Covid-19 Drug Repurposing: Evaluation of Inhibitors in SARS-Cov-2 Infected Cell Lines Clifford Fong

Covid-19 drug repurposing: evaluation of inhibitors in SARS-CoV-2 infected cell lines Clifford Fong

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Clifford Fong. Covid-19 drug repurposing: evaluation of inhibitors in SARS-CoV-2 infected celllines. [Research Report] Eigenenergy Adelaide South Australia Australia. 2021. hal-03221289

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Clifford W. Fong

Eigenenergy, Adelaide, South Australia, Australia. Email: [email protected]

Keywords: Caco-2, VeroE6, VeroCCL81, HuH, Calu-3, COVID-2019, SARS-CoV-2; ACE2 receptor binding, spike serine proteases, S-RBD, TMPRSS2, IC50, linear free energy relationships, HOMO-LUMO, quantum mechanics;

Abbreviations: Structure activity relationships SAR, ΔGdesolv,CDS free energy of water desolvation, ΔGlipo,CDS lipophilicity free energy, cavity dispersion solvent structure of the first solvation shell CDS, Dipole moment DM, Molecular Volume Vol, HOMO highest occupied molecular orbital, LUMO lowest unoccupied molecular orbital, HOMO-LUMO energy gap, linear free energy relationships LFER, Receptor binding domain of S protein of SARS-CoV-2 S- RBD, transmembrane serine 2 protease TMPRSS2, angiotensin-converting enzyme 2 ACE2.

Abstract

We have shown that the LFER method can be used to quantify the antiviral inhibition of various drugs in infected Caco-2, Vero, HuH and Calu-3 cell lines. A quantitative dependency can be found with various molecular specifiers such as the ΔGdesolv,CDS, the free energy of water desolvation, ΔGlipo,CDS lipophilicity free energy in octane or octanol, DM the dipole moment in water, Mol Vol the molecular volume in water, and HOMO-LUMO the energy gap in water. It appears that the inhibition of the various infected cell lines is the result of factors specific to the inhibitors and the different cell types, which have implications for exploratory studies of potential therapeutics of the antiviral efficacy in various human organs and tissues. There is some evidence that it may be possible to identify various drugs that may target the more solvent exposed exosite of TMPRSS2 in infected Calu-3 cells.

Introduction

The susceptibility of various cell lines to repurposed antiviral drugs offers a rapid and effective screening method for evaluating host cell responses to viral infection such as Covid-19. Examination of different human cell lines may have relevance to therapeutic treatment of various human tissues. Inhibition of SARS-CoV-2 infected Caco-2 cells can be studied using the LFER method to separate Caco-2 cell entry processes involving ACE2, TMPRSS2 or S-RBD from intracellular inhibitory processes. [1] The extensive study by Ellinger [2] likely predominantly involves the inhibition of Caco-2 cell entry processes involving ACE2, TMPRSS2 or S-RBD. Ellinger’s data for Caco-2 cells is a valuable source for evaluating the efficacy of SARS-CoV-2 therapeutics. Chu [3] evaluated the replicative capability of SARS-CoV and SARS-CoV-2 in 25 different cell lines including nine of human origin, including pulmonary (Calu-3), intestinal (Caco-2), hepatic (Huh-7), and neuronal (U251) cells. It was found that both human Calu-3 cells and Caco- 2 cells showed the greatest replication and were best suited for studying SARS-CoV-2 replicative processes. It is also known that Caco-2 cells were the only human cell type of 13 tested refractory cell lines that supported efficient SARS-CoV replication and expression of the SARS-CoV receptor, ACE2. [4]

It is well known that choice of cell lines for identifying SARS-CoV-2 antiviral drug efficacy is crucial: for example chloroquine when tested in Vero cells which are derived from the kidney cells of the African Green monkey, showed antiviral properties; but when tested in Calu-3 human lung cells, it showed no activity. It has been shown that chloroquine can inhibit the virus- activating enzyme cathepsin L in Vero cells, so chloroquine has an inhibitory effect on this enzyme and can thus prevent the coronavirus from infecting the Vero cell. Cathepsin L requires an acidic environment to function. Both hydroxychloroquine and chloroquine decrease the acidity, which then disables the cathepsin L enzyme, blocking the virus from infecting the Vero cells. But using the more clinically appropriate human lung cells Calu-3, the virus is activated by the protease TRMPSS2 to initiate cellular infection. Chloroquine and hydroxychloroquine target a pathway for viral activation that is not active in Calu-3 lung cells, which have very low levels of cathepsin L. TMPRSS2 does not require an acidic environment to function, so chloroquine or hydroxychloroquine are not inhibitory in human lung cells. [5-8]

