WO2022133182A1 - Method of treating coronavirus infection - Google Patents

Abstract

Provided herein are methods of treating a coronavirus infection in a patient in need thereof comprising administering to the patient a therapeutically effective amount of a type II transmembrane serine proteinase (TTSP) inhibitor. Also, provided herein are methods of inhibiting TMPRSS2 in a subject in need thereof comprising administering to the subject an effective amount of a compound, as described herein.

Description

METHOD OF TREATING CORONAVIRUS INFECTION

STATEMENT OF GOVERNMENT SUPPORT

[0001] This invention was made with government support under contracts LM05652; GM102365; TR002515; T32 LM_012409; and T15_LM 00703 awarded by the National Institutes of Health. The government has certain rights in the invention.

BACKGROUND

[0002] A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2), emerged in Wuhan, China in December 2019 and has since spread to 166 countries and territories around the world. With over 190,000 cases and 7,800 deaths as of March 18, 2020, the discovery of effective treatments for the virus is an urgent public health need.

[0003] Many patients have been infected globally and many have died as a result of this disease. Two therapies, remdesivir and dexamethasone, have shown effectiveness in humans in large clinical trials. However, these are indicated for patients with severe disease and must be administered intravenously in a hospital setting. Drugs targeting different stages of the viral lifecycle and pathology are needed. While the development of safe and effective new vaccines and therapeutics is progressing rapidly, it is unlikely that they widely available soon.

[0004] In view of the foregoing, there remains a need for methods of treating corona virus infections.

SUMMARY

[0005] Provided herein are methods of treating a coronavirus infection in a patient in need thereof comprising administering to the patient a therapeutically effective amount of a type II transmembrane serine proteinase (TTSP) inhibitor.

[0006] The disclosure also provides methods of inhibiting TMPRSS2 in a subject in need thereof comprising administering to the subject an effective amount of a compound selected from the group consisting of otamixaban, argatroban, letaxaban, darexaban, edoxaban, a salt thereof, and a combination thereof.

BRIEF DESCRIPTION OF THE FIGURES

[0007] Figure 1 shows homology model structures of TMPRSS2.

[0008] Figure 2 shows a Ramachandran plot for validating homology model structures.

[0009] Figure 3 shows distribution of PocketFeature scores for homology models and 5,049 druggable binding sites. [0010] Figure 4 shows the ranges of docking scores for docked conformers.

[0011] Figure 5 shows docked conformers of the homology models by docking score.

[0012] Figure 6 shows the interactions between TMPRSS2 homology model and docked otamixaban.

[0013] Figure 7A shows distribution of drug likeness scores with outliers.

[0014] Figure 7B shows distribution of drug likeness scores without outliers.

[0015] Figure 8 shows violin plots of the distribution of predicted pKi values.

DETAILED DESCRIPTION

[0016] A promising drug target for SARS-CoV-2 is the transmembrane serine protease family member II (TMPRSS2). TMPRSS2 is a host, cell-membrane protein that mediates the entry of pathogenic human coronaviruses into host cells by cleaving and activating the viral Spike (S) protein. Without wishing to be bound to any particular theory, it is currently understood that following angiotensin-converting enzyme 2 (ACE2) receptor engagement by the spike protein S1 subunit, TMPRSS2 cleaves the SARS-CoV-2 spike protein at the S1/S2 and S2’ cleavage sites. This proteolysis event permits subsequent S2 subunit-driven fusion of viral and host cell membranes as in SARS-CoV, allowing entry of virus RNA into cell. TMPRSS2 inhibition blocks entry of SARS-CoV-2 into lung cells, and prevents spread of SARS-CoV and MERS-CoV in mice airways. The compounds camostat and nafamostat inhibit TMPRSS2 and are in clinical trials; however their availability is limited.

[0017] TMPRSS2 is co-expressed in lung tissue with angiotensin converting enzyme 2 (ACE2) which acts as the cell surface receptor for SARS and SARS-CoV-2. Knockout of TMPRSS2 reduces the spread of severe acute respiratory syndrome (SARS-CoV) and Middle East respiratory syndrome (MERS-CoV) in the airway of a mouse mode. Mahoney et al. have recently shown that TMPRSS2 inhbitors block SARS-CoV-2 and MERS-CoV viral entry and protect human epithelial lung cells. Kawase et al. showed that inhibition of TMPRSS2 by serine protease inhibitor camostat blocks the entry of SARS-CoV in human Calu-3 airway epithelial cells at 10 micromolar concentrations. A 2016 screen of 1 ,017 drugs found that the serine protease inhibitor nafamostat inhibited TMPRSS2 and prevented S-protein mediated membrane fusion of MERS-CoV. Recently, Hoffman et al. showed that SARS-CoV-2 also binds the ACE2 receptor, and that camostat blocks the entry of SARS- CoV-2 into human lung cells. There are several other small molecule inhibitors that have been reported to inhibit TMPRSS2 with low nanomolar affinity, but these compounds have not been tested for safety in humans. Medications are urgently needed to treat SARS-CoV- 2, and one of the most rapid pathways would be to find existing medications that inhibit TMPRSS2. [0018] Quantitative structure activity relationship (QSAR) modeling is a knowledge based approach to drug discovery that leverages the results of past experimental assays to gain insight into chemical structure features that influence activity and predict the activities of compounds that have not yet been experimentally tested. First introduced by Willet et al. in 1956, QSAR has become a foundational technique in computational chemistry and aided in the discovery and development of numerous compounds and indications. However, because QSAR is a knowledge based approach its utility and reliability rests upon the quantity of available structure activity relationship (SAR) data; and there are very few experimental TMRPSS2 inhibition assay data in large knowledge bases such as ChEMBL, PubChem, and BindingDB.

[0019] The present disclosure provides methods of treating a coronavirus infection in a patient in need thereof comprising administering to the patient a therapeutically effective amount of a type II transmembrane serine proteinase (TTSP) inhibitor. In some embodiments, the coronavirus is SARS-CoV-2.

[0020] Described herein are the predicted TTSP binding energies for a number of currently marketed anticoagulant drugs. The predicted TTSP binding energies are close to known inhibitors of TTSP, such that the anticoagulants can be repurposed to treat a coronavirus infection and/or to inhibit TTSP, as described herein. In some cases, the methods disclosed herein comprise administering a TMPRSS2 inhibitor to a subject suffering from a coronavirus infection.

