Apparatus for determining the mode of administration of engineered virus-based nanoparticle compositions for the treatment of autoimmune conditions - Patent Application 20070123633

Abstract

Disclosed is an apparatus for determining a dosage regimen of an engineered virus-based nanoparticle composition for the treatment of rheumatoid arthritis. The apparatus may include at least one processor. The apparatus may include a memory communicatively connected to the processor, the memory including instructions for configuring the at least one processor to obtain a plurality of data regarding a plurality of dosage regimens of an engineered virus-based nanoparticle composition for the treatment of rheumatoid arthritis, compare the plurality of dosage data, and determine a critical dosage.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 63 / 289,832, filed December 15, 2021, entitled "COMPOSITION OF MATTER AND METHODS OF AN ENGINEERED VIRUS-BASED NANOPARTICLE FOR THE TREATMENT OF RHEUMATOID ARTHRITIS," the entirety of which is incorporated herein by reference.

[0002] FIELD OF THEINVENTION The present invention relates generally to the field of autoimmune diseases. In particular, the present invention relates to an apparatus for determining the dosage regimen of engineered virus-based nanoparticle compositions for the treatment of autoimmune conditions. [Background technology]

[0003] Currently, more than 1.3 million people in the United States suffer from autoimmune diseases such as rheumatoid arthritis. Plant viruses combined with peptides have been shown to ameliorate such autoimmune diseases in animals. Plant viruses are natural self-assembling nanostructures with versatile, genetically programmable shells that can be applied in a variety of applications. The design and synthesis of plant viral nanoparticles displaying peptides relevant to autoimmune conditions can help prevent or treat autoimmune diseases. Summary of the Invention [Means for solving the problem]

[0004] In one embodiment, a device for determining an administration mode of an engineered virus-based nanoparticle composition for the treatment of an autoimmune condition is disclosed. The device may include at least one processor. The device may include a memory communicatively connected to the processor, the memory including instructions for configuring the at least one processor to obtain a plurality of data related to a plurality of administration modes of the engineered virus-based nanoparticle composition for the treatment of an autoimmune condition, compare the plurality of administration data, and determine a critical administration.

[0005] The details of one or more variations of the presently disclosed subject matter are set forth in the accompanying drawings and the description below. Other features and advantages of the presently disclosed subject matter will become apparent from the description and drawings, and from the claims.

[0006] For the purpose of illustrating the invention, the following drawings show aspects of one or more embodiments of the invention, although it will be apparent that the invention is not limited to the precise arrangements and apparatus shown in the drawings. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram illustrating an exemplary embodiment of an apparatus for determining the dosage regimen of an engineered virus-based nanoparticle composition for the treatment of an autoimmune condition. [Figure 2A] 1 is a flow chart illustrating an exemplary embodiment of a method for producing engineered virus-based nanoparticles for the treatment of autoimmune conditions. [Figure 2B] 1 is a flow chart illustrating an exemplary embodiment of another method for manufacturing engineered virus-based nanoparticles for the treatment of autoimmune conditions. [Diagram 3] FIG. 1 illustrates an exemplary embodiment of a method for manufacturing engineered virus-based nanoparticles for the treatment of autoimmune conditions. [Figure 4A] FIG. 1 is a graph showing the effect of CPMV plant virus-based particles on NOD mice. [Figure 4B] FIG. 1 is a graph showing the effect of CPMV plant virus-based particles on NOD mice. [Diagram 5] AE are schematic diagrams of exemplary embodiments and characterization of engineered plant-based viral particles. [Figure 6] 3A-3C are graphs showing the effect of TBSV plant virus-based particles on the severity of CIA. [Figure 7A] FIG. 1 shows an exemplary embodiment of a composition of engineered virus-based nanoparticles for the treatment of autoimmune conditions. [Figure 7B] FIG. 1 shows an exemplary embodiment of a composition of engineered virus-based nanoparticles for the treatment of autoimmune conditions. [Figure 8] FIG. 1 illustrates an exemplary embodiment of a machine learning module. [Figure 9] FIG. 1 illustrates an exemplary embodiment of a neural network. [Figure 10] FIG. 2 illustrates an exemplary embodiment of a node of a neural network. [Figure 11] FIG. 1 is a block diagram of a computing system that can be used to implement any one or more of the methods disclosed herein or any one or more portions thereof. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] The drawings are not necessarily to scale and may be represented by phantom lines, schematic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that would obscure other details may be omitted. Like reference characters in the various drawings indicate like elements.

[0009] At a high level, aspects of the present disclosure relate to an apparatus for determining an administration mode of an engineered virus-based nanoparticle composition for the treatment of an autoimmune condition. The apparatus may include at least one processor. The apparatus may include a memory communicatively connected to the processor, the memory including instructions for configuring the at least one processor to obtain a plurality of data regarding a plurality of administration modes of an engineered virus-based nanoparticle composition for the treatment of an autoimmune condition, compare the plurality of administration data, and determine a critical administration.

[0010] Aspects of the present disclosure can be used to design plant-based viral particles that express immunodominant peptides.

[0011] Aspects of the present disclosure can prevent the onset of autoimmune diseases. Aspects of the present disclosure can also treat autoimmune diseases. Exemplary embodiments are described below that illustrate aspects of the present disclosure in the context of several specific examples.

[0012] First, FIG. 1 illustrates an exemplary embodiment of an apparatus 100 for determining a dosage regimen of an engineered virus-based nanoparticle composition for the treatment of an autoimmune condition. The apparatus 100 may include any computing device as described herein, including, but not limited to, a microcontroller, a microprocessor, a digital signal processor (DSP) and / or a system on a chip (SoC) as described herein. The computing device may include and / or be included in and / or communicate with a mobile device, such as a mobile phone or smartphone. The apparatus 100 may include a single computing device operating independently, or may include two or more computing devices operating in cooperation, parallel, or sequentially, and the two or more computing devices may be included together in a single computing device or may be included in two or more computing devices. The apparatus 100 may interface or communicate with one or more additional devices via a network interface device, as described in more detail below. The network interface device may be used to connect the apparatus 100 to one or more of various networks and one or more devices. Examples of network interface devices include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of networks include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, building, campus, or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider's data and / or voice network), a direct connection between two computing devices, and any combination thereof.The network may employ wired and / or wireless communication modes. In general, any network topology may be used. Information (e.g., data, software, etc.) may be communicated to and / or from computers and / or computing devices. The apparatus 100 may include, for example, but not limited to, a computing device or cluster of computing devices at a first location and a second computing device or cluster of computing devices at a second location. The apparatus 100 may include one or more computing devices specialized for data storage, security, traffic distribution for load balancing, etc. The apparatus 100 may distribute one or more computing tasks described below across multiple computing devices, which may operate in parallel, serial, redundant, or any other manner used to distribute tasks or memory between computing devices. In one embodiment, the apparatus 100 may be implemented using a "shared nothing" architecture where data is cached at the workers, thereby providing scalability for the apparatus 100 and / or computing devices.

[0013] With continued reference to FIG. 1 , the apparatus 100 may be designed and / or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order, and with any degree of repetition. For example, the apparatus 100 may be configured to repeatedly perform a single step or sequence of steps until a desired or commanded result is achieved, the repetitions of the step or sequence of steps may be performed iteratively and / or recursively using the output of a previous iteration as input for a subsequent iteration, the inputs and / or outputs of the iterations may be aggregated to generate an aggregate result, one or more variables, such as global variables, may be reduced or decremented, and / or a larger processing task may be divided into a set of smaller processing tasks that are addressed iteratively. The apparatus 100 may perform steps or sequences of steps as described in this disclosure in parallel, e.g., performing steps simultaneously and / or substantially simultaneously two or more times using two or more parallel threads, processor cores, etc., and the division of tasks among parallel threads and / or processes may be performed according to any protocol suitable for dividing tasks among iterations. Those skilled in the art will, upon consideration of this disclosure in its entirety, be able to envision various techniques for subdividing, sharing, or otherwise processing steps, sequences of steps, processing tasks, and / or data using iteration, recursion, and / or parallel processing.

[0014] With continued reference to FIG. 1, the term "autoimmune disease" as used in this disclosure is a condition resulting from an abnormal immune response against a functioning body part. As non-limiting examples, autoimmune diseases may include rheumatoid arthritis, type 1 diabetes mellitus (T1D), and the like. The term "immune response" as used in this disclosure is a reaction that occurs within an organism to defend the organism against invaders. Invaders can include a wide variety of different microorganisms, including but not limited to viruses, bacteria, parasites, fungi, and the like, which, if not eliminated from the body, can cause significant problems for the health of the host organism. The term "rheumatoid arthritis" as used in this disclosure, also known as "RA," is an autoimmune disease that causes joint pain and damage throughout the body. Rheumatoid arthritis typically causes joint swelling and pain. It can also affect the wrists and hands, and typically affects the same joints on both sides of the body. RA may also affect other parts of the body, such as the skin, eyes, lungs, heart, nerves, and blood. This may result in a low red blood cell count, inflammation around the lungs, and inflammation around the heart. Fever and weakness may also occur. In many cases, RA symptoms develop gradually over weeks to months. In one embodiment, a blood test may be performed to identify the presence of rheumatoid factor (RF) to diagnose RA. In another embodiment, a blood test may be performed to identify the presence of anti-cyclic citrullinated peptides (anti-CCP) to diagnose RA. In one embodiment, if the blood test results are positive for RF and / or anti-CCP, this may indicate seropositive RA. In one embodiment, if the blood test results are negative for RF and / or anti-CCP but have signs and symptoms of RA, this may indicate seronegative RA. In some embodiments, patients with seronegative RA may have a more active form of the disease and a slower response to treatment compared to patients with seropositive RA.The goal of treatment using the composition may be to reduce pain, reduce inflammation, treat rheumatoid arthritis, alleviate symptoms of RA, prevent RA, and improve overall human function. As a non-limiting example, the composition may be designed to aid in the alleviation of symptoms of RA and the treatment of RA. The composition includes at least one immunodominant peptide engineered to be expressed through a plant virus-based particle. The composition also includes sodium acetate and / or PBS buffer. The composition may be applied, studied, and / or used for the treatment of rheumatoid arthritis. In one embodiment, the composition may act as a carrier. In another embodiment, the composition may act as an adjuvant.

[0015] 1, the device 100 includes a processor 104 and a memory 108 communicatively connected to the processor 104, where the memory 108 includes instructions for configuring the processor 104 to obtain administration data 112 relating to multiple administration formats of an engineered virus-based nanoparticle composition for the treatment of autoimmune disease. As used in this disclosure, the term "communicatively connected" means connected by a connection, attachment, or coupling between two or more entities, allowing for the reception and / or transmission of information between the entities. For example, the connection may be, but is not limited to, wired or wireless, direct or indirect, and may be established between two or more components, circuits, devices, systems, etc., allowing for the reception and / or transmission of data and / or signal(s) therebetween. The data and / or signals therebetween may include, but are not limited to, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and / or signals, and combinations thereof. The communication connection may be achieved, for example, but not limited to, by wired or wireless electronic, digital, or analog communication, directly or through one or more intervening devices or components. Furthermore, the communication connection may include electrically coupling or connecting at least one output of a device, component, or circuit to at least one input of another device, component, or circuit. For example, but not limited to, the connection may be made through a bus or other facility for intercommunication between the elements of the device 100. The communication connection may also include an indirect connection, for example, but not limited to, via a wireless connection, a wireless communication, a low-power wide area network, optical communication, magnetic coupling, capacitive coupling, or optical coupling, etc. In some implementations, the term "communicatively coupled" may be used instead of the term "communicatively connected" in this disclosure. The device 100 may include and / or be included in and / or communicate with a mobile device, such as a mobile phone or a smartphone.

