Polyoma virus antiviral therapy

Abstract

Provided herein are methods, compositions, systems and kits to identify and to make use of polyoma virus antiviral therapy. In particular, provided herein are compositions and methods to treat BK polyoma virus (BKPyV) infection comprising velpatasvir or analogs or derivatives thereof.

Description

[0001] POLYOMA VIRUS ANTIVIRAL THERAPY

[0002] FIELD

[0003] Provided herein are methods, compositions, systems and kits to identify and to make use of polyoma virus antiviral therapy. In particular, provided herein are compositions and methods to treat BK polyoma virus (BKPyV) infection comprising velpatasvir or analogs or derivatives thereof.

[0004] BACKGROUND

[0005] BK Polyomavirus (BKPyV) is a member of the Polyomaviridae family, a group of small, opportunistic, non-enveloped double-stranded DNA viruses with a very high seroprevalence in adults. BKPyV often remains dormant and causes little to no symptoms in immunocompetent individuals, but the opportunistic pathogen may reactivate and lead to severe complications in those with immune systems weakened by a diversity of causes. (Ambalathingal, G. R., Francis, R. S., Smyth, M. J., Smith, C. & Khanna, R. BK Polyomavirus: Clinical Aspects, Immune Regulation, and Emerging Therapies. Clin. Microbiol. Rev. 30, 503-528 (2017).) BKPyV often targets the urinary tract, establishing a dormant state within renal epithelial cells and urothelium after initial exposure that frequently occurs in early childhood. (Shen, C.L., Wu, B.S., Lien, T.J., Yang, A H. & Yang, C.Y. BK Polyomavirus Nephropathy in Kidney Transplantation: Balancing Rejection and Infection. Viruses 13, (2021).) While the immune system effectively controls viral replication in people with normal immunity, the equilibrium may be disrupted in individuals with compromised immune responses including, for example, solid organ transplant recipients, bone marrow transplant recipients and persons with human immunodeficiency virus (HIV) infection. BKPyV infection is one of the top viral disorders that afflict kidney transplant recipients. (Vanichanan, J., Udomkamj ananun, S., Avihingsanon, Y. & Jutivorakool, K. Common viral infections in kidney transplant recipients. Kidney Res Clin Pract 37 , 323-337 (2018).) The incidence of BK polyoma virus viruria in kidney transplant recipients is 30-40%, that of viremia 13%, and that of BK virus nephropathy 8%. (Hirsch H.H., Knowles W., Dickenmann M., Passweg J., Klimkait T., Mihatsch M.J., Steiger J. Prospective study of polyomavirus type BK replication and nephropathy in renal-transplant recipients. N Engl J Med. 347, 488-496 (2002). Without screening and treatment BK virus nephropathy causes 50% graft loss. (Nickeleit V , et al. The Banff Working Group Classification of Definitive Polyomavirus Nephropathy: Morphologic Definitions and Clinical Correlations. J Am Soc Nephrol. 29, 680-693 (2018.).

[0006] One of the serious complications of BKPyV infection is polyomavirus-associates nephropathy (PVAN) that threatens the success of renal transplantation. (Hirsch, H. H., Drachenberg, C. B., Steiger, J. & Ramos, E. Polyomavirus-Associated Nephropathy in Renal Transplantation: Critical Issues of Screening and Management. (Landes Bioscience, 2013).) PVAN is associated with high rates of graft dysfunction and rejection, often leading to graft loss and re-transplantation, thereby increasing healthcare costs and patient morbidity. Up to 10% of kidney transplant patients develop PVAN, and up to 80% of those patients lose their graft. BKPyV is linked to other serious clinical manifestations, including hemorrhagic cystitis (Mohammadi Najafabadi, M., Soleimani, M., Ahmadvand, M., Soufi Zomorrod, M. & Mousavi, S. A. Treatment protocols for BK virus associated hemorrhagic cystitis after hematopoietic stem cell transplantation. Am. J. Blood Res. 10, 217-230 (2020).) and ureteral stenosis (Cohen-Bucay, A., Ramirez-Andrade, S. E., Gordon, C. E., Francis, J. M. & Chitalia, V. C. Advances in BK Virus Complications in Organ Transplantation and Beyond. Kidney Med 2, 771-786 (2020).) multiplying the challenges of preventing, managing and treating BKPyV infection and resulting complications.

[0007] The conventional approach to treating BKPyV infection primarily is reducing immunosuppressive medications necessary to maintain graft function, with the aim of gaining control over viral BKPyV replication by bolstering the immune system. (Zhong, C. et al. Therapeutic strategies against BK polyomavirus infection in kidney transplant recipients: Systematic review and meta-analysis. Transpl. Immunol. 81, 101953 (2023).) This method poses significant challenges. Scaling back immunosuppression increases the risk of j eopardizing the success of organ or bone marrow transplants and exacerbates other health conditions. No effective FDA-approved therapies for treatment and prophylaxis of BKPyV infection are available at present. Accordingly, there is a pressing need to identify treatments for BKPyV infection. SUMMARY

[0008] Provided herein are methods, compositions, systems and kits to identify and to make use of polyoma virus antiviral therapy. In particular, provided herein are compositions and methods to treat BK polyoma virus (BKPyV) infection comprising velpatasvir or analogs or derivatives thereof.

[0009] In some embodiments, the present invention provides a method of treating, ameliorating or preventing a polyoma virus infection in a subject comprising administering to the subject a therapeutically effective amount of velpatasvir or an analog, a derivative or a related compound thereof and a pharmaceutically acceptable carrier. In some embodiments, the polyoma virus infection is BKPyV infection. In some embodiments, the subject is a human subject. In some embodiments, the human subject is an immunocompromised human subject. In some embodiments, the immunocompromised human subject is a solid organ transplant recipient or a bone marrow transplant recipient. In some embodiments, the solid organ transplant recipient is a kidney transplant recipient. In some embodiments, the human subject does not have HCV infection. In some embodiments, the human subject is not being administered sofosbuvir.

[0010] In some embodiments, the present invention provides a kit comprising a pharmaceutical composition comprising velpatasvir and instructions for administering velpatasvir. In some embodiments, the present invention provides use of a kit comprising a pharmaceutical composition comprising velpatasvir and instructions for administering velpatasvir. In some embodiments, the present invention provides a kit comprising a pharmaceutical composition comprising velpatasvir and instructions for administering velpatasvir for treatment and / or prevention of a polyoma virus infection. In some embodiments, a kit comprises one or more additional antiviral agents.

[0011] In some embodiments, the present invention provides a method for testing a compound for efficacy and safety in treating and / or preventing BKPyV infection, comprising: providing one or more renal proximal tubule epithelial cells immortalized using human telomerase reverse transcriptase (RPTE-hTERT cells); infecting the one or more RPTE-hTERT cells with BKPyV; applying the compound to the one or more RPTE-hTERT cells infected with the BKPyV; scoring the one or more BKPyV infected RPTE-hTERT cells for a half maximal inhibitory concentration (IC50) of the compound; scoring the one or more BKPyV infected RPTE-hTERT cells for a 50% cytotoxicity concentration (CC50) of the compound; and calculating an IC50 / CC50 selectivity index (ST) for the compound. Tn some embodiments, the infecting the one or more RPTE- hTERT cells with the BKPyV is before the applying the compound to the one or more RPTE- hTERT infected cells. In some embodiments, the infecting the one or more RPTE-hTERT cells with the BKPyV is after the applying the compound to the one or more RPTE-hTERT cells.

[0012] In some embodiments, the present invention provides a system, comprising: at least one test compound; one or more RPTE-hTERT cells; an image acquisition device; and a computer comprising a processor configured to implement a machine vision algorithm, an artificial intelligence (AT) algorithm and / or a machine learning (ML) algorithm. In some embodiments, the system comprises one or more devices and / or one or more reagents for cell growth, replication and maintenance. In some embodiments, the image acquisition device is a high-content imaging system comprising a laser. In some embodiments, the system comprises one or more antibodies. In some embodiments, the one or more antibodies is a large tumor antigen (TAg) antibody. In some embodiments, the TAg antibody comprises a fluorescent label. In some embodiments, the system comprises fluorescence reader and / or an image display and / or a scoring display. In some embodiments, the system comprises one or more calibrants and / or one or more positive or negative virus controls and / or one or more positive or negative compound controls.

