key: cord-0802127-fqymqzv2
authors: Qu, Hua; Zheng, Yi; Wang, Yuren; Li, Hongwei; Liu, Xiufei; Xiong, Xin; Zhang, Linlin; Gu, Jing; Yang, Gangyi; Zhu, Zhiming; Zheng, Hongting; Ouyang, Qin
title: The potential effects of clinical antidiabetic agents on SARS‐CoV‐2
date: 2020-12-19
journal: J Diabetes
DOI: 10.1111/1753-0407.13135
sha: 3b30d1f8965dc76fe112934ea0ccb1653a857bd0
doc_id: 802127
cord_uid: fqymqzv2

BACKGROUND: Coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2), is currently posing significant threats to public health worldwide. It is notable that a substantial proportion of patients with sever COVID‐19 have coexisting diabetic conditions, indicating the progression and outcome of COVID‐19 may relate to diabetes. However, it is still unclear whether diabetic treatment principles can be used for the treatment of COVID‐19. METHODS: We conducted a computational approach to screen all commonly used clinical oral hypoglycemic drugs to identify the potential inhibitors for the main protease (M(pro)) of SARS‐CoV‐2, which is one of the key drug targets for anti‐COVID‐19 drug discovery. RESULTS: Six antidiabetic drugs with docking scores higher than 8.0 (cutoff value), including repaglinide, canagliflozin, glipizide, gliquidone, glimepiride, and linagliptin, were predicted as the promising inhibitors of M(pro). Interestingly, repaglinide, one of the six antidiabetic drugs with the highest docking score for M(pro), was similar to a previously predicted active molecule nelfinavir, which is a potential anti‐HIV and anti‐COVID‐19 drug. Moreover, we found repaglinide shared similar docking pose and pharmacophores with a reported ligand (N3 inhibitor) and nelfinavir, demonstrating that repaglinide would interact with M(pro) in a similar way. CONCLUSION: These results indicated that these six antidiabetic drugs may have an extra effect on the treatment of COVID‐19, although further studies are necessary to confirm these findings.

Since late December 2019, an epidemic of acute respiratory disease (ARD) in humans started in Wuhan, Hubei Province, China. 1, 2 Further deep sequencing analysis from patient samples revealed a novel coronavirus (CoV), which was named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses, and the ARD induced by this CoV was named coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO). To date, more than 881 464 people have been killed by COVID-19, a number that surpasses the toll from SARS in the 2002-03 epidemic worldwide. 2, 3 It was reported that severe COVID-19 cases showed extremely high rates of coexisting diabetes (22.2%-26.9%), 4, 5 and recent study in a cohort of 7337 confirmed COVID-19 cases suggested the progression and outcome of COVID-19 may relate to diabetes. 6 However, the diabetic treatment principles for COVID-19 patients with diabetes are unclear. Although there are various kinds of clinical antidiabetic agents, how to choose these agents wisely for COVID-19 patients with diabetes, that is, the effect of antidiabetic agents on SARS-CoV-2, remains unknown.

The replication and transcription of CoVs are regulated by the nonstructural proteins (nsps). 7, 8 The 16 mature nsps (nsp1 to 16) are processed from polyproteins (pp1a/1ab) by 2 viral-encoded proteases, including the main protease (M pro ) or chymotrypsin-like protease (3CL pro ) and papainlike protease (PLPs). 7, 9 Between these 2 proteases, M pro plays a predominant role, and it shares significant homology in amino acid sequence and three-dimensional architecture from human to animal CoVs. 10, 11 Therefore, M pro attracted more attention as a candidate target for the development of drugs to treat CoVs. By targeting M pro it may be possible to develop an anti SARS-CoV-2 inhibitor, [10] [11] [12] which is similar to an anti-HIV drug, nelfinavir, predicted to be a potential inhibitor of SARS-CoV-2 by computational approaches targeting M pro .

Here, we conducted a computational approach to screen all commonly used clinical oral antidiabetic drugs to analyze whether these drugs have a potential inhibition role for M pro of SARS-CoV-2, basing on M pro binding pocket of key residues generated for molecular dynamic simulation.

