Method, kit, device and computer program for assisting in predicting the severity of respiratory infection and monitoring biomarker measurement values

Abstract

The present invention aims to provide a new method for predicting the severity of respiratory infection. This problem is solved by a method for assisting in the prediction of the severity of respiratory infection, comprising: measuring a biomarker in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, wherein the biomarker is at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6, and CXCL9, and using the measured biomarker value as an indicator for predicting the severity of respiratory infection.

Description

Technical field

[0001] The present invention relates to methods for assisting in the prediction of severe respiratory infections. The present invention relates to methods for monitoring the measured values of biomarkers. The present invention relates to kits for use in these methods. The present invention relates to devices and computer programs for assisting in the prediction of severe respiratory infections. [Background Technology]

[0002] While most patients with respiratory infections have mild symptoms and do not require hospitalization, some may develop severe symptoms and require hospitalization. Since patients with severe symptoms must be treated first, there is a high demand for tools to predict the severity of respiratory infections.

[0003]

Prior art literature

[0004] [Patent Literature]

[0005] [Patent Document 1] Patent No. 6081699

[0006] [Summary of the Invention]

[0007] [Problems to be solved by the invention]

[0008] In view of the above, the present inventors have attempted to search for biomarkers that can predict the severity of respiratory infections. The present invention aims to provide a new means for predicting the severity of respiratory infections using such biomarkers.

[0009] Patent Document 1 describes a method for specifically measuring IL-28B (also known as IFNλ3), a cytokine, in the serum of patients with chronic hepatitis C. However, Patent Document 1 does not describe measuring IFNλ3 for the purpose of predicting the severity of respiratory infections.

[0010]

Methods for solving the problem

[0011] The present inventors discovered that IFNλ3 (interferon λ3), CCL17 (CC chemokine ligand 17), CXCL11 (CXC chemokine ligand 11), IP-10 (interferon-inducible protein-10), IL-6, and CXCL9 (CXC chemokine ligand 9) can be used as biomarkers for predicting the severity of respiratory infections, thereby completing the invention. Thus, the present invention provides a method for assisting in the prediction of the severity of respiratory infection, the method comprising the step of measuring a biomarker in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, wherein the biomarker is at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6, and CXCL9, and using the measured value of the biomarker as an indicator for predicting the severity of respiratory infection.

[0012] The present invention provides a method for monitoring biomarker values in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection. This method comprises using samples collected from the subject at multiple time points to obtain biomarker values, wherein the biomarker is at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6, and CXCL9, and using the biomarker values as an indicator for predicting the severity of the respiratory infection.

[0013] The present invention provides a method for assisting in the prediction of severe respiratory infection, comprising the steps of measuring a biomarker in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, and predicting severe respiratory infection based on the measured value of the biomarker, wherein the biomarker is at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, IL-6, and CXCL9.

[0014] The present invention provides a kit for use in the above method, comprising a reagent containing a substance capable of specifically binding to a biomarker.

[0015] The present invention provides a device for assisting in the prediction of the severity of respiratory infection, comprising a computer including a processor and a memory under the control of the processor, wherein the memory stores a computer program for causing the computer to execute the steps of calculating the measured values of biomarkers in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection, and outputting the measured values of the biomarkers, wherein the measured values of the biomarkers serve as indicators for predicting the severity of respiratory infection, and wherein the biomarkers include at least one selected from IFNλ3, CCL17, CXCL11, IP-10, IL-6, and CXCL9.

[0016] The present invention provides a computer program for assisting in the prediction of severe respiratory infections. This computer program is recorded on a computer-readable medium and causes a computer to execute the steps of calculating biomarker values in a sample collected from a subject suffering from a respiratory infection or a subject suspected of having a respiratory infection and outputting the biomarker values. The biomarker comprises at least one selected from IFNλ3, CCL17, CXCL11, IP-10, IL-6, and CXCL9.

[0017] Effects of the Invention

[0018] According to the present invention, it is possible to assist in predicting the severity of respiratory infection in a subject.

[0019] [Brief description of the accompanying drawings]

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[0090] In the method for assisting in the prediction of the severity of respiratory infection of this embodiment (hereinafter also referred to as the "prediction method"), at least one biomarker selected from IFNλ3, CCL17, CXCL11, IP-10, IL-6, and CXCL9 is first measured as a biomarker in a sample collected from a subject. In the prediction method of this embodiment, the measured value of the biomarker serves as an indicator for predicting the severity of respiratory infection.

