institute, Centre d’Etudes et de Recherche de Djibouti, IRM-CERD, Route de l’Aéroport, Djibouti

S. Al Jawad SAYEM
International Islamic University Chittagong, Department of pharmacy, Bangladesh

Mohamed AHMED
Medicinal Research Institute, Centre d’Etudes et de Recherche de Djibouti, IRM-CERD, Route de l’Aéroport, Djibouti

Fatouma MOHAMED
Medicinal Research Institute, Centre d’Etudes et de Recherche de Djibouti, IRM-CERD, Route de l’Aéroport, Djibouti

The new coronavirus type SARS-Cov 2 (severe acute respiratory syndrome), which appeared in autumn 2019 in China, became a global pandemic in a few months. In this work, we looked for the potential anti SARS-Cov 2 of the compounds isolated from three Djiboutian medicinal plants namely Acacia seyal, Cymbopogon commutatus, and Indigofera caerulea. For this we carried out a docking with nine biomolecules, β-Sitosterol , Quercetin, Catechin, Lupeol, Rutin, Kaempferol, Gallic acid, Piperitone and Limonene on three target sites which are SARS-CoV-2 main protease (Mp), SARS-CoV-2 receptor binding domain (RBD) and human furin protease. These targets are chosen because of their role in the process of penetration of the virus into human cells and its multiplication. The phenolic compounds have a very good afinity on these three target sites with binding energies of up to -9.098 kcal/mol for rutin on SARS-CoV-2 Mp, much better than the two reference drugs hydroxychloroquine (-5.816 kcal / mol) and remdesivir (-7.194 kcal/mol). These natural compounds do not present toxicities and can be used pending In vitro and In vivo evaluations.

  Bio molecules, Djibouti medicinal plant, Anticovid 19, Molecular docking
  In Bangladesh
  
  
  Development of Host and Medicinal Plants
  Medicinal Plants, COVID-19

As part of the promotion of Djiboutian medicinal plants, various bioactive compounds have been isolated for their antimicrobial and anticancer effects. In this present study we will evaluate the potential anticovid therapeutics of these biomolecules through molecular simulation on the targets SARSCoV-2 RBD, SARS-CoV-2 main protease, and human furin protease. We will determine the energies of molecule-target virus interaction, ADME (absorption, distribution, metabolism, and excretion) as well as possible toxicities generated from these molecules.

1. Study compound The nine compounds tested are β-Sitosterol, Quercetin, Catechin, Lupeol, Rutin, Kaempferol, Gallic acid, Piperitone, and Limonene. They were isolated from three Djiboutian medicinal plants: Acacia seyal, Cymbopogon commutatus, and Indigofera caerulea. The extractions and isolations of these compounds are described in our previous publications (Elmi et al. 2018; Elmi 2018; Elmi et al. 2020). Two drugs against covid are used for comparison: Remdesivir and Hydrochloroquine. 

2.1. Proteins and Chemical Compounds Studied In This Investigation Three proteins were selected for the purpose of this study; 1.SARS-CoV-2 main protease (PDB ID: 5R84) (Fearon et al. 2020), 2.Human furin protease (PDB ID:5MIM), and 3.SARS-CoV-2 receptor-binding domain (PDB ID: 6VW1)(Shang et al. 2020). Nine compounds were also selected; 1.β-Sitosterol (PubChem CID 222284), 2.Quercetin (PubChem CID 5280343), 3.Catechin (PubChem CID 9064), 4.Lupeol (PubChem CID 259846), 5.Rutin (PubChem CID 5280805), 6.Kaempferol (PubChem CID 5280863), 7.Gallic acid (PubChem CID 370), 8.piperitone (PubChem CID 6987), 9.Limonene (PubChem CID 22311), along with two reference drugs remdesivir (PubChem CID 121304016), and hydroxychloroquine (PubChem CID 3652).

2.2. Molecular Docking: Preparation of Ligand The chemical structures of eleven selected compounds were obtained from PubChem an online repository of chemical compounds (https://pubchem.ncbi.nlm.nih.gov/). The structures were obtained in 2D SDF format. A bioinformatics tool called LigPrep was used to perform ligand preparation. LigPrep is set in Schrödinger suite-Maestro (v 11.1). The following parameters were taken into consideration during this job: the structure was set as a project table, the force field was set at OPLS3, the target pH was 7.0 ± 2.0 using Epik and the output format was Maestro.

2.3. Molecular Docking: Preparation of Protein The desire proteins were taken from Protein Data Bank (PDB) an online database (https://www.rcsb.org/). The three-dimensional protein structures were downloaded in pdb format (Berman et al. 2002). The Resolution was 1.83 Å, 1.9 Å, and 2.68 Å of selected proteins with PDB ID: 5R84, 5MIM, and 5R84 respectively. Preprocessing, optimization, and minimization were done by using the Protein Preparation Wizard for preparing the proteins (Friesner et al. 2004). This wizard is also included in Schrödinger suite-Maestro (v 11.1). The following parameters were used in this job; the structures were optimized at pH 7.0, remove waters with less than 3 H-bond to non-waters, and minimized the proteins using OPLS3 force field. Then generate the receptor grid by using PockDrug an online tool for selecting the best docking site. 

2.4. Molecular Docking: Glide Molecular Docking The molecular docking was performed to understand the possible mechanism of the selected compound comparing with two reference drugs against the receptors associate with COVID19 and human. The docking was completed by using the Ligand Docking tool attaches in Schrödinger suite-Maestro (v 11.1). Then the spreadsheet and 2d interaction figures were collected for further study. Discovery Studio (v 4.1) software was used for more understanding via 3d visualization (Discovery Studio 2008).

2.5. Prediction of the Pharmacokinetic Parameter (ADME) Several pharmacokinetic properties such as absorption, distribution, metabolism, excretion (ADME) are important to developing a drug. The following properties are investigated by SwissADME an online tool to determine various biochemical properties (http://www.swissadme.ch/). Some parameters were determined for evaluating the compounds from the SwissADME database based on Lipinski’s and Veber’s Rules. The following parameters were molecular weight, hydrogen bond acceptor, hydrogen bond donor, log P value, Lipinski’s Violations value, number of the rotatable bond (NRB), and topological polar surface area (TPSA).

2.6. Prediction of Toxicological Properties Toxicological determination is the most prime considerations in case of the development of new drugs. An online bioinformatics tool named AdmetSAR was used to evaluate the toxicological properties of desired compounds. The following parameters were counted in this study such as rat acute toxicity, acute oral toxicity, ames toxicity, and carcinogenic properties. 

  Journal of Ethnopharmacology 115 (3): 387-408. https://doi.org/10.1016/j.jep.2007.10.021
  
Funding Source:
1.   Budget:  
  

The Covid 19 currently presents a major challenge in human health. To treat this viral infection different treatments are being tested without getting the real cure so far.

In this present study, we evaluated by In silico test (pathogen-therapeutic molecule target modeling) the therapeutic potential of the biomolecules isolated from three Djiboutian medicinal plants, namely Acacia seyal, Cymbopogon commutatus and Indigofera caerulea.

Phenolic compounds give the best preliminary results with minimized docking scores. On the three targeting sites, rutin has better binding energy than the two drug references Hydroxychloroquine and Remdesivir.

This encouraging result must be confronted with in vitro and in vivo tests to determine the real performance of these biomolecules in the fight against the coronavirus and before clinical trials in humans can be performed.

  Journal
  


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