[0013] DESCRIPTION OF THE FIGURES

[0014] Figure 1 shows a high-content assay for BKPyV infection in renal proximal tubule epithelial cells immortalized using human telomerase reverse transcriptase (RPTE-hTERT cells). Figure 1A. shows an assay pipeline. Figure IB. shows images of cell nuclei (cyan), BKPyV large tumor antigen (Tag) protein (magenta), and whole cell mask (orange) of infected and mock controls. Figure 1C. shows a raw percent infection violin plot for infected and mock controls including the assay Z’ to assess the quality and robustness of assay performance. Data are from N = 20 samples in mock replicate wells and N=40 samples in infected replicate wells. ****= P<0.0001

[0015] Figure 2 shows image analysis, segmentation and machine language (ML) BKPvY infection scoring. Figure 2A. shows multiplexed fields of renal proximal tubule epithelial (RPTE) cells. Figure 2B. shows CellPose segmentation of nuclei and cell body. Figure 2C. shows machine learning (ML) infection scoring using XGBoost. Figure 2D. shows drug screening with identified hits above the red dashed line. Red data points indicate high efficacy with minimal cytotoxicity for high-quality BKPyV antiviral hit detection. Figure 2E. shows uniform manifold approximation and projection (UMAP) embedding and Leiden clustering of infected cells showing distinct morphologic classes.

[0016] Figure 3A. shows the chemical structure of velpatasvir (C49H54N8O8) (methyl A'-[(17?)-

[0017] 2-[(21S1,45)-2-[5-[6-[(21$,55)-l-[(25)-2-(methoxycarbonylamino)-3-methylbutanoyl]-5- methylpyrrolidin-2-yl]-21-oxa-5,7-diazapentacyclo[l 1.8.0.03-1104,8 o14 19]henicosa-

[0018] 1 ( 13 ), 2, 4(8), 5, 9, 11 , 14(19), 15, 17-nonaen-17-yl]- 1 ff-imidazol-2-yl]-4- (methoxymethyl)pyrrolidin-l-yl]-2-oxo-l-phenylethyl]carbamate). Figure 3B. shows velpatasvir concentration response curves for BK polyoma virus (BKPyV) infection (red) and cell viability (black). Figure 3C. shows velpatasvir concentration response curves for mouse polyoma virus (MPyV) infection (red) and cell viability (black). Curves were fitted in Graphpad Prism using a semi-log 4-parameter variable slope model. IC50 and CC50 values are provided at the top of each figure.

[0019] Figure 4 shows a time-of-addition assay for velpatasvir in BKPyV infected renal RPTE- hTERT cells. Cells were treated with 2 / / M velpatasvir (5X ICso) 2 hours prior to infection with BKPyV (“-2 hrs”), 1 hour post infection (“+1 hrs p.i ”), 4 hours post infection (“+4 hrs p.i.”) or 24 hours post infection (“+24 hrs p.i ”). Results show the means + / - SEM for N = 32 sample replicates for infected controls and N = 4 sample replicates for each time point. Significance was determined in GraphPad Prism using a one-way ANOVA with multiple comparisons to the infected control. **** = P<0.0001, * = P <0.05.

[0020] Figure 5 shows the comparable efficacy of FDA-approved HCV nonstructural protein 5 A (NS5A) inhibitors with velpatasvir. Figure 5A. shows structures of 6 FDA approved HCV NS5A inhibitors. Figure 5B. shows concentration response curves for NS5A inhibitors against BKPyV infection in RPTE-hTERT cells. Figure 5C. shows the half maximal inhibitory concentration (IC50), the 50% cytotoxicity concentration (CC50), and the IC50 / CC50 selectivity index (SI) values for each NS5A inhibitor.

[0021] Figure 6 shows that velpatasvir potency is dependent on BKPyV multiplicity of infection (MOI), supporting a viral target mechanism. Figure 6A. shows RPTE-hTERT cells infected with BKPyV at MOIs of 1, 5, or 10 and treated with a concentration range of velpatasvir. Cells were fixed 48 hours post-infection and stained for the viral large T antigen (TAg). Infection was quantified using high-content imaging and expressed as percent TAg-positive cells relative to the DMSO-treated control. Figure 6B. shows ICso values extracted from fitted dose-response curves for each MOI and plotted for comparison. Potency significantly decreases with increasing MOI (***P < 0.001, ****p < 0.0001; one-way ANOVA with multiple comparisons). These data indicate that velpatasvir targets a viral factor whose abundance scales with input virus, consistent with capsid-associated proteins (e.g., VP 1 / 2 / 3). The ICso of velpatasvir shifts as a function of viral multiplicity of infection (MOI) in keeping with a viral rather than host target.

[0022] Figure 7 shows that velpatasvir protects against BKPy V-induced cytopathic effect in RPTE-hTERT cells. Figure 7A. shows quantification of cell viability following infection with BKPyV and treatment with a dose range of velpatasvir. RPTE-hTERT cells were treated with velpatasvir and infected with BKPyV under conditions permissive to CPE. Cell viability was quantified at 7 days post-infection using whole-well imaging. Velpatasvir showed dosedependent rescue of viability compared to infected controls (****p < 0.0001, **P < 0.01, ns = not significant; one-way ANOVA with multiple comparisons). Figure 7B. shows representative images of infected RPTE-hTERT cells treated with the indicated concentrations of velpatasvir. Mock-infected cells show intact monolayers, while infected untreated cells exhibit extensive CPE and cell loss. Restoration of cell monolayers with velpatasvir treatment indicates a robust protective effect. These data validate antiviral activity using a CPE-based assay in BKPyV- infected RPTE-hTERT cells. Velpatasvir treatment restored cell viability in a dose-dependent manner.

[0023] Figure 8 shows that repurchased velpatasvir from a second source (Cayman Chemical) enhances potency against BKPyV infection in RPTE-hTERT cells. Concentration-response curves for velpatasvir were generated in RPTE-hTERT cells infected with BKPyV (MOI = 1). BKPyV infection (red, left Y-axis) was quantified by percentage of TAg-positive nuclei. Cell viability (black, right Y-axis) was measured via total cell count. ICso was calculated as 151 nM, with CCso exceeding 10 pM. Results show the average of replicate wells ± SEM. These data show a three-fold improved IC50 value (151 nM) using an alternatively sourced velpatasvir compound. DEFINITIONS

[0024] Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrase “in some embodiments” as used herein does not necessarily refer to the same embodiment, though it may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention. For purposes of interpreting this specification, the following definitions will apply and whenever appropriate, terms used in the singular will also include the plural and vice versa. In the event that any definition set forth below conflicts with any document incorporated herein by reference, the definition set forth below shall control.

[0025] As used herein the terms “disease” and “pathologic condition” are used interchangeably, unless indicated otherwise herein, to describe a deviation from the condition regarded as normal or average for members of a species or group (e.g., humans), and which is detrimental to an affected individual under conditions that are not inimical to the majority of individuals of that species or group. Such a deviation can manifest as a state, signs, and / or symptoms (e.g., diarrhea, nausea, fever, pain, blisters, boils, rash, immune suppression, inflammation, etc.) that are associated with any impairment of the normal state of a subject or of any of its organs or tissues that interrupts or modifies the performance of normal functions. A disease or pathological condition may be caused by or result from contact with a microorganism (e.g., a pathogen or other infective agent (e.g., a virus or bacteria)), may be responsive to environmental factors (e.g., malnutrition, industrial hazards, and / or climate), may be responsive to an inherent or latent defect in the organism (e.g., genetic anomalies) or to combinations of these and other factors.