[0091] Respiratory infection refers to a disease caused by infection with pathogens through respiratory organs such as the nasal cavity, pharynx, trachea, bronchi and alveoli. The pathogen is not particularly limited, and examples include viruses, bacteria, fungi or parasites. Examples of viruses include coronaviruses and influenza viruses. Coronaviruses are not particularly limited, and examples include α-coronaviruses, β-coronaviruses, γ-coronaviruses and δ-coronaviruses. Examples of β-coronaviruses include SARS-CoV-2, SARS-CoV, HCoV-OC43, HCoV-HKU1, Bat SL-CoV-WIV1, BtCoV-HKU4, BtCoV-HKU5, MERS-CoV and BtCoV-HKU9.

[0092] As subjects in this embodiment, patients suffering from respiratory infection and persons suspected of having respiratory infection can be cited. Patients suffering from respiratory infection refer to those who have been confirmed to be infected by the detection of pathogens, etc., and whose respiratory infection has not yet become severe. Those suspected of having respiratory infection include, for example, those who have cold symptoms such as fever, cough, runny nose, sore throat, and / or dyspnea, shortness of breath during work, abnormal taste and smell, etc., those who have come into contact with patients with respiratory infection, and those who are suspected of having come into contact with them. Contact with a patient with respiratory infection refers to, for example, conversation with the patient at a distance of less than 1m, staying in a confined space where the patient is present, exposure to the patient's saliva, cough, etc., and other droplets.

[0093] In one embodiment, aggravation of respiratory infection refers to pneumonia requiring oxygen inhalation or a state requiring intensive medical treatment including artificial respiration.

[0094] The test subject is not particularly limited as long as it is a liquid sample collected from a subject and suspected of containing the aforementioned biomarkers. Examples of such liquid samples include blood samples, cerebrospinal fluid, sputum, bronchoalveolar wash fluid, nasopharyngeal swab fluid, lymph fluid, urine, stool, saliva, and the like. Among these, blood samples are also preferred. Examples of blood samples include whole blood, plasma, and serum, with plasma and serum being particularly preferred.

[0095] When containing insoluble impurities such as cells in the subject, for example, impurities can be removed from the subject by known means such as centrifugation, filtration, etc. In addition, the subject can also be diluted by an aqueous medium as required. Such an aqueous medium is not particularly limited as long as it does not hinder the mensuration described later, and for example, water, physiological saline, buffer solution, etc. can be enumerated. Buffer solution is not particularly limited as long as it has a buffering effect at a pH near neutrality (for example, a pH below 6 or more and 8 or less). Such a buffer solution, for example, Good's buffer, phosphate buffered saline (PBS), etc., such as HEPES, MES, Tris, PIPES, etc. can be enumerated.

[0096] The biomarkers measured in the prediction method of this embodiment are one or more protein molecules selected from IFNλ3, CCL17, CXCL11, IP-10, IL-6 and CXCL9. IFNλ3, also known as IL-28B, is a protein composed of 200 amino acids encoded by a gene of about 1.5Kb present on chromosome 19. IFNλ3 has 25 signal peptides at the N-terminus, which are cut off when IFNλ3 is secreted outside the cell. CCL17, also known as TARC (Thymus-and activation-regulated chemokine), is a type of Th2 type chemokine. CXCL11 refers to a chemokine also known as I-TAC (Interferon-inducible T-cell Chemoattractant), which is a ligand for the CXCR3 receptor. IP-10, also known as CXCL10, is a type of Th1 type chemokine. IL-6 refers to a type of TH2 type cytokine. CXCL9, also known as MIG (Monokine induced by interferon γ), is a ligand for the CXCR3 receptor and a Th1 chemokine, similar to IP-10. These protein molecules themselves are well known, and their amino acid sequences can be obtained from publicly known databases such as NCBI (National Center for Biotechnology Information). For example, IFNλ3 may have the amino acid sequence represented by SEQ ID NO:1 or SEQ ID NO:2.