So virus entry inhibition is cell type dependent, as both pH-dependent and pH-independent pathways are available for entry into cells. The spike (S) protein of SARS-CoV-2, which mediates viral entry, is activated by the endosomal pH dependent cysteine protease cathepsin L in some cell lines. However entry into airway epithelial cells, which express low levels of cathepsin L, depends on the pH-independent transmembrane TMPRSS2. [5-9]

Mykytyn et al [10] used human airway organoids (hAOs) as a better test vehicle than Calu-3 cells and found that the multibasic cleavage site (MBCS) of the SARS-CoV-2 S spike increased infectivity of the hAOs as well as forming syncytia (which are implicated in Covid-19 pathogenesis). The hAOs express ACE-2 and TMPRSS2. It was found that the MBCS increased entry speed and plasma membrane serine protease usage relative to cathepsin-mediated endosomal entry. Blocking serine proteases, but not cathepsins, effectively inhibited SARS- CoV-2 entry and replication in hAOs. Also it was shown that the SARS-CoV-2 MBCS facilitates serine protease-mediated entry in Calu-3 cells.

SARS-CoV-2 infectivity requires binding to the ACE2 receptor followed by proteolytic activation of the S protein by host proteases at the cleavage site located at the S1/S2 boundary (S1/S2 cleavage site) and within the S2 domain (S2′ cleavage site). Several host proteases, including endosomal cathepsins, cell surface TMPRSS2 proteases, furin, and trypsin, have been identified to be responsible for S protein cleavage during virus entry or viral protein biogenesis in coronaviruses. Inhibitors of TMPRSS2 and furin have been identified as having antiviral potential against the SARS-CoV-2 virus. [11,12] Dittmar et al [13] have evaluated a wide range of antivirals for activity against SARS-CoV-2 in the human HuH7.5, Vero and human Calu-3 cells. Major differences in drug sensitivity and entry pathways used by SARS-CoV-2 were found in these cells. Entry in lung epithelial Calu- 3 cells is pH-independent and requires TMPRSS2, while entry in Vero and Huh7.5 cells requires low pH and is triggered by acid-dependent endosomal proteases.

Ko et al [14] evaluated 24 FDA approved drugs (out of 28) for antiviral efficacy against SARS- CoV-2 in Vero and Calu-3 cells and found that 16 drugs were less effective in Calu-3 cells than in Vero cells, 6 drugs showed the same efficacy, and 6 drugs were more effective in Calu-3 cells, particularly nafamostat mesylate and camostat mesylate. Remdesivir, hydroxyprogesterone caproate, digitoxin, and cyclosporine showed modest increases only. Nafamostat can prevent the fusion of the envelope of the virus with surface membranes of host cells, and can inhibit membrane fusion at a concentration of less than one-tenth of that of camostat mesylate. Both nafamostat and camostat inhibit TMPRSS2. [6,7]

Study objective:

Evaluate the application of a previously described quantitative LFER method used to describe the antiviral inhibition of repurposed drugs against the SARS-CoV-2 virus, focussing on different infected cell lines to identify predictive indicators of antiviral efficacy that may be relevant to various human tissues.

Results

We have previously shown that the general equation 1 can be applicable to the inhibition of the various proteases involved in the SARS-CoV-2, SARS-CoV and MERS-CoV replication process. Also eq 1 can be useful screening tools to evaluate the potential efficacy of therapeutic drugs that may be active against the SARS-CoV-2 virus. [1,15,16]

Eq 1

Inhibition COVID-19 = ΔGdesolv,CDS + ΔGlipo,CDS + Dipole Moment + Molecular Volume + HOMO-LUMO

With the following independent variables, or molecular specifiers: ΔGdesolv,CDS is the free energy of water desolvation, ΔGlipo,CDS is the lipophilicity free energy in octane, DM is the dipole moment in water, Mol Vol is the molecular volume in water, and HOMO-LUMO is the energy gap in water. The general method is to test inhibition against all of the five molecular specifiers for the drugs totally unconstrained, then to identify which of the molecular specifiers best fits the inhibitory experimental data.