[0016] With continued reference to FIG. 1, compositions of engineered virus-based nanoparticles for the treatment of autoimmune diseases may include plant virus-based particles. As used herein, the term "plant virus-based particle" refers to a particle of a virus that infects plants, and in some embodiments, such viruses may not be infectious or harmful to animals, such as mammals. In some embodiments, the plant virus-based particle may include, be included in, constitute, or be a nanoparticle. As used herein, the term "nanoparticle" refers to a particle of material having a diameter between 1 and 100 nanometers (nm). In some embodiments, the plant virus-based particle may be negatively charged. A negatively charged plant virus-based particle may aid in the binding of two major histocompatibility complex class II antigen chains. This may therefore improve stability. In one embodiment, the plant virus-based particle may be used to deliver a drug. In another embodiment, the plant virus-based particle may be used to image molecules in specific target cells. In some embodiments, the plant virus-based particle may be used to display and / or deliver a peptide. Peptides may include, but are not limited to, antigens. As used herein, the term "antigen" refers to a molecule or molecular structure or any foreign particulate matter or pollen grain that can bind to a specific antibody or T-cell receptor. As used herein, the term "antibody" refers to a protein that the immune system uses to identify and neutralize an invader. In some embodiments, the presence of an antigen in a mammalian body may trigger an immune response. In some embodiments, the plant virus-based particles may be configured and / or modified to display an epitope. As used herein, the term "epitope" refers to a portion of an antigen that is recognized by the immune system. As non-limiting examples, an epitope may be recognized by an antibody, a B cell, a T cell, etc. In some embodiments, the plant virus-based particles may include an immunomodulatory effect.The term "immunomodulatory effect" as used in this disclosure refers to an effect of modulating the immune system. As a non-limiting example, the plant virus-based particles may modulate the immune system to induce tolerance and thus prevent autoimmune diseases such as, but not limited to, RA or type 1 diabetes. As a non-limiting example, the plant virus-based particles may modulate the immune system to induce tolerance and thus cure autoimmune diseases such as, but not limited to, RA, type 1 diabetes, and / or similar.

[0017] With continued reference to FIG. 1 , in one embodiment, the plant virus-based particle may comprise an icosahedral structure. As used in this disclosure, the term "icosahedron" refers to a geometric shape having 20 faces, each of which is an equilateral triangle. In another embodiment, the plant virus-based particle may comprise a filamentous structure. In some embodiments, the plant virus-based particle may comprise a rod-shaped structure. In some embodiments, the plant virus-based particle may comprise a helical capsid structure. In some embodiments, the plant virus-based particle may comprise a spherical structure. Furthermore, without limitation, the plant virus-based particle may comprise any structure. In some embodiments, the plurality of plant virus-based particles may be uniform in size. In some embodiments, the plurality of plant virus-based particles may be uniform in shape.

[0018] With continued reference to FIG. 1, in some embodiments, plant virus-based particles may be engineered from a host plant. By way of non-limiting example, the host plant may include Nicotiana benthamiana plants, chicory (Cycorium intybus) plants, cabbage (Brassica oleracea var. capitata) plants, Swiss chard (Beta vulgaris var cicla) plants, basil (Ocimum basilicum) plants, red beet (red beet) plants, spinach (spinach) plants, and the like. As used in this disclosure, the term "Nicotiana benthamiana plant" refers to a close relative of tobacco, a species of Nicotiana native to Australia, and a plant used to "formulate" monoclonal antibodies and other recombinant proteins. The term "Cycorium intybus plant" as used herein is a hardy plant that is widely used in folk medicine to treat a variety of ailments ranging from wounds to diabetes. In some embodiments, the chicory plant may have antibacterial, anthelmintic, antimalarial, hepatoprotective, antidiabetic, gastroprotective, anti-inflammatory, analgesic, antioxidant, tumor suppressor, and anti-allergic activity. Cabbage plants, when uncultivated, are referred to as wild cabbages. Cultivated varieties include cabbage, broccoli, cauliflower, kale, Brussels sprouts, collard greens, Savoy cabbage, kohlrabi, gai lan, and the like. The term "Beta vulgaris var cicla plant" as used herein is a plant that is native to the Mediterranean. The chard plant may contain medicinal properties. By way of non-limiting example, the medicinal properties may include boosting the immune system and / or lowering blood pressure. As used in this disclosure, the term "Ocimum basilicum plant" refers to a plant that belongs to the mint family. Red beet plants show several promising medicinal uses, such as the phytochemicals present in red beets providing protection against diseases, including cancer and cardiovascular disease.In some embodiments, the plant virus-based particles may be extracted from a spinach plant. As used in this disclosure, the term "spinach plant" is a leafy green plant that belongs to the Amaranthaceae family.

[0019] With continued reference to FIG. 1, the plant virus-based particle may include one or more viruses. As a non-limiting example, the one or more viruses may include tomato bush stunt virus, cowpea mosaic virus, and / or any combination thereof. As used in this disclosure, the term "tomato bushy stunt virus", also known as "TBSV", is a plant virus from the tombusvirus group that primarily infects vegetable crops, causing stunted growth, leaf spotting, and malformed or missing fruit in infected plants. The structure of the tomato bushy stunt virus includes a single-stranded RNA virus with a linear genome of about 4,800 nucleotides. The tomato bushy stunt virus may also have three symmetrically distinct coat protein monomers. The tomato bushy stunt virus may be icosahedral. In some embodiments, the TBSV may be produced as an infectious virus. TBSV may contain a 30 nm capsid consisting of 180 subunits of a single capsid protein, and Tomato bushy stunt virus may replicate using, but is not limited to, cytoplasmic replication. The virus can enter host cells by uncoating and releasing viral RNA into the cytoplasm. Tomato bushy stunt virus may also spread indirectly, including but not limited to, through water, soil, and / or infected seeds. When engineered to express a peptide, the behavior of Tomato bushy stunt virus may experience one or more modifications, such as, but not limited to, reduced virus concentration, percent infection, and / or disease severity. In some embodiments, TBSV may be used to reduce the severity of RA.

[0020] With continued reference to FIG. 1, the term "cowpea mosaic virus" as used in this disclosure, also known as "CPMV" and "Sunn-hemp mosaic virus", is a non-developing plant virus of the comovirus group that infects susceptible cowpea leaves, resulting in a "mosaic" pattern on the leaves and enhanced virus yield. The cowpea mosaic virus genome may comprise two molecules of RNA that are separately encapsulated. In some embodiments, the diameter of the cowpea mosaic virus virus particle may be about 30 nm. In some embodiments, the cowpea mosaic virus virus particle may comprise 60 copies each of the large (L) coat protein and the small (S) coat protein. In some embodiments, the cowpea mosaic virus particle may be heat resistant. The cowpea mosaic virus particle may be easily isolated from the plant.

[0021] With continued reference to FIG. 1, the plant virus-based particle may be engineered to express at least one immunodominant peptide. As used herein, the term "immunodominant peptide" is a peptide that is a representative epitope of a given protein antigen to the immune system. As non-limiting examples, immunodominant peptides may include p524opt, pLIP1, pFADK2, and / or the like. In some embodiments, pLIP1 and pFADK2 may be proteins that are in a cytoplasmic location. As used herein, the term "cytoplasmic location" refers to a protein that is located in the cytoplasm of a cell. In some embodiments, a coat protein of the plant-based virus particle 104 may be engineered to display an immunodominant peptide 104. As used herein, the term "coat protein" is any protein that is a component of the viral capsid. As used herein, the term "capsid" is a protein shell that surrounds and protects the viral genome. In some embodiments, at least one immunodominant peptide may be fused to a protein of Tomato bushy stunt virus. As a non-limiting example, pLIP1, pFADK2 may be fused to TBSV. For example, but not limited to, the composition may include TBSV.LIP1, TBSV.FADK2, CPMV.LIP1, CPMV.FADK2, and / or any combination thereof. In some embodiments, the presence of an immunodominant peptide may reduce the yield of plant-based virus particles in a plant. As a non-limiting example, the presence of p524opt may reduce the yield of CPMV particles. In some embodiments, the presence of an immunodominant peptide may improve the yield of plant-based virus particles in a plant. As a non-limiting example, the presence of pLIP1 may improve the yield of TBSV.

[0022] With continued reference to FIG. 1, the composition may include sodium acetate. The term "sodium acetate" as used in this disclosure is an organic sodium salt. It is also referred to as acetic acid, sodium salt, anhydrous sodium acetate, or sodium acetate salt. Sodium acetate is the anhydrous sodium salt form of acetic acid. Anhydrous sodium acetate dissociates in water to form sodium ions (Na+) and acetate ions. Sodium is the major cation in extracellular fluid and may play a major role in fluid and electrolyte replacement therapy. Sodium acetate may be used as an electrolyte replacement agent in isotonic solutions to parenterally replace acute losses of extracellular fluid without disrupting normal electrolyte balance. Sodium acetate may include a hygroscopic powder. In some embodiments, a solution of sodium acetate and acetic acid may act as a buffer to maintain a relatively constant pH level. This may be useful in biochemical applications where reactions are pH-dependent in the weakly acidic range. In this case, sodium acetate may be used to titrate the composition to change its pH, as will be further described below with reference to FIG. 2. In some embodiments, the composition may include sodium bicarbonate. As used in this disclosure, the term "sodium bicarbonate" is a chemical compound that is a salt of sodium cation and bicarbonate anion. Sodium bicarbonate can decompose in water to form sodium bicarbonate. Sodium bicarbonate can neutralize acids.

[0023] With continued reference to FIG. 1, the composition may include a phosphate buffered saline (PBS) buffer. As used in this disclosure, the term "PBS buffer" is an aqueous salt buffer. In some embodiments, the PBS buffer may include disodium hydrogen phosphate N, sodium chloride, potassium chloride, potassium dihydrogen phosphate, and the like. The PBS buffer may help maintain a constant pH. As a non-limiting example, the PBS buffer may maintain the pH of the composition. The osmolality and ion concentration may be isotonic. The PBS buffer is further described with reference to FIG. 2. In some embodiments, the composition may include a normal saline buffer. As used in this disclosure, the term "normal saline" is an aqueous solution of electrolytes and other hydrophilic molecules. The pH level of the normal saline buffer may be 5.5. In some embodiments, the composition may include a sodium bicarbonate buffer. The pH level of the sodium bicarbonate may be 8.5. Sodium bicarbonate may be used in combination with acetic acid to lower the pH level and create a buffer. In some embodiments, the composition may include a 4-(2-hydroxyethyl)-1-piperazineethanesulphonic acid (HEPES) buffer. The term "HEPES" as used in this disclosure is a zwitterionic sulfonic acid buffer. In some examples, the HEPES buffer may have a higher pH retention than phosphate buffered saline.

[0024] With continued reference to FIG. 1, the composition may include one or more anti-rheumatic ingredients. As used in this disclosure, the term "anti-rheumatic ingredient" is any drug used to treat and / or prevent rheumatoid arthritis. The anti-rheumatic ingredient may include a non-steroidal anti-inflammatory drug (NSAID), a corticosteroid, methotrexate, hydroxychloroquine, sulfasalazine, leflunomide, a tumor necrosis factor inhibitor, a T cell costimulatory blocker, a B cell depleting agent, an interleukin-1 receptor antagonist therapy agent, and / or any other immunomodulatory and / or cytotoxic agent.

