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. Author manuscript; available in PMC: 2020 Nov 5.
Published in final edited form as: J Proteome Res. 2020 Sep 27;19(11):4670–4677. doi: 10.1021/acs.jproteome.0c00397

Repurposing of FDA-Approved Toremifene to Treat COVID-19 by blocking the spike glycoprotein and NSP14 of SARS-CoV-2

William R Martin ⊥, Feixiong Cheng ⊥,▽,◊,*
PMCID: PMC7640961  NIHMSID: NIHMS1635678  PMID: 32907334

Abstract

The global pandemic of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to the death of more than 675,000 worldwide and over 150,000 in the United States alone. However, there are currently no proven effective pharmacotherapies for COVID-19. Here, we combine homology modeling, molecular docking, molecular dynamics simulation, and binding affinity calculations to determine potential targets for toremifene, a selective estrogen receptor modulator which we have previously identified as a SARS-CoV-2 inhibitor. Our results indicate the possibility of inhibition of the spike glycoprotein by toremifene, responsible for aiding in fusion of the viral membrane with the cell membrane, via a perturbation to the fusion core. An interaction between the dimethylamine end of toremifene and residues Q954 and N955 in heptad repeat 1 (HR1) perturbs the structure, causing a shift from what is normally a long, helical region to short helices connected by unstructured regions. Additionally, we found a strong interaction between toremifene and the methyltransferase non-structural protein (NSP) 14, which could be inhibitory to viral replication via its active site. These results suggest potential structural mechanisms for toremifene by blocking the spike protein and NSP14 of SARS-CoV-2, offering a drug candidate for COVID-19.

Keywords: COVID-19, Drug repurposing, SARS-CoV-2, Methyltransferase non-structural protein 14 (NSP14), Molecular docking, Spike glycoprotein, Toremifene

Graphical Abstract

graphic file with name nihms-1635678-f0001.jpg

INTRODUCTION

As of August 4th, 2020, there are over 18 million documented cases of COVID-19 (over 675,000 resulting in death), with nearly one-third of all cases occurring in the United States (over 150,000 deaths1). As of this writing, there are no FDA approved treatments or vaccines for COVID-19, both of which are sorely needed. A search on clinicaltrials.gov on June 1st for COVID-19 as the disease and the additional term “drug” yields 806 results, providing evidence for the need for an intervention. Drug repurposing allows an acceleration of the drug discovery pipeline; drugs which have already been FDA approved to treat another disease are repositioned as therapeutics for diseases for which they have not yet been used2,3.

In our initial network-based drug repurposing study4, we identified toremifene, another selective estrogen receptor modulator (SERM), as a strong candidate for the potential treatment of COVID-19. A drug repurposing study for SARS-CoV-15 indicated a low 50% effective concentration (EC50) for toremifene, and noted that estrogen signaling may not be involved in the inhibitory pathway, similar to that of inhibition of ebola6. Indeed, a crystal structure of the Ebola virus with bound toremifene indicates the interaction lies between the attachment (GP1) and fusion (GP2) protein subunits7. In a study using human organs-on-chips8, toremifene was found to significantly inhibit entry of a pseudotyped SARS-CoV-2 virus. However, the mechanism of action for toremifene in the inhibition of SARS-CoV-2 is not yet known. As noted above, experimental studies have indicated a potential for toremifene to inhibit both Ebola virus and coronaviruses (Table 1).

Table 1:

Docking results for toremifene with all potential targets.

Protein Model Percent Homology Affinity (kCal/mol)
NSP1 2HSX35 86.1 −5.1
PL-PRO 6VXS 100 −6.6
NSP4 3VCB36 61.4 −6
3CL-PRO 5R7Y 100 −5.9
NSP7 6M7137 100 −5.7
NSP9 6W4B 100 −5.7
RNA-P 7BTF37 100 −6.5
Helicase 6JYT38 99.8 −5.9
NSP14 5C8S39 95.1 −7.2
NSP15 6W0140 100 −6.1
NSP16 6W4H 100 −6.1
Spike 6VSB41 100 −6.9
Nucleocapsid 6VYO 100 −5.6
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Note: All PDBs were downloaded from RSCB database: https://www.rcsb.org. Proteins with no potential binding region were not tested. Systems listed as 100% homology used the crystal structure as opposed to a homology model.