TTSPs

[0021] A number of members of the human TTSP family have been identified to date. Members of the TTSP family are characterized by a N-terminal transmembrane, a C-terminal extracellular serine protease domain of the chymotrypsin (S1 ) fold that contains the catalytic histidine, aspartic acid, and serine residues, and a stem region that may contain various protein domains.

[0022] In some embodiments, the TTSP is a serine endopeptidase. In some cases, the serine endopeptidase is one belonging to the International Union of Biochemistry and Molecular Biology’s Enzyme Commission (EC) category EC 3.4.21 , or a subclass thereof, as listed in Table 1.

[0023] In some embodiments, the TTSP is a trypsin-like serine protease. Non-limiting illustrative trypsin-like serine proteases are listed in Table 2. Table 2. Trypsin-like serine proteases

[0024] In some embodiments the TTSP is a eukaryotic protease. In some cases, the eukaryotic protease is one as listed in Table 3.

Table 3. Family of Eukaryotic TTSPs

[0025] In some embodiments, the TTSP is a member of the hepsin/TMPRSS subfamily. Non-limiting illustrative members of the hepsin/TMPRSS subfamily include hepsin, TMPRSSS2, TMPRSS3, TMPRSS4, mosaic serine protease large-form (MSPL, also known as TMPRSS 13), spinesin (also known as TMPRSS 5), TMPRSS11 D (i.e., human airway tryptase), and enteropeptidase. [0026] In some embodiments, the TTSP is a member of the matriptase subfamily. Illustrative members of this subfamily include, matriptase, matriptase-2, matriptase-3, and polyserase-1.

[0027] In some embodiments, the TTSP is selected from the group consisting of Kallikreins (KLK), including for example, plasma kallikreins (e.g., KLKB1 ).

TTSP Inhibitors, and Identification of Same

[0028] The disclosed methods comprise administering a TTSP inhibitor to treat a coronavirus infection (e.g., a SARS-CoV-2 infection). As described herein, homology modeling and QSAR methods were used to screen a library of compounds for TTSP inhibition activity. In some embodiments, the TTSP inhibitor is selected from the group consisting of argatroban, ximelagatran, rivaroxaban, apixaban, otamixaban, dabigatran, edoxaban, nafamostat, gabexate, betrixaban, eribaxaban, letaxaban, sivelestat, camostat, darexaban, patamostat, RWJ-58643, RWJ-56423, RWJ-51084, 1-[(4S)-4-amino-5-(1 ,3- benzothiazol-2-yl)-5-oxopentyl]guanidine, sepimostat, ciluprevir, 1 -(5-chloro-2- methoxyphenyl)-3-[6-[2-(dimethylamino)-1 -methylethoxy]pyrazine-2-yl]urea, a salt thereof, and a combination thereof. In some cases, the TTSP inhibitor is selected from the group consisting of otamixaban, argatroban, letaxaban, darexaban, edoxaban, a salt thereof, and a combination thereof. In some embodiments, the disclosure provides methods of inhibiting TMPRSS2 in a subject in need thereof comprising administering to the subject an effective amount of a compound selected from the group consisting of otamixaban, argatroban, letaxaban, darexaban, edoxaban, a salt thereof, and a combination thereof.

[0029] In some embodiments, the TTSP inhibitor is an inhibitor of TMPRSS2. In some embodiments, the TMPRSS2 inhibitor is selected from the group consisting of otamixaban, argatroban, nafamostat, letaxaban, darexaban, edoxaban, a salt thereof, and a combination thereof. In some embodiments, the TMPRSS2 inhibitor is argatroban or a salt thereof. In some embodiments, the TMPRSS2 inhibitor is otamixaban or a salt thereof. In some embodiments, the TMPRSS2 inhibitor is nafamostat or a salt thereof. In some embodiments, the TMPRSS2 inhibitor is letaxaban or a salt thereof. In some embodiments, the TMPRSS2 inhibitor is darexaban or a salt thereof. In some embodiments, the TMPRSS2 inhibitor is edoxaban or a salt thereof.

[0030] Suitable salts include, for example, acid addition salts, metal salts, ammonium salts, organic amine addition salts, and amino acid addition salts. Suitable acid addition salts include, for example, inorganic acid salts such as hydrochlorides, sulfates, nitrates, and phosphates, and organic acid salts such as acetates, maleates, fumarates, citrates, malates, lactates, a-ketoglutarates, gluconates, caprylates, adipates, succinates, tartrates, and ascorbates. Suitable metal salts include, for example, alkali metal salts such as sodium salts and potassium salts, alkaline earth metal salts such as magnesium salts and calcium salts, aluminum salts, and zinc salts. Suitable ammonium salts, salts of ammonium, and tetramethylammonium. Suitable organic amine addition salts include, for example, salts of morpholine, and piperidine. Suitable amino acid addition salts include, for example, salts of glycine, phenylalanine, lysine, aspartic acid, and glutamic acid. In some embodiments, one or more combinations of the aforementioned salts can be administered, as appropriate.

[0031] Prior to the present disclosure, few TMPRSS2 inhibitors were known. Computationally modeling protein structures and protein-ligand interactions can be an effective method for predicting the binding of small molecule drugs to protein targets. As described herein, seven plausible homology models of TMPRSS2 were created using standard structural modeling and docking protocols. Further, the disclosed homology models of TMPRSS2 were screened against several approved small molecule serine protease inhibitors for activity against TMPRSS2. Compounds identified in the screen include nafamostat and camostat, two known inhibitors of TMPRSS2, as well as several other small molecule compounds that had previously advanced into the clinic. The known inhibitors provide a positive control and further validate the model. By way of example, nafamostat has been shown to inhibit the MERS coronavirus through a TMPRSS2-mediated inhibition.

[0032] The positive controls, the uniformly low docking scores give confidence that the protocol is generating homology models and docking results that are consistent. In addition, camostat's relatively lower binding affinity is consistent with its placement in the middle of the list of candidate inhibitors. The lowest energy predictions may therefore be reasonable candidates for taking forward into in vitro validation experiments — and appropriate subsequent studies if these are positive.

