Open Access | Peer-reviewed | Research Article
Syed Hussain Basha*
Innovative Informatica Technologies, Hyderabad – 500 049, Telangana, India.
Sasi priya SVS
Faculty of Pharmacy, MS. Ramaiah University of Applied Sciences, Bangalore – 560054, India.
Inamul Hasan Madar
Department of Biotechnology, Bharathidasan University, Tamil Nadu – 620024, India.
Syed Heena Kousar
Faculty of Pharmacy, MS. Ramaiah University of Applied Sciences, Bangalore – 560054, India.
Faculty of Pharmacy, MS. Ramaiah University of Applied Sciences, Bangalore – 560054, India.
Department of Computer Science, Aligarh Muslim University, Uttar Pradesh – 202002, India.
Published: February 06, 2021 DOI: 10.5281/zenodo.4564110
A novel corona virus, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused about 100 million infections and is responsible for more than 2 million deaths worldwide. The new disease caused by this SARS-CoV-2 is named Coronavirus Disease 2019 (COVID-19) by World Health Organization (WHO). There is no specific drug approved to combat the COVID-19 to date. In the current study, an In-silico screening of Indian natural compounds database approach was deployed to find potential inhibitors of SARS-CoV-2 targeting 3-chymotrypsin like protein (3CLpro). The protease 3CLpro is a potential drug target due to its critically important role in processing the polyproteins that are involved in viral replication. The Indian natural compounds library Indian Medicinal Plants, Phytochemistry And Therapeutics (IMPPAT) database, containing over 9500 manually curated compounds, were filtered and 1158 compounds were screened against the 3CLpro target protein. A combination of virtual screening, molecular docking and molecular dynamic simulations were employed for this screening purpose. Out of the 1158 compounds screened, Gmelanone and Litseferine are the two compounds which have shown to be having best binding capabilities along with preferable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Both Gmelanone and Litseferine have shown significant interactions with active site residues of 3CLpro target protein. Our present finding are deemed further in-vitro and in-vivo studies towards developing novel anti-SARS-CoV-2 drugs based on the structure and properties of the compounds identified in this study.
Keywords: SARS-CoV-2, COVID-19, 3CLpro, Gmelanone, Litseferine.
|Citation: Syed Hussain Basha et.al, (2021) In-silico screening of Indian medicinal plants active constituents database revealed Gmelanone and Litseferine as potential 3CLpro antagonists targeting SARS-CoV-2, Journal of PeerScientist 4(1): e1000030.|
|Received: September 10, 2020; Accepted: February 02, 2021; Published: February 06, 2021.|
|Copyright:© 2021 Syed Hussain Basha et.al, This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
|Data Availability: All relevant data are within the paper and its Supporting Information files.|
|Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.|
|Competing interests: The authors have declared that no competing interests exist.|
|* E-mail: firstname.lastname@example.org; email@example.com | Phone: + (91)–9177247605|
A moderate respiratory illness which is caused by novel coronavirus was reported in December 2019 in Wuhan city of China and there occurred an immediate action by the Chinese health authorities and researchers , naming the coronavirus disease as covid-2019 and the virus that causes it . The first article related to novel coronavirus was published on January 21, 2020 revealed the family of coronavirus i.e. bat coronavirus HKU9-1 which is similar to SARS-coronavirus. These viruses are found to cause severe respiratory, hepatic, gastrointestinal diseases and nerve-related problems too depending upon infected individual .
These coronaviruses are single-stranded positive-sense RNA viruses that consists of large viral RNA genomes . The studies showed that the SARS-CoV-2 has 80% similarities in organization of genomic sequence with that of beta coronaviruses which consists of a replicase complex encoding non-structural proteins, a spike protein, 5’-untranslated region, envelope protein, a nucleocapsid protein, a membrane protein, 3’UTR and unidentified non-structural open reading frames , this despite diversity in sequence and its spike proteins will strongly interacts with human ACE2 receptor and cause severe damage to lungs . Upon transcription of genome, beta coronaviruses produce ~800 kDa polypeptide which is cleaved proteolytically to generate various proteins. This proteolytic process is mediated by 3-chymotrypsin like protein (3CLpro) and papain like protease (PLpro). The generated poly proteins were cleaved by this 3CLpro at 11 distinct sites in order to get various non-structural proteins which plays an important in viral replication . Thus 3CLpro is responsible for virus replication and unlike other encoding genes, it is located at 3’ end and so exhibits numerous variability. Hence, it became a potential drug target for COVID-19 treatment . The symptoms of COVID-19 include fever, dry cough, fatigue, difficulty in breathing, sputum production, muscle pain and joint pain and severe head ache which may arise within 2-14 days after corona attack and depending upon age, sex and other health issues like cardiovascular diseases, diabetes mellitus, hypertension and the severity of symptoms i. e or severe, the person may die within 6 to 41 days period after virus attack [3, 8].