Many in silico studies seeking to find potential repurposed antivirals that may be effective against SARS-CoV-2 have used molecular docking screening of the ACE2 receptor, TMPRSS2 protein, as well as inhibitors of the Mpro involved in virus replication. The extension of these searches using antivirals with virus infected cells is more problematical because it is not clear which site(s) is actively inhibited when the various drugs are found to be effective in SARS- CoV-2 infected cell lines. Since the ACE2 and the TMPRSS2 are found on the cell surface of the many cells and tissues that are actively infected by the virus, these targets are likely inhibited first. The inhibition of Mpro requires the virus to penetrate into the infected cells as well as the drug to also be transported across the cell membrane.

Ellinger et al [17] investigated the inhibition of viral induced cytotoxicity using the human epithelial colorectal adenocarcinoma cell line Caco-2 and a SARS-CoV-2 isolate obtained from an individual originally exposed to the virus in the Wuhan region of China. A total of 64 compounds with IC50 <20 μM were identified in the final screening.

Analysis of Ellinger’s IC50 data for SARS-CoV-2 infected Caco-2 cells using the general equation 1 for 56 widely structurally diverse drugs is shown below in eq 2. No correlation was found with the dipole moment. The molecular volumes have been scaled by 35 times to allow a direct comparison of the relative magnitudes of the four molecular specifiers.

Eq 2

IC50 = 0.76ΔGdesolv,CDS + 0.83ΔGlipo,CDS + 1.71Mol Vol + 4.34 2 Where R = 0.174, SEE = 5.55, SE(ΔGdesolvCDS) = 0.30, SE(ΔGlipoCDS) = 0.34, SE(Mol Vol) = 0.54, F=3.64, Significance= 0.018

It is noted that the P-values of the coefficients of the ΔGdesolvCDS, ΔGlipoCDS (octane) and Molecular Volume molecular specifiers in eq 8(b) are significant at 0.015, 0.018 and 0.003 levels. The low regression coefficient is partly due to the low sensitivity of the coefficients to IC50, since the regression coefficient is dependent on the slope of the regression line. Eq 2 also includes 9 positively charged drugs and their neutral counterparts, since these drugs can be charged at neutral pH conditions in vitro. Eq 2 shows dependency on ΔGlipoCDS (octane) but not with ΔGlipoCDS (octanol) and no dependency on the HOMO-LUMO gap. We have previously shown that octanol better represents a cell membrane environment where some water interaction can occur, whereas octane is a better representative of a more hydrophobic environment found in deep pockets where drug-protein environments exists with very little water interaction. [1]

The data of Dittmar [13] (see Table 1) which includes the inhibition of the SARS-CoV-2 infected Calu-3, Vero CCL81 (Dittmar found that SARS-CoV-2 was cytopathic in Vero E6, but not in Vero CCL81, so Vero CCL81 was used to focus on infection rather than toxicity) and human hepatocyte Huh7.5 cells by 22 neutral and 16 potentially charged (at neutral pH experimental levels) antiviral drugs has also been analysed using the general equation 1 to find out which of the drug molecular specifiers can describe strong LFERs, and to compare these results with that found for infected Caco-2 cells. The 38 drugs were tested against ΔGdesolv,CDS, ΔGlipo,CDS, octane or octanol, DM, Molec Vol, and the HOMO-LUMO energy gap for virus infected HuH7.5, Vero CCL81 and Calu-3 cells. The best fit unconstrained relationships with EC50 are shown in eqs 3(a-c) below. Also shown in eqs 3(a-c) are the subsets of the primary 38 drug subdivided into their 22 neutral and 17 ionized drug groups.

Eq 3(a) Dittmar 38 drugs with infected HuH7.5 cells

EC50 = -0.050ΔGdesolv,CDS – 0.008DM - 0.038Mol Vol + 0.793 2 Where R = 0.192, SEE = 0.406, SE(ΔGdesolvCDS) = 0.023, SE(DM)= 0.003, SE(Mol Vol) = 0.0016, F=2.69, Significance= 0.061. P-values: ΔGdesolvCDS = 0.040, DM= 0.027, Mol Vol=0.040; Mol Vol scaled by 20 times. Eq 3(a)(i) Dittmar 22 neutral drugs with infected HuH7.5 cells: no strong correlations were found.