[0033] Docking scores provide a rough estimation of binding energy (AG, kcal/mol). Lower scores indicate higher binding affinity. Docking scores may vary depending on the system, however generally scores less than -7.5 indicate high affinity binding. Indications were compiled from FDA drug labels and clinical trial records. Side effects were compiled from the FDA adverse event reporting system (FAERS) and clinical trial records. All of the predicted low-energy binding molecules have anticoagulant activity. They also have a wide range of adverse events reported, including abnormal liver function tests, rash, anemia, and of course bleeding-related adverse effects.

[0034] Structure-activity relationship (SAR) data in the literature also have been investigated and used to develop the disclosed methods. The present literature extraction efforts have yielded the largest collection of structure activity data for TMPRSS2. Prior to the present disclosure, only three chemical compounds in chemical databases with TMPRSS2 inhibition activity annotations were known. As such, many knowledge based approaches to virtual screening were infeasible. The manually curated dataset of TMPRSS2 inhibitors described herein contain 92 compounds and corresponding activity values. These compounds were indexed in both PubChem and ChEMBL, but were associated with inhibition assays for other trypsin-like protease targets, such as ST14 and human airway tryptase. This dataset was augmented by imputing TMPRSS2 inhibition values from assay data for homologous off target proteins with highly correlated binding activities. While these imputed activity values are less reliable, the additional data added chemical diversity to the literature extracted TMPRSS2 compounds, which were dominated by a single tripeptide mimetic scaffold. IC50 measurements were converted to Ki values to facilitate combination and comparison with binding affinity values obtained under different experimental conditions. Finally, a curation pipeline was used to filter out duplicates, non-druglike molecules, and activity cliffs.

[0035] The virtual screen described herein identified two known potent inhibitors of TMPRSS2, and several promising compounds that have not been previously tested for activity against TMPRSS2. A two-step approach to affinity prediction was used. In the first classification step, molecules that were unlike inhibitors of TMRPSS2 and close homologs were excluded, and thus outside of the applicability domain of the regression models. The high accuracy observed during model validation is unsurprising. Positive examples were confined to a small region of chemical space relative to the molecules in the dark chemical matter dataset, which are structurally diverse. However, these results were expected to be applicable to the virtual screening because the repurposing compound libraries are diverse and cover a broad swath of chemical space similar to the dark chemical matter dataset. In the second step, the affinity of compounds was predicted using a random forest regression model trained only on the current binding affinity dataset. The model predicted nafamostat, a nanomolar inhibitor of TMPRSS2, as having the highest affinity in the screening set the top hit, and camostat, a high micromolar inhibitor. Several other interesting molecules were also ranked highly. For example, patamostat (E-3123) is a protease inhibitor that was discontinued in Phase III trials and has shown inhibitory activity toward trypsin, thrombin, plasmin, cathepsin-B and kallikrein, as well as effectiveness in animal models toward pancreatitis and disseminated intravascular coagulation. Also, RWJ-58643 is a mast cell tryptase inhibitor that was discontinued after phase I testing, and RWJ-56423 is also a potent inhibitor of mast cell tryptase which was discontinued after phase II testing for allergic rhinitis. Without wishing to be bound to any particular theory, it is believed that the off target activities of patamostat, RWJ-58643, and RWJ-56423 against Cathepsin-B and Plasma Kallikrein may enhance their therapeutic effectiveness. Cathepsins, which are coexpressed with TMPRSS2 in many cell types, are capable of catalyzing the same activation step during cell entry.

Combination Therapy

[0036] Also provided herein are methods of treating a coronavirus infection in subject suffering thereof by administering a TTSP, or TMPRSS2, inhibitor as disclosed herein in addition to a second antiviral therapeutic.

[0037] Without wishing to be bound to a particular theory, a strong binder is not a requisite for a clinical useful medication. Many diseases ultimately respond to several medications with different mechanisms of action, taken in combination. This strategy has two benefits. First, it makes it more difficult for the pathogen to evade treatment with a single mutation — multiple mutations would likely be required to develop resistance to multiple drugs. Second, by interfering with the pathogen at multiple points in its underlying pathways, the treatments can be more effective at eliminating the pathogen. For example, the introduction of combined therapy in HIV was pivotal in changing the success rates of medication, and many other diseases have benefited from a polypharmacy approach. Thus, the present disclosure provides one part of a developing arsenal of medications that weaken or eliminate the virus.

[0038] The second antiviral agent can be administered either concomitantly, e.g., as an admixture, separately but simultaneously or concurrently; or sequentially. This includes embodiments in which the combined agents are administered together as a therapeutic mixture, and also procedures in which the combined agents are administered separately but simultaneously, e.g., as through separate intravenous lines into the same individual. Administration "in combination" further includes the separate administration of one of the compounds or agents given first, followed by the second.

[0039] Contemplated second antiviral agents include (a) an antiretroviral agent; (b) nucleoside or nucleotide reverse transcriptase inhibitors (NRTIs); (c) non-nucleoside reverse transcriptase inhibitors (NNRTs); (d) nucleotide or nucleoside analogues; (e) protease inhibitors (Pis); (f) drugs based on "antisense" molecules; (g) ribozyme antivirals; (h) assembly inhibitors; (i) release phase inhibitors; (j) drugs which stimulate the immune system, such as interferons and synthetic antibodies; (k) fusion inhibitors/gp41 binders; (I) fusion inhibitors/chemokine receptor antagonists; (m) integrase inhibitors; (n) hydroxyurealike compounds; (o) inhibitors of viral integrase; (p) inhibitors of viral genome nuclear translocation; (q) inhibitors of HIV entry; (r) nucleocapsid zinc finger inhibitors; (s) targets of HIV Tat and Rev; (t) pharmacoenhancers; (u) cytokines; (v) lymphokines; (w) an antiinflammatory agent; and (x) any combination thereof.