According to WHO, daily cases were increasing rapidly and as of now there were 25,656,354 COVID-19 cases had been recorded all over the world and out of the total diagnosed cases, there were 855,134 people were died and 17,955,191 people were recovered . This simple data from all over the world shows how rapidly this coronavirus has been spreading but the mortality rate shows that, this virus is comparatively less dangerous than Ebola virus and the recovered people number creating the positive impact and giving hope to the one who are fighting against corona . So, accordingly it had started first in china in the end of 2019 and statistical data of January, February and March month [11-12] reveals that, in 2nd week of February there increased the number of cases and by the 1st week of march it came to below 100 and now recently in the end of July there recorded a few new cases but are still less than 100 per day as shown in the figure 1a. Whereas in Italy, the scenario is similar to china but the spike rate is high in the month of March and April  and slowly the rate was decreased. Therefore an inconsistent graph (figure 1b) has been observed in the all months till August [10-11]. But, in India the scenario is entirely different because, the COVID-19 cases were increasing day by day and as of today there are nearly 37 lakh cases reported in India [10,12] which is still in increasing  when compared to other countries. In India, the COVID-19 cases were started rapidly from May and from the month of July the cases were increasing drastically and it continues increasing with each passing days as shown in the figure 1c.
Figure 1: Statistical Data of Covid-19 cases and deaths in a) China, b) Italy and c) India.
At present, single effective antiviral treatment for COVID-19 is unavailable but a combination of antiviral, antibiotics, corticosteroids and immunomodulators are adding beneficial therapy. However, drug repositioning has become main tool to several researchers in finding of effective therapy  because traditional methods will take more time and now the present situation is demanding the researchers to find out the effective medicine and / or vaccine as early as possible and also in the view of economic aspects, the experimental tools and materials required for drug discovery are out of reach to many researchers due to its high price. So, the computational study has brightened aside which requires less price and time compared to traditional methods [17-18]. So, in search of effective drug many researchers all over the world are following drug repositioning includes all FDA approved drugs for various diseases . As a result, here is the list of drugs that are used for this pandemic treatment listed in table 1.
Table 1: List of FDA approved drugs used for treating coronavirus disease:
As these drugs are the derivatives of traditional phytochemical constituents of various valuable medicinal plants. Hence we are focusing on the traditional phytochemical constituents of valuable medicinal plants. So, the present study was aimed to dig out the bioactive phytochemical constituents which can act against this pandemic diseases (COVID-19).
Results & Discussion:
We performed the virtual screening studies and predicted the ADMET properties for all 1180 ligands which were obtained from a total of 60 medicinal plants. Using PyRx AutoDock Vina and Data Warrior software we screened the binding affinity and ADMET properties of ligands towards COVID-19 main protease 3CLpro protein designing grid box covering whole protein, obtained docking score/ binding energy were analyzed thoroughly. From the analysis, it was found that out of 1180 ligands 544 compounds are obeying Lipinski’s rule and almost 60% of compounds had exhibited the docking score ≥ -5 indicating the their binding affinity towards 3CLpro protein. Out of all, two compounds namely Gmelanone (C20H16O7) and Litseferine (C18H17NO4) had exhibited the highest binding energy of -8.5 and those compounds were extracted from two different plants i.e. Gmelina arborea and Litsea glutinosa respectively. Therefore, Molecular docking studies were performed for further analysis of these 2 compounds.
Molecular docking Studies
Comprehensive details of interactions and binding affinity, we performed molecular docking studies, where the results obtained for binding affinity and interactions suggests that the natural ligands identified from those plants may prove more useful candidates for COVID-19 therapy.