[0097] In this embodiment, from the perspective of improving the accuracy of prediction of severe disease, it is preferred to obtain the measured values of two or more biomarkers selected from IFNλ3, CCL17, CXCL11, IP-10, IL-6, and CXCL9. Examples of the two or more biomarkers include a combination of any of the following:

[0098] A combination of IFNλ3 and at least one selected from CCL17, CXCL11, IP-10, IL-6, and CXCL9;

[0099] A combination of CCL17 and at least one selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9;

[0100] A combination of CXCL11 and at least one selected from IFNλ3, CCL17, IP-10, IL-6, and CXCL9;

[0101] A combination of IP-10 and at least one selected from IFNλ3, CCL17, CXCL11, IL-6, and CXCL9;

[0102] A combination of IL-6 and at least one selected from IFNλ3, CCL17, CXCL11, IP-10, and CXCL9; and

[0103] A combination of CXCL9 and at least one selected from the group consisting of IFNλ3, CCL17, CXCL11, IP-10, and IL-6.

[0104] In this embodiment, it is particularly preferable to obtain the measurement value of IFNλ3 and the measurement value of CCL17.

[0105] In the determination of biomarkers, known methods can be used, without particular limitation. In the present embodiment, a method of capturing the marker using a substance that can specifically bind to the biomarker is preferably used. By detecting the biomarker captured by such a substance using a method known in the prior art, the biomarker contained in the subject can be measured. The measured value of the biomarker can be a value reflecting the amount or concentration in the subject. In addition, the measured value is a value that can reflect the concentration or concentration calculated based on the measurement result of the calibrator. Among them, the "value reflecting the concentration" exemplifies the measured value of fluorescence intensity, the measured value of luminescence intensity, the measured value of radioactivity, etc., according to the type of labeling substance described later.

[0106] As a substance that can specifically bind to a biomarker, for example, antibodies, aptamers, etc. can be mentioned, among which antibodies are particularly preferred. Antibodies against biomarkers are not particularly limited as long as they are antibodies that can specifically bind to a biomarker. Such antibodies can also be any of monoclonal antibodies, polyclonal antibodies, and fragments thereof (e.g., Fab, F(ab')2, etc.). In addition, commercially available antibodies can also be used.

[0107] As an antibody capable of specifically binding to IFNλ3, for example, a monoclonal antibody or a fragment thereof having the heavy chain variable region domain and light chain variable region domain described in any one of the following (1) to (5) can be used.

[0108] (1) a heavy chain variable region domain comprising a heavy chain complementarity determining region 1 comprising amino acids 31 to 35 of the amino acid sequence represented by SEQ ID NO: 3, a heavy chain complementarity determining region 2 comprising amino acids 50 to 66, and a heavy chain complementarity determining region 3 comprising amino acids 99 to 113, and a light chain variable region domain comprising a light chain complementarity determining region 1 comprising amino acids 24 to 38 of the amino acid sequence represented by SEQ ID NO: 4, a light chain complementarity determining region 2 comprising amino acids 54 to 60, and a light chain complementarity determining region 3 comprising amino acids 93 to 101;

[0109] (2) a heavy chain variable region domain comprising a heavy chain complementarity determining region 1 comprising amino acids 31 to 35, a heavy chain complementarity determining region 2 comprising amino acids 50 to 66, and a heavy chain complementarity determining region 3 comprising amino acids 99 to 109 of the amino acid sequence represented by SEQ ID NO: 5, and a light chain variable region domain comprising a light chain complementarity determining region 1 comprising amino acids 24 to 38, a light chain complementarity determining region 2 comprising amino acids 54 to 60, and a light chain complementarity determining region 3 comprising amino acids 93 to 101 of the amino acid sequence represented by SEQ ID NO: 6;

[0110] (3) a heavy chain variable region domain comprising a heavy chain complementarity determining region 1 comprising amino acids 31 to 35, a heavy chain complementarity determining region 2 comprising amino acids 50 to 66, and a heavy chain complementarity determining region 3 comprising amino acids 99 to 106 of the amino acid sequence represented by SEQ ID NO: 7, and a light chain variable region domain comprising a light chain complementarity determining region 1 comprising amino acids 24 to 38, a light chain complementarity determining region 2 comprising amino acids 54 to 60, and a light chain complementarity determining region 3 comprising amino acids 93 to 101 of the amino acid sequence represented by SEQ ID NO: 8;