The actual target for toremifene has not yet been elucidated in coronaviruses as it has in Ebola, which was determined to have an IC50 of roughly 1 μM6. Dyall et. al5 found the EC50 of toremifene for MERS-CoV and SARS-CoV-1 to be 12.9 μM and 11.97 μM, respectively; while these results are not different, the story is different for tamoxifen, which differs from toremifene by a substitution of hydrogen for the chlorine on toremifene. While the EC50 for tamoxifen is slightly lower in MERS-CoV (10.1 μM), the EC50 for SARS-CoV-1 is worse than for toremifene (92.9 μM), which could potentially indicate the inhibition is related to a viral, and not human, protein. Importantly, a recent study indicated an IC50 of 3.58 μM for toremifene with SARS-CoV-29.

There have been numerous studies recently targeting singular proteins in SARS-COV-2 using virtual screening techniques, such as 3-chymotrypsin-like protease10–12 and the papain-like protease13. Further studies have been done using large databases, such as ZINC14, to test large compound libraries across multiple viral proteins. In an effort to determine potential interactors with toremifene specifically15, we used inverse virtual screening to test 13 of the 29 viral proteins encoded by the SARS-CoV-2 genome. Proteins which did not have a crystal structure at the time of this study were modeled using homology modeling. We believe this to be a comprehensive study to combine virtual screening with molecular dynamics and molecular mechanics / Poisson Boltzmann Surface Area (MM/PBSA) calculations to determine potential protein-ligand interactions in SARS-CoV-2 proteome. Here, we have discovered two potential targets for toremifene from the entire SARS-CoV-2 proteome.

METHODS AND MATERIALS

2.1. Homology Modeling

Proteins for which crystal structures were not available were constructed using homology modeling. Each sequence was accessed from NCBI by its accession number. The protein sequence was submitted to a BLAST16 search within UCSF Chimera17, and the best matching structure was chosen for homology modeling. Each homology model was constructed using a single template using MODELLER 9.2118 within UCSF Chimera. The best model as determined by Z-score was used for docking. Proteins which do not have potential templates with high homology were not modeled. Generally, sequence identity of greater than 40% will yield an acceptable homology model19.

2.2. System Equilibration

Prior to docking, all homology models were subjected to a short minimization and equilibration period using molecular dynamics simulations. In short, each system was constructed using standard tools in GROMACS 2018.220. Systems were submerged in a water box with edges no less than 10 Å from any part of the protein, and neutralized using sodium and chloride ions to an ionic strength of 0.15 M. Parameterization for the protein, ions, and water were done using the CHARMM36 force field21. Each protein was minimized using a steepest descent algorithm for 5000 steps, followed by a short 200 ps equilibration.

2.3. Molecular Docking

The toremifene structure was downloaded from the ZINC database14, while all crystal structures used for docking were obtained from the RCSB protein data bank22. All docking was done using AutoDock Vina23 within UCSF Chimera. The highest scoring binding pose (kCal/mol) was selected for further analysis where appropriate (Figure 1). Each search generated 10 binding modes with the exhaustiveness set to the maximum value. Proteins without a clear binding region (for example, accessory protein 7a) were not included. The search grid for each protein was selected to encompass the entire protein.

Figure 1: A diagram illustrating the workflow of computational identification of viral targets of toremifene across the SARS-CoV-2 proteome.

Figure 1:

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Toremifene (center) with potential targets for molecular docking. From top right: PL-PRO, papain-like protease; NSP4, non-structural protein 4; NSP9, non-structural protein 9; RNA-P, RNA-directed RNA polymerase; NSP15, non-structural protein 15; NSP16, non-structural protein 16; NSP14, non-structural protein 14; Spike, spike glycoprotein.

2.4. Molecular Simulation System Construction

Each system was constructed using the solution builder in CHARMM-GUI24–26. Following a processing step involving the addition of hydrogens not added in docking and parameterization of the ligand, a water box with edges at least 10 Å from any part of the protein was added. The system was neutralized and brought to an ionic strength of 0.15 M using sodium and chloride ions. The CHARMM General Force Field (CGenFF)27 was used to parameterize toremifene, while the CHARMM36m force field was used to parameterize the protein, ions, and TIP3P water molecules.