Gmelanone is the chemical constituent derived from the plant Gmelina arborea had exhibited good binding energy of -5.88 Kcal/mol and pIC50 value of 49.01 µM with 3CLpro protein (PDB ID: 6LU7) with following interactions conventional hydrogen bonding with LYS5, Pi-Pi T-shaped interaction with TYR126, carbon-hydrogen bonding with GLU290, alkyl and pi-alkyl interaction with LYS137 and vanderwaal force of attraction with VAL 125, ALA7, GLU127, SER139, GLU138 and CYS128 A-chain amino acid residues which were shown in figure 2a.
Litseferine is the chemical constituent derived from the plant Litsea glutinosa had exhibited good binding energy of -6.56 Kcal/mol and pIC50 value of 15.48 µM with 3CLpro protein with following interactions like conventional hydrogen bonding with GLN110, ASP153; Pi-alkyl bonding with ILE106, carbon-hydrogen bonding with ARG105, ILE106, THR292 and vanderwaal force of attraction with SER158, ILE152, ASN151, PHE8, PHE294, THR111, ASP295, LYS102, VAL104, GLN107 of A-chain amino acid residues which were shown in figure 2b.
Figure 2: Molecular interactions observed between 3CLpro and a) Gmelanone and b) Litseferine.
MD simulations of 3CLpro protease in complex with Gmelanone
The RMSD of proteins backbone was fluctuating between 1.8 and 3.5 A0 with an average of 2.5 A0 whereas the cα, heavy atoms, side chains were fluctuated up to 2.0, 2.4, 3.4 A0 respectively (figure 3a) which was comparatively less than the apo protein backbone RMSD. Corresponding with proteins backbone, the ligands RMSD was found to be fluctuated between 5 to 7A0 but with its own structure the ligand exhibiting minimal fluctuations ranging around 0.5 A0 throughout the simulation time.
Figure 3: Simulation interaction analysis of Gmelanone in complex with 3CLpro protease showing a) Root mean square deviation (RMSD) b) Root mean square fluctuations (RMSF) c) Protein Secondary structure elements (%SSE) d) ligand interaction fractions represented in stacked bar charts normalized over the course of the trajectory: for example, a value of 0.7 suggests that 70% of the simulation time the specific interaction is maintained e) Ligand protein property profile f) protein-ligand interactions of 3CLpro protease complexed with gmelanone and g) torsions of gmelanone plot summarizing the conformational evolution of every rotatable bond (RB) in the ligand throughout the simulation trajectory.
In RMSF, to find the higher flexible regions in the protein, each residue fluctuations were calculated and averaged for the entire 100ns simulated timescale. From the graph analysis (figure 3b) of 3CLpro protease with gmelanone, it was noticed that there occurred minimal fluctuations indicating that the residues are quite stable in the presence of gmelanone. We have also monitored the total secondary structure elements (SSE) like alpha helices and beta strands present in the protein throughout the simulation trajectory. From the analysis it revealed that the protease in complex with gmelanone was maintaining an average of 45% of SSE composition (figure 3c) of the helices and strands throughout the simulated time. Most of the protein is stabilized with strands (blue), helices (red) and loops (white). Ligand protein profile (figure 3e) in complex with 3CLpro protease has also been reported by Desmond software which can predicts the ligand properties like RMSD, ROG of ligand, intra molecular H-Bonds, molecular surface area (MoLSA), polar surface area (PSA) and also the solvent accessible surface area (SASA) of ligand during simulated length of time scale. The ligands RMSD was fluctuated between 0.8 to 1.8 A0 with an average of 1.2A0 indicating the ligand is stable inside the active site of the protein throughout the simulated time. Whereas the ROG of ligand was stabilized around 4.2A0 and there were no intramolecular hydrogen bonds detected. The MoLSA, PSA and SASA were found to be maintained around 310, 136, 200A0 respectively.
