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23 June 2020

Identification of Antiviral Drug Candidates against SARS-CoV-2 from FDA-Approved Drugs

ABSTRACT

Drug repositioning is the only feasible option to immediately address the COVID-19 global challenge. We screened a panel of 48 FDA-approved drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which were preselected by an assay of SARS-CoV. We identified 24 potential antiviral drug candidates against SARS-CoV-2 infection. Some drug candidates showed very low 50% inhibitory concentrations (IC50s), and in particular, two FDA-approved drugs—niclosamide and ciclesonide—were notable in some respects.

INTRODUCTION

COVID-19 is an emerging infectious disease caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (1). Although the case fatality rate due to this viral infection varies from 1% to 12% (2), the transmission rate is relatively high (3), and recently, the WHO declared the COVID-19 outbreak a pandemic. Currently, there are no vaccines or therapeutics available, and the patients with COVID-19 are being treated with supportive care.
Drug repositioning could be an effective strategy to respond immediately to emerging infectious diseases since new drug development usually takes more than 10 years (4). FDA-approved drugs provide safe alternatives only in the case where at least modest antiviral activity can be achieved. Accordingly, several drugs are being tested in numerous clinical trials (5), including remdesivir, lopinavir, and chloroquine (6).
We screened approximately 3,000 FDA- and Investigational New Drug (IND)-approved drugs against SARS-CoV to identify antiviral drug candidates (unpublished data). Since SARS-CoV and SARS-CoV-2 are very similar (79.5% sequence identity) (1), the drugs which show antiviral activity against SARS-CoV are expected to show a similar extent of antiviral activity against SARS-CoV-2.
A total of 35 drugs were selected from the earlier SARS-CoV screening results. In addition, 13 drugs were included based on recommendations from infectious diseases specialists (Table 1). For screening experiments, Vero cells were used and each drug was added to the cells prior to the virus infection. At 24 h after the infection, the infected cells were scored by immunofluorescence analysis with an antibody specific for the viral N protein of SARS-CoV-2. The confocal microscope images of both the viral N protein and cell nuclei were analyzed using our in-house Image Mining (IM) software, and the dose-response curve (DRC) for each drug was generated (Fig. 1).
TABLE 1
TABLE 1 Pharmacological actions and registration status of drugs
Drug namePharmacological action[email protected] labelaWHO essential medicine statusbOrganization(s)c
AbemaciclibAntineoplastic agentsNDA #208855NAdUSAN, INN
Amodiaquine dihydrochlorideAntimalarialsNDA #006441EssentialUSP, INN, BAN
AnidulafunginAntifungal agentsNDA #021948NAUSAN, INN, BAN
BazedoxifeneAntiestrogenNDA #22247NAINN, USAN, JAN
Berbamine hydrochlorideNatural productsNANANA
CamostatProtease inhibitorNANAJAN, INN
CepharanthineAnti-inflammatory agentsNANAJAN
Chloroquine diphosphateAntimalarialsANDA #091621EssentialUSP, BAN
CiclesonideAntiallergic agentsNDA #021658NAUSAN, INN
Clomiphene citrateFertility agentsANDA #075528EssentialUSAN, USP
CyclosporineAntifungal agentsANDA #065017NAUSAN, USP
DigitoxinCardiovascular agentsANDA #084100NAUSP, INN, BAN, JAN
DigoxinCardiovascular agentsNDA #021648EssentialUSP, INN, BAN, JAN
Dihydrogambogic acidNatural productsNANANA
DroloxifeneAntineoplastic agentsNANAUSAN, INN
Dronedarone HClCardiovascular agentsANDA #205903NAUSAN
EbastineAntihistaminic agentsNANAUSAN, INN, BAN
EltrombopagTreatment of thrombocytopeniaANDA #209938NAINN
GilteritinibAntineoplastic agentsNDA #211349NAUSAN, INN
HexachloropheneAnti-infective agentsNANAUSP, INN, BAN
Hydroxyprogesterone caproateHormonesANDA #211777NAUSP, INN, JAN
IsoosajinNatural productsNANANA
IsopomiferinAntioxidantNANANA
IvacaftorTreatment of cystic fibrosisNDA #203188NAUSAN, INN
Lanatoside CCardiovascular agentsNANAINN, BAN, DCF, JAN, NF
LDK378Antineoplastic agentsNDA #211225NAUSAN, INN
Loperamide hydrochlorideAntidiarrhealsNDA #021855EssentialUSAN, USP, JAN
LopinavirAntiviral agentsNDA #021906EssentialUSAN, USP, INN, BAN
LusutrombopagTreatment of thrombocytopeniaNDA #210923NAUSAN, INN
MefloquineAntimalarialsANDA #076392EssentialUSAN, INN, BAN
MequitazineHistamine antagonistsNANAINN, BAN, DCF, JAN
NiclosamideAntiparasitic agentsNDA #018669EssentialUSAN, INN, BAN
OsajinNatural productsNANANA
Osimertinib mesylateAntineoplastic agentsNDA #208065NAUSAN
OuabainCardiovascular agentsNANAUSP
OxyclozanideAntiparasitic agentsNANAINN, BAN
PenfluridolAntipsychoticNANANA
Perhexiline maleateCardiovascular agentsNANAUSAN
Phenazopyridine hydrochlorideAnalgesicNDA #021105EssentialUSAN, USP
ProscillaridinCardiovascular agentsNANAUSAN, INN, BAN, JAN
Quinacrine hydrochlorideAntimalarials/antiparasitic agentsNANAINN, BAN
Remdesivir (GS-5734)Antiviral agentsNANAUSAN
Salinomycin sodiumAntibacterial agentsNANAINN, BAN
TetrandrineAntiviral agentsNANANA
Thioridazine hydrochlorideAntipsychoticANDA #088004NAUSP, JAN
TiloroneAntiviral agentsNANAINN
Toremifene citrateAntineoplastic agentsANDA #208813NAUSAN
TriparanolHypolipidemic agentsNANAINN, BAN
a