Eq 3(a)(ii) Dittmar 16 charged drugs with infected HuH7.5 cells

EC50 = – 0.007DM – 0.007HOMO-LUMO - 0.730 Where R2 = 0.439, SEE = 0.307, SE(DM)= 0.0035, SE(HOMO-LUMO) = 0.0016, F=5.081, Significance= 0.023. P-values: DM= 0.063, HOMO-LUMO=0.042;

Eq 3(b) Dittmar 38 drugs with infected VeroCCL81 cells

EC50 = -0.677ΔGdesolv,CDS + 0.328 HOMO-LUMO + 8.116 2 Where R = 0.432, SEE = 3.977, SE(ΔGdesolvCDS) = 0.139, SE(HOMO-LUMO) = 0.145, F=13.33, Significance= 0.00005. P- values: ΔGdesolvCDS = 0.00004, HOMO-LUMO=0.0008

Eq 3(b)(i) Dittmar 22 neutral drugs with infected VeroCCL81 cells

EC50 = -0.683ΔGdesolv,CDS - 2.319 HOMO-LUMO + 6.905 2 Where R = 0.493, SEE = 4.402, SE(ΔGdesolvCDS) = 0.161, SE(HOMO-LUMO) = 1.242, F=9.224, Significance= 0.0015. P- values: ΔGdesolvCDS = 0.0077, HOMO-LUMO=0.00046

Eq 3(b)(ii) Dittmar 16 charged drugs with infected VeroCCL81 cells

EC50 = -3.760 HOMO-LUMO + 17.889 Where R2 = 0.254, SEE = 3.644, SE(HOMO-LUMO) = 1.723, F=4.759, Significance= 0.0467. P-values: HOMO- LUMO=0.0467

Eq 3(c) Dittmar 38 drugs with infected Calu-3 cells

EC50 = 2.711ΔGlipo,CDS + 43.553 2 Where R = 0.427, SEE = 10.666, SE(ΔGlipo,CDS) = octane=0.523, F=26.882, Significance= 0.000009. P-values: ΔGdesolvCDS = 0.000009. No correlation was found with ΔGlipo,CDS = octanol.

Eq 3(c)(i) Dittmar 22 neutral drugs with infected Calu-3 cells

EC50 = 2.093ΔGlipo,CDS + 33.784 2 Where R = 0.309, SEE = 10.773, SE(ΔGlipo,CDS) = octane=0.700, F=8.946, Significance= 0.0072. P-values: ΔGdesolvCDS = 0.0072. No correlation was found with ΔGlipo,CDS = octanol.

Eq 3(c)(ii) Dittmar 16 charged drugs with infected Calu-3 cells

EC50 = 3.354ΔGdesolv,CDS + 2.948ΔGlipo,CDS + 78.348 2 Where R = 0.757, SEE = 7.916, SE(ΔGdesolvCDS) = 1.173, SE(ΔGlipo,CDS) = octane=0.617, F=20.297, Significance= 0.0001. P- values: ΔGdesolvCDS = 0.013, ΔGdesolvCDS = 0.00036. No correlation was found with ΔGlipo,CDS = octanol.

Analysis of Ko’s data [14] (see Table 2) for 28 drugs for antiviral efficacy against SARS-CoV-2 in Vero and Calu-3 gives eqs 4(a) and (b):

Eq 4(a) Ko 28 drugs with infected Vero cells

IC50 = 0.626DM - 0.828 Where R2 = 0.390, SEE = 7.38, SE(DM)= 0.003, F=16.645, Significance= 0.00038 = P-values. No dependence was found with any other molecular specifier, including ΔGlipo,CDS = octanol or octane. Eq 4(b) Ko 28 drugs with infected Calu-3 cells

IC50 = -3.743ΔGlipo,CDS - 9.687HOMO-LUMO + 57.108 2 Where R = 0.414, SEE = 14.15, SE(ΔGlipo,CDS) = 1.227, SE(HOMO-LUMO)= 2.996, F=8.828, Significance= 0.00125. P- values: ΔGlipo,CDS (octanol) = 0.0053, HOMO-LUMO=0.00343. No dependence was found with any other molecular specifiers, including ΔGlipo,CDS = octane.