[0111] (4) a heavy chain variable region domain comprising a heavy chain complementarity determining region 1 comprising amino acids 31 to 35, a heavy chain complementarity determining region 2 comprising amino acids 50 to 66, and a heavy chain complementarity determining region 3 comprising amino acids 99 to 106 of the amino acid sequence represented by SEQ ID NO: 9, and a light chain variable region domain comprising a light chain complementarity determining region 1 comprising amino acids 24 to 38, a light chain complementarity determining region 2 comprising amino acids 54 to 60, and a light chain complementarity determining region 3 comprising amino acids 93 to 101 of the amino acid sequence represented by SEQ ID NO: 10; or

[0112] (5) A heavy chain variable region domain comprising a heavy chain complementarity determining region 1 comprising the 31st to 35th amino acid sequence of the amino acid sequence represented by SEQ ID NO: 11, a heavy chain complementarity determining region 2 comprising the 50th to 66th amino acid sequence, and a heavy chain complementarity determining region 3 comprising the 99th to 106th amino acid sequence, and a light chain variable region domain comprising a light chain complementarity determining region 1 comprising the 24th to 38th amino acid sequence of the amino acid sequence represented by SEQ ID NO: 12, a light chain complementarity determining region 2 comprising the 54th to 60th amino acid sequence, and a light chain complementarity determining region 3 comprising the 93rd to 101st amino acid sequence.

[0113] The method for measuring biomarkers using antibodies is not particularly limited and can be appropriately selected from known immunoassays. In this embodiment, enzyme-linked immunosorbent assay (ELISA) is preferred, and sandwich ELISA is particularly preferred. As an example of a measurement process, the following describes the measurement of biomarkers in a subject by sandwich ELISA.

[0114] First, a complex containing a biomarker, an antibody for capturing the biomarker (hereinafter also referred to as a "capture antibody"), and an antibody for detecting the biomarker (hereinafter also referred to as a "detection antibody") is formed on a solid phase. When the subject contains a biomarker, a complex can be formed by mixing the subject, the capture antibody, and the detection antibody. Furthermore, the above-mentioned complex can be formed on the solid phase by bringing a solution containing the complex into contact with a solid phase on which the capture antibody can be fixed. Alternatively, a solid phase on which the capture antibody is pre-fixed can be used. That is, the above-mentioned complex can be formed on the solid phase by bringing the solid phase on which the capture antibody is fixed, the subject, and the detection antibody into contact. Furthermore, when both the capture antibody and the detection antibody are monoclonal antibodies, it is preferred that their epitopes are different from each other.

[0115] The embodiment of fixing the capture antibody to the solid phase is not particularly limited. For example, the capture antibody and the solid phase can be directly bonded, or the capture antibody and the solid phase can be indirectly bound via another substance. As direct binding, for example, physical adsorption can be mentioned. As indirect binding, for example, binding via a combination of biotin and avidin can be mentioned. In this case, by pre-modifying the capture antibody with biotin and pre-binding the solid phase with avidin, the capture antibody and the solid phase can be indirectly bound via the binding of biotin and avidin. Biotin includes biotin and biotin analogs such as desthiobiotin. Avidin includes avidin and avidin analogs such as streptavidin and Tamavidin (registered trademark).

[0116] The raw material of the solid phase is not particularly limited, for example, it can be selected from organic polymer compounds, inorganic compounds, biopolymers, etc. As organic polymer compounds, latex, polystyrene, polypropylene, etc. can be mentioned. As inorganic compounds, magnetic bodies (iron oxide, chromium oxide and ferrite, etc.), silicon oxide, aluminum oxide, glass, etc. can be mentioned. As biopolymers, insoluble agarose, insoluble dextran, gelatin, cellulose, etc. can be mentioned. It is also possible to use two or more of these in combination. The shape of the solid phase is not particularly limited, for example, particles, membranes, microplates, microtubes, test tubes, etc. can be mentioned. Among them, particles are also preferred, and magnetic particles are particularly preferred.

[0117] In this embodiment, B / F (Bound / Free) separation can also be performed between the complex formation step and the complex detection step to remove unreacted free components that did not form a complex. Unreacted free components refer to components that do not constitute a complex. For example, capture antibodies and detection antibodies that are not bound to biomarkers can be mentioned. The means of B / F separation are not particularly limited. When the solid phase is a particle, B / F separation can be performed by recovering only the solid phase that captures the complex by centrifugal separation. When the solid phase is a container such as a microplate or microtube, B / F separation can be performed by removing the liquid containing unreacted free components. In addition, when the solid phase is a magnetic particle, B / F separation can be performed by removing the liquid containing unreacted free components by suctioning it with a nozzle while the magnetic particles are magnetically bound by a magnet. This is preferred from the perspective of automation. After removing the unreacted free components, the solid phase that captures the complex can also be washed with a suitable aqueous medium such as PBS.