Latest New Drug Application (NDA) and Abbreviated New Drug Application (ANDA) information retrieved from [email protected] (https://www.accessdata.fda.gov/scripts/cder/daf/; accessed March 2020).
b
According to the WHO Model List of Essential Medicines, 21st List (2019).
c
BAN, British Approved Name; DCF, Data Clarification Form; INN, International Nonproprietary Names; JAN, Japanese Accepted Name; USAN, United States Adopted Names; USP, The United States Pharmacopeial Convention; NF, USP-National Formulary.
d
NA, not available.
FIG 1
FIG 1 (A) Dose-response curve analysis by immunofluorescence for reference drugs. The blue squares represent inhibition of SARS-CoV-2 infection (%), and the red triangles represent cell viability (%). The confocal microscope images show cell nuclei (red) and viral N protein (green) at each drug concentration. Means ± SD were calculated from duplicate experiments. (B) Dose-response curve analysis by immunofluorescence for 45 drugs that were tested in this study. The blue squares represent inhibition of SARS-CoV-2 infection (%), and the red triangles represent cell viability (%). Means ± SD were calculated from duplicate experiments.
Chloroquine, lopinavir, and remdesivir were used as reference drugs with 50% inhibitory concentration (IC50) values of 7.28, 9.12, and 11.41 μM, respectively (Fig. 1A). Among the 48 drugs that were evaluated in our study, 24 drugs showed potential antiviral activities against SARS-CoV-2, with IC50 values in between 0.1 and 10 μM, namely, tilorone, cyclosporine, loperamide, mefloquine, amodiaquine, proscillaridin, digitoxin, digoxin, hexachlorophene, hydroxyprogesterone caproate, salinomycin, ouabain, cepharanthine, ciclesonide, oxyclozanide, anidulafungin, gilteritinib, berbamine, tetrandrine, abemaciclib, ivacaftor, bazedoxifene, niclosamide, and eltrombopag.
Among these 24 drugs, 2 FDA-approved drugs drew our attention. First, niclosamide, an anthelminthic drug, exhibited very potent antiviral activity against SARS-CoV-2 (IC50, 0.28 μM). Not surprisingly, its broad-spectrum antiviral effect has been well documented in the literature (7), including antiviral properties against SARS-CoV and Middle East respiratory syndrome (MERS)-CoV (8, 9). Recently, Gassen et al. demonstrated that niclosamide inhibits SKP2 activity, which enhances autophagy and reduces MERS-CoV replication (9). A similar mechanism might be attributable to the inhibition of SARS-CoV-2 infection by niclosamide. Although niclosamide has a pharmacokinetic flaw of low absorption, further development or drug formulation could enable an effective delivery of this drug to the target tissue (10).
Second, ciclesonide is another interesting drug candidate for further development, although its antiviral potency was much lower (IC50, 4.33 μM) than niclosamide. It is an inhaled corticosteroid used to treat asthma and allergic rhinitis (11). A recent report by Matsuyama et al. corroborated our finding of ciclesonide as a potential antiviral drug against SARS-CoV-2 (12). A treatment report of three patients who were infected by SARS-CoV-2 in Japan (13) warrants further clinical investigation of this drug in patients with COVID-19. Intriguingly, an underlying mechanism for the suppression of viral infection by ciclesonide has been revealed by the isolation of a drug-resistant mutant (12). The isolation of the drug-resistant mutant indicated that NSP15, a viral endoribonuclease, is the molecular target of ciclesonide. Together, these findings suggest that it is not unreasonable to consider that ciclesonide exhibits direct-acting antiviral activity in addition to its intrinsic anti-inflammatory function. In the future, small interfering RNA (siRNA) targeting the hormone receptor will allow for an assessment of the extent of this direct-acting antiviral activity. With its proven anti-inflammatory activity, ciclesonide may represent a potent drug which can manifest dual roles (antiviral and anti-inflammatory) for the control of SARS-CoV-2 infection.
Prior to our evaluation of 48 drugs against SARS-CoV-2 infection, we also tested the antiviral activity of several other drugs based on the cytopathic effect of the virus in the presence of each drug (Fig. 2). In particular, the effects of favipiravir and atazanavir on SARS-CoV-2 were compared with those of the reference drugs (chloroquine, lopinavir, and remdesivir) because favipiravir is considered a drug candidate for clinical trials and atazanavir was recently predicted as the most potent antiviral drug by artificial intelligence (AI)-inference modeling (14). However, in the current work, we did not observe any antiviral activity of either favipiravir or atazanavir.
FIG 2
FIG 2 Dose-response curve analysis by cytopathic effect. The blue squares represent inhibition of SARS-CoV-2 infection (%), and the red triangles represent cell viability (%). Means ± SD were calculated from duplicate experiments.
In summary, we selected and screened 48 FDA-approved drugs based on our SARS-CoV screening, and our screening campaign revealed 24 potential antiviral drug candidates against SARS-CoV-2. Our findings could be further validated in an appropriate animal model and, hopefully, developed through subsequent clinical trials in order to provide additional therapeutic options for patients with COVID-19.

