Cordycepin anti sarcovi 2

Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2 Cordycepin anti sarcovi 2

Received: October 2020  DOI: 10.1111/cbdd.13812 |   Revised: 15 November 2020  |   Accepted: 29 November 2020 RESEARCH ARTICLE Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm Akalesh Kumar Verma1  | Cell and Biochemical Technology Laboratory, Department of Zoology, Cotton University, Guwahati, India Cosmic Cordycep Farms, Badarpur Said Tehsil Tigaon, Faridabad, Haryana, India Correspondence Akalesh K Verma, Cell and Biochemical Technology Laboratory, Department of Zoology, Cotton University, Guwahati 781001, India Email: akhilesh@cottonuniversity.ac.in Rohit Aggarwal2 Abstract The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA-approved and investigational) against SARS-CoV-2 target proteins that facilitates viral entry and replication into the host body To evaluate molecular affinities between antiviral drug candidates and SARS-CoV-2 associated target proteins such as spike protein (S) and main protease (Mpro), a molecular interaction simulation was performed by docking software (MVD) and subsequently the applicability score was calculated by machine learning algorithm Furthermore, the STITCH algorithm was used to predict the pharmacology network involving multiple pathways of active drug candidate(s) Pharmacophore features of active drug(s) molecule was also determined to predict structure–activity relationship (SAR) The molecular interaction analysis showed that cordycepin has strong binding affinities with S protein (−180) and Mpro proteins (−205) which were relatively highest among other drug candidates used Interestingly, compounds with low IC50 showed high binding energy Furthermore, machine learning algorithm also revealed high applicability scores (0.42–0.47) of cordycepin It is worth mentioning that the pharmacology network depicted the involvement of cordycepin in different pathways associated with bacterial and viral diseases including tuberculosis, hepatitis B, influenza A, viral myocarditis, and herpes simplex infection The embedded pharmacophore features with cordycepin also suggested strong SAR Cordycepin's anti-SARS-CoV-2 activity indicated 65% (E-gene) and 42% (N-gene) viral replication inhibition after 48h of treatment Since, cordycepin has both preclinical and clinical evidences on antiviral activity, in addition the present findings further validate and suggest repurposing potential of cordycepin against COVID-19 KEYWORDS 2019nCov, antiviral drugs, cordycepin, coronavirus, drug repurposing |   IN TRO D U C T ION 1  COVID-19 disease is caused by a positive-sense, singlestranded RNA containing novel coronavirus, named severe acute respiratory syndrome coronavirus (SARS-CoV-2) (previously provisionally known as 2019 novel coronavirus; | 2019-nCoV) (Zu et  al.,  2020) The virus, which is now a pandemic (WHO, 2020), has infected at least 54.1 M people across the world, killing 1.31 and 34.8 M people have been recovered (till November, 15, 2020) The clinical symptoms associated with COVID-19 include high fever, mild cough, body aches, lack of smell and taste, self-limiting respiratory 836    wileyonlinelibrary.com/journal/cbdd 2020 John Wiley & Sons Ltd Chem Biol Drug Des 2021;97:836–853 |       837 VERMA and AGGARWAL tract illness to severe progressive pneumonia leading to multi-organ failure leading to death (Shi et  al.,  2020; Liu et al., 2020) The COVID-19 pandemic poses a big challenges in the near future to global public health (Phelan et al., 2020), appealing for the development of effective prophylactics and therapies against the causative agent Due to lack of potent antiviral drug(s) to treat the COVID-19 patients leads to a clamoring to test existing antiviral drugs (alone or in combination) that are previously approved for the use of genetically close human viruses, and the procedure specifically known as drug repurposing (Nishimura & Hara, 2018) Drug repurposing or repositioning of launched or even failed drugs against new emerging viral diseases provides unique opportunities in translational research It bears a substantially higher probability of success as compared to developing new virus-specific drugs and vaccines, in terms of cost, time, and clinical availability (Ianevski et al., 2018) SARS-CoV-2 genome comprised of open reading frames and code for several structural and non-structural proteins (Walls et  al.,  2020) The structural protein includes spike protein (S), membrane protein (M), envelope protein (E), and the nucleocapsid protein (Ncp) (Kahn & McIntosh,  2005; Walls et  al.,  2020) that are tightly bound to surface of the mature virion Spike protein (S) made up of two distinct functional subunits (S1 and S2) responsible for binding (S1 subunit) and fusion (S2 subunit) with the host cell membranes (Kirchdoerfer et  al.,  2016; Walls et  al.,  2020) The distal S1 subunit accommodates receptor-binding domain and stabilized the prefusion state of the membrane-anchored S2 subunit that contains the fusion machinery For all CoVs, S is further cleaved and processed by host proteases, TMPRSS2 (Walls et al., 2020) at the S2′ site and in a recent