It is noted that Ko’s data base was mainly comprised of neutral species with only five charged species under the experimental conditions so testing of charged species alone was not possible. The results from eq 4(b) for infected Calu-3 cells showing predominant dependency on the HOMO-LUMO gap and to a lesser extent on ΔGlipo,CDS in octanol is very different from those in eqs 3(c)(i,ii) which show predominant dependency on the lipophilicity, ΔGlipo,CDS in octane.

Discussion

It has been shown that the LFER method applies to a wide and diverse range of drugs that can inhibit the SARS-CoV-2 infected cell lines: Caco-2, HUH, Vero and Calu3, as shown in eqs 2-4. Since virus entry inhibition is cell type dependent, with both pH-dependent and pH-independent pathways are available for entry into cells. Entry in lung epithelial Calu-3 cells is pH- independent and requires TMPRSS2, while entry in Vero and Huh7.5 cells requires low pH and is triggered by acid-dependent endosomal proteases. It can be seen that the charged status of the drugs has a major influence on inhibitory behaviour, as shown in eqs 3(a)-(c).

Eqs 3(c)(i) and 3(c)(ii) are consistent with previous findings that entry in lung epithelial Calu-3 cells is pH-independent and requires TMPRSS2, since the neutral inhibitors show similar behaviour to those for the charged drugs, except for a significant water desolvation effect ΔGdesolv,CDS in eq 3(c)(ii) which is not present for the neutral species in eq 3(c)(i).

However eq 4(b) for a different set of antiviral drugs with Calu-3 cells gives a different outcome with a strong dependence on the HOMO-LUMO gap, and a lesser dependence on the ΔGlipo,CDS lipophilicity in octanol rather than octane. This lipophilicity differs from previous dependency in all eqs 3(a-c). We have previously found that octanol better represents a cell membrane environment where some water interaction can occur, whereas octane is a better representative of a more hydrophobic environment found in deep pockets where drug-protein environments exists with very little water interaction. [1]

TMPRSS2 is a type II transmembrane serine protease (TTSPs), with an extracellular region composed of a low-density lipoprotein (LDL) receptor class A domain, a scavenger receptor cysteine-rich (SRCR) domain and a peptidase S1 domain containing the catalytic triad. There is also a transmembrane sector and a cytoplasmic domain. [18] Singh et al [19] have used docking studies to characterize drugs which are more likely to bind with the peptidase S1 domain containing the catalytic triad or a different set of drugs which can bind with the exosites. The exosites are more solvent-exposed than the catalytic site pocket and drugs binding to exposed pockets have different physico-chemical properties than drugs that bind to the deeper catalytic cavity. [19] Nafamostat and camostat are known to bind to the catalytic cavity site in human lung cells. [6,7] It is not clear why eq 4(b) is quite different from eq 3(c)(i) and 3(c)(ii) but a different antiviral drug set and possibly differences in the notional Calu-3 cell lines used in the Dittmar [13] and Ko [14] studies may be responsible. However the dependence on ΔGlipo,CDS ,octanol in eq 4(b) and ΔGlipo,CDS ,octane in eq 3(c)(i) and 3(c)(ii) is important, especially if ΔGlipo,CDS ,octanol better represents an exosite which is solvent exposed, and ΔGlipo,CDS ,octane better represents a buried site, such as the catalytic site of TMPRSS2 as defined by Singh. [19]

Conclusions

It appears that the inhibition of the various infected cell lines is the result of factors specific to the inhibitors and the different cell types, which have implications for exploratory studies of potential therapeutics of the antiviral efficacy in various human organs and tissues.

There is some evidence that it may be possible to identify various drugs that may target the more solvent exposed exosite of TMPRSS2 in infected Calu-3 cells.

Materials and methods

All calculations were carried out using the Gaussian 09 package. Energy optimizations were at the DFT/B3LYP/6-31G(d) (6d, 7f) level of theory for all atoms in water. Selected optimizations at the DFT/B3LYP/6-311+G(d,p) (6d, 7f) level of theory gave very similar results to those at the lower level. Energy calculations were conducted at the DFT/B3LYP/6-31G(d,p) (6d, 7f) for neutral and cationic compounds with optimized geometries in water, using the IEFPCM/SMD solvent model. With the 6-31G* basis set, the SMD model achieves mean unsigned errors of 0.6 - 1.0 kcal/mol in the solvation free energies of tested neutrals and mean unsigned errors of 4 kcal/mol on average for ions. [20] The 6-31G** basis set has been used to calculate absolute free energies of solvation and compare these data with experimental results for more than 500 neutral and charged compounds. The calculated values were in good agreement with experimental results across a wide range of compounds. [21,2 2] Adding diffuse functions to the 6-31G* basis set (ie 6-31+G**) had no significant effect on the solvation energies with a difference of less than 1% observed in solvents, which is within the literature error range for the IEFPCM/SMD solvent model. It is noted that high computational accuracy for each species in different environments is not the focus of this study, but comparative differences between various species is the aim of the study. Experimental errors in inhibitory and docking binding studies are substantially higher than those for calculated molecular specifiers.