[0040] Conventional antiviral treatments include, but are not limited to (1 ) amantadine and rimantadine, which combat influenza and act on penetration/uncoating; (2) pleconaril, which works against rhinoviruses, which cause the common cold; (3) nucleotide or nucleoside analogues, such as acyclovir, zidovudine (AZT), lamivudine; (4) drugs based on "antisense" molecules, such as fomivirsen; (5) ribozyme antivirals; (6) protease inhibitors; (7) assembly inhibitors, such as rifampicin; (8) release phase inhibitors, such as zanamivir (Relenza™) and oseltamivir (Tamiflu™); (9) drugs which stimulate the immune system, such as interferons, which inhibit viral synthesis in infected cells (e.g., interferon alpha), and synthetic antibodies (a monoclonal drug is now being sold to help fight respiratory syncytial virus in babies, and antibodies purified from infected individuals are also used as a treatment for hepatitis B). Examples of antiviral drugs include, but are not limited to, abacavir, aciclovir, acyclovir, adefovir, amantadine, amprenavir, arbidol, atazanavir, atripla, boceprevir, cidofovir, combivir, darunavir, delavirdine, didanosine, docosanol, edoxudine, efavirenz, emtricitabine, enfuvirtide, eentecavir, entry inhibitor, famciclovir, fixed dose combination (antiretroviral), fomivirsen, fosamprenavir, foscarnet, fosfonet, fusion inhibitors, ganciclovir, ibacitabine, imunovir, idoxuridine, imiquimod, indinavir, inosine, integrase inhibitor, interferon type III, interferon type II, interferon type I, interferon, lamivudine, lopinavir, loviride, maraviroc, molixan (NOV-205), moroxydine, nelfinavir, nevirapine, nexavir, nucleoside analogues, oseltamivir (TAMIFLU™.), Peginterferon a-2a, penciclovir, peramivir, pleconaril, podophyllotoxin, protease inhibitor (pharmacology), raltegravir, Reverse transcriptase inhibitor, ribavirin, rimantadine, ritonavir, saquinavir, stavudine, Synergistic enhancer (antiretroviral), tenofovir, tenofovir disoproxil, tipranavir, trifluridine, trizivir, tromantadine, truvada, valaciclovir (VALTREX™), valganciclovir, vicriviroc, vidarabine, viramidine, zalcitabine, zanamivir (RELENZA™), and zidovudine.

[0041] In certain embodiments, the TTSP inhibitor (e.g., TMPRSS2 inhibitor) is administered in combination with antiretroviral agents, nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and/or protease inhibitors (Pls). NRTIs that may be administered in combination with the TTSP inhibitor (e.g., TMPRSS2 inhibitor), include, but are not limited to, RETROVIR™ (zidovudine/AZT), VIDEX™ (didanosinelddl), HMD™ (zalcitabine/ddC), ZERIT™ (stavudine/d4T), EPIVIR™ (lamivudine/3TC), and COMBIVIR™ (zidovudine/lamivudine). NNRTIs that may be administered in combination, include, but are not limited to, VIRAMUNE.TM. (nevirapine), RESCRIPTOR™ (delavirdine), and SUSTIVA™ (efavirenz). Protease inhibitors that may be administered in combination with the TTSP inhibitor (e.g., TMPRSS2 inhibitor) include, but are not limited to, CRIXIVAN™ (indinavir), NORVIR™ (ritonavir), INVIRASE™ (saquinavir), and VIRACEPT™ (nelfinavir).

[0042] Additional NRTIs include LODENOSINE™ (F-ddA; an acid-stable adenosine NRTI; Triangle/Abbott; COVIRACIL™ (emtricitabine/FTC; structurally related to lamivudine (3TC) but with 3- to 10-fold greater activity in vitro; Triangle/Abbott); dOTC (BCH-10652, also structurally related to lamivudine but retains activity against a substantial proportion of lamivudine-resistant isolates; Biochem Pharma); adefovir (refused approval for anti-HIV therapy by FDA; Gilead Sciences); PREVEON™. (Adefovir Dipivoxil, the active prodrug of adefovir; its active form is PMEA-pp); TENOFOVIR™ (bis-POC PMPA, a PMPA prodrug; Gilead); DAPD/DXG (active metabolite of DAPD; Triangle/Abbott); D-D4FC (related to 3TC, with activity against AZT/3TC-resistant virus); GW420867 (Glaxo Wellcome); ZIAGEN™ (abacavir/1591189; Glaxo Wellcome Inc.); CS-87 (3'azido-2',3'-dideoxyuridine; WO 99/66936); and S-acyl-2-thioethyl (SATE)-bearing prodrug forms of beta-L-FD4C and P-L- FddC (WO 98/17281).

[0043] Additional NNRTIs include COACTINON™ (Emivirine/MKC442, potent NNRTI of the HEPT class; Triangle/Abbott); CAPRAVIRINE™ (AG-1549/S-1153, a next generation NNRTI with activity against viruses containing the K103N mutation; Agouron); PNU-142721 (has 20- to 50-fold greater activity than its predecessor delavirdine and is active against K103N mutants; Pharmacia & Upjohn); DPC-961 and DPC-963 (second-generation derivatives of efavirenz, designed to be active against viruses with the K103N mutation; DuPont); GW420867X (has 25-fold greater activity than HBY097 and is active against K103N mutants; Glaxo Wellcome); CALANOLIDE A (naturally occurring agent from the latex tree; active against viruses containing either or both the Y181C and K103N mutations); and propolis (WO 99/49830).

[0044] Additional protease inhibitors include LOPINAVIR™ (ABT378/r; Abbott Laboratories); BMS-232632 (an azapeptide; Bristol-Myres Squibb); TIPRANAVIR.TM. (PNU- 140690, a non-peptic dihydropyrone; Pharmacia & Upjohn); PD-178390 (a nonpeptidic dihydropyrone; Parke-Davis); BMS 232632 (an azapeptide; Bristol-Myers Squibb); L- 756,423 (an indinavir analog; Merck); DMP450 (a cyclic urea compound; Avid & DuPont); AG-1776 (a peptidomimetic with in vitro activity against protease inhibitor-resistant viruses; Agouron); VX-175/GW433908 (phosphate prodrug of amprenavir; Vertex & Glaxo Welcome); CGP61755 (Ciba); and AGENERASE™ (amprenavir; Glaxo Wellcome Inc.).