study it has been reported that a serine protease inhibitor, which act on TMPRSS2 significantly inhibit novel coronavirus entry (Wan et al., 2020) Thus, it is evident that coronavirus entry into susceptible cells is a complex and multistep process that requires the concerted action of receptor-binding and proteolytic processing of the spike protein to promote successful virus-cell fusion (Walls et al., 2017) Furthermore, the maturation of SARS-CoV-2 also required a series of highly complex proteolytic events mediated by main protease (CoV Mpro; also known as 3Cl protease or 3CLpro) on the polyproteins to control viral gene expression (Zhang et al., 2020) Mpro (~306 amino acid) is a cysteine protease with a chymotrypsin-like two-domain fold at the N terminus consists of three functional domains (IIII) The structural analysis of Mpro revealed that two Mpro molecules form an active homodimer A Cys-His catalytic dyad is positioned in a cleft located between domains I and II, and the Mpro N-terminal residues mostly to (or N finger) are involved in the proteolytic activity, whereas, Highlights • Cordycepin showed strong chemical interactions with SARS-CoV-2 RBD domain • Cordycepin served as a pivot molecule against target proteins (S and Mpro) • The repurposing potential of cordycepin was validated by ECFP6 and Bayesian algorithm • Pharmacology network also revealed the involvement of cordycepin in different pathways the C-terminal domain III is reported to be required for dimerization (Xue et al., 2008; Zhang et al., 2020) Most maturation cleavage events within the precursor polyprotein are mediated by the CoV main protease to forestall the spread of disease by restraining the cleavage of the viral polyprotein (Xue et al., 2008) Till now, no clinically proven vaccines or antiviral drug (s) are available for the prevention and treatment of COVID-19 pandemic Due to the gravity of the situation and worldwide rapid spread of SARS-CoV-2, an urgent and complementary efforts are necessary to find new preventive methods The combination of α-interferon and the anti-HIV drugs Lopinavir/Ritonavir (Kaletra®) has been tested at a different levels of infection, but the curative effect is limited due to severe side effect in the host (Cao et al., 2020) A broad-spectrum antiviral drug, remdesivir, (by Gilead Sciences, Inc.) is also in the race of trial for the treatment of COVID-19, but lacking satisfactory data to prove its efficacy (Wang et al., 2020) It is also worth mentioning, the Indian Council of Medical Research (ICMR), under the Ministry of Health and Family Welfare (MHFW), has recommended the use of hydroxychloroquine (400  mg twice on day 1, then 400  mg once a week thereafter) as chemoprophylaxis for asymptomatic healthcare workers directly involved in treating COVID19 patients with suspected or confirmed COVID-19, and for asymptomatic household contacts of confirmed cases (Rathi et al., 2020) Thus, the existing data confirmed that spike protein (S) and main protease (Mpro) in SARS-CoV-2 play vital roles during viral entry, genome replication, and self multiplication in the host body (Huang et al., 2020; Wang et al., 2020; Wrapp et  al.,  2020; Zhang et  al.,  2020) Therefore, in the present study, these protein targets have been utilized during molecular interactions simulation with FDA-approved and investigational drugs using different computational tools The present paper highlights the repurposing potential of known antiviral drug candidates against SARS-CoV-2 Drug Favipiravir Nitazoxanide Remdesivir Mycophenolic acid Chloroquine Niclosamide BCX4430 (Galidesivir) Gemcitabine Rapamycin (Sirolimus) ABT-263 (Navitoclax) Cyclosporine Emetine Ribavirin Luteolin Tilorone (Amixin) Glycyrrhizin Eflornithine Monensin Sl Nos 10 11 12 13 14 15 16 17 18 Approved antibacterial HMPV HEV-B EBV CHIKV SARS-CoV LASV HEV-B HCV FLUBV FLUAV FLUAV DENV LASV KSHV JUNV JUNV ADV ANDV Virus (-)ssRNA (+)ssRNA dsDNA (+)ssRNA (+)ssRNA (-)ssRNA (+)ssRNA (+)ssRNA (-)ssRNA (-)ssRNA (-)ssRNA (+)ssRNA (-)ssRNA dsDNA (-)ssRNA (-)ssRNA dsDNA (-)ssRNA Group Unknown An irreversible inhibitor of ornithine decarboxylase Unknown Interferon inducer Unknown Nucleoside inhibitor Unknown Calcineurin inhibitor Bcl-2/Bcl-xL inhibitor IL-2-dependent T-cell proliferation inhibitor Antimetabolite; Nucleoside analog RNA-dependent RNA polymerase inhibitor Unknown Antimalarial agent; chemo/radio sensitizer Inosine monophosphate dehydrogenase inhibitor Viral RNA polymerase inhibitor Pyruvate inhibitor Viral RNA polymerase inhibitor Mode of action 3.8 0.026 20 1.41254 1.12 0.04 0.00874 0.004 0.46613 0.00018 1–5 0.062 0.1 0.306 0.05 0.032 0.21 0.044 IC50 (µM) DENV-2 Trypanosoma brucei SARS coronavirus Ebola virus Hepatitis C virus Hepatitis C virus Zika virus HCV genotype 1b MOLT-13 HIV1 CVB3 and EV71 Zika virus SARS coronavirus HCoV-OC43 Encephalitis virus MERS-CoV Hepatitis C virus Influenza C virus IC50 reported against Mazzon et al., 2019) AID 1317209 AID 297145 AID 1117314 AID 658257 AID 1306320 (Continues) Bleasel & Peterson, 2020) AID 555954 AID 742372 AID 406228 Kang et al., 2015) Eyer et al., 2017) AID 297149 AID 558298 AID 588727 Gordon et al., 2020) AID 