Molecular interactions of 3CLpro protease in complex with gmelanone during Molecular dynamic simulations
Desmond’s software was used to interpret molecular interactions of the complex in detail. There found 16 contacts between 3CLpro protease and gmelanone in which 6 contacts were involved in hydrogen bonds, 8 contacts were involved in hydrophobic interactions and 5 contacts in water briding interactions respectively as shown in figure 3d,f. The table 2 shows the molecular interaction profile of gmelanone with the 3CLpro protease. Hydrogen bonds with ASN142, GLY143, SER144, CYS145, GLU166, GLN189; Hydrophobic interactions with HIS41, THR45, SER46, MET49, HIS163, MET165, LEU167, PRO168; water bridging interactions with ASN142, GLU166, PRO168, GLN189, THR190. We have also analyzed rotatable bonds in the ligand. The analysis shows the 2D schematic of a ligand with color-coded rotatable bonds. Each rotatable bond torsion is accompanied by a dial plot and bar plots of the same color. Dial (or radial) plots describe the conformation of the torsion throughout the course of the simulation. The beginning of the simulation is in the center of the radial plot and the time evolution is plotted radially outwards. The bar plots summarize the data on the dial plots, by showing the probability density of the torsion. If torsional potential information is available, the plot also shows the potential of the rotatable bond (by summing the potential of the related torsions). The values of the potential are on the left Y-axis of the chart in Kcal/mol. For Gmelanone there presents a total of 2 rotatable bonds consuming an equal amount of energy 3.41 Kcal/mol of energy which are shown in the figure 3g.
Table 2: Molecular interactions profile of Gmelanone with 3CLpro protease during MD simulations:
MD simulations of 3CLpro protease in complex with Litseferine
The RMSD (figure 4a) of proteins backbone was fluctuating between 1.0 and 2.0 A0 with an average of 1.5 A0 whereas the cα, heavy atoms, side chains were fluctuated around 2.2, 2.5, 3.0 A0 respectively, which was comparatively similar to that of apo protein backbone RMSD. Corresponding with the proteins backbone, the ligands RMSD was found to be fluctuated between 1.5A0 but with its own structure the ligand exhibiting minimal fluctuations below 0.5A0 throughout the simulation time.
In RMSF (figure 4b), to find the higher flexible regions in the protein, each residue fluctuations were calculated and averaged for the entire 100ns simulated timescale. From the graph analysis of 3CLpro protease with Litseferine, it was noticed that there occurred minimal fluctuations indicating that the residues are quite stable in the presence of Litseferine. We have also monitored the total secondary structure elements (SSE) like alpha helices and beta strands present in the protein throughout the simulation trajectory. From the analysis it revealed that the protease in complex with Litseferine was maintaining an average of 45% of SSE composition (figure 4c) of helices and strands throughout the simulated time. Most of the protein is stabilized with strands (blue), helices (red) and loops (white). Ligand protein profile (figure 4e) in complex with 3CLpro protease has also been reported by Desmond software which can predicts the ligand properties like RMSD, ROG of ligand, intra molecular H-Bonds, molecular surface area (MoLSA), polar surface area (PSA) and also the solvent-accessible surface area (SASA) of ligand during the simulated length of time scale. The ligands RMSD was fluctuated between 0.15 and 0.30A0 with an average of 0.20A0 indicating the ligand is stable inside the active site of the protein throughout the simulated time. Whereas the ROG of ligand was stabilized around 3.54A0 and there are no intra molecular hydrogen bonds detected. The MoLSA, PSA and SASA were found to be maintained around 80, 110, 150A0 respectively.
Figure 4: Simulation interaction analysis of Litseferine in complex with 3CLpro protease showing a) Root mean square deviation (RMSD) b) Root mean square fluctuations (RMSF) c) Protein Secondary structure elements (%SSE) d) ligand interaction fractions represented in stacked bar charts normalized over the course of the trajectory: for example, a value of 0.7 suggests that 70% of the simulation time the specific interaction is maintained e) Ligand protein property profile f) protein-ligand interactions of 3CLpro protease complexed with Litseferine and g) torsions of Litseferine plot summarizing the conformational evolution of every rotatable bond (RB) in the ligand throughout the simulation trajectory.
Molecular interactions of 3CLpro protease in complex with Litseferine during Molecular dynamic simulations
There found 16 contacts between 3CLpro protease and Litseferine in which 3 contacts were involved in hydrogen bonding, 6 contacts involved in hydrophobic interactions and about 10 contacts involved in water bridging respectively. The table 3 shows the molecular interaction profile of Litseferine (figure 4d,f) with the 3CLpro protease. Polar interactions with HIS41, THR190, GLN192, GLN189; Hydrophobic interactions with MET165, LEU167, ALA193, PRO168; negatively charged bonding with GLU166.