[0026] The terms “host,” “subject,” or “patient” are used interchangeably herein to refer to an individual to be treated by (e.g., administered) the compositions and methods of the present invention. Subjects include, but are not limited to, mammals (e.g., murines, simians, equines, bovines, porcines, canines, felines, and the like), and most preferably includes humans. In the context of the invention, the term “subject” generally refers to an individual who will be administered or who has been administered one or more compositions of the present invention (e.g., genetically modified immune cells described herein).

[0027] The term “solution” refers to an aqueous or non-aqueous mixture. A “disorder” is any condition or disease that would benefit from treatment with a composition or method of the invention. This includes chronic and acute disorders including those pathological conditions which predispose the mammal to the disorder in question. Nonlimiting examples of disorders to be treated herein include conditions such as cancer.

[0028] The terms “antiviral agent” and “antiviral” and “antiviral drug” as used herein, refer to a therapeutic agent used to treat an infection caused by a virus.

[0029] An “effective amount” refers to an amount effective, at dosages and for periods of time necessary, to achieve a desired therapeutic or prophylactic result.

[0030] The term “therapeutically effective amount,” as used herein, refers to that amount of the therapeutic agent sufficient to result in amelioration of one or more symptoms of a disorder, or prevent advancement of a disorder, or cause regression of the disorder. For example, with respect to the treatment or the prevention of polyomavirus infection, in one embodiment, a therapeutically effective amount will refer to the amount of a therapeutic agent that decreases the rate of viral proliferation, or increases survival time by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or at least 100%.

[0031] As used herein, the terms “administration” and “administering” refer to the act of giving a composition of the present invention to a subject. Exemplary routes of administration to the human body include, but are not limited to, through the eyes (ophthalmic), mouth (oral), skin (transdermal), nose (nasal), lungs (inhalant), oral mucosa (buccal), ear, rectal, by injection (e.g., intravenously, subcutaneously, intraperitoneally, intratumorally, etc.), topically, and the like.

[0032] As used herein, the terms “co-administration” and “co-administering” refer to the administration of at least two agent(s) or therapies to a subject. In some embodiments, the coadministration of two or more agents or therapies is concurrent. In other embodiments, a first agent / therapy is administered prior to a second agent / therapy. In some embodiments, coadministration can be via the same or different route of administration. Those of skill in the art understand that the formulations and / or routes of administration of the various agents or therapies used may vary. The appropriate dosage for co-administration can be readily determined by one skilled in the art. In some embodiments, when agents or therapies are coadministered, the respective agents or therapies are administered at lower dosages than appropriate for their administration alone. Thus, co-administration is especially desirable in embodiments where the co-administration of the agents or therapies lowers the requisite dosage of a potentially harmful (e.g., toxic) agent(s), and / or when co-administration of two or more agents results in sensitization of a subject to beneficial effects of one of the agents via co- administration of the other agent.

[0033] The terms “pharmaceutically acceptable” or “pharmacologically acceptable,” as used herein, refer to compositions that do not substantially produce adverse reactions (e.g., toxic, allergic or other immunologic reactions) when administered to a subject.

[0034] As used herein, the term “pharmaceutically acceptable carrier” refers to any of the standard pharmaceutical carriers including, but not limited to, phosphate buffered saline solution, water, and various types of wetting agents (e.g., sodium lauryl sulfate), any and all solvents, dispersion media, coatings, sodium lauryl sulfate, isotonic and absorption delaying agents, disintegrants (e.g., potato starch or sodium starch glycolate), polyethylene glycol, and the like. The compositions also can include stabilizers and preservatives. Examples of carriers, stabilizers and adjuvants have been described and are known in the art (see, e.g., Martin, Remington's Pharmaceutical Sciences, 15th Ed., Mack Publ. Co., Easton, Pa. (1975), incorporated herein by reference).

[0035] As used herein, the term “kit” refers to any delivery system for delivering materials. In the context of antiviral agents, such delivery systems include systems that allow for the storage, transport, or delivery of antiviral agents and / or supporting materials (e.g., written instructions for using the materials, etc.) from one location to another. For example, kits include one or more enclosures (e.g., boxes) containing the relevant antiviral agents. As used herein, the term “fragmented kit” refers to delivery systems comprising two or more separate containers that each contain a sub-portion of the total kit components. The containers may be delivered to the intended recipient together or separately. For example, a first container may contain a composition comprising an antiviral composition for a particular use, while a second container contains a second agent (e.g., a second antiviral). Any delivery system comprising 2 or more separate containers that each contains a sub-portion of the total kit components are included in the term “fragmented kit.” In contrast, a “combined kit” refers to a delivery system containing all of the components of an antiviral agent needed for a particular use in a single container (e.g., in a single box housing each of the desired components). The term “kit” includes both fragmented and combined kits.

[0036] The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”

[0037] The phrase “and / or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and / or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and / or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and / or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc. As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0038] The term “about” as used herein means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within an acceptable standard deviation, per the practice in the art. Alternatively, “about” can mean a range of up to ± 20 %, preferably up to ± 10 %, more preferably up to ± 5 %, and more preferably still up to ± 1 % of a given value. Where particular values are described in the application and claims, unless otherwise stated, the term “about” is implicit and in this context means within an acceptable error range for the particular value.

[0039] As used herein, a “system” refers to a plurality of components operating together for a common purpose. In some embodiments, a “system” is an integrated assemblage of hardware and / or software components. In some embodiments, each component of the system interacts with one or more other components and / or is related to one or more other components. In some embodiments, a system refers to a combination of components and software for controlling and directing methods. For example, a “system” or “subsystem” may comprise one or more of, or any combination of, the following: mechanical devices, hardware, components of hardware, circuits, circuitry, logic design, logical components, software, software modules, components of software or software modules, software procedures, software instructions, software routines, software objects, software functions, software classes, software programs, files containing software, etc., to perform a function of the system or subsystem. Thus, the systems or methods provided herein, or certain aspects or portions thereof, may take the form of program code (e.g., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, flash memory, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the embodiments. In the case of program code execution on programmable computers, the computing device generally includes a processor, a storage medium readable by the processor (e.g., volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. One or more programs may implement or utilize the processes described in connection with the embodiments, e.g., through use of an application programming interface (API), reusable controls, or the like. Such programs are preferably implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language, and combined with hardware implementations.

[0040] As used herein, the term “machine learning algorithm” refers to a method that produces a machine learning model (e.g., by receiving data as an input and performing the algorithm on the data). In some embodiments, a machine learning algorithm comprises recognizing patterns in data to determine or “learn” from the data how to generate output or make a prediction based on input data. In some embodiments, a machine learning algorithm is described using a mathematical equation, pseudocode, or using code in a specific programming language e.g., BASIC, Java, C, C++, C#, Objective-C, MATLAB, Mathematica, Python, R, PHP, Ruby, Perl, Object Pascal, Swift, Scala, Common Lisp, or Smalltalk, etc.). In some embodiments, computer science techniques may be used to evaluate the efficiency of a machine learning algorithm. In some embodiments, a machine learning algorithm comprises an optimization method that minimizes an error calculated from data and / or a prediction algorithm for a training dataset.

[0041] As used herein, the term “machine learning model” or “model” refers to the output of a machine learning algorithm. In some embodiments, a machine learning model is a machine learning algorithm that has been optimized (e.g., having optimized parameters) using training data to identify certain patterns in data or produce certain outputs. In some embodiments, a machine learning model comprises a saved set of rules, parameterized algorithms, numbers, methods, and / or data structures that are produced by the machine learning algorithm using training data and that may be used to make predictions or produce output using new data as input. That is, a machine learning model is a program created by performing the machine learning algorithm on data to produce a trained model that is used for prediction or output when provided with new data.

[0042] As used herein, the term “model training” or “training a model” and the like refers to a method comprising inputting a dataset (called training data) to a machine learning algorithm and optimizing the algorithm to identify certain patterns or produce certain outputs. The resulting rules, parameterized algorithms, numbers, methods, and / or data structures are termed collectively the trained machine learning model. Accordingly, a machine learning algorithm may be trained to produce a machine learning model and, because a machine learning model is an optimized machine learning algorithm, a machine learning model may be trained (and retrained) by inputting a dataset into the machine learning model.