[0118] Furthermore, by detecting the complex formed on the solid phase using methods known in the art, it is possible to obtain a measurement value of the biomarker contained in the test subject. For example, when using an antibody labeled with a labeling substance as the detection antibody, the measurement value of the marker in the liquid sample can be obtained by detecting the signal generated by the labeling substance. Alternatively, when using a labeled secondary antibody specific to the detection antibody, the measurement value of the biomarker in the liquid sample can also be obtained.

[0119] Furthermore, as an example of a method for measuring a biomarker using an antibody, the immune complex transfer method described in Japanese Patent Application Laid-Open No. 1-254868 can also be used.

[0120] In this specification, "detection of a signal" includes qualitative detection of the presence of a signal, quantitative detection of signal intensity, and semi-quantitative detection of signal intensity. Semi-quantitative detection refers to expressing the signal intensity in stages, such as "no signal," "weak," "medium," or "strong." In this embodiment, quantitative or semi-quantitative detection of signal intensity is preferred.

[0121] The labeling substance is not particularly limited. For example, the labeling substance may be a substance that generates a signal by itself (hereinafter also referred to as a "signal generating substance"), or a substance that catalyzes the reaction of other substances to generate a signal. Examples of signal generating substances include fluorescent substances and radioactive isotopes. Examples of substances that catalyze the reaction of other substances to generate a detectable signal include enzymes. Examples of enzymes include alkaline phosphatase, peroxidase, β-galactosidase, and luciferase. Examples of fluorescent substances include fluorescent dyes such as fluorescein isothiocyanate (FITC), rhodamine, and Alexa Fluor (registered trademark), and fluorescent proteins such as GFP. Examples of radioactive isotopes include 125 I. 14 C. 32 P, etc. Among them, enzymes are also preferred as labeling substances, and alkaline phosphatase and peroxidase are particularly preferred.

[0122] The method of detecting a signal itself is well known in the prior art. In the present embodiment, a method of measuring the type of signal derived from the above-mentioned labeling substance can be appropriately selected. For example, when the labeling substance is an enzyme, the method can be performed by measuring the signal of light, color, etc. generated by reacting the substrate of the enzyme using a known device such as a spectrophotometer.

[0123] The substrate of the enzyme can be appropriately selected from known substrates according to the type of the enzyme. For example, when alkaline phosphatase (ALP) is used as an enzyme, as substrates, chemiluminescent substrates such as CDP-Star (registered trademark) (4-chloro-3-(methoxyspiro[1,2-dioxetane-3,2'-(5'-chloro)tricyclo[3.3.1.13,7]decane]-4-yl)phenyl phosphate and CSPD (registered trademark) (3-(4-methoxyspiro[1,2-dioxetane-3,2-(5'-chloro)tricyclo[3.3.1.13,7]decane]-4-yl)phenyl phosphate) and chromogenic substrates such as 5-bromo-4-chloro-3-indolyl phosphate (BCIP), 5-bromo-6-chloro-indolyl phosphate and p-nitrophenyl phosphate can be mentioned. When peroxidase is used as the enzyme, examples of the substrate include chemiluminescent substrates such as LUMINOR and its derivatives, and colorimetric substrates such as 2,2'-azinobis(3-ethylbenzothiazoline-6-ammonium sulfonate) (ABTS), 1,2-phenylenediamine (OPD), and 3,3',5,5'-tetramethylbenzidine (TMB).

[0124] When the labeling substance is a radioisotope, the radiation as a signal can be measured using a known device such as a scintillation counter. Alternatively, when the labeling substance is a fluorescent substance, the fluorescence as a signal can be measured using a known device such as a fluorescence microplate reader. Furthermore, the excitation wavelength and fluorescence wavelength can be appropriately determined depending on the type of fluorescent substance used.