[0045] Additional antiretroviral agents include fusion inhibitors/gp41 binders. Fusion inhibitors/gp41 binders include T-20 (a peptide from residues 643-678 of the HIV gp41 transmembrane protein ectodomain which binds to gp41 in its resting state and prevents transformation to the fusogenic state; Trimeris) and T-1249 (a second-generation fusion inhibitor; Trimeris).

[0046] Additional antiretroviral agents include fusion inhibitors/chemokine receptor antagonists. Fusion inhibitors/chemokine receptor antagonists include CXCR4 antagonists such as AMD 3100 (a bicyclam), SDF-1 and its analogs, and ALX404C (a cationic peptide), T22 (an 18 amino acid peptide; T rimeris) and the T22 analogs T 134 and T140; CCR5 antagonists such as RANTES (9-68), AOP-RANTES, NNY-RANTES, and TAK-779; and CCR5/CXCR4 antagonists such as NSC 651016 (a distamycin analog). Also included are CCR2B, CCR3, and CCR6 antagonists. Chemokine receptor agonists such as RANTES, SDF-1 , MEP-1 a, MIP-1 (3, etc., may also inhibit fusion.

[0047] Additional antiretroviral agents include integrase inhibitors. Integrase inhibitors include dicaffeoylquinic (DFQA) acids; L-chicoric acid (a dicaffeoyltartaric (DCTA) acid); quinalizarin (QLC) and related anthraquinones; ZINTEVIR™ (AR 177, an oligonucleotide that probably acts at cell surface rather than being a true integrase inhibitor; Arondex); and naphthols such as those disclosed in WO 98/50347.

[0048] Additional antiretroviral agents include hydroxyurea-like compounds such as BCX- 34 (a purine nucleoside phosphorylase inhibitor; Biocryst); ribonucleotide reductase inhibitors such as DIDOX. TM. (Molecules for Health); inosine monophosphate dehydrogenase (IMPDH) inhibitors such as VX-497 (Vertex); and mycopholic acids such as CellCept (mycophenolate mofetil; Roche).

[0049] Additional antiretroviral agents include inhibitors of viral integrase, inhibitors of viral genome nuclear translocation such as arylene bis(methylketone) compounds; inhibitors of HIV entry such as AOP-RANTES, NNY-RANTES, RANTES-IgG fusion protein, soluble complexes of RANTES and glycosaminoglycans (GAG), and AMD-3100; nucleocapsid zinc finger inhibitors such as dithiane compounds; targets of HIV Tat and Rev; and pharmacoenhancers such as ABT-378.

[0050] Other antiretroviral therapies and adjunct therapies include cytokines and lymphokines such as MIP-1 alpha, MIP-1 beta, SDF-1 alpha, IL-2, PROLEUKIN. TM. (aldesleukin/L2-7001 ; Chiron), IL4, IL-10, IL-12, and IL-13; interferons such as IFN-alpha2a, IFN-alpha2b, or IFN-beta; antagonists of TNFs, NFkappaB, GM-CSF, M-CSF, and IL-10; agents that modulate immune activation such as cyclosporin and prednisone; vaccines such as Remune.TM. (HIV Immunogen), APL 400-003 (Apollon), recombinant gp120 and fragments, bivalent (B/E) recombinant envelope glycoprotein, rgp120CM235, MN rgp120, SF-2 rgp120, gp120/soluble CD4 complex, Delta JR-FL protein, branched synthetic peptide derived from discontinuous gp120 C3/C4 domain, fusion-competent immunogens, and Gag, Pol, Nef, and Tat vaccines; gene-based therapies such as genetic suppressor elements (GSEs; WO 98/54366), and intrakines (genetically modified CC chemokines targeted to the ER to block surface expression of newly synthesized CCR5 (Yang et aL, PNAS, 94:11567- 72 (1997); Chen et al., Nat. Med., 3:1110-16 (1997)); antibodies such as the anti-CXCR4 antibody 12G5, the anti-CCR5 antibodies 2D7, 5C7, PA8, PA9, PA10, PA11 , PA12, and PAM, the anti-CD4 antibodies Q4120 and RPA-T4, the anti-CCR3 antibody 7B11 , the anti- gp120 antibodies 17b, 48d, 447-52D, 257-D, 268-D and 50.1 , anti-Tat antibodies, anti-TNF- alpha antibodies, and monoclonal antibody 33A; aryl hydrocarbon (AH) receptor agonists and antagonists such as TCDD, 3,3',4,4',5-pentachlorobiphenyl, 3,3',4,4'-tetrachlorobiphenyl, and alpha-naphthoflavone (WO 98/30213); and antioxidants such as gamma-L-glutamyl-L- cysteine ethyl ester (gamma-GCE; WO 99/56764).

[0051] The methods described herein comprise administering a therapeutically effective amount of a type II transmembrane serine proteinase (TTSP) inhibitor or a salt thereof. In some embodiments, the TTSP inhibitor is administered in the form of a pharmaceutical composition. In these embodiments, a pharmaceutical composition will comprise the TTSP inhibitor or a salt thereof and a pharmaceutically acceptable carrier. Suitable pharmaceutically acceptable carriers include, for example, excipients, vehicles, adjuvants, and diluents, which are well known to those who are skilled in the art and which are readily available. Typically, the pharmaceutically acceptable carrier is one that is chemically inert to the active compounds and one that has no detrimental side effects or toxicity under the conditions of use.

EMBODIMENTS

1 . A method of treating a coronavirus infection in a patient in need thereof comprising administering to the patient a therapeutically effective amount of a type II transmembrane serine proteinase (TTSP) inhibitor.

2. The method of embodiment 1 , wherein the TTSP is selected from the group consisting of TMPRSS2, TMPRSS6, and TMPRS11 D.

3. The method of embodiment 1 or 2, wherein the TTSP is TMPRSS2.

4. The method of any one of embodiments 1-3, wherein the TTSP inhibitor is selected from the group consisting of argatroban, ximelagatran, rivaroxaban, apixaban, otamixaban, dabigatran, edoxaban, nafamostat, gabexate, betrixaban, eribaxaban, letaxaban, sivelestat, camostat, darexaban, patamostat, RWJ-58643, RWJ-56423, RWJ- 51084, 1 -[(4S)-4-amino-5-(1 ,3-benzothiazol-2-yl)-5-oxopentyl]guanidine, sepimostat, ciluprevir, 1 -(5-chloro-2-methoxyphenyl)-3-[6-[2-(dimethylamino)-1 -methylethoxy]pyrazine-2- yl]urea, a salt thereof, and a combination thereof.