Crystal structure of SARS-CoV-2 M pro was retrieved from RCSB Protein Data Bank (PDB ID: 6 LU7), which was recently reported by Yang et al. 13 The crystal structure of the complex of M pro with noncovalent ligand X77 was also retrieved (PDB ID: 6W63). 14 The structure was prepared by SYBYL-X 2.0 (Tripos Associates, St. Louis, MO, USA), following methods reported previously. 15 

Molecular dynamics (MD) simulations were conducted by using AMBER14, gaff force field for small molecules and with ff14SB force field for protein. 15, 16, 17 The structures were prepared as previously reported. The N3 inhibitor was bonded with the S atom of CYS145. The MD simulations were carried out under periodic boundary conditions by using NPT ensemble at 300 K after proper minimization and equilibration, according to methods reported previously. 17 

The trajectory analysis was performed using Amber 14, cpptraj module. 18 The root-mean-square deviation (RMSD) was evaluated and equilibrium of the system was assessed by the RMSD values. The average structures of models were calculated based on the equilibrium time in MD simulation, using the cpptraj module.

The binding free energies of proteins to ligands were calculated when reached equilibrium state in aforementioned MD simulation, using the molecular mechanics generalized Born surface area (MM/GBSA) method 19 implemented in Amber 14. Protocols and parameters were reported previously. 19 Based on the calculated binding free energies, the key residues employing more contribution to the binding interaction would be identified.

The crystal structures of SARS-CoV-2 main protease was extracted from its complex by using an inhibitor N3 (PDB ID: 6LU7). Docking studies were performed using Surflex-Dock in SYBYL-X 2.0 software with Surflex-Dock Geomx (SFXC) mode. The pre-dock minimization, post-dock minimization, consider ring flexibility, molecule fragmentation, and the soft grid treatment were set as on. Based on the key residues with default setting (Threshold 0.5 and Bloat 0), the binding pocket was generated. The key residues for SARS-CoV-2 main protease included LEU27, HIS41, MET49, CYS145, MET165, GLU166, PRO168, ASP187, and GLN189. The docked complex with the highest score was chosen for the molecular dynamic simulation. 20 The binding free energies were calculated by MM/GBSA method. The interactions of binding between the M por and ligands were determined using LigPlot+. 21, 22 2.6 | Cell culture and reagents Human alveolar type II cells (A549) were cultured in DMEM (Gibco) with 10% fetal bovine serum (FBS) and human umbilical vein endothelial cells (HUVECs) were cultured in 1640 medium (Gibco) with 10% FBS according to the recommendation from the suppliers. Cell identities and mycoplasma determinations were done by Shanghai Biowing Biotechnology Co. Commercial antidiabetic drugs for humans including repaglinide (Novo Nordisk), canagliflozin (Janssen Pharmaceuticals), glipizide (Zibo Wanjie Pharmaceutical), gliquidone (Beijing Wanhui Shuanghe Pharmaceutical), glimepiride (Sanofi Aventis), and linagliptin (Boehringer Ingelheim Pharmaceuticals) from the listed companies were also used.

qRT-PCR analyses were performed as previously described. 23 In brief, by using Trizol (Takara), total RNA of cells was isolated according to the instructions, and then 1 μg of total RNA was reverse transcribed to cDNA by PrimeScript Reagent Kit (Takara). The PCR amplification was performed using SYBR Green (Takara). Expression levels of mRNA were calculated by the ΔCt-method. The following primer pairs were used in this study: angiotensinconverting enzyme 2 (ACE2):forward 5 0 -GAGGAAAAG GCCGAGAGCTT-3 0 , and reverse 5 0 -GACGCTTGATGGTC GCATTC-3 0 ; L-SIGN: forward 5 0 -CTCCTGGGGTGTCTT GGC-3 0 , and reverse 5 0 -GTCCAGTCCTTGGGACAGTG-3 0 ; DC-SIGN: forward 5 0 -GCAAGACGCGATCTACCAGA-3 0 , and reverse 5 0 -CCAGGGGAAATTGGAGGCAT-3 0 .

Cells were treated as indicated and were collected in lysis buffer and prepared as previously described. 23 The concentrations of protein in each cell lysate were analyzed by the Protein Assay Kit (BCA assay). Then, proteins in the lysates were separated by SDS-PAGE and immune-blotted with the ACE2 primary antibodies (1:1000, proteintech) and its corresponding secondary antibodies. Images were acquired using fusion FX5s system (Vilber Lourmat). 23 

All data were analyzed by the GraphPad Prism 7.0 (Macintosh). Quantitative values were presented as the mean ± SEM. For multiple comparison analysis, one-way analysis fo variance with Tukey's multiple comparison tests was used. P values <0.05 were considered to be statistically significant.