642456 AID 392526 PubChem AID/bioassay record/ References | Approved antiprotozoal Approved anti-inflammatory Approved antiviral Approved anticancer Approved antiviral Approved antiprotozoal Approved immunosuppressant Investigational anticancer Approved immunosuppressant Approved anticancer Approved antiviral Approved antihelminthics Approved antimalarial Approved immunosuppressant Investigational antiviral Approved antiprotozoal Approved antiviral Primary indication T A B L E   Details of drug candidates with their functional annotations used in the present study for target based molecular interaction simulations 838       VERMA and AGGARWAL Drug Arbidol (Umifenovir) Silvestrol Amiodarone Dasatinib Lopinavir Nelfinavir Oritavancin Hydroxychloroquine Ritonavir Dalbavancin Teicoplanin Homoharringtonine Alisporivir Cepharanthine Hexachlorophene Imatinib Nafamostat Chlorpromazine Sl Nos 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 T A B L E   (Continued) Approved antipsychotic agent Approved anticoagulant Approved anticancer Approved antibacterial Approved anti-inflammatory Investigational antiviral Approved anticancer Approved antibacterial Approved antibacterial Approved antiviral Approved antimalarial Approved antibacterial Approved antiviral Approved antiviral Approved anticancer Approved antiarrhythmic Investigational anticancer Approved antiviral Primary indication SARS-CoV MERS-CoV MERS-CoV MERS-CoV HCoV-OC43 HCoV-229E SARS-CoV-2 MERS-CoV MERS-CoV HIV-1 HIV-1 EBOV CHIKV SARS-CoV-2 SARS-CoV RRV MERS-CoV HSV-1 Virus (+)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA ssRNA-RT ssRNA-RT (-)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA dsDNA Group Inhibits assembly of clathrin-coated pits Synthetic serine protease inhibitor Abl; Kit; PDGFRB inhibitor Unknown Unknown Unknown Protein translation inhibitor Peptidoglycan polymerization inhibitor Inhibits cathepsin L HIV protease inhibitor Unknown Transglycosylation and transpeptidation inhibitor HIV protease inhibitor HIV protease inhibitor Abl; Src; Kit; EphR inhibitor Non-competitive ß-adrenergic inhibitor Inhibitor of translation initiation by targeting the RNA helicase Unknown Mode of action 9.15 0.1 1.75 0.46 4.7 0.008 0.139 0.33 0.33 0.00006 8.28 1.73 ~0.95 17 5.47 5.60 0.00189 12.2 IC50 (µM) MERS-CoV MERS corona virus HCMV HT-29 viability SW480 cell HIV1 subtype A Herpes simplex virus type Ebola virus Ebola virus HIV MERS-CoV Ebola virus SIV MERS-CoV MERS-CoV Ebola virus Chikungunya virus Chikungunya virus IC50 reported against (Continues) Bleasel & Peterson, 2020) Yamamoto et al., 2016) Wolf et al., 2012) AID 1117330 AID 745,562 AID 581785 Dong et al., 2018) Zhou et al., 2016) Zhou et al., 2016) AID 82723 Bleasel & Peterson, 2020) Zhou et al., 2016) Deng et al., 2012) Bleasel & Peterson, 2020) Bleasel & Peterson, 2020) Bleasel & Peterson, 2020) Henss et al., 2018) AID 1073347 PubChem AID/bioassay record/ References VERMA and AGGARWAL       839 | Drug Camostat Memantine Indomethacin Saracatinib Telavancin Promethazine Trametinib Mefloquine Cordycepin Sl Nos 37 38 39 40 41 42 43 44 45 T A B L E   (Continued) Investigational anticancer and Phase II (Leukemia) Approved antimalarial Approved anticancer Approved antihistamine Approved antibacterial Approved anticancer Approved anti-inflammatory Approved anti-Alzheimer's Approved anticancer Primary indication HSV-1 JCV FLUAV SARS-CoV MERS-CoV MERS-CoV HIV-1 FLUAV FLUAV Virus dsDNA dsDNA (-)ssRNA (+)ssRNA (+)ssRNA (+)ssRNA ssRNA-RT (-)ssRNA (-)ssRNA Group Polyadenylation inhibitor Unknown Mitogen activated protein-kinase inhibitor Inhibits assembly of clathrin-coated pits Bacterial cell wall synthesis inhibitor Unknown Cyclo-oxygenase (COX) enzyme or prostaglandin G/H synthase inhibitor N-methyl-D-aspartate glutamate receptor antagonist Unknown Mode of action 0.0005 4.36 0.004 11.8 1.89 0.0027 1.5 0.00045 IC50 (µM) Herpes simplex virus type-1 Dengue virus type SW1463 MERS-CoV Ebola virus NIH3T3 SARS coronavirus NMDA SARS-CoV IC50 reported against AID 216185 Bleasel & Peterson, 2020) AID 1340171 Bleasel & Peterson, 2020) Zhou et al., 2016) AID 272211 Amici et al., 2006) AID 388528 Zhou et al., 2015) PubChem AID/bioassay record/ References 840       | VERMA and AGGARWAL |       841 VERMA and AGGARWAL 2  |  M ATE R IA L A N D ME T HODS 2.1  |  Selection and acquisition of chemical compounds The information of broad-spectrum antiviral agents (BSAAs, i.e., compounds targeting viruses belonging to two or more viral families) was collected from a freely accessible database (https:// drugv​ irus.info/) (Andersen et  al.,  2020) The selected drug molecules were belonged to investigational or FDA-approved category against SARS-CoV-2, HCoV-229E, HCoV-OC43, MERS-CoV, and SARS-CoV as well as other human diseases Forty-five potential drug candidates as shown in Table 1 were identified from the PubMed-NCBI literature with strong antiviral activity against genetically close human viruses The present status of all the drug candidates against SARS-CoV-2 target proteins is also shown in Table 2 The 3D structures of all the compounds were downloaded in SDF/Mol file format embedded with 3D properties