2.5. Simulation Parameters

All systems were simulated using GROMACS 2020.1 on the AiMOS Supercomputer at the Rensselaer Polytechnic Institute Center for Computational Innovations in a three-step process. Initial minimization of the systems was run until changes in the potential energy of the system reached machine precision. Following minimization, an NVT equilibration step was completed with a 2 fs timestep for 500,000 steps using 400 kJ mol−1 nm−2 and 40 kJ mol−1 nm−2 positional restraints on the backbone and sidechains, respectively. A 500 ns production step was completed using the NPT ensemble with no position restraints and a 2 fs timestep.

Hydrogen atoms were constrained using the LINCS28 algorithm. Temperature for the system was held at 300 K using a Nose-Hoover thermostat29 with a 1 ps coupling constant. For the production simulation, pressure was coupled isotropically using a Parrinello-Rahman barostat30 with a 5.0 ps coupling constant and compressibility of 4.5e-05 bar−1 to maintain a pressure of 1 bar. The pair-list cutoff was constructed using the Verlet scheme31 with a cutoff distance of 1.2 nm. Particle mesh Ewald electrostatics32 were used to describe coulombic interactions with a 1.2 nm cutoff, while van der Waals forces were smoothly switched to using between 1.0 and 1.2 nm using a force-switch modifier to the cut-off scheme. Linear center of mass translation was removed every 100 steps for the entire system.

2.6. MM/PBSA calculations

For systems which were chosen for further analysis, MM/PBSA (Molecular Mechanics / Poisson-Boltzmann Surface Area) calculations were done using g_mmpbsa33, a GROMACS tool used to calculate an estimated binding affinity. The final 50 nanoseconds of the trajectory are selected for the calculation, sampled every 200 picoseconds, for a total of 251 frames. MM/PBSA calculations were completed on the Pitzer supercomputer at the Ohio Supercomputer Center.

RESULTS

3.1. Molecular Docking

SARS-CoV-2 has a roughly 30 kb genome which encodes 29 proteins34. These 29 proteins include 16 non-structural proteins (NSP), 4 structural proteins, and 9 accessory proteins. The non-structural proteins include proteinases (NSP3, NSP5), RNA polymerases (NSP12), helicases (NSP13), ribonucleases (NSP14, NSP15) and methyltransferases (NSP14, NSP16), while structural proteins are involved in viral assembly (envelope, membrane) and binding with the host protein (spike glycoprotein).

Molecular docking over the entire protein was carried out on 13 of the 29 possible viral proteins in SARS-CoV-2. These 13 were selected based on a combination of criteria: 1) Whether a crystal structure for the protein exists. 2) if a crystal structure does not exist, is there a template protein (generally from SARS-CoV-1) available to use for homology modeling. 3) Based on either the crystal structure or homology modeling, is there a potential binding pocket. Proteins which did not meet these criteria (for example, the membrane protein has partial homologous coverage at ~20% homology; protein 3a does not appear to have a potential binding pocket) were not chosen for the docking study. The best scoring poses are tabulated in Table 1, with a larger negative number indicating a better binding affinity. Unsurprisingly, the affinity was not high for a few of the smaller systems (NSP1 and the nucleocapsid, for example), which were included as a sort of negative control; it was not expected that good binding would be achieved with these systems. Interestingly, the spike glycoprotein and NSP14 had the highest binding affinities based on molecular docking.

To determine which systems would be selected for further analysis via simulation to better determine if a particular protein-ligand binding pose maintains its integrity, we visually inspected the binding of each system. While a hard cutoff was not selected, certain systems were rejected for further analysis. Systems which did not have strong binding within a clear pocket were not simulated, including NSP1, NSP7, NSP9, the helicase, and the nucleocapsid. As an example, the interaction with NSP9 does not involve a binding pocket, but sits on the surface of the protein. We have no expectation that such an interaction would be maintained throughout a simulation, and therefore did not include it in the next step.

3.2. Molecular Dynamics

Each of the above systems with a strong predicted binding was simulated according to the protocol listed in the methods. Here, we have monitored the protein-ligand interaction over the entire 500 nanosecond trajectory. Unsurprisingly, due to the low binding affinities, the interaction between the ligand and the protein was not maintained throughout the trajectory in most systems. Table 2 indicates the length of simulation before a particular protein-ligand system lost its interaction.