We first conducted a molecular dynamics simulation for 200 ns to determine the key residues in the binding pocket of M pro and investigated the stability of the M pro / N3 inhibitor complex by monitoring the RMSD. The N3 inhibitor was connected with M pro by C-S covalent bond. After molecular dynamics, the N-terminus residues of M pro (near the binding interface) showed slight fluctuation, and the loop at the C-terminus showed greater variation ( Figure 1A ). M pro attained its equilibrium (plateau) state at 50 ns, with the RMSD value of 2.70 Å ( Figure 1B) , whereas the values for the N3 inhibitor were 10 ns, and 1.84 Å ( Figure 1B) . Because of the covalent bond, the conformation of the ligand was slightly shaking.

To assess the interactions between these two systems, the hydrogen bond analyses were carried out based on the MD simulation. In the crystal structure of M pro and N3, seven residues, including THR190, GLN189, HIS164, GLU166, HIS163, PHE140, and GLY143, to form N3 hydrogen bonds ( Figure S1 ). During the MD simulation, the hydrogen bonds between GLN189, HIS164, GLU166, THR24, HIS41, and GLU143 with N3 were found in more than 10% of the frames of the MD trajectory. To calculate the contribution of each residue, the MM/GBSA free energy and the decomposition energy M pro /N3 system were calculated ( Figure 1C ), which suggested that GLN189, MET165, MET49, and PRO168 contributed favorable energies lower than −2.0 kcal/mol. Among these residues, there are hydrogen bonds between GLN189 and N3, whereas the binding interaction between MET165, MET49, PRO168, and N3 was hydrophobic interaction from the side chain of M pro and N3. Other residues, including GLU166 and HIS41, also proved important to the interaction of hydrogen bonds with energy contributions lower than −0.8 kcal/ mol, and LEU27, CYS145, and ALA191 was binding with ligand by hydrophobic interaction.

To further confirm the residues for the binding pocket, 200 ns dynamics simulation and corresponding MM/GBSA free energy were also carried out for the complex of M pro with noncovalent ligand X77 ( Figure S2 ). The total energy in M pro -X77 system was F I G U R E 1 Molecular dynamics study to explore the binding pocket of M pro . A, The crystal structure superimposed on the last configuration after 200 ns of simulation for M pro and N3 inhibitor. The initial and the last configurations were shown in green and magenta, respectively. B, The root-mean-square deviation (RMSD) of M pro and N3 inhibitor. C,)Binding free energy decomposition of M pro and N3 inhibitor system. D, The key residues for the binding interaction of M pro and N3 inhibitor and the binding pocket of M pro −30.58 ± 4.29 kcal/mol, resulting from Van der Waals (VDW) energy (ΔE vdw ), electrostatic energy (ΔE electrostatic ), electrostatic contribution solvation free energy (ΔG GB ), and nonpolar solvation free energy (ΔG SA ) with the values of −41.74 ± 5.07, −12.26 ± 4.28, 28.74 ± 4.46, and − 5.33 ± 0.51 kcal/mol, respectively (- Table S1 ). Obviously, ΔE vdw terms made the greatest contribution to this binding, indicating that VDW interactions generated the primary binding energy in the system. As initial structure, in the crystal structure, there are three hydrogen binding energy between X77 and residues GLU166, HIS163, and GLY143; however, their energy contributions are greater than −0.8 kaL/mol according to the decomposition energy calculation. On the other side, HIS41, MET49, LEU50, CYS145, MET165, ASP187, and GLN189 contributed favorable energies lower than −0.8 kcal/mol. The hydrogen bond analyses suggested there are hydrogen bonds between HIS41, GLU189, and X77. Other residues might interact with X77 by hydrophobic interaction. Based on these residues, we established the binding pocket by SYBYL program ( Figure 1D ).