from the PubChem compound database (http://pubch​ em.ncbi.nlm.nih.gov/), DrugBank (https://www drugb​ank.ca/) and ZINC database (https://zinc.docki​ng.org/) The molecular arrangement and geometry of all the compounds were fully optimized using the semiempirical quantum chemistry method (PM3) (Gogoi et al., 2019) At last, the fully optimized 3D structure of all the drug molecules was exported in Mol2 format and used for molecular interaction simulation using different computational tools 2.2  |  Selection of target protein The SARS-CoV-2 associated target proteins, namely spike protein receptor-binding domain (ID: 6VW1) (Shang et al., 2020) and main protease (6LU7) (Jin et al., 2020), were used in the present study The crystal structures of both the target proteins were obtained from the RCSB Protein Data Bank (http://www.rcsb.org/pdb/home/home.do) These target proteins have been reported for their pivotal roles during SARS-CoV-2 infection, replication, survival, and multiplication in the host body (Liu et al., 2020b; Xi et al., 2020) 2.3  |  Molecular interaction simulation The intermolecular interactions between all the FDAapproved and investigational drug candidates (Table 1) with aforesaid target proteins were studied by using Molegro Virtual Docker (MVD 2010.4.0 software for windows-7, trial) (Kusumaningrum et  al.,  2014; Puspaningtyas,  2014) The active site selection and protein preparations were carried out by the inbuilt program of the software (Thomsen & Christensen,  2006) Moreover, postdocking generated protein–ligand complex along with chemical interactions was further analyzed and visualized by Discovery Studio (Sakkiah et al., 2010) (https://www.3dsbi​ovia.com/produ​cts/colla​borat​ ive-scien​ce/biovi​a-disco​very-studi​o/) and Chimera software (https://www.cgl.ucsf.edu/chime​ra/) (Goddard et  al.,  2007; Thomsen & Christensen, 2006) 2.4  |  Machine learning models for drug repurposing Bayesian machine learning models (from FDA-approved drug screens) from Assay Central software (https://assay​ centr​al.github.io/#) were used (Ekins et al., 2019) for identifying the possibility of compounds that may work against SARS-CoV-2 A total of 45 drug molecules from previously reported screens for antiviral activities against different human viruses were used to validate their potential repurposing ability using Bayesian machine learning models Each model in Assay Central used different metrics for evaluating predictive performance such as recall, precision, specificity, F1-Score, receiver operating characteristic (ROC) curve, Cohen's Kappa (CK), and the Matthews correlation coefficient (MCC) These models utilize extended-connectivity fingerprints of maximum diameter (ECFP6) descriptors generated from the library 2.5  |  Pharmacology network of potent compound(s) Interactions between proteins and bioactive compounds or drugs are an integral part of biological processes in living organisms In the present study, the pharmacology interaction network of the most active drug candidate (s) (in context with molecular interactions) was determined by STITCH (Search Tool for Interacting Chemicals) algorithm The interactions between drugs and receptors include direct (physical) and indirect (functional) associations and generated by computational prediction from knowledge transfer between organisms, and from interactions aggregated from other databases (primary) Interactions in STITCH are derived from different sources such as genomic context predictions, (conserved) coexpression, automated text mining, and previous knowledge in databases (Szklarczyk et al., 2016) 2.6  |  Pharmacophore modeling Pharmacophore features of the most active drug candidate (s) in terms of higher affinity for target proteins were determined using the Ligandscout software which also reveals structural activity relationship (SAR) (Wolber & Langer, 2005) with a specific biological target (s) The fully optimized 3D structure | 842       VERMA and AGGARWAL T A B L E   Status of all the compounds used in the present study against SARS-CoV-2 target proteins for molecular interaction simulation Sl nos Compounds Favipiravir Nitazoxanide Remdesivir Mycophenolic acid Chloroquine Niclosamide BCX4430 (Galdecivir) Gemcitabine Rapamycin (Sirolimus) 10 ABT-263 11 Cyclosporine 12 Emetine 13 Ribavirin 14 Luteolin 15 Tilorone (Amixin) 16 Glycyrrhizin 17 Eflornithine 18 Monensin 19 Arbidol (Umifenovir) 20 Silvestrol 21 Amiodarone 22 Dasatinib 23 Lopinavir 24 Nelfinavir 25 Oritavancin 26 Hydroxychloroquine 27 Ritonavir 28 Dalbavancin 29 Teicoplanin 30 Homoharringtonine 31 Alisporivir 32 Cepharanthine 33 Hexachlorophene 34 Imatinib 35 Nafamostat 36 Chlorpromazine 37 Camostat 38 Memantine 39 Indomethacin 40 Saracatinib 41 Telavancin 42 Promethazine SARS-CoV−2 HCoV−229E HCoV-OC43 MERS-CoV SARSCoV (Continues) |       843 VERMA and AGGARWAL T A B L E   (Continued) Sl nos Compounds 43 Trametinib 44 Mefloquine 45 Cordycepin SARS-CoV−2 HCoV−229E HCoV-OC43 MERS-CoV SARSCoV Note: Dark blue: Cell culture/co-cultures, yellow: Primary cells/organoids, green: animal model, pink: Phase II, orange: Phase III, purple: phase IV, red: approved, black: investigational