[0125] The detection result of the signal can be used as the measured value of the biomarker. For example, when the intensity of the quantitative detection signal is detected, the measured value of the signal intensity itself or the value obtained from the measured value can be used as the measured value of the biomarker. As the value obtained from the measured value of the signal intensity, for example, the value obtained by subtracting the measured value of the negative control sample or the value of the background from the measured value, the value obtained by applying the measured value to a calibration curve, etc. can be cited. The negative control sample can be appropriately selected, for example, a subject obtained from a mild patient (for example, a person who has recovered from a severe illness among an infectious disease patient), a subject obtained from a healthy person, etc.

[0126] In this embodiment, it is preferred to measure the biomarkers contained in the subject by immunoassay such as EIA or ELISA. The measurement of biomarkers can be performed using commercially available devices and reagents such as the HISCL series (manufactured by Sysmex Corporation) and the Bio-Plex Multiplex System (manufactured by Bio-Rad Corporation).

[0127] In this embodiment, the measured values of the above-mentioned biomarkers can be used as indicators to indicate whether the subject's respiratory infection has become severe. For example, by comparing the measured values of the obtained biomarkers with the threshold values corresponding to the biomarkers, the measured values of the biomarkers can be used as indicators to indicate whether the subject's respiratory infection is likely to become severe. In this embodiment, the likelihood of severe respiratory infection is the risk of the subject's respiratory infection becoming severe after a specified period (e.g., 1 day to 1 month) has passed since the date the sample was collected from the subject.

[0128] As shown in the Examples described below, IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 show high values in patients with severe respiratory infections and low values in patients with mild respiratory infections. The measured values of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 can be used as indicators indicating the likelihood of severe respiratory infections by comparing them with threshold values corresponding to each biomarker. In one embodiment, the biomarker includes IFNλ3, and when the measured value of IFNλ3 is above the threshold value corresponding to IFNλ3, it indicates that the subject has a high likelihood of severe respiratory infections. When the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3, it indicates that the subject has a low likelihood of severe respiratory infections.

[0129] In one embodiment, the biomarker includes CXCL11, and when the measured value of CXCL11 is above a threshold value corresponding to CXCL11, it indicates that the subject has a high likelihood of developing severe respiratory infection. When the measured value of CXCL11 is below the threshold value corresponding to CXCL11, it indicates that the subject has a low likelihood of developing severe respiratory infection.

[0130] In one embodiment, the biomarker includes IP-10, and when the measured value of IP-10 is above a threshold value corresponding to IP-10, it indicates that the subject has a high probability of developing a severe respiratory infection. When the measured value of IP-10 is below the threshold value corresponding to IP-10, it indicates that the subject has a low probability of developing a severe respiratory infection.

[0131] In one embodiment, the biomarker includes IL-6, and when the measured value of IL-6 is above a threshold value corresponding to IL-6, it indicates that the subject has a high probability of developing severe respiratory infection. When the measured value of IL-6 is below the threshold value corresponding to IL-6, it indicates that the subject has a low probability of developing severe respiratory infection.

[0132] In one embodiment, the biomarker includes CXCL9, and when the measured value of CXCL9 is above a threshold value corresponding to CXCL9, it indicates that the subject has a high possibility of developing severe respiratory infection, and when the measured value of CXCL9 is below the threshold value corresponding to CXCL9, it indicates that the subject has a low possibility of developing severe respiratory infection.

[0133] In one embodiment, the biomarkers may include at least two selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and their measured values may indicate a subject's risk of developing severe respiratory infection. Specifically, if the measured value of at least one of the biomarkers selected from the above group exceeds a threshold value corresponding to that biomarker, the subject is likely to develop severe respiratory infection. If the measured values of all the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker, the subject is likely to develop severe respiratory infection.

[0134] As an example, the biomarkers are 2 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when any one or both of them are above the corresponding threshold, it indicates that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 3 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when at least one of them is above the corresponding threshold, it indicates that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 4 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when at least one of them is above the corresponding threshold, it indicates that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 5 consisting of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when at least one of them is above the corresponding threshold, it indicates that the subject has a high probability of severe respiratory infection.

[0135] In another embodiment, the biomarkers may include at least two selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and their measured values may be used to categorize the risk of severe respiratory infection in a subject into three stages. Specifically:

[0136] When all the measured values of the biomarkers selected from the above group are equal to or higher than the threshold value corresponding to each biomarker, it indicates that the possibility of the respiratory infection becoming severe is high;

[0137] When the measured value of at least one of the biomarkers selected from the above group is equal to or higher than the threshold value corresponding to the biomarker, it indicates that the possibility of the respiratory infection becoming severe is moderate;

[0138] When all the measured values of the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker, it indicates that the possibility of the respiratory infection becoming severe is low.