Virus and cells.

Vero cells were obtained from the American Type Culture Collection (ATCC CCL-81) and maintained at 37°C with 5% CO2 in Dulbecco’s modified Eagle’s medium (DMEM; Welgene), supplemented with 10% heat-inactivated fetal bovine serum (FBS) and 1× antibiotic-antimycotic solution (Gibco). SARS-CoV-2 (βCoV/KOR/KCDC03/2020) was provided by Korea Centers for Disease Control and Prevention (KCDC) and was propagated in Vero cells. Viral titers were determined by plaque assays in Vero cells. All experiments using SARS-CoV-2 were performed at Institut Pasteur Korea in compliance with the guidelines of the Korea National Institute of Health (KNIH), using enhanced biosafety level 3 (BSL3) containment procedures in laboratories approved for use by the KCDC.

Reagents.

Chloroquine diphosphate (CQ; C6628) was purchased from Sigma-Aldrich (St. Louis, MO), lopinavir (LPV; S1380) was purchased from SelleckChem (Houston, TX), and remdesivir (HY-104077) was purchased from MedChemExpress (Monmouth Junction, NJ). Chloroquine was dissolved in Dulbecco’s phosphate-buffered saline (DPBS; Welgene), and all other reagents were dissolved in dimethyl sulfoxide (DMSO) for the screening. Anti-SARS-CoV-2 N protein antibody was purchased from Sino Biological Inc. (Beijing, China). Alexa Fluor 488 goat anti-rabbit IgG (H+L) secondary antibody and Hoechst 33342 were purchased from Molecular Probes. Paraformaldehyde (PFA) (32% aqueous solution) and normal goat serum were purchased from Electron Microscopy Sciences (Hatfield, PA) and Vector Laboratories, Inc. (Burlingame, CA), respectively.