[0043] As used herein, the term “machine learning network” refers to a machine learning algorithm or machine learning model having a defined organization of algorithms, functions, methods, weights, parameters, data flows between algorithms or methods, and / or data formats used for input and output. In some embodiments, a machine learning network comprises weights applied to data communicated within the network; and / or weights applied to parameters used in algorithms, functions, or methods. In some embodiments, the organization is described as a hierarchy of layers between which inputs and outputs are communicated.

[0044] As used herein, the term “machine learning architecture” refers to a specific organizational structure of a machine learning network. A machine learning architecture may be described in terms of a map or topology (e.g., comprising nodes, connections between nodes, weights of nodes, directions of flow between nodes).

[0045] As used herein, a machine learning “node” refers to a computational unit that has one or more weighted input connections, a function (e.g., comprising an algorithm, method, function) that transforms the inputs, and an output connection. In some embodiments, nodes are organized into layers to comprise a machine learning network comprising a particular machine learning architecture.

[0046] As used herein, the terms "processor" and "central processing unit" or "CPU" are used interchangeably and refer to a device that is able to read a program from a computer memory (e.g., ROM or other computer memory) and perform a set of steps according to the program.

[0047] As used herein, the terms "computer memory" and "computer memory device" refer to any storage media readable by a computer processor. Examples of computer memory include, but are not limited to, RAM, ROM, computer chips, digital video disc (DVDs), compact discs (CDs), hard disk drives (HDD), and magnetic tape.

[0048] As used herein, the term "computer readable medium" refers to any device or system for storing and providing information (e.g., data and instructions) to a computer processor. Examples of computer readable media include, but are not limited to, DVDs, CDs, hard disk drives, memory chip, magnetic tape and servers for streaming media over networks.

[0049] DETAILED DESCRIPTION

[0050] Provided herein are methods, compositions, systems and kits to identify and to make use of polyoma virus antiviral therapy. In particular, provided herein are compositions and methods to treat BK polyoma virus (BKPyV) infection comprising velpatasvir or analogs or derivatives thereof. BKPyV Antiviral Screening Assay

[0051] In some embodiments, the present invention provides a cell painting-style BKPyV assay comprising a physiologically relevant renal cell system that combines high content imaging, machine vision, and machine learning-based cell classification and morphologic profiling to identify inhibition of BKPyV infection while maintaining high renal cell health. A challenge for developing a BKPyV antiviral arises from its small genome that encodes 6 proteins. This constrains conventional target-centric drug discovery and identification of small molecule candidates suitable for antiviral intervention. Another challenge is that there are no satisfactory animal models for studying BKPyV infection. Accordingly, in some embodiments the present invention provides physiologically relevant cell models and analysis pipelines for polyoma virus antiviral drug screening development.

[0052] In some embodiments, methods, compositions, systems and kits of the present invention provide a phenotypic, single cell bioimaging assay that expands antiviral drug discovery capabilities to identify clinically relevant antiviral effects unaccounted for by conventional drug discovery methods. In some embodiments, high-content assays of the present invention employ advanced imaging and analytic techniques to simultaneously capture a diversity of cellular parameters and characteristics in a high-throughput manner. In some embodiments, the assay discriminates between infected and uninfected cells through image analysis of more than 500 distinct morphological traits to accurately quantify infection levels. In some embodiments, the morphological traits include, but are not limited to, intensity, intensity distribution, texture, shape / area, spatial arrangements, and colocalization features for each fluorescent channel in each cellular compartment such as cell nuclei, cytoplasm, nucleoli, mitochondria, endoplasmic reticulum which are delineated by fluorescent dyes / antibodies and identified via image analysis algorithms. (Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 2006;7(10):R100.) Together the morphological features capture and describe a phenotypic fingerprint of each cell, and distinguish between infected and uninfected cells. For example, cells infected with BKPyV may have an increase in nuclear Large T antigen expression, while also having an increase in the total area of the nuclei and other textural / intensity distribution changes. Compounds that reverse the infected phenotype to the uninfected control are candidate drug leads in contrast to compounds that may reduce intensity of large T antigen alone but also disrupt key cellular functions leading to a divergent uninfected phenotype.

[0053] In turn, the assay system captures intrinsic heterogeneity within infection patterns, and provides contextual data from infected cells and drugs that inhibit BKPyV infection.

[0054] In some embodiments, the present invention provides automated, multiplexed high- content imaging coupled with machine vision and artificial intelligence methods and systems to support drug discovery that comprise: 1) adapting a cell painting approach to identify antiviral hit selection in physiologically relevant cell systems (Bray MA, Singh S, Han H, Davis CT, Borgeson B, Hartland C, Kost-Alimova M, Gustafsdottir SM, Gibson CC, Carpenter AE. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. NatProtoc. 2016; 11 (9): 1757-1774.); 2) artificial intelligence (Al)-based image analysis that identifies clinically relevant efficacy and rapid selection of compounds with high probability of in vivo activity with maintained cell health; and 3) morphologic cell analysis to aid prioritize clinically-relevant antiviral agents. (Wotring JW, McCarty SM, Shafiq K, Zhang CJ, Nguyen T, Meyer SR, Fursmidt R, Mirabelli C, Clasby MC, Wobus CE, O’Meara MJ, Sexton JZ. In Vitro Evaluation and Mitigation of Niclosamide’ s Liabilities as a COVID-19 Treatment. Vaccines (Basel). 2022; 10(8)., Mirabelli C, Wotring JW, Zhang CJ, McCarty SM, Fursmidt R, Pretto CD, Qiao Y, Zhang Y, Frum T, Kadambi NS, Amin AT, O’Meara TR, Spence JR, Huang J, Alysandratos KD, Kotton DN, Handelman SK, Wobus CE, Weatherwax KJ, Mashour GA, O’Meara MJ, Chinnaiyan AM, Sexton JZ. Morphological cell profiling of SARS-CoV-2 infection identifies drug repurposing candidates for COVID-19. Proc Natl Acad Sci USA. 2021 ; 118(36)., Procario MC, Sexton JZ, Halligan BS, Imperiale MJ. Single-Cell, High-Content Microscopy Analysis of BK Polyomavirus Infection. Microbiol Spectr. May 8, 2023:e0087323., Sexton JZ, Fursmidt R, O’Meara MJ, Omta W, Rao A, Egan DA, Haney SA. Machine Learning and Assay Development for Image-Based Phenotypic Profiling of Drug Treatments. Eli Lilly & Company and the National Center for Advancing Translational Sciences; 2023.)

[0055] In some embodiments, the present inventions provides methods, compositions, systems and kits for efficient and cost-effective antiviral drug screening comprising machine vision and machine learning in a phenotypic RPTE infection cell model. (Figure 1.) In some embodiments the assay is formatted for high-throughput screening within a 384-well framework. (Figure 1) In some embodiments, the RPTE cells are immortalized with human telomerase reverse transcriptase (RPTE-hTERT cells). RPTE-hTERT cells overcome multiple challenges associated with working with primary cells. For example, primary cells have finite lifespans and high variability among donor samples thereby impairing the reproducibility and scalability of a screening assay. RPTE-hTERT cells maintain their functional state thereby providing a consistent and enduring supply for screening without variability commonly encountered with primary cells. (Zhao L, Imperiale MJ. Establishing renal proximal tubule epithelial-derived cell lines expressing human telomerase reverse transcriptase for studying BK Polyomavirus. Microbiol Resour Announc. 2019;8(42).) As well, RPTE-hTERT cells sustain viability across 30+ passages without loss of functionality and are highly permissive to BKPyV infection including infection by the rearranged Dunlop BKPyV variant associated with severe human clinical outcomes. In some embodiments, a multivariate scoring system further comprises staining for large tumor antigen (TAg), one of 6 proteins encoded by the BKPyV genome.