Table 3: Molecular interactions profile of Litseferine with 3CLpro protease during MD simulations:
We have also analyzed rotatable bonds in the ligand. For Litseferine there presents a total of 2 rotatable bonds consuming an amount of energy 7.83 and 3.36 Kcal/mol of energy which are shown in the figure 4g.
The analysis of results suggests that, out of 1158 compounds which were screened against SARS-CoV 2 3CLpro protease, two compounds were found to be more potent in binding with the protein target and these two compounds namely Gmelanone and litseferine were obtained from two different plants i.e. Gmelina arborea and Litsea glutinosa respectively. Interestingly, the antiviral properties of these two plants were not mentioned in the IMPPAT database. Molecular interactions with CYS145, MET165, PRO168, HIS41, GLN189, HIS163, ASN142, SER144, GLY143 and GLU166 were found to be critical for Gmelanone binding. While, molecular interactions with MET165, LEU167, ALA193, PRO168, HIS41, THR190, GLN192, GLN189 and GLU166 were found to be critical for Litseferine binding. We believe that the knowledge of atomic-level interactions revealed in this present study critical for compounds binding with 3CLpro is of high value when further optimizing or designing novel inhibitors against 3CLpro of SARS-CoV-2 in specific, based on the structure and properties of the compounds identified in this study. However, the present finding are deemed further in-vitro and in-vivo studies based validation towards developing novel anti-SARS-CoV-2 drugs.
Materials & Methods
Target collection and preparation
The structure of the main protease 3CLpro was retrieved from the pool of the protein receptors in the protein data bank (https://www.rcsb.org/). We identified the crystal structure of SARS-CoV-2 main protease in complex with an inhibitor N3 (PDB ID: 6LU7) which is determined from X-ray crystallography diffraction with a resolution of 2.16Ao, classified as viral protein Bat SARS-like coronavirus organism and E.coli BL21 (DE3) expression system and composed of 3 domains namely domain I (residue 8-101), domain II (102-184), domain III (201-303) and along with a long loop (185-200) binding from domain II to domain III. For the preparation of 3CLpro protease, all residues, Hetatm and water molecules were removed while polar hydrogens, kollman and computer gasteiger charges were added to produce favourable bonding for molecular docking.
Ligands collection and preparation
The phytochemical constituents (ligands) of 60 valuable Indian medicinal plants were collected using IMPPAT database  and few plant constituents were collected form literature survey and the chemical structures of the those phytoconstituents were retrieved from Pubchem (http://pubchem.nchi.nlm.nih.gov) database. A total of 1180 ligands were extracted from IMPPAT and Pubchem database. Those obtained compounds were optimized by minimizing the energy for proper binding with the protein.
For carrying out any biological activity, a molecule should obey Lipinski’s rule of five  i.e.,
- Molecular weight should not exceed ≥ 500 daltons
- Hydrogen bond donors should not be < 5
- Hydrogen bond acceptors should be < 10
- Partition coefficient clogP > 5
- of rotatable bonds should be < 10
Data warrior software was used to determine the ADMET properties of the compounds. This rule won’t define the pharmacological activity but speaks about the pharmacokinetic properties of the drug and mostly used in drug discovery.
Virtual screening of the 1158 compounds which passed the ADMET test towards 3CLpro protein was done using PyRx AutoDock Vina (Version 1.1.2). Basically, it is used to filter off the compounds which were showing good binding energy values from the pool of compounds and the actual molecular docking and interaction analysis were identified using AutoDock version 1.5.6 default protocol, explained in detail elsewhere . This version helps us to identify the binding energy, pIC50 values and biding pose of the compounds in detail. It employs two steps grid generation and docking. Grid generation builds a compound wall directing the ligand to bind in a particular area and the dimensions include X-axis = 126, Y-axis =126 and Z-axis =126 with the spacing of 0.375 and docking uses Lamarckian genetic algorithms which creates 2500000 evolutions and generates 27000 docking poses and finally shows us the top 10 best outfitted ligands with the protein. Interaction analyses were done using Bovia Discovery Studio Visualizer  and all the interaction diagrams were obtained using Bovia Discovery Studio Visualizer software.