5. The method of embodiment 4, wherein the TTSP inhibitor is selected from the group consisting of otamixaban, argatroban, letaxaban, darexaban, edoxaban, a salt thereof, and a combination thereof.

6. The method of any one of embodiments 1-5, wherein the coronavirus is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

7. A method of inhibiting TMPRSS2 in a subject in need thereof comprising administering to the subject an effective amount of a compound selected from the group consisting of otamixaban, argatroban, letaxaban, darexaban, edoxaban, a salt thereof, and a combination thereof.

8. The method of any one of embodiments 1-7, further comprising administering to the patient an antiviral agent.

EXAMPLES

[0052] The following examples further illustrate the disclosed methods, but of course, should not be construed as in any way limiting its scope.

[0053] The following abbreviations are used in the Examples: KMC refers to Knowledge Management Center (KMC); IDG refers to Illuminating the Druggable Genome program; NIH refers to National Institutes of Health; DG refers to druggable genome; GPCRs refers to G- protein coupled receptors; IC refers to ion channels; TMPRSS2 refers to transmembrane serine protease family member II; SMILES refers to Simplified Molecular-Input Line-entry System; and BLAST refers to basic local alignment search tool.

[0054] The repurposing lead compounds are either currently marketed or have already undergone extensive human testing, which significantly decreases regulatory burden and development times compared to new drugs that have not yet been approved or tested in clinical trials. Unlike compounds that target viral proteins such as Remdesivir, the compounds identified herein by the disclosed method target TMPRSS2, thus they are less susceptible to evolved resistance on the part of the virus. Unlike compounds such baricitinib, which target human proteins involved in the immune response, the compounds identified using the method described herein target a protein with redundant function, and are less likely to exacerbate SARS-CoV-2 infection. Unlike hydroxycholoroquine or chloroquine, the mechanism of action for the compounds described herein is well known; and they are not associated cardiotoxocity. Finally, the off target effects of the compounds may be beneficial for patients since their primary indication is for thrombosis and abnormal clotting has been observed in COVID patients.

Example 1

[0055] Using known 3D structures of close homologs, seven homology models of TMPRSS2 were created. A set of serine protease inhibitor drugs were identified, conformers of each identified drug were generated and docked with the model. Three known chemical (non-drug) inhibitors and one validated inhibitor of TMPRSS2 in MERS were used as benchmark compounds. Six compound were identified having high binding affinity in the range of the known inhibitors. It was also shown that a previously published weak inhibitor, camostat, had a significantly lower binding score than our six compounds. All six compounds are anticoagulants with significant and potentially dangerous clinical effects and side effects. METHODS

Ligand Library Preparation

[0056] Pharos is the user interface to the Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) program. The goal of KMC is to develop a comprehensive, integrated knowledge-base for the Druggable Genome (DG) to illuminate the uncharacterized and/or poorly annotated portion of the DG, focusing on three of the most commonly drug-targeted protein families: G-protein-coupled receptors (GPCRs); Ion channels (ICs); and Kinases.

[0057] Pharos was searched for active ligands of TMPRSS2. The sequence of the TMPRSS2 homologs (e-value < 10**-10, described in next section) was used to identify a set of serine protease inhibitor drugs that might also bind TMPRSS2. The likely binding pocket was assessed through sequence alignment. Without wishing to be bound to any particular theory, it was believed that drugs that bind the most similar pockets are most likely to bind TMPRSS2. The DrugBank database was then searched for these related serine protease targets and inhibitors were selected that are currently marketed or discontinued in human trials. The list of these possible inhibitors is shown in Table 4.

Table 4. A list of repurposing candidates by searching Drugbank for targets.

[0058] The targets listed in Table 5 had similarities in sequence (BLAST e-value < 10**- 10) and structure (PocketFEATURE similarity < -4.0). Compounds were chosen that had advanced into clinical trials. Most of the compounds are anticoagulants that target human serine proteases which are active in the clotting cascade.

[0059] Isomeric and canonical Simplified Molecular-Input Line-entry System (SMILES) strings for these molecules from DrugBank and PubChem and generated conformers, tautomers, and ionization states at pH 7.0 ± 2.0 for each compound were obtained using LigPrep, as available from Schrodinger, Inc. (New York, NY USA) . All enantiomers in cases where chiral centers were present was generated, but stereochemistry was unspecified. The resulting set of ligand conformations was the "ligand library."

Homology Ensemble Preparation

[0060] The amino acid sequence for the TMPRSS2 catalytic domain was downloaded from UniprotKB [Uniprot Accession 015393] using the following workflow using the Schrodinger Advanced Homology Modeling Interface: (1 ) the Protein Data Bank (PDB) was searched for template structures based on sequence similarity (using BLASTp); (2) globally conserved residues were identified in the sequence (HMMER/Pfam], from e.g., hmmer.org); (3) high scoring (used BLASTp e-value) templates were selected from three closely related proteins (Human Plasma Kallikrein, Factor Xia, Hepsin); (4) secondary structure motifs were predicted and aligned using the single template alignment (STA) setting; and (5) resulting homology structures were refined by optimizing hydrogen bond assignments and minimizing side chain energies, using the protocol of Ramachandran et al.

[0061] The homology model is shown in Figure 1. Aligned homology model structures with template structure 1O5E (Hepsin/TMPRSSI , not shown) is depicted to highlight the differences between the model structures and the active binding site. The model structures varied in loop regions outside of the active site. The large variation in loop at the three o’clock position (Res 83-88, NKTKSD) suggests that this region is important for controlling the size of ligands that can enter the pocket. The average energy of the structures is -8573 ± 167 kcal/mol. The RMSD of the binding site prediction centroids was 2.4 angstroms.