Next, we in silico screened all commonly used clinical oral antidiabetic drugs suggested by guideline from the Chinese Diabetes Society 24 to predict the potential inhibitors for M pro . Molecular docking was carried out between M pro and 22 different hypoglycemic drugs listed in Table S2 . Nelfinavir, a previously predicted active molecule bound to M pro , was selected as the positive control and produced a total docking score of 9.63. After evaluating the interaction by analyses of the docking scores and binding poses, repaglinide, canagliflozin, glimepiride, glipizide, gliquidone, and linagliptin were predicted as the promising chemical agents. Our results showed that these six antidiabetic drugs with docking scores higher than 8.0 (Table S2) shared similar binding poses to the reported ligand N3 inhibitor in the crystal structure ( Figure 2 ). Among these six antidiabetic drugs, repaglinide employed the highest docking score for M pro (9.3). In these binding conformations, we found some hydrogen bonds existing between drugs and HIS164, GLU166, GLY143, and ASP187. The hydrophilichydrophobic interactions should also be important for their binding interaction, because the VDW interaction was the primary contribution to the binding of reported ligand N3 inhibitor with M pro . We also compared the binding residues of each ligand with the key residues identified by molecular dynamic simulation of M pro -N3 system and M pro -X77 ( Figure S3 and Table S3 ). We found gliquidone, repaglinide, and glipizide interacted with most of these key residues, which implied that these ligands have more possibility of inhibiting M pro .

Based on binding conformations alignment and similarity calculation, we found repaglinide shared similar pharmacophores with nelfinavir and the reported ligand N3 among these six drugs. Repaglinide is an insulinotropic agent, classified as the nonsulfonylurea type, whose mass weight was lower than N3 inhibitor or nelfinavir, whereas their shapes and pharmacophores were similar. The docking model of repaglinide with M pro turned out to be similar with that of the reported ligand (N3 inhibitor) and nelfinavir, where two hydrogen bonds involving GLU166 and HIS164 maintained the binding interface between repaglinide and SARS-CoV-2 M pro . Moreover, repaglinide also shared an analogous docking pose ( Figure 3A and B) with N3 inhibitor and nelfinavir, as well as similar pharmacophores including hydrophobic alkyl, hydrophilic ring, aromatic ring, and hydrophobic ring. In particular, repaglinide and N3 inhibitor shared a similar leucine (LEU) analogue structure in their hydrophobic alkyl moiety.

To gain more insights into the binding mechanism, docking complex of repaglinide-M pro were subjected to 50 ns molecular dynamics simulations using Amber 14. The binding free energies were calculated by MM/GBSA approaches. The results showed that (a) the binding free energies of repaglinide-M pro are −28.48 ± 3.27 kcal/mol, similar with the recently reportednNelfinavir and X77 inhibitor; (b) VDW interaction (E vdw ) makes a more significant contribution than the electrostatic interaction (E ele ) (Table S1), indicating that E vdw is the driving force for binding. The energy decomposition in the repaglinide-M pro system was calculated (Figure S4) , where GLN192, MET165, ALA191, GLN189, and MET49 contributed favorable energies lower than −1.0 kcal/mol, consistent with the key residues of the binding pocket defined by ligand (N3 and X77 inhibitor) . These observations further demonstrated that repaglinide interacted with M pro in a similar way with that of the existing inhibitors.

On the other hand, cell penetration is also a pivotal step during the infection of CoVs. Recent studies found that ACE2 was a possible cellular entry receptor for SARS-CoV-2, because the virus was proved unable to infect cells with absence of ACE2. 25, 26 Therefore, we assessed the effects of these six antidiabetic drugs on ACE2 expression in human alveolar type II cell line A549 and vascular endothelial cells (HUVEC), which represented the major expression position of ACE2 and showed an upregulated ACE2 expression under carcinogen exposures or inflammation. [27] [28] [29] [30] Our results showed that they had no significant influences on the expressions of ACE2 ( Figure 3C and D) . In addition to ACE2, other receptors, such as L-SIGN (also known CD209L) and DC-SIGN (also known as CD209), were reported to affect SARS-CoV-2 host entry . 31 Therefore, we further detected the expression of these two receptors in A549 and HUVEC cells after treated by the selected antidiabetic drugs, and we did not find significant changes at the mRNA expression levels of both L-SIGN and DC-SIGN ( Figure S5 ).

In this study, we performed a novel strategy to in silico screen 22 clinical oral antidiabetic drugs and identify six medicines with potential inhibition for M pro of SARS-CoV-2. Interestingly, repaglinide, an anti-HIV drug 20 employed the similar pharmacophores, LEU analogical substrate, and M pro docking score to nelfinavir, is predicted as a promising candidate for treatment of COVID-19.