[0139] As an example, the biomarkers are 2 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when both of them are above the corresponding threshold value, it indicates that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 3 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when all of them are above the corresponding threshold value, it indicates that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 4 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when all of them are above the corresponding threshold value, it indicates that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 5 consisting of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when all of them are above the corresponding threshold value, it indicates that the subject has a high probability of severe respiratory infection.

[0140] In this embodiment, CCL17 in the subject can also be measured. As shown in the examples described below, CCL17 is different from other biomarkers. It shows low values in patients with severe respiratory infections and high values in patients with mild respiratory infections. The measured value of CCL17 can be used as an indicator to indicate the possibility of severe respiratory infections by comparing it with a threshold value corresponding to CCL17. In one embodiment, the biomarker contains CCL17. When the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it indicates that the possibility of severe respiratory infections in the subject is high. When the measured value of CCL17 becomes above the threshold value corresponding to CCL17, it indicates that the possibility of severe respiratory infections in the subject is low.

[0141] In a preferred embodiment, CCL17 is used in combination with at least one biomarker selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9. For example, the biomarker may contain at least one selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 and CCL17, and their measured values may indicate the risk of severe respiratory infection in the subject. Specifically, when at least one of the measured values of the biomarkers selected from the above group is above the threshold value corresponding to the biomarker, and / or the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it indicates that the subject has a high probability of severe respiratory infection. When all the measured values of the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker, and the measured value of CCL17 is above the threshold value corresponding to CCL17, it indicates that the subject has a low probability of severe respiratory infection.

[0142] In another embodiment, the biomarkers may include at least one selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and CCL17, and their measured values may be used to categorize the risk of severe respiratory infection in a subject into three stages. Specifically:

[0143] If all of the measured values of the biomarkers selected from the above group are above the threshold values corresponding to the respective biomarkers, and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it indicates that the subject is highly likely to have a severe respiratory infection.

[0144] If at least one of the measured values of the biomarkers selected from the above group is equal to or higher than the threshold value corresponding to the biomarker, and the measured value of CCL17 is higher than the threshold value corresponding to CCL17, it indicates that the possibility of the respiratory infection in the subject becoming severe is moderate;

[0145] If at least one of the measured values of the biomarkers selected from the above group is lower than the threshold value corresponding to the biomarker, and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it indicates that the possibility of the respiratory infection in the subject becoming severe is moderate;

[0146] When all the measured values of the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker, and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it indicates that the possibility of the respiratory infection in the subject becoming severe is low.

[0147] In a more specific example, the biomarkers include IFNλ3 and CCL17,

[0148] If the measured value of IFNλ3 is equal to or higher than the threshold corresponding to IFNλ3 and the measured value of CCL17 is lower than the threshold corresponding to CCL17, it indicates that the possibility of the respiratory infection in the subject becoming severe is high;

[0149] When the measured value of IFNλ3 is equal to or higher than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it indicates that the possibility of the subject's respiratory infection becoming severe is moderate;

[0150] If the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3, and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it indicates that the possibility of the subject's respiratory infection becoming severe is moderate;

[0151] When the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it indicates that the possibility of the respiratory infection in the subject becoming severe is low.

[0152] The threshold value corresponding to each biomarker is not particularly limited and can be appropriately set. For example, a subject is collected from a plurality of respiratory infection patients, and the biomarkers in the subject are measured to obtain a measured value. After a specified period (such as 2 weeks) has passed since the subject was collected, it is confirmed whether the respiratory infection has become severe or not. The data of the measured value obtained are classified into the data of the patient group of severe disease and the data of the patient group of non-severe disease. And then, for each biomarker, the value that can distinguish the patient group of severe disease and the patient group of non-severe disease with the highest accuracy is obtained, and the value is set as the threshold value. In the setting of the threshold value, sensitivity, specificity, positive rate, negative rate, etc. can be considered.