files of drug candidate (s) (Mol2 format) were loaded into the working space of Ligandscout software, and key pharmacophore features were identified The unique pharmacophore features considered during analysis were H–bond acceptor, H–bond donor, hydrophobic, aromatic, halogen bond donor, and positively and negatively ionizable groups (Wolber & Kosara, 2006) 2.7  |  Cytotoxicity assay The in vitro antiviral SARS-CoV-2 testing service was availed by Translational Health Science and Technology Institute (THSTI), NCR Biotech Science Cluster, Faridabad-01, Haryana, India Since the antiviral activity was tested in the Vero E6 cells, the test substance(s), that is, cordycepin, should not be cytotoxic to host cells at the test concentration/s; therefore, cytotoxicity test was performed before anti-SARS-CoV-2 experiment The assay is done in a 96-well plate (Thermo Scientific Nunc Edge 2.0) format in wells for each sample 1x10e4 VeroE6 cells were seeded per well and incubated at 37°C overnight for the monolayer formation Next day, cells were incubated with the test substance (cordycepin) at the different concentration (1, 5, 10, 20, and 50  μM) The cell without test substance was used as negative control, and Remdesivir was used as positive reference drug After 24 and 48 hr, cells were stained with Hoechst 33342 and Sytox orange dye Images were taken at 10X, 16 images per well, which covers 90% of well area using ImageXpress Microconfocal (Molecular Devices, LLC, San Jose, CA-95134 USA) Hoechst 33342 nucleic acid stain is a popular cell-permeant nuclear counter stain that emits blue fluorescence when bound to dsDNA It stains all the live and dead cells Sytox orange dye stains nucleic acids in cells with compromised membranes This stain is an indicator of cell death Finally, percentage of cell viability was determined in cordycepin treatment group as compared to untreated control 2.8  |  Anti-SARS-CoV-2 testing Briefly, the assay was performed in a 96-well plate (Thermo Scientific Nunc Edge 2.0) format in wells for each sample (Caly et al., 2020) 1 × 10e4 cells were plated per well and incubated at 37°C overnight for the monolayer formation Cells were incubated with the culture medium with cordycepin at the potent non-cytotoxic concentration (10  μM) determined as mentioned above Soon after (within 5 min), virus was added to each well at a defined multiplicity of infection (MOI; 0.1 for 2 hr) Control cells were incubated with culture medium with corresponding concentration of vehicle Then, the plate was incubated at 37°C and culture supernatant was harvested at 24 and 48  hr later Viral RNA was extracted using the QIAamp 96 Virus QIAcube HT Kit (Qiagen, Hilden, Germany) from 100  μl culture supernatant Reverse transcription was carried out using the BioLine SensiFAST cDNA kit (Bioline, London, UK), total reaction mixture (20 μl), containing 10 μl of RNA extract, 4 μl of 5× TransAmp buffer, 1 μl of Reverse Transcriptase, and 5 μl of nuclease free water The reactions were incubated at 25°C for 10 min, 42°C for 15 min, and 85°C for 5 min qRT-PCR (Applied Biosystems, Foster City, CA, USA) was performed using cycling conditions of 95°C for 2 min, 95°C for 5 s, and 60°C for 24 s, and Ct values for N and E gene sequence were determined The obtained data were used for calculating the % virus inhibition, if any Ramdesivir was used as a positive control (Caly et al., 2020) 2.9  |  Statistical analysis The data of molecular docking score of five different poses were expressed as mean  ±  SD The data were analyzed by using one-way ANOVA followed by Tukey's range test considering *p ≤ 0.05 as statically significant values 3  |  RESULTS AND DISCUSSION On the highest point of all questions associated with COVID-19 is; what are the most effective therapeutic options to cure COVID-19? To answer this, the one among many approaches may be the use of pre-existing antiviral drugs alone or in combination to combat COVID-19 Similar was the case when FDA-approved hydroxychloroquine was implemented to modulate cellular response | 844       VERMA and AGGARWAL F I G U R E   Comparative docking scores of different drug candidates are shown with SARS-CoV-2 spike protein Data are mean ± SD of different poses (n = 5), One-way ANOVA, *p ≤ 0.05 [Colour figure can be viewed at wileyonlinelibrary.com] by suppressing the inflammatory response, thus improving organ functions in COVID-19 patients (Zhou et al., 2020) To date, no treatment specific to SARS-CoV-2 has been reported to tackle the pandemic situation In this context, drug repurposing, also known as repositioning or reprofiling, is a unique and alternative strategy for generating additional value to the existing investigational or approved drugs by targeting disease other than that for which it was originally reported (Huang et al., 2020; Nishimura & Hara,  2018) This has several advantages over new drug discovery since chemical synthesis, preclinical (animal model), and clinical information (phase 0, I, and IIa) including safety data, doses, and pharmacokinetics results are already available for the molecules that may assist the rapid drug development process Therefore, the present