Table 2:

Number of water molecules, ions, total number of atoms, and residence time of toremifene in the binding region as determined by molecular docking.

Protein TIP3P Water Na+/Cl− System Size Residence Time (ns)
PL-PRO 8912 26/26 29387 34.4
NSP4 12155 34/34 38019 123.6
3CL-PRO 26898 79/76 85553 284.2
RNA-P 75727 222/215 242404 60.4
NSP14 94844 270/272 293433 500
NSP15 25929 84/73 83481 35.3
NSP16 13133 37/41 44220 11.5
Spike 277372 794/785 885596 500
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Note: A time of 500 nanoseconds indicates the drug did not leave the binding pocket. Please see the supplementary materials for details on proteins with residence time less than 500 nanoseconds.

Both the interaction between toremifene and NSP14, as well as that between toremifene and the spike glycoprotein, were maintained (and within the original binding pocket) throughout the entire 500 nanosecond trajectory. These two systems were analyzed further to determine the nature of the protein-ligand interactions.

To ensure the results were not simply a result of poor initial docking poses, all systems for which toremifene did not maintain contact with the initial docked region were redocked using SwissDock42 selecting the “accurate” setting under “extra parameters“, with flexible residues within 5 angstroms of the docked ligand. The implementation of AutoDock23 used sacrifices the inclusion of flexible residues in the binding pocket for significantly improved speed, while SwissDock allows us to implement a different algorithm, while also including flexible residues (at significantly higher computational cost).

A specific region of interest was not defined. These newly docked systems were also simulated in the same fashion as those docked with AutoDock, and yielded similar results; all simulations resulted in a residence time less than 200 nanoseconds.

3.3. Toremifene interacts with the spike glycoprotein

The initial docking position (Figure 2a) lies at the interface between two separate domains in the spike glycoprotein, with one domain having its receptor binding domain (RBD) in the “up” position, while the other having its RBD in the “down” position. In the B domain of PDB ID 6VSB, the interaction with the spike glycoprotein is with the helical region between the loop separating the S1 and S2 subunits and the fusion peptide, shown in SARS-CoV-1 to mediate membrane fusion in a calcium-dependent manner43, while the interaction in the A domain involves the N-terminal region of the RBD, as well as heptad repeat 1 (HR1), a key component of the fusion core (Figure 2b,4a). While the non-helical linker between T941 and L945 did extend to K947 in the B and C domains, the remainder of this helical region remained unperturbed when compared to the crystal structure, with a long helix between L948 and S967. However, the interaction with toremifene resulted in a helical region between S943 and Q954, with a short helical region from A958 through T961 and the remainder unstructured. An interaction between the dimethylamine region of toremifene and Q954 and N955 appears to be key in perturbing the secondary structure of HR1.

Figure 2: Toremifene likely interacts with the spike glycoprotein of SARS-CoV-2.

Figure 2:

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(a) The full-length crystal structure (PDB ID: 6VSB) of the homotrimeric spike glycoprotein with toremifene. (b) The final conformation of toremifene (blue, ball and stick) with the spike glycoprotein at 500 nanoseconds of simulation. (c) Key interacting residues with toremifene. Edges are labeled with MM/PBSA interaction energies.

Figure 4: Toremifene interacts with the spike glycoprotein and NSP14.

Figure 4:

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Stick representations of the final pose at 500 nanoseconds for toremifene with (a) the spike glycoprotein and (b) NSP14. Wireframe residues represented in each image are within 3.0 angstroms of toremifene in the final frame of the 500 nanosecond (ns) simulation.

In an effort to determine the strength of the interaction, we carried out an MM/PBSA binding affinity calculation between the entire protein and toremifene. Our calculation resulted in a final binding energy of −91.036 (±0.933) kJ/mol. The main contributors (Figure 2c) to this binding energy were V772 and L861 in the B domain due to large non-polar interactions (−9.7 and −4.0 kJ/mol, respectively), And Y313 in the A domain due to a chlorine-pi interaction (−3.7 kJ/mol). The chlorine-pi interaction could indicate a potential mechanism by which toremifene has a stronger inhibitory action than tamoxifen as seen in SARS-CoV-1.