[0056] BKPyV Antiviral Drug Selection

[0057] The RPTE-hTERT assay was used to identify small molecule inhibitors of BKPyV infection with a wide cell-based therapeutic index (IC50 / CC50). (Figure 2.) With a Z-prime >0.5 bioassay developed to detect BKPyV inhibition while preserving RPTE cell health, drug screening was performed using a library of 1,425 bioactive compounds. Figure 2D shows a scatterplot of compounds screened from five 384 well plates. 20 dose-responsive compounds were identified through assessment of efficacy, lead-likeness, and cell-based IC50 / CC50. Velpatasvir had an IC50 of 480nM, a CC50 exceeding 20 pM, and the greatest therapeutic index (>42) indicating strong potency with minimal cytotoxicity in kidney cells. (Figure 3.) Figure 3A. provides the chemical structure of velpatasvir. Figure 3B. shows velpatasvir potency and toxicity dose response curves against BKPyV. Figure 3C. shows velpatasvir potency and toxicity dose response curves against MPy V indicating the utility of C57BL6 / J mice as an in vivo BKPyV antiviral efficacy model. Velpatasvir administered from 2 hours before to 4 hours after BKPyV infection provides 100% efficacy, with up to 50% efficacy retained 24 hours after infection. (Figure 4.) These data indicate that velpatasvir prevents as well as treats BKPyV infection, and that velpatasvir does not act as an entry or binding inhibitor, denoting activity at a later stage to inhibit viral replication. Velpatasvir is an FDA-approved antiviral medication that targets and inhibits the hepatitis C virus (HCV) (Gilead Sciences, Foster City, CA). It is used in combination therapy with sofosbuvir under the brand name Epclusa for the treatment of chronic HCV infection. Velpatasvir inhibits the HCV multifunctional NS5A protein essential for RNA replication and virion assembly, thereby disrupting the viral replication cycle and supporting clearance of the virus. The efficacy of velpatasvir to inhibit BKPyV infection in the RPTE-hTERT assay was compared with 5 other NS5A inhibitors sharing a polyaromatic core flanked on either side by dipeptides including elbasvir, daclatasvir, pibrentasvir, ledipasvir, and ombitasvir. (Figure 5A.) Daclatasvir exhibited >10-fold less potency than velpatasvir against BKPyV. The other NS5A inhibitors had no efficacy against BKPyV infection. Moreover, daclatasvir exhibited minimal separation between its potency and cytotoxicity (SI = 2.5). (Figure 4C.) These results indicate that velpatasvir is well suited for antiviral efficacy against BKPyV among other NS A inhibitors, and that its BKPyV efficacy does not arise from NS5A inhibition in BKPyV that lacks the NS5A protein. Velpatasvir is not a broad spectrum antiviral agent in the high-content imaging-based screening assay of the present invention with lack of efficacy against SARS-CoV-2. Astrovirus, and Dengue Virus. (Mirabelli, C. et al. Morphological cell profiling of SARS-CoV-2 infection identifies drug repurposing candidates for COVID-19. Proc. Natl. Acad. Set. U. S. A. 118, (2021), Hoffstadt, J. G. et al. High-Content Screening to Identify Inhibitors of Dengue Virus Replication. bioRxiv 2023.03.24.534108 (2023) doi: 10.1101 / 2023.03.24.534108.) In some embodiments, the methods, compositions, systems and kits of the present invention comprise a velpatasvir analog. In some embodiments, the methods, compositions, systems and kits of the present invention comprise a velpatasvir derivative. In some embodiments, the velpatasvir derivative is a compound of formula (I): Ela-Via— C(=O) — P;it— Wla— Plb— C(~O) — Vlb-Elb(I ). In some embodiments, the compound is an analog and / or derivative of formula (I ) as described, for example, in U.S. Patent No. 9, 156,823 incorporated by reference herein in its entirety.

[0058] Methods of Use

[0059] Methods for treatment of diseases are also encompassed by this disclosure. The methods include administering a therapeutically effective amount of a polyoma virus antiviral as described above. In some embodiments, provided herein are methods of treating a subject in need thereof, the method comprising administering a therapeutically effective dose of any of the compositions disclosed herein.

[0060] In some embodiments, the antiviral therapy is administered systemically, which refers to the administration of an antiviral other than directly into a target site, tissue, or organ, such that it enters, instead, the subject's circulatory system and, thus, is subject to metabolism and other like processes. Suitable modes of administration include oral, injection, infusion, instillation, or ingestion. Injection includes, without limitation, intravenous, intramuscular, intra-arterial, intrathecal, intraventricular, intracapsular, intraorbital, intracardiac, intradermal, intraperitoneal, transtracheal, subcutaneous, subcuticular, intraarticular, subzcapsular, subarachnoid, intraspinal, intracerebrospinal, and intrasternal injection and infusion. In some embodiments, the route is intravenous administration.

[0061] A subject may be any subject for whom diagnosis, treatment, or therapy is desired. In some embodiments, the subject is a mammal. In some embodiments, the subject is a human. In some embodiments, the human subject has a polyoma virus infection. In some embodiments, the polyoma virus infection is BKPyV infection. In some embodiments, the polyoma virus infection is JCPyV virus infection. In some embodiments, the JCPyV virus infection causes progressive multifocal leukoencephalopathy (PM1) and / or aseptic meningitis. In some embodiments, the polyoma virus is Merkel cell polyoma virus (MCPyV) in a subject with Merkel-cell carcinoma. In some embodiments, the polyoma virus infection is human polyoma virus 7 (HPvV7) infection. In some embodiments, the polyoma virus infection is a murine, bovine, avian or simian polyoma virus infection.

[0062] In some embodiments, the human subject has an immunodeficiency and / or an immunocompromised condition. In some embodiments, the immunodeficiency is caused by drug induced immunosuppression wherein the drug is used as an anti -rejection drug after organ or bone marrow transplantation, a glucocorticoid, a drug used to treat an autoimmune disorder, or a cancer chemotherapy. In some embodiments, the immunodeficiency arises from an inborn deficiency in the immune system or a genetic disorder, for example, severe combined immunodeficiency (SCID), sickle cell disease, or a congenital immunodeficiency. In some embodiments, the immunodeficiency arises from an acquired deficiency in the immune system, for example, asplenia after trauma. In some embodiments, the immunodeficiency arises from an infection, for example, HIV infection and acquired immune deficiency syndrome (AIDS). In some embodiments, the immunodeficiency is a humoral immune deficiency, a T cell deficiency, a granulocyte deficiency, or a complement deficiency,

[0063] An effective amount refers to the amount of an antiviral therapy of the present invention needed to prevent or alleviate at least one or more signs or symptoms of a medical condition and relates to a sufficient amount of a composition to provide the desired effect, e.g., to treat a subject’s signs or symptoms of polyoma virus infection. An effective amount also includes an amount sufficient to prevent or delay the development of a symptom of the disease, alter the course of a symptom of the disease (for example but not limited to, slow the progression of a symptom of the disease), or reverse a symptom of the disease. It is understood that for any given case, an appropriate effective amount can be determined by one of ordinary skill in the art using routine experimentation.

[0064] The efficacy of a treatment using the polyoma antivirals disclosed herein can be determined by a skilled clinician. A treatment is considered “effective”, if any one or all of the signs or symptoms of a polyoma virus infection are altered in a beneficial manner (e.g., reduced by at least 10%), or other clinically accepted symptoms or markers of disease (e.g., polyoma virus infection) are improved or ameliorated. Efficacy can also be measured by failure of a subject to worsen as assessed by hospitalization or need for medical interventions (e.g., progression of the disease is halted or at least slowed). Methods of measuring these indicators are known to those of skill in the art and / or described herein. Treatment includes any treatment of a disease in subject and includes: (1) inhibiting the disease, e.g., arresting, or slowing the progression of symptoms; or (2) relieving the disease, e.g., causing regression of symptoms; and (3) preventing or reducing the likelihood of the development of symptoms and signs.