Docking

[0062] Active binding site regions were identified in the homology model structures using the SiteMap tool, which scans protein structures for putative binding sites. The proximity of the predicted binding site to the serine protease catalytic triad (Ser-Asp-His) was inspected to check the accuracy of the specified regions. If no predicted binding sites were located near the catalytic triad of a homology model structure, that structure was not used for docking. The conformers in our ligand library were docked with the active site of each homology model using Glide. To ensure that compounds were docked in the correct site, a comparison of homology structures docked with active ligands to their original template structures was made by visual inspection and by using PocketFEATURE, a program that compares two 3D pockets in proteins and assesses their overall similarity with a score, available for download at simtk.org/projects/pocketfeature. A PocketFEATURE cutoff of -4.0 was an indication that the pocket had not been greatly disrupted during modeling and docking. [0063] Figure 2 shows a Ramachandran plot for validating homology model structures (3ANY -hepsin/TMPRSSI ). Triangles denote glycine residues and squares denote proline residues. The data in Figure 2 demonstrates that over 90 percent of residues fall into the core regions of the plot with very few residues falling into unfavorable regions.

[0064] Figure 3 shows the distribution of PocketFeatures scores for homology models and asset of 5,049 druggable binding sites from scPDB. Fewer than one percent of PocketFeatures scores for scPDB pockets were less than -4.0, while all homology models had PocketFeature scores less than -4.0.

[0065] Figure 4 shows a box-plot of the docking scores of these drug conformers to all seven homology models more quantitatively, wherein the box for each conformer shows the range of docking scores over all homology models. Different conformations of the same molecule are appended with numbers (i.e., dabigatran, dabigatran-2, etc.) Lower scores indicate better docking an scores below -7.5 are considered promising. Known active ligands 4689977, 56677007, and 56663319 had the lowest docking scores. Agatroban, nafamostat, otamixaban, and letaxaban scored below -7.5 for at least one model.

[0066] Figure 5 shows a heatmap of the docking scores normalized by rank, with the most stable being dark (lowest energy) and the least stable light (high energy, unfavorable), of docked conformers for known active ligands of TMPRSS2 and FDA-approved serine protease inhibitors to an ensemble of homology models generated from protein structure templates. The compounds are clustered by docking score rank. Darker cells indicate better docking scores. Columns correspond to homology models of TMPRSS2. Rows correspond to conformers. Molecules appended with numbers (i.e. dabigatran, dabigatran- 2, etc.) denote different conformers. Known active ligands (Pubmed CIDs 4689977, 56677007, 56663319) ranked highest across all homology models. Argatroban, Otamixaban, Letaxaban, Darexaban, Edoxaban, Betrixaban, and Nafamostat also ranked highly across a majority of model structures and clustered together with known active ligands. Nafamostat is reported to inhibit TMPRSS2 mediated cell fusion of MERS-CoV in vitro at high nanomolar concentrations. Camostat, which was shown to inhibit SARS-CoV-2 entry in vitro at micromolar concentrations, was close to the median.

RESULTS

[0067] The ligand library consisted of 19 small molecules, each adopting an average of 1 .89 steric conformations for a total library size of 36. Seven homology models were created to ensure that results were robust to anomalies that might arise from sources of variability such as differences between serine protease family members with similarly high e-values, or bound ligands that influence protein conformation and binding site geometry. The resulting seven models represent an ensemble of structures with an average root mean squared deviation (RMSD) of 1 .27 Angstroms and a maximum RMSD (between model 105E and model 4NA8) of 1 .675 Angstroms. Figure 1 summarizes the way in which these homology models differ, the location of their active site, and their energy. Table 5 shows their pairwise RMS distances.

[0068] RMSD values were computed for each pair of homology model structures in the ensemble. The average RMSD was 1 .27 angstroms. The maximum RMSD was 1 .675 angstroms. This is close to the length of an alkane bond (1 .54 Angstroms), and less than what is considered good resolution for a protein crystal structure (2.4 Angstroms). PocketFeature computes the similarity of binding sites by comparing sets of protein microenvironments. Scores below -4.0 indicate very high similarity. Highly similar pockets are likely to bind the same ligands. The binding sites of the homology models exhibit extremely high similarity in a range which is typically observed for different crystal structures of the same protein.

[0069] Table 6 shows a summary of the predicted high-binding drugs, their lowest and average docking scores, their status as marketed or experimental, and the marketed indications.

Table 6. Docking scores of selected compounds

[0070] Figure 6 shows key electrostatic interactions between the docked chemical structure of the best scoring ligand (otamixaban) and residues in the binding pocket of a TMPRSS2 homology model.

Example 2

Datasets for Large Library

[0071] Structure-activity relationship (SAR) data was extracted from the literature. A number of chemical structures had been deposited in Pubchem and ChEMBL, but associated with assays for homologous serine proteases such as Matriptase and Y. Two independent curators matched compounds in the publication to Pubchem records by visual inspection. Assay data was downloaded for ST14, KLKB1 , TMPRSS11 D, and TMPRSS6, which are homologous trypsin-like serine proteases that shared compounds with the current TMPRSS2 SAR dataset. An independent curator manually converted reported IC50 activity values to Ki values wherever possible to increase the size of the training dataset. Data for which Ki values could not reliably be generated were dropped from the training dataset. All compounds were assigned a binary classification of “active” if its Ki value against its native target was less than or equal to 50 pM; compounds were labeled inactive if this criterion was not true. Compounds that were not associated with either Ki/IC50 values or binary activity labels were excluded. A set of negative training examples that do not bind TMPRSS2, as obtained from the dark chemical matter (DCM) dataset, was also included.

[0072] A combined screening library was assembled from three sources: ReFRAME library, Drugbank, and Drug Repurposing Hub. The entirety of DrugBank and Drug Repurposing Hub was screened. ReFRAME compounds were limited to those that have previously been evaluated in clinical trials and meet at least one of the following criteria: a) contained benzamidine group, a terminal 4-(diaminomethylideneamino)benzoate moiety, or guanidino moieties or b) those that are active against any class of serine protease. SAR Dataset Curation

[0073] The counter ions from salts and standardized molecules for all molecules were removed using RDKit. Non-druglike molecules were removed using quantitative estimation of drug-likeness (QED) package to generate distributions of molecular weight, polar surface area, estimated Log partitioning (logP) coefficient, rotatable bond count, hydrogen-bond donor count, and hydrogen-bond acceptor count; outliers were then removed using the 1 .5*IQR (interquartile range) rule. If a compound was tested multiple times against the same target, the median activity value was used. Duplicates which had a Tanimoto similarity greater than 0.9 were removed, as was all molecules associated with activity cliffs where two compounds had a Tanimoto similarity of 0.85 or higher, but an activity difference of two or more orders of magnitude.