DRC analysis by immunofluorescence.

Ten-point DRCs were generated for each drug. Vero cells were seeded at 1.2 × 104 cells per well in DMEM, supplemented with 2% FBS and 1× antibiotic-antimycotic solution (Gibco), in black, 384-well μClear plates (Greiner Bio-One) 24 h prior to the experiment. Ten-point DRCs were generated, with compound concentrations ranging from 0.1 to 50 μM. For the viral infections, plates were transferred into the BSL3 containment facility and SARS-CoV-2 was added at a multiplicity of infection (MOI) of 0.0125. The cells were fixed at 24 hours postinfection (hpi) with 4% PFA and analyzed by immunofluorescence. The acquired images were analyzed using in-house software to quantify cell numbers and infection ratios, and antiviral activity was normalized to positive (mock) and negative (0.5% DMSO) controls in each assay plate. DRCs were fitted by sigmoidal dose-response models, with the following equation: Y = bottom + (top − bottom)/[1 + (IC50/X)Hillslope], using XLfit 4 software or Prism7. IC50 values were calculated from the normalized activity data set-fitted curves. All IC50 and 50% cytotoxic concentration (CC50) values were measured in duplicate, and the quality of each assay was controlled by Z’-factor and the coefficient of variation in percent (%CV).

DRC analysis by CPE.

Ten-point DRCs were generated for each drug. Vero cells were seeded at 1.2 × 104 cells per well in DMEM, supplemented with 2% FBS and 1× antibiotic-antimycotic solution (Gibco) in white, 384-well μClear plates (Greiner Bio-One) 24 h prior to the experiment. Ten-point DRCs were generated, with compound concentrations ranging from 0.1 to 50 μM. For the viral infections, plates were transferred into the BSL3 containment facility and SARS-CoV-2 was added at a multiplicity of infection (MOI) of 0.05 and incubated at 37°C for 72 h. Cell viability was measured using the CellTiter-Glo luminescent cell viability assay (Promega), according to the manufacturer’s instructions. Antiviral activity was determined by the degree of inhibition of viral cytopathic effect (CPE). The results were normalized to positive (mock) and negative (0.5% DMSO) controls in each assay plate. DRCs were fitted by sigmoidal dose-response models, with the following equation: Y = bottom + (top − bottom)/[1 + (IC50/X)Hillslope], using XLfit 4 software or Prism7. IC50 values were calculated from the normalized activity data set-fitted curves. All IC50 and CC50 values were measured in duplicate, and the quality of each assay was controlled by Z’-factor and the coefficient of variation in percent (%CV).

ACKNOWLEDGMENTS

We thank Wang-Shick Ryu and Spencer Shorte for their helpful discussion and review of the manuscript. The pathogen resource (NCCP43326) for this study was provided by the National Culture Collection for Pathogens.
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2017M3A9G6068245 and NRF-2020M3E9A1041756).

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Information & Contributors

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Published In

Antimicrobial Agents and Chemotherapy cover image
Antimicrobial Agents and Chemotherapy
Volume 64Number 723 June 2020
eLocator: e00819-20
PubMed: 32366720

History

Received: 27 April 2020
Returned for modification: 27 April 2020
Accepted: 30 April 2020
Published online: 23 June 2020

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KEYWORDS

  1. COVID-19
  2. FDA-approved drug
  3. SARS-CoV-2

Contributors

Authors

Sangeun Jeon
Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam, South Korea
Meehyun Ko
Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam, South Korea
Jihye Lee
Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam, South Korea
Inhee Choi
Medicinal Chemistry, Institut Pasteur Korea, Seongnam, South Korea
Soo Young Byun
Screening Discovery Platform, Institut Pasteur Korea, Seongnam, South Korea
Soonju Park
Screening Discovery Platform, Institut Pasteur Korea, Seongnam, South Korea
David Shum
Screening Discovery Platform, Institut Pasteur Korea, Seongnam, South Korea
Seungtaek Kim
Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam, South Korea

Notes

Address correspondence to Seungtaek Kim, [email protected].
Sangeun Jeon and Meehyun Ko contributed equally to this work. Author order was determined alphabetically.

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