[0065] In some embodiments, the methods, compositions, systems and kits of the present invention comprise one or more additional drugs or compositions in combination with a therapeutically effective dose of a polyoma virus antiviral identified in an RPTE-hTERT cellbased assay. In some embodiments, a first drug is velpatasvir and a second drug is leflunomide, cidofovir, an intravenous immunoglobulin (IVIg), and / or a monoclonal antibody. In some embodiments, a second composition comprises a virus-specific T-cell therapy (VST). The drug or composition may be administered prior to, concurrently with, simultaneously with, and / or subsequent to administration of any of the compositions disclosed herein. The drug or composition may be administered serially. The drug or composition may be administered at separate intervals than e.g., prior to or subsequent to) administration of any of the compositions disclosed herein. The drug or composition may be administered both concurrently and simultaneously as well as at separate intervals. The drug or composition and the polyoma virus antiviral agent may be administered via different routes, e.g., the polyoma virus antiviral agent may be administered orally and a second composition may be administered intraperitoneally, intravenously, subcutaneously, or any other route appropriate for administration.

[0066] In some embodiments, the specific dose level and frequency of dosage for any particular patient may be varied and will depend upon a variety of factors including the activity of the specific compound employed, the metabolic stability and length of action of that compound, the age, body weight, general health, sex, diet, mode and time of administration, rate of excretion, drug combination, the severity of the particular condition, and the host undergoing therapy.

[0067] Polyoma virus antiviral identification systems

[0068] In some embodiments, the present invention comprises a system for identification of safe and effective polyoma virus antiviral agents. In some embodiments, the system comprises one or more test compounds, one or more cells, an image acquisition device, a computer comprising a processor, a machine vision device and algorithm, and an image analysis algorithm. In some embodiments, the one or more cells are RPTE-hTERT cells. In some embodiments, the system further comprises devices and reagents for cell growth, replication, and maintenance. In some embodiments, the imaging acquisition device is a high content imaging system. In some embodiments, the high content imaging system comprises a laser. In some embodiments, the processor comprises non-transitory computer readable media comprising instructions that when executed by the processor cause a computer to execute instructions for image acquisition and analysis. In some embodiments, the image analysis algorithm comprises an artificial intelligence (Al) and / or machine learning (ML) algorithm configured for image analysis. In some embodiments, the ML algorithm comprises semi-supervised ML. In some embodiments, the algorithm configured for image analysis comprises a random Forest (RF) classifier model. In some embodiments, the algorithm configured for image analysis identifies and scores cell segmentation. In some embodiments, the system comprises one or more antibodies. In some embodiments, the antibody is a large tumor antigen (TAg) antibody. In some embodiments, the antibody is tagged with a label. In some embodiments, the label is a fluorescent label. In some embodiments, the system comprises a fluorescence reader. In some embodiments, the system comprises a multi-well plate and a plate reader. In some embodiments, the system comprises a display that displays infection scoring and cytotoxicity scoring. In some embodiments, the system comprises one or more calibrants, and / or one or more positive compound and / or cell controls, and / or one or more negative compound and / or cell controls.

[0069] Polyoma virus antiviral kits

[0070] In some embodiments, the present invention provides kits of use in administering a safe and effective polyoma virus antiviral agent. The kit may further comprise instructions for administration of the therapeutic antiviral agent. Alternatively, or in addition, the kit may further comprise a description of selecting a subject suitable for treatment based on identifying whether the subject is in need of the treatment. Instructions relating to the use of the therapeutic polyoma virus antivirals described herein include information as to dosage, dosing schedule, and route of administration for the intended treatment. Instructions supplied in the kits of the disclosure are typically written instructions on a label or package insert. The label or package insert indicates that the polyoma virus antiviral is used for treating, delaying the onset, and / or alleviating a disease or disorder in a subject. In some embodiments, the present invention provides kits of use in identifying a safe and effective polyoma virus antiviral agent.

[0071] The kits provided herein are in suitable packaging. Suitable packaging includes, but is not limited to, vials, bottles, jars, flexible packaging, and the like. Also contemplated are packages for use in combination with a specific device, such as an infusion device for administration. A kit may have a sterile access port (for example, the container may be an intravenous solution bag or a vial having a stopper pierceable by a hypodermic injection needle). The container may also have a sterile access port. Kits optionally may provide additional components such as buffers and interpretive information. In some embodiments, the kit comprises a container and a label or package insert(s) on or associated with the container. Containers may be unit doses, bulk packages (e.g., multi-dose packages) or sub-unit doses. In some embodiments, the disclosure provides articles of manufacture comprising contents of the kits described above.

[0072] One of ordinary skill in the art, based on the present disclosure, can utilize the compositions, methods, systems and kits described to their fullest extent. The specific embodiments are, therefore, to be construed as merely illustrative, and not limitative of the remainder of the disclosure in any way. All publications cited herein are incorporated by reference for the purposes or subject matter referenced herein. From the above description, one skilled in the art can ascertain the essential characteristics of the present disclosure, and without departing from the spirit and scope thereof, can make various changes and modifications of the disclosure to adapt it to various usages and conditions.

[0073] EXPERIMENTAL EXAMPLES

[0074] Experimental Methods

[0075] Cells and viruses

[0076] RPTE-hTERT cells were grown in renal epithelial basal medium (REBM™, Lonza 3191) supplemented with SingleQuots™ (Lonza #CC-4127) to yield renal epithelial cell growth medium (REGM™). (Zhao, L. & Imperiale, M. J. Establishing Renal Proximal Tubule Epithelial-Derived Cell Lines Expressing Human Telomerase Reverse Transcriptase for Studying BK Polyomavirus. Microbiol Resour Announc 8, (2019).) 293TT cells were maintained in DMEM with 10% fetal bovine serum and 100 U / mL penicillin, 100 pg / ml streptomycin. (Buck, C. B., Pastrana, D. V., Lowy, D. R. & Schiller, J. T. Efficient intracellular assembly of papillomaviral vectors. J. Virol. 78, 751-757 (2004).) 3T3 cells were maintained in DMEM supplemented with 10% calf bovine serum (Iron fortified, ATCC 30-2030) and 100 U / mL penicillin, 100 pg / ml streptomycin. All cells were tested for mycoplasma and were negative. Cell lines were each grown in a humidified incubator at 37°C and 5% CO2. For viral stocks, the BKPyV Dunlop variant was propagated in 293TT cells as described previously. (Procario, M. C., Sexton, J. Z., Halligan, B. S. & Imperiale, M. J. Single-Cell, High-Content Microscopy Analysis of BK Polyomavirus Infection. Microbiol Spectr 11, e0087323 (2023).) Viral titers were determined using a fluorescent focus assay (FFA). RPTE-hTERT, 293TT and 3T3 cells, and MPyV and BKPyV (Dunlop) stocks were provided to us by the Imperiale lab. BKPy V high content assay

[0077] Compounds to be evaluated were dry spotted onto 384-well plates (Revvity PhenoPlate, 6057300) using an Echo 650 series acoustic liquid handler. RPTE-hTERT cells were then plated on top of compounds at 5000 cells per well in a total of 40 / J.L complete media. Plated cells were placed in an incubator at 37C and 5% CO2 for 2 hours for preincubation with compounds. Following the pre-incubation period, cells were infected with BKPy V Dunlop at a multiplicity of infection (MOI) of 1 in a volume of 20 / rL / well complete media. The first and last columns of every plate were always reserved for mock-infection controls with 20 complete media added. The final working assay volume was 60 ^L / well. Cells were returned to the incubator for 48 hours post-infection. Following the infection period, media was discarded, and plates were fixed with 4% Paraformaldehyde (Electron Microscopy Sciences, 15713) for 20 minutes at room temperature. Then, PFA-fixed plates were permeabilized with 0.3% Triton-XlOO (Acros Organics, 21568-2500) for 15 minutes at room temperature. Plates were then allowed to incubate overnight at 4C in anti-TAg primary antibody (1 : 1000 dilution) in antibody buffer. The antibody buffer used throughout these experiments consisted of 1.5% bovine serum albumin (Sigma Aldrich, A7030), 1% Goat Serum (Gibco, 16210064), and IX TBS-Tween (Thermo Fisher, 28360) dissolved in IX phosphate-buffered saline. Cells were then washed twice with PBS and stained with a dye cocktail containing a 1 : 1000 dilution of Hoechst 33342 (Invitrogen H3570), 1 : 10,000 of HCS CellMask Orange (Invitrogen H32713) and 1 : 1000 of Goat Anti-Rabbit Secondary antibody (A21235) for 1 hour at room temperature.