[0025] With continued reference to FIG. 1, the term "administration data" as used in this disclosure is data regarding the administration of a composition of engineered virus-based nanoparticles for the treatment of an autoimmune condition. The term "administration" as used in this disclosure is the technique of introducing a drug, fluid, poison, or other substance into the body. In an embodiment, administration 126 may include oral administration. The term "oral dosage" as used in this disclosure is the ingestion of a composition by mouth. As non-limiting examples, oral administration may include the use of tablets, syrups, pills, thin films, liquid solutions, powders, solid crystals, natural or herbal plants, seeds, or foods, pastes, and the like. In another embodiment, administration 126 may include intravenous injection. The term "intravenous injection" as used in this disclosure is the injection of a drug or another substance into a vein and directly into the bloodstream. Intravenous injection may be administered intravenously in any suitable manner, including administration as a bolus and / or as an infusion. In another embodiment, administration 126 may include intraperitoneal injection. As used herein, the term "intraperitoneal injection" refers to an injection of a substance into the peritoneum. In another embodiment, administration 126 may include a subcutaneous injection. As used herein, the term "subcutaneous injection" refers to an injection given into the fatty tissue just below the skin. In some embodiments, administration 126 may include any suitable administration and / or delivery form. In an embodiment, the composition may be administered with an adjuvant. As used herein, the term "adjuvant" refers to a substance that enhances the body's response to an antigen. In another embodiment, the composition may be administered without an adjuvant. In some embodiments, administration 126 may include one or more administrations 126. As non-limiting examples, administration 126 may include one administration 126, two administrations 126, three administrations 126, five administrations 126, ten administrations 126, etc. In some embodiments, administration 126 may include a dosage. As used in this disclosure, the term "dosage" refers to an amount of a medicine or drug.As non-limiting examples, dosage amounts may include 10 μg, 18 μg, 30 μg, 50 μg, 75 μg, etc. In some embodiments, administration 126 may be performed on an animal. As non-limiting examples, animals may include mice, fish, rabbits, etc. As a non-limiting example, administration 126 of an engineered virus-based nanoparticle composition for the treatment of an autoimmune condition may be performed on dilute, brown, and non-agouti (DBA) mice based on their susceptibility to collagen-induced arthritis (CIA). As another non-limiting example, administration 126 of an engineered virus-based nanoparticle composition for the treatment of an autoimmune disease may be performed on nonobese diabetic (NOD) mice. In some embodiments, administration data 112 may be stored in an autoimmune database 124. In some embodiments, administration data 112 may be obtained from an autoimmune database 124.

[0026] With continued reference to FIG. 1 , in some embodiments, an administration classifier 110 may be used to determine an administration 126. In some embodiments, the administration 126 may include an amount, an administration frequency, an administration type, an administration type, and the like. In some embodiments, the administration type may include intermittent drip, continuous drip, and the like. As used in this disclosure, the term "administration classifier" refers to a machine learning model and / or models used to determine administration. The administration classifier 110 may include one or more classifiers, as disclosed in more detail below, one or more supervised machine learning models, such as linear regression models or other machine learning models, as disclosed below, and the like. In some embodiments, training data is used to train one or more models of the administration classifier 110, and such training data may include patient data and / or other clinical data that correlates with previous administration data. The training examples used for the training data may be selected according to results. For example, but not limited to, a user may specify data corresponding to cases in which a desired or optimal clinical outcome was achieved, and data from such cases may be used for and / or in the training data. As non-limiting examples, one or more decisions on delivery options, such as choosing between intravenous, intramuscular, subcutaneous, or other delivery, at least a bolus, continuous administration such as a "drip", and / or whether to administer a series of doses, may be made using one or more classifiers, each of which may be trained using training examples such as those described above to match clinical data to likely optimal or likely optimal administration options. As further non-limiting examples, one or more doses in a course, amount of a bolus, amount of dose per dose, number of days in a course, number of courses in a period such as a year, etc., may be determined by machine learning models designed to calculate such values ​​based on clinical and / or patient data.Alternatively or additionally, a query and / or classification algorithm such as k-nearest neighbors may be used to retrieve similar cases to the current case from a database and / or lookup table of successful and / or optimal cases, such as, but not limited to, the database and / or lookup table described below. In one embodiment, previous administrations may be retrieved from the autoimmune database 124, such as administrations used to provide training examples. In another embodiment, training data used to train the administration classifier 100 may be input by a user. In some embodiments, training data used to train the administration classifier 110 may be stored in a database. In some embodiments, the administration 126 may be determined using newer cases, supervised models, etc. As a non-limiting example, the supervised model may include a linear regression model, etc. In some embodiments, the administration 126 may be determined using a lookup table. As used in this disclosure, the term "lookup table" is an array of data that maps input values ​​to output values. A lookup table may be used to replace run-time calculations with array indexing operations. With continued reference to FIG. 1, but not by way of limitation, Table 1 shows exemplary methods for testing compositions of engineered virus-based nanoparticles for the treatment of autoimmune conditions using multiple modes of administration.

[0027] [Table 1]

[0028] With continued reference to FIG. 1 , in some embodiments, the administration data 112 may include a clinical score 114. As used herein, the term "clinical score" is a score that represents the severity of a disease on a numerical scale. As a non-limiting example, the clinical score 114 may include a four-point scale from 0 to 4. As a non-limiting example, 0 represents the lowest level of disease severity and 4 represents the highest level of disease severity. In some embodiments, the clinical score 114 may be stored in the autoimmune database 124. In some embodiments, the clinical score 114 may be retrieved from the autoimmune database 124. For example, but not limited to, the clinical score 114 may include an arthritis score. As used herein, the term "arthritic score" is a clinical score for arthritis. As a non-limiting example, the administration data 112 may include an arthritis score of 1 for TBSV.LIP1 administered via intraperitoneal injection. As another non-limiting example, the administration data 112 may include an arthritis score of 5 for TBSV.LIP1 administered via intraperitoneal injection. In some embodiments, the arthritis score for each group of multiple administration formats of engineered plant-based particles may be evaluated periodically. As a non-limiting example, the arthritis score may be evaluated every 1st, 2nd, 3rd, 4th, etc. day after administration. In some embodiments, the arthritis score may be calculated using the following formula: Mean arthritis score ± standard error of the mean (SEM). As used in this disclosure, the term "standard error" is the standard deviation of that sampling distribution. As used in this disclosure, the term "standard deviation" is a measure of the amount of variability or dispersion of a set of values. In some embodiments, the mean arthritis score may be calculated using the following formula:

[0029]

number

[0030] where n represents the total number of samples and x represents the sample. As a non-limiting example, the samples may include the administration data 112. As a non-limiting example, the number of samples may include the number of administration data 112. In some embodiments, the mean arthritis score may be stored in the autoimmune database 124. In some embodiments, the mean arthritis score may be obtained from the autoimmune database 124. In some embodiments, the standard error may be calculated using the following formula:

[0031]

number

[0032] Here, SE represents the standard error and σ represents the standard deviation. In some embodiments, the standard deviation may be calculated using the following formula:

[0033]

number

[0034] In some embodiments, statistical significance may be determined by applying the Kruskal-Wallis test. As used in this disclosure, the term "statistical significance" is a determination that the relationship between two or more variables is caused by something other than chance. As used in this disclosure, the term "Kruskal-Wallis test" is a nonparametric method to test whether samples come from the same distribution. The Kruskal-Wallis test can be used to compare two or more independent samples of equal or different sample sizes. The Kruskal-Wallis test is performed by ranking all data from all groups together from 1 to n. Then, the test statistic (H) for the Kruskal-Wallis test is:

[0035]

number

[0036] Here, R i is the sum of ranks of the ith sample, and k is the number of groups. In some embodiments, statistical significance may be determined by applying Dunn's multiple comparison test. The term "Dunn's multiple comparison test" as used in this disclosure is a test that compares the difference in the sum of ranks between two columns to the expected mean difference based on the number of groups and their sizes. In some embodiments, a p-value may be used. The term "p-value" as used in this disclosure is the probability for a given statistical model that the statistical summary will be equal to or more extreme than the actual observations if the null hypothesis is true. This arthritis score is further described below with reference to FIG. 6.

[0037] Continuing with reference to FIG. 1 , in some embodiments, the score machine-learning model 128 may be used to calculate the clinical score 114. As used in this disclosure, the term “score machine-learning model” is a machine-learning model used to calculate the clinical score. The score machine-learning model 128 may correspond to the machine-learning module illustrated in FIG. 8. In some embodiments, the training data used to train the score machine-learning model 128 may include administration data 112 that correlates to an output that may include the clinical score 114. In another embodiment, the training data used to train the score machine-learning model 128 may be input by a user. In some embodiments, the training data used to train the score machine-learning model 128 may be obtained from the autoimmune database 124. In some embodiments, the training data used to train the score machine-learning model 128 may be stored in a database. In some embodiments, the clinical score 114 may be determined using a look-up table.

[0038] Continuing to refer to FIG. 1, the device 100 includes a processor 104 and a memory 108 communicatively connected to the processor 104, where the memory 108 includes instructions for configuring the processor 104 to compare a plurality of administration data 112. In some embodiments, the plurality of administration data 112 may be compared to a plurality of administration data 112 including different compositions. As a non-limiting example, an arthritis score of TBSV.pLIP1 may be compared to an arthritis score of saline buffer. As a non-limiting example, an arthritis score of TBSV.pFADK2 may be compared to an arthritis score of dexamethasone. In some embodiments, the plurality of administration data 112 may be compared to a plurality of administration data 112 including different administrations. As a non-limiting example, an arthritis score of TBSV.pLIP1 administered subcutaneously may be compared to an arthritis score of TBSV.pLIP1 administered intravenously. As another non-limiting example, the arthritis score of TBSV.pFADK2 administered orally may be compared to the arthritis score of TBSV.pFADK2 administered intravenously. In some embodiments, multiple administration data 112 may be compared manually by a human.

[0039] Continuing with reference to FIG. 1, in some embodiments, a critical machine-learning model 116 may be used to compare multiple dosing data 112. As used herein, the term "critical machine-learning model" refers to a machine-learning model configured to compare multiple dosing data. The critical machine-learning model 116 may correspond to a machine-learning module described with reference to FIG. 8. In some embodiments, training data used to train the critical machine-learning model 116 may include multiple dosing data 112 correlated to an output that may include a critical dosing. The critical dosing is described in more detail below. As a non-limiting example, inputs to the critical machine-learning model 116 may include arthritis scores for TBSV.pLIP1 administered orally and wild type TBSV (wt TBSV) administered intravenously. As used herein, the term "wild type virus" refers to a naturally occurring, unmutated strain of the virus. As another non-limiting example, the inputs for the administration machine learning 116 may include arthritis scores for TBSV.pLIP1 administered orally and arthritis scores for TBSV.pLIP1 administered intravenously. In an embodiment, the training data used to train the critical machine learning model 116 may be input by a user. As used in this disclosure, the term "user" refers to a person using the device. As a non-limiting example, a user may include a clinician, a physician, and the like. In another embodiment, the training data used to train the critical machine learning model 116 may be obtained from an autoimmune database 124, which is described in more detail below. As a non-limiting example, the training data used to train the critical machine learning model 116 may include previous administration data. As used in this disclosure, the term "previous administration data" refers to previously obtained administration data.The previous administration data may include critical administrations, which are described in more detail below.

[0040] Continuing to refer to FIG. 1, the device 100 includes a processor 104 and a memory 108 communicatively connected to the processor 104, where the memory 108 includes instructions for configuring the processor 104 to determine a critical administration 120. As used herein, the term "critical administration" refers to an administration of a composition, where the composition is most effective. In some embodiments, the critical administration 120 may include a minimum arthritic score. As used herein, the term "minimum arthritic score" is the smallest arthritic score. As a non-limiting example, TBSV.pLIP1 administered by intravenous injection may be a critical administration 120 if arthritic score 2 is the minimum arthritic score, where the arthritic score of TBSV.pLIP1 administered by intravenous injection is higher than other arthritic scores of the plurality of administration data 112, such as TBSV.pLIP1 administered by oral administration and wt TBSV administered by oral administration. As another non-limiting example, TBSV.pLIP1 administered intravenously may be a critical dosing 120 if an arthritis score of 3 is the minimum arthritis score, where the arthritis score of TBSV.pLIP1 administered intravenously is higher than other arthritis scores in the plurality of dosing data 112, such as TBSV.pLIP1 administered intravenously and wt TBSV administered subcutaneously. In some embodiments, the critical dosing 120 may be determined manually by a human.