study was undertaken with a strong commitment to identify potential therapeutic agent(s) against SARS-CoV-2 from the available antiviral drug candidates by using computational approach The antiviral potential (Table  1) of all the drug candidates used in the present study has been validated in cell cultures/co-cultures, primary cells, animal model, and phase II-IV and also approved against SARSCoV-2, HCoV-229E, HCoV-OC43, MERS-CoV, and SARS-CoV (Table 2) 3.1  |  Molecular interaction simulation Molecular interaction simulation is the most commonly used method particularly for lead identification in computer-aided drug designing (CADD) The binding energy revealed the affinities between ligands and their corresponding target receptor molecule Lower binding energy (negative) indicates a higher affinity of the ligand for the receptor (Kusumaningrum et al., 2014; Puspaningtyas, 2014) The heavily glycosylated large transmembrane spike glycoprotein (type I) of SARS-CoV-2 accounts for its notable feature and plays an important role during viral attachment, fusion, and entry into the host body (Wrapp et al., 2020), and therefore, it is suggested that the inhibition of spike protein may be associated with inhibition of viral multiplication The transmembrane spike glycoprotein exists in a heterotrimeric form with three separate polypeptide chains: chain A, B, and C, forming each monomer The spike glycoprotein has two functional domains, named as S1 and S2, both of which are responsible for successfully entry of coronavirus into the host cells (Wrapp et  al.,  2020) The molecular interaction study revealed that cordycepin has a strong binding affinity followed by nitazoxanide, rapamycin, monensin, silvestrol, amiodarone, cepharanthine, VERMA and AGGARWAL |       845 F I G U R E   Docking structure and chemical interactions of cordycepin are shown along with ligand atoms and interacting amino acids in the binding sites of the SARS-CoV-2 spike protein [Colour figure can be viewed at wileyonlinelibrary.com] indomethacin, promethazine, and mefloquine with SARSCoV-2 RBD domain (spike protein) (Figures  and 2) Cordycepin showed strong chemical interactions with RBD domain–human ACE2 interface with His34, Glu35, and Lys353 in the active site (Figure 2) It is worth mentioning that all these amino acids are localized in the interface region of spike glycoprotein and host receptors which ultimately facilitates receptor-mediated endocytosis (Hoffmann et al., 2020) during primary infection In a recent study, it has been reported that pan-coronavirus fusion inhibitor (EK1C4), targeting spike protein, successfully restricted (IC50 range: 1.3–15.8 nM) the viral entry into the host cells against different types of coronaviruses such as SARS-CoV, MERS-CoV, SARS-CoV-2, HCoV-OC43, and SARSr-CoVs (Xia et al., 2020) An additional and alluring drug target among coronaviruses is the ~306 amino acid long main protease (Mpro, 3CLpro), to forestall the spread of disease by restraining the cleavage of the viral polyprotein Mpro is essential for processing the polyproteins that led to the proteolytic activation of the viral functional proteins (Zhang et  al.,  2020) An in silico molecular interaction analysis revealed that the cordycepin (−205) binds strongly with Mpro followed by monensin, promethazine, mefloquine, nitazoxanide, rapamycin, amiodarone, cepharanthine, silvestrol, and indomethacin (Figure 3) The Mpro active site amino acids such as Thr26, Gly143, Cys145, Ser144, Leu141, His172, Phe140, Glu166, His163, and His164 have been involved during chemical interactions with cordycepin (Figure 4) 3.2  |  Machine learning models for drug repurposing The Assay central software uses Extended-Connectivity Fingerprints (ECFP6) which is among the most popular similarity search tools in drug discovery (Ekins et al., 2020) ECFP6 are circular topological fingerprints designed for molecular characterization, similarity searching, and structure–activity modeling (Rogers & Hahn,  2010) Bayesian algorithm is also useful in the identification of unique leads from a small molecules library with potent bioactivity and | 846       VERMA and AGGARWAL F I G U R E   Comparative docking scores of different drug candidates are shown with SARS-CoV-2 main proteases Data are mean ± SD of different poses (n = 5), One-way ANOVA, *p ≤ 0.05 [Colour figure can be viewed at wileyonlinelibrary.com] cytotoxicity The Assay Central Bayesian model for coronavirus disease, COVID-19 (SARS-Cov-2), had a fivefold cross-validation receiver operating characteristic (ROC) of 0.79, precision 0.76, recall 0.76, specificity 0.78, F1-score 0.76, CK 0.53, and MCC 0.54 (Figure  5) The SARS proteases model had a fivefold cross-validation ROC of 0.92, precision 0.85, recall 0.98, specificity 0.78, F1-score 0.90, CK 0.77, and MCC 0.79 (Figure 5) For SARS-CoV-2, prediction and applicability score for cordycepin were 0.60 and 0.47, respectively, whereas for SARS, proteases prediction and applicability scores were 0.60 and 0.42, respectively The results were further corroborated by comparing reported IC50 with interaction scores Interestingly, the compound with low IC50 has shown strong