[0078] High content mouse polyoma virus (MPyV) assay

[0079] NIH-3T3 cells were seeded in Perkin Elmer 384-well plates at a density of 5,000 cells per well and allowed to adhere overnight. Compounds were selected based on hits from the BKPy V assay and used at the same range of concentrations. These compounds were added to the cells using an HPD300e for a 2-hr preincubation before infecting the cells with Murine polyomavirus (MPyV). As in the BKPyV assay, the virus was titrated to achieve -30% infection at the desired time point of 48 hours. At 48 hours post-infection the cells were fixed and stained as described for the parallel assay. An antibody specific to MPyV large-T-antigen was used to identify infected cells. Cell growth prior to infection was carried out using DMEM (Gibco, 11995-065) supplemented with Penicillin / Streptomycin and 10% calf bovine serum. The serum concentration was reduced to 2% for the 2-hour drug treatment, infection, and subsequent incubation. All incubations were carried out in a humidified 37°C incubator with 5% CO2.

[0080] BKPyV time-of-addition assay

[0081] RPTE-hTERT cells were seeded at a density of 5,000 cells per well, in a volume of 40 pl per well. The day after seeding, cells were incubated at 4°C for 15 min, then either mock- infected or infected at an MOI of 0.6. The cells were returned to 4°C for a 1-hour adsorption. After the adsorption, the inoculum was aspirated from the wells and washed with roomtemperature PBS. The wash was then replaced with pre-warmed media. Compounds were added 2 hours before, during, or 1 hour post-infection, 4 hours post-infection or 24 hours post-infection. The cells were returned to 37°C for 48 hours. At 48 hours post-infection, media was aspirated from cells and replaced with room temperature 4% paraformaldehyde for a 15-minute incubation. Wells were washed thoroughly with PBS after fixation and then permeabilized with 0.3% Triton-XlOO for 15 minutes. Staining was performed as previously described for the BKPyV high-content assay.

[0082] Imaging

[0083] Imaging was performed using a confocal Yokogawa Cell Voyager 8000 (CV8000) high- content imaging system with a 10X objective. The optical configuration and fluorophores were as follows: nuclei - Hoechst-33342 (405nm laser, 445 / 45 emission filter), HCS CellMask Orange Stain (561 nm laser, 600 / 37 emission filter), and TAg - Alexa-647 (640nm laser, 676 / 29 emission filter). Laser power and exposure times were adjusted to optimize signal-to- background. Maximum projection images were collected over a 12 pm Z-range at 3 pm intervals to ensure that all cells were in focus. Four fields were imaged per well. Post-acquisition background and geometry correction were performed for each channel. Each 384 well plate provides 10,368 images or 124 GB of image data.

[0084] Image Processing and infection scoring

[0085] Individual nuclei in the Hoechst-33342 images were identified and nuclear masks were generated using open-source CellPose 2.0 software (diameter = 23). CellPose’s native edge removal to prevent generating masks of partial cells and the built-in cyto2 model were used. (Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat. Methods 19, 1634-1641 (2022). Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100-106 (2021).) Nuclear masks were imported into CellProfiler 4.2.1 along with the fluorescent images for feature extraction. (McQuin, C. et al. CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).) Over 500 morphological “features” are measured from each cell using Cell Profder to establish a single-cell phenotypic fingerprint. Cell-feature matrices are joined with the drug treatment and data pre-processing is performed in Python using Seiki t-Leam to center / scale data to account for plate-to-batch variation.

[0086] All cells were classified as TAg positive or negative with CellProfiler Analyst 3.0.4 using a semi-supervised machine learning approach. (Stirling, D. R., Carpenter, A. E. & Cimini, B. A. CellProfiler Analyst 3.0: accessible data exploration and machine learning for image analysis. Bioinformatics 37, 3992-3994 (2021).) Approximately 100 each of the TAg positive and negative cells were hand-selected, and a Random Forest (RF) classifier model was trained using 5-fold cross-validation. The classification model resulted in high accuracy (>98%) on the training set. Infected cell counts and total cell counts were exported at the field level and joined with treatment metadata for further analysis. Field-level data were grouped at the well level using Knime and used to determine normalized percent infection and percent viability scores. (Berthold, M. R. et al. KNIME: the Konstanz information miner, p 319—326. Data analysis, machine learning and applications. Springer, Berlin, Germany, http: dx. doi. org lO. 10074978- 3-540-78246-9 38 (2008).) The raw percent infection per well was determined by taking the ratio of infected cells to total nuclei and multiplying by 100. Normalized percent infection was then generated such that “100% infection” was equivalent to the average raw percent infection of the viral control for each plate. Cell counts for the entire plate were normalized, and 100% viability was determined using the average cell count of the infected DMSO control wells. An unbiased Mahalanobis distance score was calculated and dimensionality reduction / clustering was performed using UMAP-leam (Figure 2E) to identify natural phenotypic clusters and then we map features by drug to identify highly efficacious compounds with minimal cellular perturbation indicating high cell health enabling high-quality hit detection. (Becht E, Mclnnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, Ginhoux F, Newell EW. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. Published online December 3, 2018. doi : 10.1038 / nbt.4314., Mclnnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv [statML], Published online February 9, 2018. http: / / arxiv.org / abs / 1802.03426., Caldera M, Muller F, Kaltenbrunner I, Licciardello MP, Lardeau CH, Kubicek S, Menche J. Mapping the perturbome network of cellular perturbations. Nat Commun. 2019; 10(1): 5140.) Drug doses at which features significantly deviate from control are modeled using an XGBoost and compared between drugs to assess similarity in phenotypic outcomes. (Nyffeler J, Willis C, Lougee R, Richard A, Paul- Friedman K, Harrill JA. Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling. Toxicol Appl Pharmacol. 2020;389: 114876., Willis C, Nyffeler J, Harrill J. Phenotypic Profiling of Reference Chemicals across Biologically Diverse Cell Types Using the Cell Painting Assay. SLAS Discov. 2020;25(7):755-769.).

[0087] Concentration response analysis and ICso / CCso determinations

[0088] Concentration-response curves were plotted in GraphPad Prism 9 (GraphPad Software) and fitted using a semi-log 4-parameter variable slope model. IC50 and CC50 values were extracted from percent infection curves and percent viability curves, respectively. Selectivity indices (SI) were determined by taking the ratio of the CC50 and the IC50.

[0089] Example 1 - Small molecule screening

[0090] A library of 1,425 compounds was screened in a high-content imaging-based screening assay of the present invention comprising a renal proximal tubule epithelial (RPTE) cell model for evaluating the antiviral efficacy of small molecules against BKPyV. (Figure 1. and Figure 2.) Velpatasvir exhibited strong potency (IC50 = 480 nM), efficacy (100%), and low cytotoxicity (CC50 > 10 M) resulting in a selectivity index (SI) > 42. (Figures 3 and Figure 5) These data were unexpected because velpatasvir is known to target the NS5A protein of the Flavivirus RNA virus HCV whereas polyoma virus BKPyV does not have a NS5A protein target.

[0091] Example 2 - Velpatasvir polyoma virus specificity

[0092] To test whether velpatasvir is safe and effective in the treatment of another polyoma virus besides BKPyV in another species, velpatasvir was tested in a high content imaging-based assay of the present invention comprising a mouse 3T3 cell line for safety and efficacy against murine polyomavirus (MPyV). Velpatasvir exhibited strong potency (IC50 = 212 nM), efficacy (100%), and low cytotoxicity (CC50 > 10 jizM) resulting in a selectivity index (SI) > 42. (Figure 3) indicating that velpatasvir is a pan-polyomavirus inhibitor.