[0041] 1, in some embodiments, the device 100 may include an autoimmune database 124. The autoimmune database 124 may be implemented as a key-value lookup database, such as, but not limited to, a relational database, a NOSQL database, or any other format or structure that one of ordinary skill in the art would recognize as suitable for use as a database upon review of the entirety of this disclosure. Alternatively or additionally, the autoimmune database 124 may be implemented using a distributed data storage protocol and / or data structure, such as a distributed hash table. The autoimmune database 124 may include multiple data entries and / or records, as described above. Data entries in the autoimmune database 124 may be flagged or linked with one or more additional information elements, which may be reflected in the data entry cells and / or in linked tables, such as tables related by one or more indexes in a relational database. Those skilled in the art, upon review of this disclosure in its entirety, will be able to envision various ways in which data entries within autoimmune database 124 may store, retrieve, organize, and / or reflect data and / or records as used herein, as well as categories and / or collections of data consistent with this disclosure.

[0042] With continued reference to FIG. 1, the development of viral nanoparticles (VNPs) for medical applications has thus far focused primarily on the delivery of drugs to specific target cells and molecular imaging. However, the ability of VNPs to display peptide antigens or epitopes has also been applied to vaccines, for example, displaying pathogen-derived peptides as a proactive immunization strategy against infectious diseases, and displaying self-antigens found on cancer cells as a means to prime the immune system against tumors. Research into these approaches and preclinical studies have provided mounting evidence that VNPs also have immunomodulatory effects. For example, pVNPs have been shown to activate local (tumor microenvironment) and systemic anti-cancer immune responses involving cytokine secretion and the activation state of different immune cell populations, suppressing cancer cell proliferation and metastasis. Based on this, we hypothesize that pVNPs displaying autoantigens for autoimmune diseases may also modulate the immune system to induce tolerance and prevent or treat diseases such as T1D and RA.

[0043] With continued reference to Figure 1, the coat proteins of CPMV and TBSV can be engineered to display immunodominant peptides associated with T1D (p524opt) and RA (pLIP1 or pFADK2), respectively. Although these viruses are similar in shape and size, the density of peptide display is three times lower in CPMV due to virions assembled from two distinct coat protein subunits compared to a single coat protein in TBSV. As a non-limiting example, icosahedral nanoparticles may tend to circulate in vivo for several hours, while filamentous and rod-shaped viruses may persist longer.

[0044] Continuing to refer to FIG. 1, as an exemplary experimental result, previous experiments have shown that the presence of p524opt reduced CPMV particle yields in plants by 10-fold compared to wild-type virus, likely due to loss of stability or enhanced aggregation, despite the presence of the recombinant particle's pI. In contrast, the presence of either pLIP1 or pFADK2 improved TBSV yields, likely due to the opposite effect of increased stability. Recombinant pVNP was tested in appropriate mouse models of T1D and RA to compare its efficacy with the corresponding peptide without the viral scaffold. The peptide was administered with adjuvants necessary to ensure a strong immune response, whereas pVNP was administered without adjuvant, as the repeat structure of the viral scaffold is known to achieve a similar effect. Adjuvants can induce local or systemic reactogenicity, negatively impacting public perception of the risk-benefit balance of vaccination. In contrast, the safety profile of plant viruses is positive due to their ubiquity in the environment and especially in all plant-based foods.

[0045] Continuing with reference to FIG. 1, analysis of NOD mice revealed that two treatments with the CPMV.p524opt formulation partially protected against the development of diabetes, whereas the free p524opt peptide had no protective effect. The presence of fewer Foxp3+ cells around the islets in mice treated with the pVNP formulation suggests that the protective mechanism induced by CPMV.p524opt is not due to Treg cells. Similarly, analysis of recently diagnosed NOD mice treated with the bacterium Lactococcus lactis engineered to secrete GAD65370-575 (and the anti-inflammatory cytokine IL10) in the intestine in combination with a short course of low-dose anti-CD3 therapy (combi-GAD therapy) achieved stabilization of insulitis, maintained functional beta cell mass, and restored normoglycemia, but the number of FOXP3+ cells in and around the islets was not significantly different compared to untreated mice. The favorable results in the above examples may reflect differences in cytokine activation by the various treatment groups, with IL10 and IL2 playing a major role in dual-dose treatment with CPMV.p524. Analysis of DBA mice after the establishment of CIA showed intriguing preliminary evidence that administration of TBSV particles displaying pFADK2 or pLIP1 could achieve complete remission of arthritic signs, even if pVNP was delivered after the onset of the disease. CIA in DBA mice is the most commonly used model of chronic arthritis dependent on T helper cells (TH1) because it is highly reproducible and allows the antibody-induced effector phase of arthritis to be studied in detail. Tolerance to citrullinated proteins has been shown in DBA mice, and prophylactic administration of citrullinated filaggrin peptides protected these mice from arthritis.

[0046] With continued reference to Figure 1, administration of multi-epitope citrullinated peptides derived from major prevalent autoantigens immediately after disease induction significantly reduced the severity of symptoms in an adjuvant-induced rat arthritis model. These results and data support the hypothesis that antigen-specific tolerance is achieved in RA by repeated administration of peptides that cause immunomodulation of T cell populations involved in the immune response (mainly TH1, TH17, and Treg cells) and downregulation of inflammatory cytokines. Specifically, reductions in TNFα, IL1β, IL17, and IFNγ are observed in serum, lymph nodes, and (to a lesser extent) joints. These cytokines may be major mediators of innate (TNFα), adaptive (IFNγ), and TH17-mediated (IL17) immune responses in RA. The beneficial effects of pVNP may be related to the secretion of anti-inflammatory cytokines, such as IL10 for TBSV.pLIP1 and TBSV.pFADK2, and both TGFβ and IL10 for TBSV.pFADK2, and the accumulation of CD25+Foxp3+Treg cells, which suppress autoreactive lymphocytes by direct contact or by promoting the secretion of TGFβz and IL10. The high proportion of Treg cells in the lymph nodes of treated mice supports the role of this T cell subset in suppressing immune-mediated inflammation during the pathogenesis of RA.

[0047] Continuing with reference to Figure 1, the role of liprins and focal adhesion kinases (FADKs) in the immunopathogenesis of RA is unclear. The liprin family of multidomain proteins is required for cell motility on the extracellular matrix and efficient control of focal adhesion during cell migration. Liprin α1 dimers are highly expressed in lymphatic endothelial cells and regulate lymphatic vessel integrity. The pFADK2 sequence is derived from protein tyrosine kinase 2β (FAK2 / PTK2B), which controls cell adhesion, migration, and invasion. In mouse models of RA, FAK2 / PTK2B promotes inflammatory angiogenesis, induces synovial fibroblast invasion, and induces synovial pannus formation, suggesting that it may be involved in the development and progression of synovial hyperplasia. Thus, the positive effects of pFADK2 administration in DBA mice may in part reflect the inhibition of synovial hyperplasia, supporting a role for focal adhesion kinase in the pathogenesis of RA and the value of peptide-driven therapy using adjuvant viral scaffolds.

[0048] With continued reference to Figure 1, these results indicate that immunodominant peptides delivered by pVNPs provide therapeutic benefit in both T1D and RA models, but the underlying mechanisms are different, with Treg cell activation being required only in the RA model. This indicates that pVNP-expressed peptides strongly influence the immunomodulatory mode of action. However, the viral scaffold achieved an adjuvant effect in both cases, likely by stabilizing the peptides and concentrating multiple peptides in a small area. The combination of the viral scaffold and the immunodominant peptides resulted in the secretion of IL10, which was more abundant in the serum of both disease models after treatment with pVNP. The requirement of both the scaffold and peptide is evidenced by the negligible increase in IL10 levels for either wild-type virus or synthetic peptide formulations.

[0049] 2A is a flow chart illustrating an exemplary embodiment of a method 200 for engineered viral-based nanoparticles for the treatment of autoimmune conditions. The engineered viral-based nanoparticles 100 may include, but are not limited to, any of the nanoparticles described herein with reference to FIGS. 1, 3, and 4.

[0050] 2A, in step 205, the method 200 includes infecting a plant with a virus to produce plant virus-based particles. The term "infection" as used in this disclosure refers to the process of delivering an infectious viral gene to a plant so that the plant can produce the tomato bushy stunt virus. Upon infection, the plant may begin to produce the required plant virus-based particles. In one embodiment, the infection may be performed by natural infiltration. As a non-limiting example, natural infiltration may include spraying the plant with an infectious viral gene, spraying a viral solution obtained from a previously infected leaf, and the like. The term "infiltration" as used in this disclosure refers to the penetration of a substance from the surface to the depth of the plant tissue. In another embodiment, the infection may be performed by infiltration of a viral gene into the plant. In another embodiment, the infection may be performed by infiltration of a viral solution obtained from a previously infected leaf. In some embodiments, the infection may be performed by forced infiltration. As a non-limiting example, forced infiltration may include syringe infiltration, vacuum infiltration, and the like. As non-limiting examples, vacuum infiltration may include vacuum infiltration using a vacuum pump, vacuum infiltration using a syringe, etc. Those skilled in the art can, upon review of the entirety of this disclosure, envision a variety of infiltrations that may be applied to generate plant virus-based particles as described herein.

[0051] Continuing to refer to FIG. 2A, in step 210, the method 200 includes sampling symptomatic leaves from the plant. As used in this disclosure, the term "sampling" refers to the act of taking samples from the plant leaves to see if they have produced the required plant virus-based particles. Because it takes time for the plant to produce particles, sampling can only be done after the infection process has occurred. Samples may be taken from symptomatic plant leaves. In the case of Tomato bushy stunt virus, a lapse of at least six days may be required for a sample of Tomato bushy stunt virus to form on the plant. Once the plant virus-based particles are detected, they can be extracted as further described below.

[0052] With continued reference to FIG. 2A, in step 215, the method 200 includes homogenizing the plant virus-based particles. As used in this disclosure, the term "homogenizing" refers to a process of blending elements into a mixture to make them uniform. The homogenization process may include the use of an extraction buffer. An "extraction buffer" is an aqueous solution used to disrupt cells and may include salts to adjust the acidity of the buffer. That is, the plant tissue containing the plant virus-based particles is combined with the extraction buffer. In the case of tomato bushy stunt virus, sodium acetate is used as the extraction buffer.

[0053] 2A , in step 220, the method 200 includes incubating the plant virus-based particles. The homogenous mixture of plant leaves and extraction buffer is incubated in ice for a period of time before commencing centrifugation, as described below. As used in this disclosure, the term "incubating" is the process of immersing something in a high or low temperature for a period of time. In this scenario, the plant virus-based particles are exposed to ice for a period of time.

[0054] 2A, in step 225, the method 200 includes centrifuging the plant virus-based particles. As used in this disclosure, the term "centrifugation" refers to the process of separating various components using a centrifuge device that uses centrifugal force. Each virus particle has a certain amount of centrifugation time before it can be removed, and in the case of tomato bushy stunt virus, the centrifugation may be performed two or three times.

[0055] Continuing to refer to FIG. 2A, in optional step 230, the method 200 may include filtering the plant virus-based particles. The filtering method used in the present disclosure may be gel filtration. The term "gel-filtration" as used in the present disclosure is molecular chromatography. In this process, separation is based on the difference in the ability of molecules in a sample to enter the pores of the gel filtration medium (due to differences in molecular size). This is also called size exclusion chromatography. The chromatography column is filled with fine porous beads made of dextran polymers, agarose, or polyacrylamide. The pore size of these beads estimates the size of the macromolecules, and separation is performed according to their size. The main application of gel filtration chromatography is the fractionation of proteins and other water-soluble polymers, while gel permeation chromatography is used to analyze the molecular weight distribution of organic soluble polymers.