chemical interactions with target proteins (Figures  and 7) and similar was the case with cordycepin The advantage of this approach is that the method is more time and cost-efficient in discovering novel therapeutics, and interestingly, it does not require crystal structures Moreover, it enables small-molecule structures to be scored against many models simultaneously (Ekins et al., 2020) A similar model was used to identify a TB drug lead, TCMDC-125802 which exhibited promising in vitro bactericidal activity against Mycobacterium tuberculosis with acceptable mammalian cellular cytotoxicity and in vivo mouse safety (Ekins et al., 2013) 3.3  |  Pharmacology network analysis The biological response of small molecules in a living organisms is largely regulated by their interaction partners (Sharan et  al.,  2007) The role of the interaction network becomes more important in CADD since diseases are often reflected by alterations of protein complex of certain pathways (Oti et al., 2006) The association between drug-proteins and the topology of the network itself can lead to a better understanding of a drug mediated cellular response (Hopkins,  2008) Taking into account, the possible protein interaction profiles of cordycepin was determined and it is evident that cordycepin can modulate multiple pathways associated with apoptosis, cancer, hepatitis B, tuberculosis, influenza A, herpes simplex infection, and many more Cordycepin specific network analysis revealed that (Figure 8) it may interfere with multiple cellular activities in normal and pathological conditions which are as follows: apoptosis (4.2e-16): APAF1, CASP3, CASP8, CASP9, FADD, FAS, TNFRSF10A, TNFRSF10B, TNFSF10 and XIAP; hepatitis B (2.38e-10): APAF1, CASP3, CASP8, CASP9, FADD, FAS, MYC and TLR4; tuberculosis (9.08e-10): APAF1, CASP3, CASP8, CASP9, FADD, IL10, IL10RA, and TLR4; pathways in cancer (3.06e-09): CASP3, CASP8, CASP9, FADD, FAS, HGF, MET, MYC and XIAP; influenza A(8.92e-07): CASP9, FAS, TLR4, TNFRSF10A, |       847 VERMA and AGGARWAL F I G U R E   Docking structure and chemical interactions of cordycepin are shown along with ligand atoms and interacting amino acids in the binding sites of Mpro [Colour figure can be viewed at wileyonlinelibrary.com] TNFRSF10B and TNFSF10; natural killer cell mediated cytotoxicity (5.76e-06): CASP3, FAS, TNFRSF10A, TNFRSF10B and TNFSF10; viral myocarditis (0.000489): CASP3, CASP8, and CASP9; herpes simplex infection (0.000562): CASP3, CASP8, FADD, and FAS (Figure 8) The value in bracket indicates false discovery rate (FDR); the FDR is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons 3.4  |  Pharmacophore model A pharmacophore model of any drug like molecule is a collection of steric and electronic features that described the stability of ligand–receptor complex and denotes corresponding biological response (Yang, 2010) in terms of activation or inhibition Pharmacophore features also revealed drug likeness properties about a new drug candidate(s) before setting preclinical experiments (Buyya et al., 2003) The top-ranked ligands, that is, cordycepin which showed high binding affinity (−180 to −205) with both the receptors, were further subjected to investigate essential pivot chemical descriptors that may be responsible for biological activity The 2D and 3D pharmacophore features of cordycepin were determined by LigandScout 4.1 and shown in Figure  The identified pharmacophoric features of the said compound include hydrogen bond donors and acceptors shown as directed vectors and aryl group that is illustrated by a blue circle All these obtained pharmacophore features reflect SAR (Figure 10) and would enable to develop a strategy for repurposing cordycepin against COVID-19; at the same time open up new avenues to design new and more potent SARS-CoV-2 inhibitors as prospective antiviral agents It is worth noting that the pharmacophore model has contributed to the development of more stable drugs with no or minimal host toxicity, since they are more specifically targeted (Buyya et al., 2003) 3.5  |  Cytotoxicity and anti-SARSCoV-2 testing Anti-SARS-CoV-2 activity of cordycepin was tested in the Vero E6 cells The test substance(s), that is, cordycepin, | 848       VERMA and AGGARWAL F I G U R E   Fivefold cross-validation receiver operating characteristic curve (ROC) plots Bayesian machine learning models using ECFP6 fingerprints were used for scoring and selecting compounds to predict potent binding affinity against SARS-CoV-2 target proteins [Colour figure can be viewed at wileyonlinelibrary.com] F I G U R E   Radar display showing comparative IC50 of different compounds reported for antiviral activities against different human viruses [Colour figure can be viewed at wileyonlinelibrary.com] VERMA and AGGARWAL |       849 F I G U R E   Radar display showing comparative mean interaction scores of different drug candidates used against spike protein and main proteases [Colour figure can be viewed at wileyonlinelibrary.com] F I G U R E   Interaction network determined by STITCH algorithm between cordycepin and target proteins, showing the involvement of cordycepin in multiple pathways This is the