We demonstrated six antidiabetic drugs, including repaglinide, canagliflozin, glipizide, gliquidone, glimepiride, and linagliptin, possessed the ability to bind with M pro binding pocket, indicating they may act to suppress the replication and transcription of SARS-CoV-2. Here we established the receptor-based virtual screening approach for the potential inhibitor of M pro . Compared with the standard assay that was used to test whether the existing hypoglycemic drugs are effective in treating the viral infections, 32 our method could quickly and with high throughput screen drugs for repurposing or easily obtained compounds and shorten the time for new drug development. 33, 34 Through this model, six antidiabetic drugs are predicted as promising inhibitors for M pro , as they gained the docking scores between 8.5~9.3. In general, a great docking score means more possibility to bind to the target protein with high binding affinity. Here, a relative higher cutoff value of 8.0 was selected to find the more promising candidates. The binding conformations of these antidiabetic drugs with M pro suggested these molecules may inhibit the replication and transcription of CoV, which in accordance with previous studies that lopinavir, a marketed status drug developed by targeting F I G U R E 2 Prediction of the potential ligands for M pro . The docking mode of M pro and repaglinide, canagliflozin, glimepiride, glipizide, gliquidone, and linagliptin, respectively M pro , now was found to improve the outcome of Middle East respiratory syndrome (MERS)-CoV infected common marmosets, as well as SARS patients in nonrandomized trials. 35, 36 We also found lopinavir may bind to the binding pocket of M pro with docking score of 10.04 (Table S2) center and open-label trial that evaluated the therapeutic effects of lopinavir-ritonavir treatment in hospitalized adult patients with severe COVID-19; however, they found no benefit from this treatment compared with standard care. Consistently, another trial conducted in a mild/moderate COVID-19 population also reported a negative result of lopinavir-ritonavir therapy. 38 Recently, WHO announced the results of the SOLIDARITY trial, the largest international randomized trial regarding antiviral drugs for COVID-19, and they also found no effect of lopinavir on hospitalized COVID-19. 39 These findings remind us to confront the gap between molecular structural information and clinical effectiveness. Thus, the molecular docking results need to be interpreted with caution.

Others also reported the potentially druggable target of M pro by the molecular docking approach and identify numerous classes of protease inhibitors against SARS-CoV, such as GRL-001. 10, 40, 41 More important, our structure analysis showed repaglinide, belonging to the meglitinide class of short-acting insulin secretagogues, employed a similar structure and docking score (9.3) as an anti-HIV drug nelfinavir (9.6), which was recently suggested to be a potential inhibitor against SARS-CoV-2 M pro . 20 In addition, based on binding conformations alignment and similarity calculation of these six drugs with nelfinavir, we found repaglinide shared similar docking pose ( Figure 3A and B) and pharmacophores with the reported ligand in the crystal structure of M pro (N3 inhibitor) and nelfinavir, including hydrophobic alkyl, hydrophilic ring, aromatic ring, and hydrophobic ring. In particular, we identified the similar LEU amino acid substrate as a reported ligand N3 inhibitor shared by repaglinide and nelfinavir, suggesting that LEU analogical fragments might be important for the discovery of new M pro inhibitors. Although the docking scores and predicted binding affinity of the other five antidiabetic drugs were lower than those of nelfinavir, we may take advantage of these hypoglycemic drugs for the additional benefits of anti-SARS-CoV-2 effects when hypoglycemic treatment is necessary to COVID-19 patients with diabetes mellitus. Notably, although our results indicated that all these six antidiabetic drugs might not affect the expression of ACE2 and other two potential receptors, that is, L-SIGN and DC-SIGN, a recent study also found other influence factors of SARS-CoV-2 host entry, such as cellular serine protease TMPRSS2, 42 which required to be examined in future studies. Besides M pro , various factors have been found to affect the infection of SARS-CoV-2, and several drugs including metformin and sitagliptin that were assessed at lower docking scores in our study were reported to show positive outcomes in COVID-19 patients. 43, 44 Therefore, we cannot exclude the possibility that drugs that rank lower in our docking results of M pro may have a beneficial role in hindering the replication and infection of SARS-CoV-2 through other ways. In addition, dipeptidyl peptidase 4 (DPP4) has been reported as a receptor for human coronavirus (hCoV-EMC) that directly binds to DPP4 through its S1 domain. Antibodies against DPP4 could inhibit hCoV-EMC infection of primary human bronchial epithelial cells and Huh-7 cells, whereas DPP4-inhibitors including sitagliptin, vildagliptin, and saxagliptin were not able to block the infections. 45 These findings indicated that the binding interface between the virus and receptor might differ from the developed receptor inhibitors that were designed for lowering blood glucose.