[0093] Example 3 - Velpatasvir time-of-addition assay

[0094] To test the efficacy of velpatasvir before and after infection with BKPy V, 2 / zM velpatasvir was added to cells in a high-content imaging-based screening assay of the present invention comprising a RPTE cell at 2 hours before infection, and at 1 hour, 4 hours, and 24 hours after exposure. These data showed that the efficacy of velpatasvir against BKPy V infection is 100% regardless of whether velpatasvir is added before or after infection up to 4 hours after exposure. (Figure 4) Administration of velpatasvir 24 hours after infection retained up to 50% efficacy compared to infected control cells. (Figure 4.) These data denote that velpatasvir does not act as a BKPy V entry or binding inhibitor, but has impacts at a later stage of infection to inhibit viral replication.

[0095] Example 4 - NS5A inhibitor screening

[0096] In view of the strong efficacy of velpatasvir against BKPyV in a high-content imagingbased screening assay of the present invention comprising a RPTE cell, 5 other NS5A inhibitors were tested in concentration response experimental design including elbasvir, daclatasvir, pibrentasvir, ledipasvir, and ombitasvir. (Figure 5A) The drugs share similarities with velpatasvir including a polyaromatic core flanked by dipeptides but are not structural analogs. Only daclatasvir exhibited weak efficacy against BKPyV with potency greater than 10-fold less than velpatasvir with minimal separation between the daclatasvir potency and cytotoxicity (SI = 2.5). (Figure 5B. and Figure 5C.) These data indicate that velpatasvir is unique among NS5A inhibitors for antiviral safety and efficacy against BKPyV that lacks the NS5A protein.

[0097] Example 5 - ICso shift with viral load supports a viral target for velpatasvir

[0098] To test whether velpatasvir potency is influenced the viral multiplicity of infection (MOI), RPTE-hTERT cells were infected with BKPyV at MOIs of 1, 5, or 10 and treated with a concentration range of velpatasvir. The ICso of velpatasvir increased in a dose-dependent manner with higher viral inputs (Figure 6A.) indicating that target abundance scales with the quantity of virus present. Quantitative analysis showed a significant increase in ICso values at MOIs of 5 and 10 compared to MOI 1 (Figure 6B.). Because cellular protein levels remain constant under varying MOI conditions, these data indicate that the molecular target of velpatasvir is of viral origin rather than a host factor. A viral target also provides a more specific and safer therapeutic strategy because it reduces the risk of disrupting host cell pathways with a lower risk of off-target toxicity. BKPyV encodes fewer than ten proteins thereby substantially narrowing the scope of potential molecular targets. In combination with the time-of-addition data of Example 3 showing that velpatasvir remains effective up to 4 hours post-infection, the MOI-dependence indicates that velpatasvir efficacy occurs at least in part during a post-entry stage of the viral life cycle. At this stage, viral capsid proteins VP1, VP2, and VP3 remain present within a host cell before disassembly is complete. This temporal correlation indicates that velpatasvir may interact directly with one or more of the viral capsid proteins to inhibit a critical step in trafficking and / or uncoating processes.

[0099] Example 6 - Velpatasvir protects against BKPyV-induced cytopathic effect (CPE) in RPTE-hTERT cells

[0100] To test velpatasvir’ s antiviral efficacy using an alternative readout, we tested its ability to prevent BKPyV-induced cytopathic effect (CPE) in RPTE-hTERT cells. This assay tests antiviral activity across multiple rounds of viral replication without relying on detection of a specific viral protein. Cells were treated with a dilution series of velpatasvir, and then infected with BKPyV under conditions that induce robust CPE and cell loss over time. A clear, dosedependent rescue of cell viability upon velpatasvir treatment was observed as quantified by cell counts (Figure 7A.). At intermediate concentrations partial recovery of the monolayer architecture was observed. At higher doses, cells were largely protected from CPE mirroring the potency range established in the high-content TAg assay (Figure 7B.). Recovery of CPE provides a non-marker-dependent validation of antiviral activity, confirming that velpatasvir inhibits a critical step in the viral life cycle sufficient to prevent cell death. Because CPE results arise from cumulative viral cytotoxicity over several replication cycles, this assay captures longer-term antiviral effects and reinforces velpatasvir’ s capacity to protect renal epithelial cells from virus-induced injury in a physiologically relevant model. Example 7 - Potency of velpatasvir from a second source

[0101] To test the reproducibility of velpatasvir’ s antiviral potency, we evaluated a second lot of compound purchased from a different commercial source (Cayman Chemical). Using the same high-content imaging assay in RPTE-hTERT cells, we observed that velpatasvir inhibited BKPyV infection with an ICso of 151 nM (Figure 8 ), a 3-fold increase in potency compared to the previously reported value of 429 nM previousl observed. Cytotoxicity was minimal, with a CCso exceeding 10 pM, maintaining a high selectivity index. These data indicate the robustness of velpatasvir's antiviral activity and support velpatasvir for repurposing efforts.

[0102] INCORPORATION BY REFERENCE

[0103] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.

Claims

CLAIMSWe claim:

1. A method of treating, ameliorating or preventing a polyoma virus infection in a subject comprising administering to said subject a therapeutically effective amount of velpatasvir or an analog or derivative thereof and a pharmaceutically acceptable carrier.

2. The method of claim 1, wherein said polyoma virus infection is BKPyV infection.

3. The method of claim 1, wherein said subject is a human subject.

4. The method of claim 3, wherein said human subject is an immunocompromised human subject.

5. The method of claim 4, wherein said immunocompromised human subject is a solid organ transplant recipient or a bone marrow transplant recipient.

6. The method of claim 6, wherein said solid organ transplant recipient is a kidney transplant recipient.

7. A kit comprising a pharmaceutical composition comprising velpatasvir and instructions for administering velpatasvir.

8. Use of a kit of claim 7.

9. Use of a kit of claim 7 for treatment and / or prevention of a polyoma virus infection.

10. A method for testing a compound for efficacy and safety in treating and / or preventing BKPyV infection, comprising:a) providing one or more renal proximal tubule epithelial cells immortalized using human telomerase reverse transcriptase (RPTE-hTERT cells); b) infecting said one or more RPTE-hTERT cells with BKPy V; c) applying said compound to said one or more RPTE-hTERT cells infected with said BKPy V; d) scoring said one or more BKPy V infected RPTE-hTERT cells for a half maximal inhibitory concentration (IC50) of said compound; e) scoring said one or more BKPyV infected RPTE-hTERT cells for a 50% cytotoxicity concentration (CC50) of said compound; and f) calculating an IC50 / CC50 selectivity index (SI) for said compound.

11. The method of claim 10 wherein said infecting said one or more RPTE-hTERT cells with said BKPyV is before said applying said compound to said one or more RPTE-hTERT cells.

12. The method of claim 10 wherein said infecting said one or more RPTE-hTERT cells with said BKPyV is after said applying said compound to said one or more RPTE-hTERT cells.

13. A system, comprising: a) at least one test compound; b) one or more RPTE-hTERT cells; c) an image acquisition device; and d) a computer comprising a processor configured to implement a machine vision algorithm, an artificial intelligence (Al) algorithm and / or a machine learning (ML) algorithm.

14. The system of claim 13 comprising one or more devices and / or one or more reagents for cell growth, replication and maintenance.

15. The system of claim 13, wherein said image acquisition device is a high-content imaging system comprising a laser.

16. The system of claim 13 comprising one or more antibodies.

17. The system of claim 16 wherein said one or more antibodies is a large tumor antigen (TAg) antibody.

18. The system of claim 17, wherein said TAg antibody comprises a fluorescent label.

19. The system of claim 18 comprising a fluorescence reader and / or an image display and / or a scoring display.

20. The system of claim 13 comprising one or more calibrants and / or one or more positive or negative virus controls and / or one or more positive of negative compound controls.

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