[0056] 2B is a flow chart illustrating an exemplary embodiment of another possible method 200 of engineered viral-based nanoparticles for the treatment of autoimmune conditions. The engineered viral-based nanoparticles 100 may include, but are not limited to, any of the nanoparticles described herein with reference to FIG.

[0057] With continued reference to FIG. 2B, in step 205, the method 200 includes agroinfiltration of the plant with the virus to produce plant virus-based particles. For cowpea mosaic virus, the process of agroinfiltration may be used. The term "agroinfiltration" as used in this disclosure is a method used in plant biology to induce transient expression of genes in plants to produce desired proteins. In the process of agroinfiltration, Agrobacterium tumefaciens may be directly injected into plant leaves or may be associated with plant cells immobilized on a porous support. The bacteria may then introduce the desired gene into the plant cells by introduction of T-DNA. Agroinfiltration is considered beneficial for its speed and convenience compared to more traditional plant transformation, but the yield of recombinant protein is also generally higher and may be more stable. Other production processes may be used to produce any type of plant virus-based particle. Once the plant has undergone the production process, it is capable of producing the plant virus-based particles for use in the composition. The plant virus-based particles can be any of the plant virus-based particles described herein. In the case of cowpea mosaic virus, a sample of cowpea mosaic virus may need to be allowed to elapse for at least six days before it is formed in the plant.

[0058] 2B, in step 210, the method 200 includes harvesting the leaves of the plant. This step can be performed using any of the harvesting processes as described above.

[0059] 2B, in step 215, the method 200 includes homogenizing the plant virus-based particles. This step may be performed using any process of homogenization, as described above. In the case of the cow pea mosaic virus, a sodium phosphate buffer may be used. Another buffer that may be used is the PBS buffer described above with reference to FIG. 1.

[0060] 2B, in step 220, the method 200 includes centrifuging the plant virus-based particles. A person skilled in the art can, by reviewing the entirety of this disclosure, envision various centrifugations that may be applied to centrifuge the plant virus-based particles as described in this disclosure, and any centrifugation process may be utilized to perform this step. Cowpea mosaic virus production does not have to include culturing.

[0061] With continued reference to FIG. 2B, in step 225, the method 200 includes filtering the plant virus-based particles. This step may be performed using any process of filtering as described above. Furthermore, without limitation, once the nanoparticles displaying immunodominant peptides have been infected or otherwise processed into a plant, the plant may begin to produce the genetically modified plant virus. The plant may function as a bioreactor. Once the plant-derived virus particles are found, they may be homogenized in an extraction buffer as described herein with reference to FIG. 2A and FIG. 2B. After homogenization, the particles may be filtered and the supernatant may then be centrifuged. After centrifugation is complete, the particles may be gel filtered and analyzed.

[0062] With continued reference to Figure 4, embodiments of the present disclosure may be used to treat type 1 diabetes. VNP GAD65 and / or other insulin-derived peptides may be expressed, rather than the immunodominant peptides associated with rheumatoid arthritis as discussed above.

[0063] FIG. 3 now shows an exemplary embodiment of a method for the manufacture of engineered virus-based nanoparticles for the treatment of autoimmune conditions. In the figure, the immunodominant peptide is cloned into an expression vector harboring the viral genome or coat protein (CP) gene. The term "expression vector", also known as "expression construct", as used in this disclosure, is usually a plasmid or virus designed for gene expression in cells. The immunodominant peptide then undergoes either retro-transcription in vitro or Agrobacterium tumefaciens transformation. N. benthamiana plants may be infected with infectious RNA in retro-transcription in vitro. N. benthamiana plants may undergo infiltration with infectious RNA in retro-transcription in vitro. Infiltration as referred to here is described in more detail below. In the case of Agrobacterium tumefaciens transformation, N. benthamiana plants may undergo agroinfiltration as described above. In this embodiment, Nicotiana benthamiana plants are used, however, any of the plants described herein may be used for the production of plant virus-based particles.

[0064] 4A-B, a graph 400 shows the effect of CPMV plant virus-based particles on nonobese diabetic (NOD) mice. FIG. 4A shows the survival curves for groups treated with CPMV-derived particles. NOD female mice were treated prior to the onset of insulitis by intraperitoneal injection of synthetic peptides, p524 404 and p524opt 408, plant virus nanoparticles (pVNP), where pVNP was a single dose of CPMV.p524opt 412 and two doses of CPMV.p524opt 416, or wild type (wt) CPMV as a control 420. None of the peptides altered disease progression, but two doses of CPMV.p524opt nanoparticles 416 induced a protective effect compared to the other treatments. Two doses of CPMV.p524opt 416 overall included P=0.01, where p-values ​​(P) are as described in detail above. More specifically, as shown in FIG. 4A, Kaplan-Meier analysis revealed that NOD mice treated with two doses of CPMV.p524opt 416 were significantly less likely to progress to diabetes compared to wild-type CPMV 420 at P=0.04, p524opt 408 at P=0.008, or p524 404 at P=0.001. As used in this disclosure, the term "Kaplan-Meier analysis" is an analysis used to measure the proportion of subjects who survive a period of time following treatment. As a non-limiting example, Kaplan-Meier analysis may be calculated using the following formula:

[0065]

number

[0066] where Π is the product of sequences, t i is the time when at least one event occurs, and d i is the time t i is the number of events that occurred in n i is the time t iThe untreated group showed the same survival curve as wild-type CPMV420, with first clinical signs at postnatal day 71, 30% prevalence at day 121, 50% at day 142, and 80% at day 168. Nondiabetic NOD female mice were examined for islet infiltration at different time points including 5, 10, and 27 weeks after treatment. Histological analysis of pancreatic samples showed a significant overall difference in insulitis scores between the different treatment groups by Kruskal-Wallis test with P<0.0001. More specifically, as shown in FIG. 4B, significant differences were identified at all three time points between CPMV.p524opt-treated animals 416 and wild-type virus-treated animals 420, with P<0.0001, and significant differences were observed at weeks 10 and 27 between CPMV.p524opt-treated animals 416 and p524-treated animals 408, with P<0.0001. In FIG. 4A, survival curves for groups treated with CPMV-derived particles show partial protection from the development of diabetes in mice 416 treated twice with CPMV.p524opt compared to wt CPMV 420, p524opt synthetic peptide + IFA 408, and p524 synthetic peptide + IFA 404. Single administration of CPMV.p524opt 412 did not prevent the development of diabetes. The x-axis indicates the number of days after the first treatment. In FIG. 4B, islet cell histopathology is determined by counting infiltrating cells in a subset of treated mice. After 5 and 10 weeks (5w and 10w) of treatment, and at the end of treatment, some animals were euthanized to characterize disease progression, where CPMV.p524opt 416 is the group of animals that received two treatments. Here, FIG. 5 shows the characterization 500 of engineered plant-based virus particles. Plant-based-virus particles displaying wild-type TBSV (wt TBSV), pLIP1 and pFADK2 immunodominant peptides (TBSV.pLIP1 and TBXV.pFADK2, respectively) were analyzed after purification from infected N. benthamiana plants. FIG. 5A shows the results of gel electrophoresis under denaturing conditions followed by silver staining of purified particles. As used in this disclosure, the term "gel electrophoresis" is a technique used to separate DNA fragments according to their size.The term "denaturation" as used in this disclosure refers to the process whereby a protein loses its native structure due to the application of some external stress or chemical compound. The term "silver staining" as used in this disclosure refers to the technique of detecting proteins on polyacrylamide gels with metallic silver ions. FIG. 5B shows the structure of wt TBSV. FIG. 5C shows the structure of TBSV.pLIP1. FIG. 5D shows the structure of TBSV.pFADK2. FIG. 5E shows the structural conformation of plant-based virus particles such as wt TBSV, TBSV.pLIP1, and TBSV.pFADK2 accessed by transmission electron microscopy (TEM) and dynamic light scattering (DLS) analysis, where 504 represents the size distribution of wt TBSV, 508 represents the size distribution of TBSV.pFADK2, and 512 represents the size distribution of TBSV.pLIP1. As used in this disclosure, the term "dynamic light scattering" is an intensity-based technique that emphasizes larger particle sizes. As used in this disclosure, the term "transmission electron microscopy" is a number-based technique that emphasizes the smallest components in the particle size distribution.

[0067] Here, FIG. 6 shows a graph 600 depicting the effect of TBSV plant virus-based particles on the severity of CIA. The graph 600 shows the arthritis scores of each group assessed every 2-3 days from days 24-59 after immunization. A P value of p<0.05 was considered significant. Statistical significance was determined by applying Kruskal-Wallis test and Dunn's multiple comparison test. The first signs of arthritis in DBA mice appeared 28 days after collagen injection, and 100% of immunized mice showed arthritis after 33 days. FIG. 6A shows a comparison of saline-treated CIA mice 604, where saline-treated CIA mice 604 are the control units, and CIA mice treated with dexamethasone 608, TBSV.pFADK2 612, and pFADK2+IFA 616. FIG 6B is a comparison of saline treated CIA mice 604, where the CIA mice 604 are the control units, and compared with CIA mice treated with dexamethasone 608, TBSV.pLIP1 620, and pLIP1+IFA 624. FIG 6C is a comparison of saline treated CIA mice 604, where the saline treated CIA mice 604 are the control units, and compared with CIA mice treated with wt TBSV 628, TBSV.pLIP1 620, and TBSV.pFADK2 612. As shown in FIG 6B, treatment with TBSV.pLIP1 may be the most effective, eliminating all signs of arthritis by the end of the treatment period.

[0068] FIG. 7A illustrates an exemplary embodiment of an engineered virus-based nanoparticle composition 700 for the treatment of an autoimmune condition. In some embodiments, the engineered virus-based nanoparticle may include a cowpea mosaic virus, where the cowpea mosaic virus is as described in detail above. In some embodiments, the composition 700 may include a large coat protein subunit 704. As a non-limiting example, the composition 700 may include 60 copies of the large coat protein subunit 704. In some embodiments, the composition 700 may include a small coat protein subunit 708. As a non-limiting example, the composition 700 may include 60 copies of the small coat protein subunit 708. In some embodiments, the composition 700 may include at least one peptide. In some embodiments, the at least one peptide may include an immunodominant peptide 712. In some embodiments, the immunodominant peptide 712 may be embedded on the coat protein. In some embodiments, the immunodominant peptide 712 may be located on the exterior of the coat protein. In some embodiments, the immunodominant peptide 712 may be exposed on the surface of the large coat protein subunit 704. In some embodiments, the immunodominant peptide 712 may be located in the interior of the particle. In some embodiments, the immunodominant peptide 712 may be located in the interior of the coat protein. In some embodiments, the composition 700 may comprise an icosahedral structure. In some embodiments, the composition 700 may comprise a thread-like structure. In some embodiments, the composition 700 may comprise a rod-like structure. In some embodiments, the composition 700 may comprise a helical capsid structure. In some embodiments, the composition 700 may comprise a spherical structure. Additionally, the composition 700 may comprise any structure, including but not limited to the following. In some embodiments, the composition 700 may be uniform in size. In some embodiments, the composition 700 may be uniform in shape.

[0069] FIG. 7B illustrates an exemplary embodiment of an engineered virus-based nanoparticle composition 700 for the treatment of an autoimmune condition. The composition 700 disclosed herein may be consistent with the composition described with respect to FIG. 7A. In some embodiments, the engineered virus-based nanoparticle may include a tomato bushy stunt virus, as described in detail above. In some embodiments, the composition 700 may include a single protein subunit 716, as shown in FIG. 7B. As a non-limiting example, the composition 700 may include 90 copies of the single protein subunit 716. In some embodiments, the composition 700 may include at least one peptide. In some embodiments, the at least one peptide may include an immunodominant peptide 712. In some embodiments, the immunodominant peptide 712 may be exposed on the surface of the single protein subunit 716.