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Table 1. Inhibition of SARS-CoV-2 infected HuH, VeroCCL81, Calu-3 cells [13] and molecular specifiers of inhibitors

EC50 EC50 EC50 ΔGdesolv,CDS ΔGlipo,CDS ΔGlipo,CDS Dipole Molec HOMO- HuH Vero Calu3 Kcal/mol octane octanol Moment Vol LUMO 3 μM μM μM kcal/mol kcal/mol D cm /mol eV AM-1241 0.008 16.7 20 -7.02 -9.94 0.76 11.72 318 2.69 AM-1241 Ion 0.008 16.7 20 -10.62 -9.62 1.54 35.5 330 2.59 AZD8055 1.1 1.1 0.003 -6.17 -13.98 -3.28 1.28 357 3.76 Bemcentinib 0.1 0.47 2.1 -3.37 -18.44 -8.59 5.16 379 3.71 Bemcentinib Ion 0.1 0.47 2.1 -6.14 -18.52 -7.96 35.7 411 3.77 BIX01294 0.09 0.48 4.9 -3.48 -14.35 -6.01 1.23 387 4.31 BIX01294 DiIon 0.09 0.48 4.9 -9.89 -14.62 -4.57 81.1 375 4.36 Cyclosporine 0.87 24 0.24 -30.29 -19.73 3.66 4 955 4.05 Dacomitinib 0.79 1.4 0.04 -5.81 -12.51 -2.87 4.65 324 4.25 Dacomitinib Ion 0.79 1.4 0.04 -9.52 -12.66 -2.03 21.57 341 4.19 Dp44mT 1.6 0.18 0.09 -4.63 -8 -1.83 8.12 226 3.57 Ebastine 1.2 5.45 1.9 -8.27 -13.9 -5 5.17 370 4.22 Ebastine Ion 1.2 5.45 1.9 -11.81 -14.03 -4.17 7.93 347 4.83 FRAX486 0.006 1.1 10.8 -4.27 -15.1 -5.9 5.1 319 3.50 FRAX486 Ion 0.006 1.1 10.8 -7.75 -15.4 -5.33 7.93 402 3.63 MG132 0.002 0.011 0.477 -14.02 -9.36 2.42 4 401 4.45 Naquotinib 0.055 0.311 4.85 -4.2 -12.77 -3.01 4.12 440 3.74 Naquotinib DiIon 0.055 0.311 4.85 -10.8 -13.02 -1.49 82.64 397 3.74 PD0166285 0.02 0.72 6.4 -5.45 -15.6 -5.38 3.2 369 3.62 PD0166285 Ion 0.02 0.72 6.4 -9.33 -15.76 -4.49 39 331 3.66 PF-04691502 0.1 1.5 5.5 -7.05 -11.85 -1.2 11.22 324 3.84 Salinomycin Ion 0.005 0.17 0.003 -14.98 -13 -1.54 34.44 596 5.03 WYE-125132 0.075 0.63 0.14 -6.06 -12.4 -1.59 3.53 447 1.08 Y-320 0.17 0.12 0.09 -6.48 -10.42 -1.53 11.32 346 4.45 Y-320 Ion 0.17 0.12 0.09 -9.77 -10.54 -0.77 38.04 321 4.58 Pyronaridine 0.18 3.204 1.396 -4.96 -12.42 -4.28 6.35 400 3.13 Pyronaridine DiIon 0.18 3.204 1.396 -8.74 -12.5 -3.42 34.4 322 3.47 N- 0.4 2.948 40 -2.93 -7.59 -2.38 7.8 246 3.91 Desethylamodiaquine N- 0.4 2.948 40 -6.06 -7.81 -1.82 28.2 255 3.97 Desethylamodiaquine Ion Amodiaquine 0.58 1.617 22.39 -4.11 -8.45 -2.72 7.59 307 3.70 Amodiaquine DiIon 0.58 1.617 22.39 -8.84 -8.84 -1.96 7.93 258 3.52 Chloroquine 0.85 5.03 25.53 -3.4 -7.71 -3.69 7.66 217 4.07 Chloroquine Ion 0.85 5.03 25.53 -6.19 -7.81 -3.05 30.85 232 4.29 Chloroquine DiIon 0.85 5.03 25.53 -7.98 -8.1 -2.97 12.02 244 4.32 Hydroxychloroquine 0.14 1.32 39.16 -3.61 -7.93 -3.11 7.97 261 4.18 Hydroxychloroquine 0.14 1.32 39.16 -6.3 -8.02 -2.49 28.61 298 4.30 Ion Hydroxychloroquine 0.14 1.32 39.16 -8.09 -8.31 -2.41 10.29 279 4.34 DiIon Remdesivir 0.002 0.457 0.005 -13.44 -10.79 -2.95 12.76 381 4.78