confidence view; stronger associations are represented by thicker lines Protein–protein interactions are shown in gray, chemical–protein interactions in green, and interactions between chemicals in red Number of nodes: 20, number of edges: 67, average node degree: 6.7, clustering coefficient: 0.784, PPI enrichment p-value: 9.58e-12 [Colour figure can be viewed at wileyonlinelibrary com] should not be cytotoxic at the test concentration in the Vero E6 cells, so the cytotoxicity test was performed prior to the anti-SARS-CoV-2 experiment After the SARS-CoV-2 was isolated from a lower respiratory tract of COVID-19 infected patient, a diagnostic RT-PCR test was developed RT-PCR tests were based on the RNA-dependent RNA polymerase (RdRp) gene of the ORF1ab sequence, E gene, N gene, and S gene of the SARS-CoV-2 genome (Corman et  al.,  2020; Konrad et  al.,  2020) The cell viability results showed that cordycepin was non-toxic (96%–100% cell viability) to the host cells (Vero E6 cells) and the similar result was also obtained for reference drug remdesivir (Figure  11a) The anti-SARS-CoV-2 results of cordycepin at most potent dose (10 μM) did not show encouraging result after 24 hr of treatment Interestingly, after 48  hr of treatment, 65% and 42% viral replication inhibition was observed considering expression profiles of E gene and N gene (Figure 11b) Taken together, these results demonstrate that cordycepin has antiviral action against the SARS-CoV-2 clinical isolate in vitro, with a single potent dose (10 μM) able to inhibit viral replication | 850       VERMA and AGGARWAL F I G U R E   Pharmacophore features (2D and 3D) of top-ranked compounds, that is, cordycepin (based on docking scores with the target proteins) predicted using Ligandscout software The pharmacophore color code is red for hydrogen acceptors, blue ring for cyclic ring, and green for hydrogen donors 2D Pharmacophore features represent HBA is hydrogen bond acceptor, HBD is hydrogen bond donor, and AR is aryl [Colour figure can be viewed at wileyonlinelibrary.com] F I G U R E   Structure–activity relationships based on the molecular interaction of drug candidates (https://cspade.fimm.fi/) are shown Web-application serves C-SPADE was used to cluster drug candidates based on their structural similarities (ECFP6) and visualize them as a dendrogram of compounds augmented with their functional annotations (red bullet: spike protein; green bullet: main protease and star: highest binding energy) [Colour figure can be viewed at wileyonlinelibrary.com] within 48  hr in our system Cordycepin therefore warrants further investigation for possible benefits in humans 4  |   CO NC LU S ION The present study aimed to identify molecules/ or drug candidate (s) from a molecular library of FDA-approved and investigational drugs that may inhibit SARS-CoV-2 by acting on the major target proteins such as spike protein (S) and main proteases (Mpro) The obtained results by molecular interaction simulation showed that cordycepin followed by nitazoxanide, rapamycin, monensin, silvestrol, amiodarone, cepharanthine, indomethacin, promethazine, and mefloquine showed strong chemical interactions with SARS-CoV-2 RBD domain of spike protein Similarly, cordycepin binds strongly with Mpro followed by monensin, promethazine, mefloquine, nitazoxanide, rapamycin, amiodarone, cepharanthine, silvestrol, and indomethacin Interestingly, against both the target proteins (S and Mpro), cordycepin served as a pivot molecule The repurposing potential of cordycepin was further validated by ECFP6 and Bayesian algorithm and obtained satisfactory results Pharmacophore features demonstrated SAR of cordycepin due to the presence of unique descriptors At the same time, the pharmacology network also revealed the involvement of cordycepin in different pathways associated with bacterial and viral diseases such as tuberculosis, hepatitis B, influenza A, viral myocarditis, and herpes simplex infection The anti-SARS-CoV-2 activity of cordycepin revealed 65% (E gene) and 42% (N gene) viral replication inhibition after 48 hr of treatment The obtained results encourage further in vitro and in vivo investigations and open up new avenue to test efficacy and safety of cordycepin against COVID-19 which is an urgent need of the hour Therefore, it is suggested to the world community to undertake repurposing clinical studies to test the efficacy and safety of cordycepin for the treatment of COVID-19 ACKNOWLEDGMENTS Cotton University is highly acknowledged for all the supports related to this study No special fund allocated to execute the work The laboratory facilities and resources required for the execution of present work was provided by Cotton VERMA and AGGARWAL |       851 F I G U R E 1   Cell viability (a) and antiSARS-CoV-2 activity (b) of cordycepin Data are mean ± SD, n = 3 Remdesivir was used as positive reference drug [Colour figure can be viewed at wileyonlinelibrary com] University, Department of Zoology No special fund was allocated for the present work AUTHORS CONTRIBUTION Study design, data collection, data analysis and manuscript writing was 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