3.4. Toremifene potentially displaces functional ligands in NSP14

NSP14 has both exoribonuclease and methyl transferase activity; here, we have found a strong interaction with the N7-methyl transferase domain (Figure 3a). Throughout the molecular dynamics simulation, very little movement of the ligand was observed. The docked position appears as though it would potentially be inhibitory to interaction with the functional ligand S-adenosyl methionine, while clearly being inhibitory to interaction with its substrate, Gppp-RNA (Figure 3c,4b). As with the spike protein, we assessed the binding affinity using MM/PBSA, finding a significant pi-pi interaction with F426 (−8.0 kJ/mol), a chlorine-pi interaction with F506 (5.0 kJ/mol), a strong hydrophobic interaction with C309 (−4.2 kJ/mol), and a total binding energy of −119.805 (±1.013) kJ/mol. Many of the residues identified here as interacting with toremifene (Figure 3b) were identified in the crystal structure used to generate our homology model as interacting with GpppA.

Figure 3: Toremifene likely displaces functional ligands in NSP14 in SARS-CoV-2.

Figure 3:

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(a) The homology model of NSP14 with toremifene. (b) Key interacting residues with toremifene, with edges labeled with MM/PBSA interaction energy. (c) Final pose of toremifene (blue, ball and stick) with NSP14 at 500 nanoseconds of simulation. The RMSD for all simulations can be found in Supplementary Figure 7.

DISCUSSION

We have demonstrated two plausible targets for toremifene in SARS-CoV-2. Previous work has indicated that, as noted earlier, toremifene is likely to be inhibitory to viral entry6,7. A proposed mechanism for Ebola posited a mechanism by which fusion between the viral and endosomal membranes is disrupted7. The interaction with the spike glycoprotein proposed here could prevent such a fusion through its disruption of the HP1 helix. The interaction with NSP14 elucidated here would indicate an inhibition of viral reproduction by interfering with interaction with the substrate.

Toremifene, a first generation nonsteroidal SERM, shows striking activity in blocking multiple viral infections, including Ebola6,7 (IC50 = ~1 μM), MERS-CoV5 (EC50 = 12.9 μM), SARS-CoV-15 (EC50 = 11.97 μM), and SARS-CoV-29 (IC50 = 3.58 μM), in established, virus infected human cell lines. The inhibition in Ebola by SERMs has been found to not be via the estrogen receptor pathway, but instead disrupt endolysosomal calcium, leading to accumulation and thereby blocking Ebola entry44. Toremifene has been approved for the treatment of breast cancer for over 25 years45, and has been investigated as a treatment for prostate cancer46. In this study, we have determined two potential targets for toremifene, the spike glycoprotein and NSP14. Toremifene has been used in post-menopausal women with breast cancer, pre-menopausal women and men with prostate cancer in clinical trials involving thousands of participants since its inception in 199747. Liver toxicity has occurred in only long-term studies and not shown in short-term studies of <8 weeks48. Therefore, there is low risk of safety concern with use of toremifene 60mg daily for short-term treatment of COVID-19, such as 2 weeks. Based on human studies, the mean plasma concentration of toremifene during administration of 60 mg/day was 0.88 mg/L (2.17 μM) in postmenopausal breast cancer patients49. The peak plasma concentration of toremifene (60 mg/day) was over 10 μM at the 4 hours administration in patients50, which is ~ 3-fold of antiviral effect on SARS-CoV-2 (IC50 = 3.58 μM). An animal model study showed that toremifene (peak plasma concentration of 2.98 μM) was needed to protect 50% of mice from death caused by Ebola virus infection, a more fatal virus than SARS-CoV-2. The combination of consistent findings from in vitro antiviral data supporting the antiviral effects of toremifene against coronaviruses, along with reasonable tolerability, provide the basis to pursue toremifene as a viable candidate therapeutic against SARS-CoV-2.