[0070] 8 illustrates an example embodiment of a machine learning module 800 that may perform one or more machine learning processes as described in this disclosure. The machine learning module may use a machine learning process to perform the determining, classifying, and / or analyzing steps, methods, processes, etc. as described in this disclosure. As used in this disclosure, the term "machine learning process" refers to a process that automatically uses training data 804 to generate an algorithm executed by the device 110 / module to generate output 808 from data provided as input 812, as opposed to a non-machine learning software program in which the commands to be executed are predetermined by a user and written in a programming language.

[0071] With further reference to FIG. 8, as used herein, the term "training data" refers to data that includes correlations that a machine learning process can use to model relationships between two or more categories of data elements. For example, but not limited to, training data 804 can include a plurality of data entries, each of which represents a set of data elements recorded, received, and / or generated together, where the data elements may be correlated by shared presence in a given data entry, by proximity in a given data entry, or the like. The plurality of data entries of training data 804 may indicate one or more trends of correlation between categories of data elements, for example, but not limited to, that higher values ​​of a first data element belonging to a first category of data elements tend to correlate with higher values ​​of a second data element belonging to a second category of data elements, indicating a proportional relationship or other mathematical relationship that may link values ​​belonging to the two categories. Categories of data elements may be related in the training data 804 based on various correlations, which may indicate causal and / or predictive links between categories of data elements, which may be modeled as relationships, such as mathematical relationships, by a machine learning process, as described in further detail below. The training data 804 may be formatted and / or organized by categories of data elements, for example, by associating the data elements with one or more descriptors that correspond to the categories of the data elements. As a non-limiting example, the training data 804 may include data entered into a standardized form by a person or process, such that entry of a given data element in a given field of the form may be mapped to one or more descriptors of a category.Elements of training data 804 may be linked to category descriptors by tags, tokens, or other data elements; for example, but not limited to, training data 804 may be provided in a format that links the location of data to categories, such as a fixed-length format, a comma-separated value (CSV) format, and / or a self-describing format, such as extensible markup language (XML), JavaScript Object Notation (JSON), etc., that allows a process or device to discover the category of the data.

[0072] Alternatively or additionally, and with continued reference to FIG. 8, the training data 804 may include one or more elements that are not categorized, i.e., the training data 804 may be unformatted or may not include descriptors for some elements of the data. Machine learning algorithms and / or other processes may sort the training data 804 according to one or more classifications, for example, using natural language processing algorithms, tokenization, detecting correlation values ​​in the raw data, etc., and may generate categories using correlation and / or other processing algorithms. As a non-limiting example, in a corpus of text, phrases that make up "n" compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams that contain such words in a particular order, and such n-grams are classified as elements of language such as "words" that are tracked similarly to single words, and new categories are generated as a result of the statistical analysis. Similarly, in a data entry containing text data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, allowing for ad-hoc classification by a machine learning algorithm and / or automatically associating the data in the data entry with a descriptor or a given format. The ability to automatically classify data entries allows the same training data 804 to be applied to two or more different machine learning algorithms, as described in more detail below. The training data 804 used by the machine learning module 800 may associate any of the input data described in this disclosure with any of the output data described in this disclosure.

[0073] With further reference to FIG. 8, the training data may be filtered, sorted, and / or selected using one or more supervised and / or unsupervised machine learning processes and / or models, as described in more detail below, including, but not limited to, a training data classifier 818. The training data classifier 818 may include a "classifier," which as used in this disclosure is a machine learning model defined below, e.g., a mathematical model, neural net, or program generated by a machine learning algorithm known as a "classification algorithm," which sorts inputs into categories or bins of data and outputs the categories or bins of data and / or their associated labels, as described in more detail below. The classifier may be configured to output at least one of the data that is clustered together and labels or otherwise identifies the sets of data that are found to be close based on a distance metric, as described below. The machine learning module 800 may generate the classifier using a classification algorithm, which is defined as a process by which the device 110 and / or any modules and / or components operating thereon derive a classifier from the training data 804. Classification may be performed using, but is not limited to, linear classifiers such as logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbor classifiers, support vector machines, least squares support vector machines, Fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and / or neural network based classifiers.

[0074] With further reference to FIG. 8, the machine learning module 800 may be configured to execute a lazy-learning process 820 and / or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and / or protocol, which may be a process that performs machine learning upon receipt of an input to be converted into an output by combining the input with a training set and deriving an algorithm that is used to generate an output on demand. For example, an initial set of simulations may be run to cover initial heuristics and / or “first guesses” at the outputs and / or relationships. As a non-limiting example, the initial heuristics may include ranking of associations between the inputs and elements of the training data 804. The heuristics may include selecting some number of the highest ranked associations and / or elements of the training data 804. Lazy learning may implement any suitable lazy learning algorithm, including but not limited to, k-nearest neighbor algorithms, lazy Naive Bayes algorithms, etc., and one of skill in the art, upon review of this disclosure in its entirety, will be able to envision a variety of lazy learning algorithms that may be applied to generate outputs as described in this disclosure, including but not limited to lazy learning applications of machine learning algorithms as described in further detail below.

[0075] Alternatively or additionally, and with continued reference to FIG. 8, the machine learning process described in this disclosure may be used to generate the machine learning model 224. As used in this disclosure, the term "machine-learning model" refers to a mathematical and / or algorithmic representation of a relationship between inputs and outputs, generated using any machine learning process, including, but not limited to, any process as described above, and stored in memory 118, where the inputs are submitted to the machine learning model 824 once created, which generates an output based on the derived relationship. For example, but not limited to, a linear regression model generated using a linear regression algorithm calculates output data by calculating a linear combination of the input data using coefficients derived during the machine learning process. As a further non-limiting example, the machine learning model 824 may be generated by creating an artificial neural network, such as a convolutional neural network, that includes an input layer of nodes, one or more hidden layers, and an output layer of nodes. The connections between nodes may be created through a process of "training" the network, in which elements from a training data 804 set are applied to the input nodes and an appropriate training algorithm (e.g., Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithm) is used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce desired values ​​at the output nodes. This process is sometimes referred to as deep learning.

[0076] With further reference to FIG. 8, the machine learning algorithm may include at least one supervised machine learning process 828. The at least one supervised machine learning process 828, as defined herein, includes an algorithm that receives a training set that associates a number of inputs with a number of outputs and seeks to find one or more mathematical relationships that associate the inputs with the outputs, where each of the one or more mathematical relationships is optimal according to some criteria specified to the algorithm using some scoring function. For example, the supervised learning algorithm may include a scoring function that represents the inputs and outputs as described above and the type of desired relationship to be found between the inputs and outputs, where the scoring function may seek, for example, to maximize the probability that a given input and / or combination of constituent inputs is associated with a given output and minimize the probability that a given input is not associated with a given output. The scoring function may be expressed as a risk function that represents the "expected loss" of the algorithm that associates inputs with outputs, where the loss is calculated as an error function that represents the degree to which the predictions generated by the relationships are inaccurate when compared to a given input-output pair provided in the training data 804. Those skilled in the art will be able to envision, upon review of this disclosure in its entirety, various possible variations of at least one supervised machine learning process 828 that may be used to determine the relationship between the inputs and the outputs. The supervised machine learning process may include a classification algorithm as defined above.

[0077] 8, the machine learning process may include at least one unsupervised machine learning process 832. As used herein, an unsupervised machine learning process is a process that derives inferences in a dataset without regard to labels, such that the unsupervised machine learning process is free to discover any structure, relationships, and / or correlations provided in the data. Unsupervised processes do not require a response variable, and unsupervised processes may be used to discover interesting patterns and / or inferences between variables, determine the degree of correlation between two or more variables, etc.

[0078] With further reference to FIG. 8, the machine learning module 800 may be designed and configured to create the machine learning model 824 using techniques for the development of linear regression models. The linear regression model may include ordinary least squares regression, which aims to minimize the squared difference between predicted and actual results according to an appropriate criterion for measuring such difference (e.g., a vector space distance criterion), and the coefficients of the resulting linear equation may be modified to improve the minimization. Linear regression models include ridge regression methods, where the function to be minimized includes a least squares function plus a term in which each coefficient squared is multiplied by a scalar amount to penalize large coefficients. Linear regression models include least absolute shrinkage and selection operator (LASSO) models, where ridge regression is combined with multiplying the least squares term by a factor of 1 divided by twice the number of samples. Linear regression models may include multitask LASSO models, where the norm applied to the least squares term of the LASSO model is the Frobenius norm, which is equivalent to the square root of the sum of the squares of all the terms. The linear regression model may include an elastic net model, a multitask elastic net model, a least-angle regression model, a LARS LASSO model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive-aggressive algorithm, a robustness regression model, a Huber regression model, or other suitable models that may occur to one of skill in the art upon review of the entirety of this disclosure. It will be apparent to one of skill in the art upon review of the entirety of this disclosure that the linear regression model may, in embodiments, be generalized to a polynomial regression model, whereby a polynomial (e.g., quadratic, cubic, or higher order) that provides the best predicted output / actual output fit is found, and a method similar to that described above can be applied to minimize the error function.

[0079] With continued reference to FIG. 8 , the machine learning algorithm may include, but is not limited to, linear discriminant analysis. The machine learning algorithm may include quadratic discriminant analysis. The machine learning algorithm may include kernel ridge regression. The machine learning algorithm may include support vector machines, including, but not limited to, regression processes based on support vector classification. The machine learning algorithm may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. The machine learning algorithm may include nearest neighbor algorithms. The machine learning algorithm may include various forms of latent space regularization, such as variational regularization. The machine learning algorithm may include Gaussian processes, such as Gaussian process regression. The machine learning algorithm may include cross-decomposition algorithms, including partial least squares and / or canonical correlation analysis. The machine learning algorithm may include naive Bayes methods. The machine learning algorithm may include decision tree based algorithms, such as decision tree classification or regression algorithms. The machine learning algorithms may include ensemble methods such as bagging meta-estimation, forests of randomized trees, AdaBoost, gradient tree boosting, and / or voting classifier methods. The machine learning algorithms may include neural net algorithms, including convolutional neural net processes.

[0080] FIG. 9 illustrates an exemplary embodiment of a neural network 900. A neural network 900, also known as an artificial neural network, is a network of "nodes" or data structures having one or more inputs, one or more outputs, and a function that determines the output based on the inputs. Such nodes are organized into a network, such as, but not limited to, a convolutional neural network, that includes an input layer 904 of nodes, one or more hidden layers 908, and an output layer 912 of nodes. Connections between the nodes may be created through a process of "training" the network, in which elements from a training data set are applied to the input nodes, and then a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce desired values ​​at the output nodes. This process is sometimes referred to as deep learning. In a "feed-forward" network, connections run only from input nodes to output nodes, while in a "recurrent" network, the output of one layer can be fed back to the input of the same or a different layer. As a further non-limiting example, a neural network can include a convolutional neural network that includes an input layer of nodes, one or more hidden layers, and an output layer of nodes. As used in this disclosure, the term "convolutional neural network" is a neural network in which at least one hidden layer is a convolutional layer that convolves the input to that layer with a subset of the inputs known as "kernels," along with one or more additional layers, such as pooling layers, fully connected layers, etc.