Imputation of TMPRSS2 Activity Values

[0074] To enlarge the SAR dataset, TMPRSS2 Ki values from activity measurements of related trypsin-like serine proteases with correlated binding activity were incorporated into the dataset. For each related protease, the subset of molecules assayed against both targets was selected, and used them to generate a linear regression model relating activity values in units of log(Ki) . If a model had R2 value > 0.7, it was then used to impute TMPRSS2 activity values.

Random Forest Classification and Regression

[0075] A two-step virtual screening pipeline (1) binary classification was used, followed by (2) affinity prediction. In the first step, a binary classification model was used to remove molecules from the screening set that are unlikely to bind TMPRSS2. In the second step, a regression model was used to predict affinity values. For classification, a random forest model was used with parameters X, Y, Z using molecules with experimentally measured and imputed TMPRSS2 affinity values as positive training examples, and an equivalent number of molecules (negative training examples) from the dark chemical weight, polar surface area, estimated Log partitioning coefficient, rotatable bond count, hydrogen-bond donor count, and hydrogen-bond acceptor count; and then removing outliers using the 1.5*IQR rule. If a compound was tested multiple times against the same target, the median activity value was used. Duplicates having a Tanimoto similarity greater than 0.9 were removed. All molecules associated with activity cliffs where two compounds had a Tanimoto similarity of 0.85 or higher, but an activity difference of two or more orders of magnitude were removed.

[0076] To test the accuracy of the classifier, a bootstrapped metric X by randomly splitting the data 2:1 into training and test sets and evaluating on the held out test data for 50 iterations. For regression, a random forest model using parameters X, Y, Z, with experimentally measured and imputed TMPRSS2 activity values was calculated as for the regression dataset. A computed bootstrapped mean squared error (MSE) for our regression model, with the same procedure as for classification.

Results

[0077] After curation, the positive training dataset consisted of 92 compounds which had been assayed against TMPRSS2 and over 800 compounds compounds which bind to related serine proteases ST 14, KLKB1 , TMPRSS11 D, and TMPRSS6. The screening library contained more than 21 ,000 molecules that are marketed or were abandoned in clinical trials. Figures 7A and 7B show the distributions of druglikeness scores for active molecules, dark chemical matter, and screening library compounds.

[0078] The distribution of druglikeness scores were calculated using the QED package for compounds active against TMPRSS2, ST 12, TMPRSS11 D, and KLKB1 before (Figure 7A) and after (Figure 7B) outlier removal. Removal of molecules with outlying molecular weights, polar surface areas, numbers of rotatable bonds, and number of hydrogen bond donors and acceptors significantly reduces the proportion of non-druglike molecules in the dataset.

[0079] Many compounds that have been assayed against TMPRSS2 have been assayed against ST14, TMPRSS6, and TMPRSS11 D. Table 7 shows the BLAST scores and binding affinity correlations for TMPRSS2 against ST 14, KLKB1 , TMPRSS1 1 D, and TMPRSS6 showing the concordance between TMPRSS2 and homologous trypsin-like serine proteases. For overlapping assay compounds, the observed inhibition activities against ST and TMPRSS6 are highly correlated with TMPRSS2. The random forest classifier had precision of 1 .0, recall of 1 .0, and F-score 1.0. The regression model had RMSE of 0.34 pKi units.

Table 7. TMPRSS2 and homologous trypsin-like serine proteases.

[0080] Figure 8 shows the distribution of predicted activities for molecules in our screening library. As shown in Figure 8, violin plots showing the distribution of predicted pKi values for compounds in repurposing libraries from sourced from DrugBank, ReFrame, and The Broad Institute. ReFrame compounds were selected for virtual screening on the basis of prior intuition for activity against TMPRSS2 and their predicted activity values skew higher than compounds from other repurposing libraries. Table 8 shows the top 10 molecules ordered by predicted affinity.

[0081] Top 10 compounds predicted by the random forest regression model to be active against TMPRSS2. Nafamostat and camostat have been shown to inhibit TMPRSS2 and SARS-CoV-2 entry in cell assays.

[0082] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[0083] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (for example, “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention. REFERENCES

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Claims

What is Claimed:

1 . A method of treating a coronavirus infection in a patient in need thereof comprising administering to the patient a therapeutically effective amount of a type II transmembrane serine proteinase (TTSP) inhibitor.

2. The method of claim 1 , wherein the TTSP is selected from the group consisting of TMPRSS2, TMPRSS6, and TMPRS11 D.

3. The method of claim 1 or 2, wherein the TTSP is TMPRSS2.

4. The method of any one of claims 1-3, wherein the TTSP inhibitor is selected from the group consisting of argatroban, ximelagatran, rivaroxaban, apixaban, otamixaban, dabigatran, edoxaban, nafamostat, gabexate, betrixaban, eribaxaban, letaxaban, sivelestat, camostat, darexaban, patamostat, RWJ-58643, RWJ-56423, RWJ-51084, 1 -[(4S)-4-amino- 5-(1 ,3-benzothiazol-2-yl)-5-oxopentyl]guanidine, sepimostat, ciluprevir, 1 -(5-chloro-2- methoxyphenyl)-3-[6-[2-(dimethylamino)-1 -methylethoxy]pyrazine-2-yl]urea, a salt thereof, and a combination thereof.

5. The method of claim 4, wherein the TTSP inhibitor is selected from the group consisting of otamixaban, argatroban, letaxaban, darexaban, edoxaban, a salt thereof, and a combination thereof.

6. The method of any one of claims 1-5, wherein the coronavirus is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

7. A method of inhibiting TMPRSS2 in a subject in need thereof comprising administering to the subject an effective amount of a compound selected from the group consisting of otamixaban, argatroban, letaxaban, darexaban, edoxaban, a salt thereof, and a combination thereof.

8. The method of any one of claims 1-7, further comprising administering to the patient an antiviral agent.