Molecular dynamic simulations
Molecular dynamic simulations were performed to understand the binding interactions in the molecular level and also to analyze those interactions at the atomic level using default protocol as described in detail elsewhere . In brief, OPLS 2005 force field  parameters have been applied to simulation TIP3P water models  at neutral pH conditions. Periodic boundary conditions were used to determine the specific size and shape of the water box buffered at 10 Å distances and box volume was calculated as ~370000 cubic Ås of simulation box volume respectively. During the equilibration process, van der Waals and short-range electrostatic interactions were cut off at 9 Å and long-range electrostatic interactions were computed using the Particle Mesh Ewald method . A RESPA integrator  was used with a time step of 2 fs, and long-range electrostatics were computed every 6 fs. One system for each of the four conditions containing approximately 37056 atoms were equilibrated using Desmond in the NPT ensemble at 300 K temperature and 1 bar using the Nose-Hoover chain relaxation thermostat method along with Martyna-Tobias-Klein relaxation Barostat method with isotropic coupling style at 1ps & 2ps timescale respectively. Before starting the analysis, we have made sure that all the simulations were carried out at the same temperature, pressure and volume conditions throughout the simulated timescale. As part of the simulation quality analysis, it was revealed that the simulated systems’ average total energy remained approximately -96000 kcal/mol.
Authors’ contribution: SHB designed the study. SHB, SP, SHK & JN executed the work and collected the data. SHB, SP, IHM, SHK, JN & GS analyzed the results. SHB, SP, IHM & GS wrote and edited the manuscript. All authors have read and approved the final manuscript.
- Xu, Xintian, et al. “Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission.” Science China Life Sciences3 (2020): 457-460.
- Ji, Wei, et al. “Cross‐species transmission of the newly identified coronavirus 2019‐nCoV.” Journal of medical virology4 (2020): 433-440.
- Hassan, Syed Adeel, et al. “Coronavirus (COVID-19): a review of clinical features, diagnosis, and treatment.” Cureus3 (2020).
- Chen, Yu, Qianyun Liu, and Deyin Guo. “Emerging coronaviruses: genome structure, replication, and pathogenesis.” Journal of medical virology4 (2020): 418-423.
- Zhu, Na, et al. “A novel coronavirus from patients with pneumonia in China, 2019.” New England journal of medicine(2020):727-733
- Anand, Kanchan, et al. “Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs.” Science5626 (2003): 1763-1767.
- Needle, Danielle, George T. Lountos, and David S. Waugh. “Structures of the Middle East respiratory syndrome coronavirus 3C-like protease reveal insights into substrate specificity.” Acta Crystallographica Section D: Biological Crystallography5 (2015): 1102-1111.
- Marinho, Emanuelle Machado, et al. “Virtual screening based on molecular docking of possible inhibitors of Covid-19 main protease.” Microbial Pathogenesis148 (2020): 104365.
- Ritchie, Hannah. “Coronavirus source data.” European Center for Disease Prevention and Control. (2020).
- info. 2020. Coronavirus Update (Live): 25,909,460 Cases And 861,346 Deaths From COVID-19 Virus Pandemic – Worldometer. [online] Available at: https://www.worldometers.info/coronavirus/
- Sarkodie, Samuel Asumadu, and Phebe Asantewaa Owusu. “Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques.” Heliyon4 (2020): e03747.
- GitHub: Owid/Covid-19-Data. [online] (2020) Available at: https://github.com/owid/covid-19-data/tree/master/public/data
- De Natale, Giuseppe, et al. “The COVID-19 infection in Italy: a statistical study of an abnormally severe disease.” Journal of clinical medicine5 (2020): 1564.
- Livingston, Edward, and Karen Bucher. “Coronavirus disease 2019 (COVID-19) in Italy.” Jama14 (2020): 1335-1335.
- Hoseinpour Dehkordi, Amirhoshang, et al. “Understanding epidemic data and statistics: A case study of COVID‐19.” Journal of medical virology7 (2020): 868-882.
- Karim, Hira, and Muhammad Shahzeb Khan. “A Systematic Review on Coronavirus Disease 2019 (COVID-19).” Journal of PeerScientist 3.2 (2020) : e1000025.
- de Oliveira, Osmair Vital, et al. “Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening.” Journal of Biomolecular Structure and Dynamics(2020): 1-10.
- Anand, Kanchan, et al. “Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs.” Science5626 (2003): 1763-1767.
- Basha, Syed Hussain. “Coronavirus drugs–a brief overview of past, present and future.” Journal of PeerScientist2 (2020): e1000013.