[0081] FIG. 10 illustrates an exemplary embodiment of a node of a neural network. A node may include, but is not limited to, multiple inputs xi, which may receive values ​​from inputs to the neural network that includes the node and / or from other nodes. The node may perform a weighted sum of the inputs using weights wi that are multiplied with each input xi. Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit of the neural network layer that is independent of the inputs to the layer. The weighted sum may be input to a function φ to generate one or more outputs y. The weights wi applied to the inputs xi may indicate whether the input is "excitatory," e.g., by having a large numerical value for the corresponding weight, indicating a strong influence on one or more outputs y, and / or "inhibitory," e.g., by having a small influence on one or more inputs y, by the corresponding weight.

[0082] Any one or more of the aspects and embodiments described herein may be suitably implemented in digital electronic circuitry, integrated circuits, specially designed application specific integrated circuits (ASICs), field programmable gate array (FPGA) computer hardware, firmware, software, and / or combinations thereof implemented in one or more machines (e.g., one or more computing devices utilized as user computing devices for electronic documents, one or more server devices, e.g., document servers, etc.) programmed according to the teachings herein, as would be apparent to one of ordinary skill in the computer art. These various aspects or features may include implementation in one or more computer programs and / or software executable and / or interpretable on a programmable system including at least one dedicated or general-purpose programmable processor coupled to receive data and instructions from a storage system, at least one input device, and at least one output device, and to transmit data and instructions to the storage system, at least one input device, and at least one output device. Those skilled in the art may readily produce appropriate software coding based on the teachings of the present disclosure. The aspects and implementations described above that employ software and / or software modules may include appropriate hardware to assist in implementing the machine-executable instructions of the software and / or software modules.

[0083] Such software may be a computer program product using a machine-readable storage medium. A machine-readable storage medium may be any medium capable of storing and / or encoding sequences of instructions for execution by a machine (e.g., a computing device) and causing the machine to perform any one of the methods and / or embodiments described herein. Examples of machine-readable storage media include, but are not limited to, magnetic disks, optical disks (e.g., CDs, CD-Rs, DVDs, DVD-Rs, etc.), magneto-optical disks, "ROM" devices which are read-only memory, "RAM" devices which are random access memory, magnetic cards, optical cards, solid-state memory devices, EPROMs, EEPROMs, Programmable Logic Devices (PLDs), and / or any combination thereof. As used herein, machine-readable media is intended to include not only a single medium, but also a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, the term machine-readable storage medium does not include any form of ephemeral signal transmission.

[0084] Such software may include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, the machine-executable information may be contained as a data carrier signal embodied in a data carrier that encodes sequences of instructions, or portions thereof, for execution by a machine (e.g., a computing device) and any associated information (e.g., data structures and data) that cause the machine to perform any one of the methods and / or embodiments described herein.

[0085] Examples of computing devices include, but are not limited to, e-book reading devices, computer workstations, terminal computers, server computers, handheld devices (e.g., tablet computers, smartphones, etc.), web appliances, network routers, network switches, network bridges, any machine capable of executing a set of instructions that specify actions to be performed by that machine, and any combination thereof. In one example, a computing device may include and / or be included in a kiosk.

[0086] 11 shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system 1100 capable of executing a set of instructions to cause a control system to perform any one or more of the aspects and / or methods of the present disclosure. Multiple computing devices may also be utilized to execute specially configured sets of instructions to cause one or more of the devices to perform any one or more of the aspects and / or methods of the present disclosure. The computer system 1100 includes a processor 1104 and a memory 1108 that communicate with each other and with other components via a bus 1112. The bus 1112 may include any of several types of bus structures, including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combination thereof, using any of a variety of bus architectures.

[0087] The memory 1108 may include a variety of components (e.g., machine-readable media), including, but not limited to, random access memory components, read-only components, and any combination thereof. In one example, a basic input / output system 1116 (BIOS), including the basic routines that help to transfer information between elements within the computer system 1100, such as during start-up, may be stored in the memory 1108. The memory 1108 may also store instructions (e.g., software) 1120 (e.g., stored on one or more machine-readable media) that embody any one or more of the aspects and / or methods of the present disclosure. In another example, the memory 1108 may further include any number of program modules, including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combination thereof.

[0088] Computer system 1100 may also include storage 1124. Examples of storage (e.g., storage 1124) include, but are not limited to, hard disk drives, magnetic disk drives, optical disk drives in combination with optical media, solid-state memory devices, and any combination thereof. Storage 1124 may be connected to bus 1112 by an appropriate interface (not shown). Exemplary interfaces include SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combination thereof. In one example, storage device 1124 (or one or more components thereof) may be removably interfaced with computer system 1100 (e.g., via an external port connector (not shown)). In particular, storage 1124 and associated machine-readable media 1128 may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for computer system 1100. In one example, the software 1120 may reside, completely or partially, within the machine-readable medium 1128. In another example, the software 1120 may reside, completely or partially, within the processor 1104.

[0089] The computer system 1100 may also include input devices 1132. In one example, a user of the computer system 1100 may input commands and / or other information to the computer system 1100 via the input devices 1132. Examples of the input devices 1132 include, but are not limited to, an alphanumeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touch screen, and any combination thereof. The input devices 1132 may interface to the bus 1112 via any of a variety of interfaces (not shown), including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE® interface, a direct interface to the bus 1112, and any combination thereof. The input devices 1132 may include a touch screen interface, which may be part of or separate from the display 1136, described further below. The input device 1132 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface, as described above.

[0090] A user may input commands and / or other information to the computer system 1100 via storage device 1124 (e.g., a removable disk drive, a flash drive, etc.) and / or network interface device 1140. A network interface device such as network interface device 1140 may be utilized to connect the computer system 1100 to one or more of a variety of networks, such as network 1144, and one or more remote devices 1148 connected thereto. Examples of network interface devices include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of networks include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, building, campus, or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider's data and / or voice network), a direct connection between two computing devices, and any combination thereof. A network such as network 1144 may employ wired and / or wireless communication modes. In general, any network topology may be used. Information (eg, data, software 1120 , etc.) may be communicated to and from computer system 1100 via network interface device(s) 1140 .

[0091] The computer system 1100 may further include a video display adapter 1152 for communicating images displayable on a display device, such as the display device 1136. Examples of display devices include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combination thereof. The display adapter 1152 and the display device 1136 may be used in combination with the processor 1104 to provide graphical representations of aspects of the disclosure. In addition to a display device, the computer system 1100 may include one or more other peripheral output devices, including, but not limited to, audio speakers, a printer, and any combination thereof. Such peripheral output devices may be connected to the bus 1112 via a peripheral interface 1156. Examples of peripheral interfaces include, but are not limited to, a serial port, a USB connection, a FIREWIRE® connection, a parallel connection, and any combination thereof.

[0092] Exemplary embodiments of the present invention have been described above in detail. Various modifications and additions can be made without departing from the spirit and scope of the present invention. Each feature of the various embodiments described above may be appropriately combined with features of other described embodiments to provide various combinations of features in related new embodiments. Furthermore, although a number of separate embodiments have been described above, what has been described herein is merely illustrative of the application of the principles of the present invention. Furthermore, although certain methods may be illustrated and / or described herein as being performed in a particular order, the order may be changed with a high degree of freedom within ordinary skill in order to realize the methods, systems, and software according to the present disclosure. Thus, the description herein is for illustrative purposes only and is not intended to limit the scope of the present invention.

[0093] In the above description and in the claims, a consecutive list of elements or features may be followed by a phrase such as "at least one of" or "one or more of." Also, a list of two or more elements or features may be preceded by the phrase "and / or." Such phrases are intended to refer to any of the listed elements or features individually, and to any of the listed elements or features in combination with any of the other listed elements or features, unless otherwise implicitly or explicitly contradicted by the context in which they are used. For example, "at least one of A and B," "one or more of A and B," and "A and / or B" are intended to mean "A alone, B alone, or both A and B," respectively. A similar interpretation applies to lists containing more than two items. For example, "at least one of A, B, and C," "one or more of A, B, and C," and "A, B, and / or C" are intended to mean "A alone, B alone, C alone, both A and B, both A and C, both B and C, or all of A, B, and C," respectively. Additionally, use of the term "based on" above and in the claims also means "based at least in part on," and thus allows for the presence of unspecified features or elements.

[0094] The subject matter described herein may be embodied in a system, an apparatus, a method, and / or an article, depending on the desired configuration. The embodiments described in the above description do not represent all embodiments consistent with the subject matter described herein. That is, they are only some examples consistent with aspects related to the subject matter described herein. Although some variations have been described in detail, other modifications or additions are possible. In particular, further features and / or variations may be provided in addition to those described herein. For example, the embodiments described above may apply to various combinations and subcombinations of the features disclosed herein, and / or combinations and subcombinations of some further features described above. Furthermore, the logic flow depicted in the accompanying figures and / or described herein does not necessarily require the particular order depicted, or sequential order, to achieve desirable results. Other embodiments may be included within the scope of the following claims.

Claims

1. 1. An apparatus for determining a dosage regimen for an engineered virus-based nanoparticle composition for the treatment of an autoimmune condition, comprising: At least one processor; a memory communicatively connected to the at least one processor, the at least one processor comprising: obtaining multiple dosing data for multiple dosing formats of a composition of engineered virus-based nanoparticles for the treatment of an autoimmune condition; Compare multiple administration data, and a memory containing instructions configuring the at least one processor to determine a critical dose.

2. 2. The apparatus of claim 1, The composition a plant virus-based particle engineered to express at least one peptide associated with said autoimmune condition, said plant virus-based particle having multiple modes of administration; A buffer solution; 13. An apparatus comprising:

3. 3. The apparatus according to claim 2, At least one of the peptides is an immunodominant peptide.

4. 4. The apparatus according to claim 3, At least one of the immunodominant peptides comprises pLIP1.

5. 4. The apparatus according to claim 3, At least one of the immunodominant peptides comprises pFADK2.

6. 3. The apparatus according to claim 2, At least one of the peptides comprises a peptide associated with type 1 diabetes.

7. 7. The apparatus according to claim 6, The peptide associated with type 1 diabetes comprises p524.

8. 3. The apparatus according to claim 2, At least one of the peptides is engineered with Tomato Bushy Stunt Virus (TBSV).

9. 3. The apparatus according to claim 2, The plant virus-based particles are sourced from a Nicotiana benthamiana plant.

10. 3. The apparatus according to claim 2, The plant virus-based particles are sourced from a chicory (Cycorium intybus) plant.

11. 3. The apparatus according to claim 2, The plant virus-based particles are sourced from a cabbage (Brassica oleracea var. capitata) plant.

12. 3. The apparatus according to claim 2, The plant virus-based particles are sourced from a Swiss chard (Beta vulgaris var cicla) plant.

13. 3. The apparatus according to claim 2, The plant virus-based particles are sourced from a basil (Ocimum basilicum) plant.

14. 3. The apparatus according to claim 2, The plant virus-based particles are sourced from a spinach plant.

15. 3. The apparatus according to claim 2, The plant virus-based particles are sourced from a red beet plant.

16. 2. The apparatus of claim 1, The autoimmune condition comprises rheumatoid arthritis.

17. 2. The apparatus of claim 1, The multiple modes of administration include oral administration.

18. 2. The apparatus of claim 1, The multiple modes of administration include subcutaneous injection.

19. 2. The apparatus of claim 1, The multiple modes of administration include intraperitoneal injection.

20. 2. The apparatus of claim 1, The multiple modes of administration include intravenous injection.

21. 3. The apparatus according to claim 2, At least one of the peptides is fused to a protein of Tomato Bushy Stunt Virus (TBSV).

22. 3. The apparatus according to claim 2, The device, wherein the plant virus-based particles further comprise cowpea mosaic virus (CPMV).

23. 23. The apparatus of claim 22, The cowpea mosaic virus comprises at least protein subunits, at least one of the protein subunits comprising a large coat protein.

24. 24. The apparatus of claim 23, At least one of the protein subunits further comprises a small coat protein.