Caution, however, must be exercised when interpreting these results. There are several limitations for molecular docking approaches. For example, molecular docking scores could yield false positives when proteins having significant conformational changes. Although we employed molecular dynamics simulation to verify our findings from molecular docking, further experimental validation is warranted in the near future. It should be noted that, to this date, there are no crystal structures published for the spike glycoprotein with an inhibitory ligand. Similarly, no inhibitors have been crystallized with NSP14, nor has a structure for NSP14 in SARS-CoV-2 been crystallized, though the homology with SARS-CoV-1 is indeed high. However, work on the flavivirus methyltransferase38 has indicated there is a potential for inhibition. While the proposed mechanisms for SARS-CoV-1 in toremifene are posited to not involve human proteins, further study would be required to confirm the mechanism. While in vitro studies on toremifene do exist for SARS-CoV-2, there are no studies to date which allow a comparison with other SERMs, as has been done with SARS-CoV-1 and MERS-CoV.

Future work will be needed to confirm these results; optimally, the determination of a co-crystal structure with NSP14 and/or the spike glycoprotein from SARS-CoV-2 with toremifene would be solved. Alternatively, other functional validation analyses (in vitro assays, for example) could be carried out to determine the correct protein target. The final poses could also be used to inform further work to refine a potential inhibitor using medicinal chemistry techniques.

CONCLUSION

The present study has demonstrated two potential targets for toremifene in SARS-CoV-2. We have demonstrated a potential mechanism for inhibition via the spike glycoprotein, through which an interaction with Q954 and N955 in heptad repeat 1 appears to disturb the secondary structure, resulting in what is normally a long alpha helix instead having a lack of secondary structure. Additionally, the interaction we’ve found with NSP14 appears as though it could be inhibitory in two ways: First, it appears as though there would be steric hindrance between toremifene and any functional ligands (such as S-adenosyl methionine), as well as interfering with substrate interaction in the catalytic pocket. Further experimental and functional studies would be needed to validate these findings, such as a co-crystal structure of the spike glycoprotein or NSP14 with toremifene.

Supplementary Material

SI files

Figure S1: a) NSP3 (PL-PRO) in the initial docked conformation b) NSP3 at 34.4 nanoseconds of simulation.

Figure S2: a) NSP4 in the initial docked conformation. b) NSP4 at 123.6 nanoseconds of simulation.

Figure S3: a) NSP5 (3CL-PRO) in its initial docked conformation. b) NSP5 at 284.2 nanoseconds of simulation.

Figure S4: a) NSP12 (RNA-Polymerase) in its initial docked conformation. b) NSP12 at 60.4 nanoseconds of simulation.

Figure S5: a) NSP15 in its initial docked conformation. b) NSP15 at 35.3 nanoseconds of simulation.

Figure S6: a) NSP16 in its initial docked conformation. b) NSP16 at 11.5 nanoseconds of simulation.

Figure S7: Root-Mean-Squared Deviation (RMSD) plot for all proteins simulated.

Acknowledgements

We acknowledge support from the Ohio Supercomputer Center and the Center for Computational Innovation at Rensselaer Polytechnic Institute for computational resources.

Funding: This work was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) under Award Number R00 HL138272 and the National Institute of Aging under Award Number R01AG066707 to F.C. This work was supported, in part, by the VeloSano Pilot Program (Cleveland Clinic Taussig Cancer Institute).

Footnotes

Supporting Information

The Supporting information is available free of charge at https://pubs.acs.org/

Competing interests: The authors declare that they have no conflict of interest. The content of this publication does not necessarily reflect the views of the Cleveland Clinic.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

SI files

Figure S1: a) NSP3 (PL-PRO) in the initial docked conformation b) NSP3 at 34.4 nanoseconds of simulation.

Figure S2: a) NSP4 in the initial docked conformation. b) NSP4 at 123.6 nanoseconds of simulation.

Figure S3: a) NSP5 (3CL-PRO) in its initial docked conformation. b) NSP5 at 284.2 nanoseconds of simulation.

Figure S4: a) NSP12 (RNA-Polymerase) in its initial docked conformation. b) NSP12 at 60.4 nanoseconds of simulation.

Figure S5: a) NSP15 in its initial docked conformation. b) NSP15 at 35.3 nanoseconds of simulation.

Figure S6: a) NSP16 in its initial docked conformation. b) NSP16 at 11.5 nanoseconds of simulation.

Figure S7: Root-Mean-Squared Deviation (RMSD) plot for all proteins simulated.

NIHMS1635678-supplement-SI_files.pdf (583.9KB, pdf)

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