- FDA Drug Safety Communication: FDA cautions against use of hydroxychloroquine or chloroquine for COVID-19 outside of the hospital setting or a clinical trial due to risk of heart rhythm problems. (2020).
- Pastick, Katelyn A., et al. “Hydroxychloroquine and chloroquine for treatment of SARS-CoV-2 (COVID-19).” Open Forum Infectious Diseases. Vol. 7. No. 4. US: Oxford University Press, 2020.
- CYP2C8: The Pharmacogene Variation (PharmVar) Consortium, https://pharmvar.org/gene/CYP2C8 (2020).
- CYP2D6: The Pharmacogene Variation (PharmVar) Consortium, https://pharmvar.org/gene/CYP2D6 (2020).
- CYP3A4: The Pharmacogene Variation (PharmVar) Consortium, https://pharmvar.org/gene/CYP3A4 (2020).
- Takahashi, Takuto, et al. “Pharmacogenomics of COVID-19 therapies.” npj Genomic Medicine1 (2020): 1-7.
- Goldman, Jason D., et al. “Remdesivir for 5 or 10 days in patients with severe Covid-19.” New England Journal of Medicine19 (2020): 1827-1837.
- Madelain, Vincent, et al. “Ebola virus infection: review of the pharmacokinetic and pharmacodynamic properties of drugs considered for testing in human efficacy trials.” Clinical pharmacokinetics8 (2016): 907-923.
- Allegra, Sarah, et al. “Role of pharmacogenetic in ribavirin outcome prediction and pharmacokinetics in an Italian cohort of HCV-1 and 4 patients.” Biomedicine & Pharmacotherapy69 (2015): 47-55.
- CYP3A4: The Pharmacogene Variation (PharmVar) Consortium, https://www. pharmvar.org/gene/CYP3A4 (2020)
- Jiménez Morales, Alberto, et al. “FCGR2A/FCGR3A gene polymorphisms and clinical variables as predictors of response to tocilizumab and rituximab in patients with rheumatoid arthritis.” The Journal of Clinical Pharmacology4 (2019): 517-531.
- Jakafi (ruxolitinib) [package insert]. https://www.accessdata.fda.gov/drugsatfda_docs/label/2011/202192lbl.pdf (2011).
- Olumiant (baricitinib) [package insert]. https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/207924s000lbl.pdf (2018).
- Link, Jenny, et al. “Human leukocyte antigen genes and interferon beta preparations influence risk of developing neutralizing anti-drug antibodies in multiple sclerosis.” PloS one3 (2014): e90479.
- Trezza, Alfonso, et al. “An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors.” Scientific reports1 (2020): 1-8.
- Mohanraj, Karthikeyan, et al. “IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics.” Scientific reports1 (2018): 1-17.
- Lipinski, Christopher A. “Chris Lipinski discusses life and chemistry after the Rule of Five.” Drug discovery today1 (2003): 12-16.
- Reddy, S. V. G., et al. “Molecular docking and dynamic simulation studies evidenced plausible immunotherapeutic anticancer property by Withaferin A targeting indoleamine 2, 3-dioxygenase.” Journal of Biomolecular Structure and Dynamics12 (2015): 2695-2709.
- Biovia, Dassault Systèmes. “Discovery studio visualizer.” San Diego, CA, USA936 1.0 (2019)
- Sastry, V. Girija, and Syed Hussain Basha. “3, 5-dinitrophenyl clubbed azoles against latent tuberculosis-a theoretical mechanistic study.” Journal of PeerScientist1 (2018): e1000001.
- Jorgensen, William L., and Nora A. McDonald. “Development of an all-atom force field for heterocycles. Properties of liquid pyridine and diazenes.” Journal of Molecular Structure: THEOCHEM1-2 (1998): 145-155.
- Mark, Pekka, and Lennart Nilsson. “Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K.” The Journal of Physical Chemistry A43 (2001): 9954-9960.
- Darden, Tom, Darrin York, and Lee Pedersen. “Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems.” The Journal of chemical physics12 (1993): 10089-10092.
- Tuckerman, M. B. B. J. M., Bruce J. Berne, and Glenn J. Martyna. “Reversible multiple time scale molecular dynamics.” The Journal of chemical physics3 (1992): 1990-2001.