In sum, our results indicated the potential extra effects on anti-SARS-CoV-2 by the six oral antidiabetic drugs. Further preclinical experiment and clinical research are necessary to confirm these findings, and the effects of insulin and GLP1 analogues on SARS-CoV-2 also need to be tested in the future.

Clinical features of patients infected with 2019 novel coronavirus in Wuhan

WHO Coronavirus Disease (COVID-19) Dashboard. 2020

Summary of Probable SARS Cases with Onset of Illness from 1

Clinical characteristics of 2019 novel coronavirus infection in China

Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan

Association of blood glucose control and outcomes in patients with COVID-19 and preexisting type 2 diabetes

Coronaviruses post-SARS: update on replication and pathogenesis

SARS and MERS: recent insights into emerging coronaviruses

Coronaviruses: an overview of their replication and pathogenesis

Design of wide-spectrum inhibitors targeting coronavirus main proteases

Structures of two coronavirus main proteases: implications for substrate binding and antiviral drug design

Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs

The crystal structure of 2019-nCoV main protease in complex with an inhibitor N3. 2020

Structure of COVID-19 main protease bound to potent broadspectrum non-covalent inhibitor X77

PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations

ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB

PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data

Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations

Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation. bioRxiv

LigPlot+: multiple ligand-protein interaction diagrams for drug discovery

Phenytoin-bovine serum albumin interactions -modeling plasma protein -drug binding: a multi-spectroscopy and in silico-based correlation

Deficiency of mitochondrial glycerol 3-phosphate dehydrogenase contributes to hepatic steatosis

Guidelines for the prevention and treatment of type 2 diabetes in China (2017 edition)

A pneumonia outbreak associated with a new coronavirus of probable bat origin

Pathogenesis of SARS-CoV-2 in transgenic mice expressing human angiotensin-converting enzyme 2

Cigarette smoke exposure and inflammatory signaling increase the expression of the SARS-CoV-2 receptor ACE2 in the respiratory tract

Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis

Altered COVID-19 receptor ACE2 expression in a higher risk group for cerebrovascular disease and ischemic stroke

acting through ERalpha, induces endothelial non-classic reninangiotensin system increasing angiotensin 1-7 production

CD209L/L-SIGN and CD209/DC-SIGN act as receptors for SARS-CoV-2 and are differentially expressed in lung and kidney epithelial and endothelial cells

Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro

Identification of potential isoform-selective histone deacetylase inhibitors for cancer therapy: a combined approach of structure-based virtual screening, ADMET prediction and molecular dynamics simulation assay

Outbreak of pneumonia of unknown etiology in Wuhan, China: the mystery and the miracle

Treatment of severe acute respiratory syndrome with lopinavir/ritonavir: a multicentre retrospective matched cohort study

Role of lopinavir/ritonavir in the treatment of SARS: initial virological and clinical findings

A trial of lopinavir-ritonavir in adults hospitalized with severe COVID-19

Efficacy and safety of Lopinavir/ ritonavir or arbidol in adult patients with mild/moderate COVID-19: an exploratory randomized controlled trial

Repurposed antiviral drugs for COVID-19 -interim WHO SOLIDARITY trial results. medRxiv

Antiviral drugs specific for coronaviruses in preclinical development

The newly emerged SARS-like coronavirus HCoV-EMC also has an "Achilles' heel": current effective inhibitor targeting a 3c-like protease

SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor

Metformin use is associated with reduced mortality rate from coronavirus disease 2019 (COVID-19) infection

Sitagliptin treatment at the time of hospitalization was associated with reduced mortality in patients with type 2 diabetes and COVID-19: a multicenter, case-control, retrospective, observational study

Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC

We thank Zihe Rao and Haitao Yang group from Shanghai Institute for Advanced Immunochemical Studies (SIAIS) for the crystal structure of M pro (PDB ID: 6LU7). 

None declared.