Table 2. Inhibition of SARS-CoV-2 infected Vero and Calu-3 cells [14] and molecular specifiers of inhibitors

IC50 IC50 ΔGdesolv,CDS ΔGlipo,CDS ΔGlipo,CDS Dipole Molec HOMO- Vero Calu3 Kcal/mol octane octanol Moment Vol LUMO 3 μM μM kcal/mol kcal/mol D cm /mol eV Tetrandrine 3 13.5 -9.38 -10.73 -0.59 7.85 485 5.27 Abemaciclib 6.62 43.7 -1.52 -13.3 -5.81 12.65 357 3.89 Cepharanthine 4.47 30 -7.56 -11.38 -1.09 8.13 463 4.93 Gilteritinib 6.76 50 -1.86 -10.09 -2.84 2.29 477 3.12 Salinomycin (0) 0.24 0.5 -15.31 -12.94 -2.07 6.25 537 5.77 Lopinavir 9.12 21.7 -13.93 -16.77 -2.83 5.82 492 5.76 Ciclesonide 4.33 10.64 -12.23 -11.82 -0.51 11.96 429 5.00 Proscillaridin 2.04 5.95 -11.33 -10.24 0.45 12.3 334 4.53 Niclosamide 0.28 0.84 -8.38 -7.65 1.57 10.1 227 3.62 Anidulafungin 4.64 17.23 -23.13 -24.31 2.44 2.46 770 4.30 Digoxin 0.19 0.72 -15.31 -14.63 -0.31 10.42 478 5.57 Bazedoxifene 3.44 12.63 -8.11 -13.16 -3.45 6.12 391 4.49 Remdesivir 11.41 1.3 -13.44 -10.79 2.95 12.76 381 4.78 Hydroxyprogesterone 6.3 3.87 -10.35 -10.35 -1.26 10.46 284 5.26 Caproate Digitoxin 0.23 0.16 -14.73 -14.37 -0.29 11.86 501 5.61 Cyclosporine 5.82 4.69 30.29 -19.73 3.66 4 955 4.05 Ouabain 0.1 0.1 11.38 -9.67 1.67 11.96 362 5.18 Eltrombopag 8.27 8.38 -10.17 2.01 2.01 4.62 252 3.20 Hexachlorophene 0.9 1.48 -5.66 -8.31 -3.6 2.65 215 5.34 Ivacaftor 6.57 11.55 -10.57 -9.51 0.56 15.22 310 4.13 Oxyclozanide 3.71 6.78 -6.4 -8.58 -2.05 9.07 205 4.41 Mefloquine 4.33 50 -6.4 -4.33 -0.7 9.43 259 3.95 Salinomycin Sodium 0.24 0.5 -14.98 -13 -1.54 34.44 596 5.03 Amodiaquine DiHCl 5.15 50 -9.51 -8.84 -1.96 8.87 243 2.04 Nafamostat Mesylate 13.88 0.0022 -9.51 -9.38 0.36 19.85 227 4.06 Loperamide HCl 9.27 12.53 -10.18 -11.59 -2.73 4.72 353 5.92 Berbamine DiHCL 7.87 50 -14.91 -11.37 -0.59 23.55 430 5.28 Camostat Mesylate 50 0.187 -10.69 -10.7 2.28 43.63 274 4.66