25. 9. The apparatus of claim 8, The Tomato bushy stunt virus comprises an icosahedral capsid, the icosahedral capsid comprising a single capsid protein.

26. 3. The apparatus according to claim 2, The composition further comprises an anti-rheumatic component.

27. 27. The apparatus of claim 26, The device, wherein the antirheumatic component further comprises methotrexate.

28. 2. The apparatus of claim 1, The composition is administered to prevent the onset of the autoimmune condition.

29. 2. The apparatus of claim 1, The composition is administered for early treatment of the autoimmune condition.

30. 2. The apparatus of claim 1, The plurality of administration data includes a clinical score.

31. 2. The apparatus of claim 1, The critical administration comprises a minimum clinical score.

32. 2. The apparatus of claim 1, The at least one processor is further configured to compare the plurality of administration data using a critical machine learning model.

33. 1. A method of making a composition for an engineered virus-based nanoparticle for the treatment of an autoimmune condition, comprising: Infecting a plant with a virus to produce a plant virus-based particle; harvesting symptomatic leaves from said plant; homogenizing the plant virus-based particles; culturing the plant virus-based particles; centrifuging the plant virus-based particles; and gel filtration of the plant virus-based particles; The method includes:

34. 32. The method of claim 31 , The composition a plant virus-based particle engineered to express at least one peptide associated with an autoimmune condition, the plant virus-based particle having multiple modes of administration; With buffer solutions. A method comprising:

35. 35. The method of claim 34, The method, wherein at least one of the peptides is an immunodominant peptide.

36. 36. The method of claim 35, The method, wherein at least one of the immunodominant peptides comprises pLIP1.

37. 36. The method of claim 35, The method, wherein at least one of the immunodominant peptides comprises pFADK2.

38. 35. The method of claim 34, The method, wherein at least one of the peptides comprises a peptide associated with type 1 diabetes.

39. 39. The method of claim 38, The method, wherein the peptide associated with type 1 diabetes comprises p524.

40. 35. The method of claim 34, The method, wherein at least one of said peptides is engineered using Tomato Bushy Stunt Virus (TBSV).

41. 35. The method of claim 34, The method, wherein the plant virus-based particles are sourced from a Nicotiana benthamiana plant.

42. 35. The method of claim 34, The method, wherein the plant virus-based particles are sourced from a chicory (Cycorium intybus) plant.

43. 35. The method of claim 34, The method, wherein the plant virus-based particles are sourced from a cabbage (Brassica oleracea var. capitata) plant.

44. 35. The method of claim 34, The method, wherein the plant virus-based particles are sourced from a Swiss chard (Beta vulgaris var cicla) plant.

45. 35. The method of claim 34, The method, wherein the plant virus-based particles are sourced from a basil (Ocimum basilicum) plant.

46. 35. The method of claim 34, The method, wherein the plant virus-based particles are sourced from a spinach plant.

47. 35. The method of claim 34, The method, wherein the plant virus-based particles are sourced from a red beet plant.

48. 34. The method of claim 33, The method, wherein the autoimmune condition comprises rheumatoid arthritis.

49. 34. The method of claim 33, The method, wherein the multiple modes of administration include oral administration.

50. 34. The method of claim 33, The method, wherein the multiple modes of administration include subcutaneous injection.

51. 34. The method of claim 33, The method, wherein said multiple modes of administration include intraperitoneal injection.

52. 34. The method of claim 33, The method, wherein said multiple modes of administration include intravenous injection.

53. 35. The method of claim 34, The method, wherein at least one of the peptides is fused to a protein of Tomato Bushy Stunt Virus (TBSV).

54. 35. The method of claim 34, The method, wherein the plant virus-based particle further comprises cowpea mosaic virus (CPMV).

55. 55. The method of claim 54, The method, wherein the cowpea mosaic virus comprises at least protein subunits, at least one of the protein subunits comprising a large coat protein.

56. 56. The method of claim 55, The method, wherein at least one of the protein subunits further comprises a small coat protein.

57. 41. The method of claim 40, The method of claim 1, wherein the Tomato bushy stunt virus comprises an icosahedral capsid, the icosahedral capsid comprising a single capsid protein.

58. 35. The method of claim 34, The composition further comprises an anti-rheumatic component.

59. 59. The method of claim 58, The method, wherein the anti-rheumatic component further comprises methotrexate.

60. 34. The method of claim 33, The method, wherein the composition is administered to prevent the onset of the autoimmune condition.

61. 34. The method of claim 33, The method, wherein the composition is administered for early treatment of the autoimmune condition.

62. 1. A method of making a composition for an engineered virus-based nanoparticle for the treatment of an autoimmune condition, comprising: agroinfiltrating a plant with the virus to produce a plant virus-based particle; harvesting leaves of said plant; homogenizing the plant virus-based particles; centrifuging the plant virus-based particles; and gel filtration of the plant virus-based particles; A method comprising:

63. 63. The method of claim 62, The composition a plant virus-based particle engineered to express at least one peptide associated with an autoimmune condition, the plant virus-based particle having multiple modes of administration; A buffer solution; A method comprising:

64. 64. The method of claim 63, The method, wherein at least one of the peptides is an immunodominant peptide.

65. 65. The method of claim 64, The method, wherein at least one of the immunodominant peptides comprises pLIP1.

66. 65. The method of claim 64, The method, wherein at least one of the immunodominant peptides comprises pFADK2.

67. 64. The method of claim 63, The method, wherein at least one of the peptides comprises a peptide associated with type 1 diabetes.

68. 68. The method of claim 67, The method, wherein the peptide associated with type 1 diabetes comprises p524.

69. 64. The method of claim 63, The method, wherein at least one of said peptides is engineered using Tomato Bushy Stunt Virus (TBSV).

70. 64. The method of claim 63, The method, wherein the plant virus-based particles are sourced from a Nicotiana benthamiana plant.

71. 64. The method of claim 63, The method, wherein the plant virus-based particles are sourced from a chicory (Cycorium intybus) plant.

72. 64. The method of claim 63, The method, wherein the plant virus-based particles are sourced from a cabbage (Brassica oleracea var. capitata) plant.

73. 64. The method of claim 63, The method, wherein the plant virus-based particles are sourced from a Swiss chard (Beta vulgaris var cicla) plant.

74. 64. The method of claim 63, The method, wherein the plant virus-based particles are sourced from a basil (Ocimum basilicum) plant.

75. 64. The method of claim 63, The method, wherein the plant virus-based particles are sourced from a spinach plant.

76. 64. The method of claim 63, The method, wherein the plant virus-based particles are sourced from a red beet plant.

77. 63. The method of claim 62, The method, wherein the autoimmune condition comprises rheumatoid arthritis.

78. 63. The method of claim 62, The method, wherein the multiple modes of administration include oral administration.

79. 63. The method of claim 62, The method, wherein the multiple modes of administration include subcutaneous injection.

80. 63. The method of claim 62, The method, wherein said multiple modes of administration include intraperitoneal injection.

81. 63. The method of claim 62, The method, wherein said multiple modes of administration include intravenous injection.

82. 64. The method of claim 63, The method, wherein at least one of the peptides is fused to a protein of Tomato Bushy Stunt Virus (TBSV).

83. 64. The method of claim 63, The method, wherein the plant virus-based particle further comprises cowpea mosaic virus (CPMV).

84. 84. The method of claim 83, The method, wherein the cowpea mosaic virus comprises at least protein subunits, at least one of the protein subunits comprising a large coat protein.

85. 85. The method of claim 84, The method, wherein at least one of the protein subunits further comprises a small coat protein.

86. 83. The method of claim 82, The method of claim 1, wherein the Tomato bushy stunt virus comprises an icosahedral capsid, the icosahedral capsid comprising a single capsid protein.

87. 64. The method of claim 63, The composition further comprises an anti-rheumatic component.

88. 88. The method of claim 87, The method, wherein the anti-rheumatic component further comprises methotrexate.

89. 63. The method of claim 62, The method, wherein the composition is administered to prevent the onset of the autoimmune condition.

90. 63. The method of claim 62, The method, wherein the composition is administered for early treatment of the autoimmune condition.

91. 1. A composition of engineered virus-based nanoparticles for the treatment of an autoimmune condition, comprising: A plant virus-based particle engineered to express at least one peptide associated with an autoimmune condition, the plant virus-based particle having multiple dosage forms and nanoparticles; A buffer solution; A composition comprising:

92. 92. The composition of claim 91, The composition, wherein the buffer comprises a phosphate-buffered saline (PBS) buffer.

93. 92. The composition of claim 91, The composition, wherein the buffer comprises a normal saline buffer.

94. 92. The composition of claim 91, The composition, wherein the buffer comprises a sodium bicarbonate buffer.

95. 92. The composition of claim 91, The composition, wherein the buffer comprises a HEPES buffer.

96. 92. The composition of claim 91, A composition, wherein the composition further comprises sodium acetate.

97. 92. The composition of claim 91, A composition, wherein at least one of the immunodominant peptides comprises pLIP1.

98. 92. The composition of claim 91, A composition, wherein at least one of said immunodominant peptides comprises pFADK2.

99. 92. The composition of claim 91, A composition, wherein at least one of the peptides comprises a peptide associated with type 1 diabetes.

100. 100. The composition of claim 99, The composition, wherein the type 1 diabetes associated peptide comprises p524.

101. 92. The composition of claim 91, The composition, wherein at least one of the peptides is engineered using Tomato Bushy Stunt Virus (TBSV).

102. 92. The composition of claim 91, The composition, wherein the plant virus-based particles are sourced from a Nicotiana benthamiana plant.

103. 92. The composition of claim 91, A composition, wherein the plant virus-based particles are sourced from a chicory (Cycorium intybus) plant.

104. 92. The composition of claim 91, A composition, wherein the plant virus-based particles are sourced from a cabbage (Brassica oleracea var. capitata) plant.

105. 92. The composition of claim 91, A composition, wherein the plant virus-based particles are sourced from a Swiss chard (Beta vulgaris var cicla) plant.

106. 92. The composition of claim 91, A composition, wherein the plant virus-based particles are sourced from a basil (Ocimum basilicum) plant.

107. 92. The composition of claim 91, A composition, wherein the plant virus-based particles are sourced from a spinach plant.

108. 92. The composition of claim 91, A composition, wherein the plant virus-based particles are sourced from a red beet plant.

109. 92. The composition of claim 91, The composition, wherein the autoimmune condition comprises rheumatoid arthritis.

110. 92. The composition of claim 91, The composition, wherein the multiple administration modes include oral administration.

111. 92. The composition of claim 91, The composition, wherein the multiple administration modes include subcutaneous injection.

112. 92. The composition of claim 91, The multiple administration modes include intraperitoneal injection.

113. 92. The composition of claim 91, The composition, wherein said multiple modes of administration include intravenous injection.

114. 92. The composition of claim 91, The composition, wherein at least one of the peptides is fused to a protein of Tomato Bushy Stunt Virus (TBSV).

115. 92. The composition of claim 91, The composition, wherein the plant virus-based particle further comprises cowpea mosaic virus (CPMV).

116. 116. The composition of claim 115, The cowpea mosaic virus comprises at least protein subunits, at least one of the protein subunits comprising a large coat protein.

117. 117. The composition of claim 116, The composition, wherein at least one of the protein subunits further comprises a small coat protein.

118. 115. The composition of claim 114, The composition, wherein the Tomato bushy stunt virus comprises an icosahedral capsid, the icosahedral capsid comprising a single capsid protein.

119. 92. The composition of claim 91, The composition further comprises an anti-rheumatic component.

120. 121. The composition of claim 120, A composition wherein the anti-rheumatic component further comprises methotrexate.

121. 92. The composition of claim 91, The composition is administered to prevent the onset of the autoimmune condition.

122. 92. The composition of claim 91, The composition is administered for early treatment of the autoimmune condition.

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