[0153] In this embodiment, the threshold value corresponding to IFNλ3 is set within a range of, for example, 4 pg / mL to 15 pg / mL. The threshold value corresponding to CXCL11 is set within a range of, for example, 20 pg / mL to 40 pg / mL. The threshold value corresponding to IP-10 is set within a range of, for example, 400 pg / mL to 1200 pg / mL. The threshold value corresponding to IL-6 is set within a range of, for example, 4 pg / mL to 6 pg / mL. The threshold value corresponding to CXCL9 is set within a range of, for example, 30 pg / mL to 40 pg / mL. The threshold value corresponding to CCL17 is set within a range of, for example, 40 pg / mL to 100 pg / mL.

[0154] Medical professionals such as physicians can also combine biomarker measurement values with other information to assess the risk of severe respiratory infections. "Other information" includes insights from lung X-ray or CT images and other medical insights.

[0155] As an index for predicting the severity of respiratory infection of the experimenter of the present embodiment, the time variation of the measured value of the biomarker in the experimenter can also be obtained.The time variation of the measured value of the biomarker is not particularly limited as long as it is information showing the transition of the measured value of the biomarker in the subject collected regularly or irregularly for many times from the experimenter.As such a time variation, for example, a value calculated from a plurality of measured values (for example, a difference, a ratio, etc. of the measured values of 2 subjects collected at any 2 time points), a record of measured values (for example, a table of measured values or a coordinate graph plotted on the measured values, etc.) etc. can be cited.

[0156] In this embodiment, when the obtained biomarker measurement values indicate that the subject's respiratory infection is likely to become severe, the subject can be subjected to medical intervention for the severe development of the respiratory infection. Examples of medical intervention include the administration of pharmaceuticals, surgery, immunotherapy, gene therapy, oxygen supply treatment, treatment using an artificial heart-lung device, and the like. The pharmaceutical agent can be appropriately selected from known therapeutic drugs for respiratory infections or pharmaceuticals that are candidates for such drugs. For example, when the respiratory infection is SARS-CoV-2 infection, pharmaceutical agents that can be cited include drugs with antiviral effects, drugs that reduce inflammation, ACE inhibitors, and the like. Specific examples include Favipiravir, Lopinavir, Ritonavir, Nafamostat, Camostat, Remdesivir, Ribavirin, Ivermectin, Ciclesonide, Chloroquine, Hydroxychloroquine, Interferon, Tocilizumab, Sarilumab, Tofacitinib, Baricitinib, Ruxolitinib, Acalabrutinib, Ravulizumab, Eritoran, Ibudilast, HLCM051, and LY3127804.

[0157] The prediction method of this embodiment may also include a step of predicting the severity of a respiratory infection based on the measured values of biomarkers obtained from a sample collected from a subject. In this step, for example, the measured values of the obtained biomarkers may be compared with threshold values corresponding to the biomarkers, and based on the comparison results, the likelihood of the subject's respiratory infection becoming severe may be determined to be high or low. The details of the threshold values corresponding to the biomarkers are described above.

[0158] In one embodiment, the biomarker includes IFNλ3, and when the measured value of IFNλ3 is equal to or higher than a threshold value corresponding to IFNλ3, it can be determined that the subject has a high likelihood of developing severe respiratory infection. When the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3, it can be determined that the subject has a low likelihood of developing severe respiratory infection.

[0159] In one embodiment, the biomarker includes CXCL11, and when the measured value of CXCL11 is equal to or higher than a threshold value corresponding to CXCL11, it can be determined that the subject has a high likelihood of developing severe respiratory infection. When the measured value of CXCL11 is lower than the threshold value corresponding to CXCL11, it can be determined that the subject has a low likelihood of developing severe respiratory infection.

[0160] In one embodiment, the biomarker includes IP-10, and when the measured value of IP-10 is above a threshold value corresponding to IP-10, it can be determined that the subject has a high probability of developing a severe respiratory infection. When the measured value of IP-10 is below the threshold value corresponding to IP-10, it can be determined that the subject has a low probability of developing a severe respiratory infection.

[0161] In one embodiment, the biomarker includes IL-6, and when the measured value of IL-6 is equal to or higher than a threshold value corresponding to IL-6, it can be determined that the subject has a high probability of developing severe respiratory infection. When the measured value of IL-6 is lower than the threshold value corresponding to IL-6, it can be determined that the subject has a low probability of developing severe respiratory infection.

[0162] In one embodiment, the biomarker includes CXCL9, and when the measured value of CXCL9 is equal to or higher than a threshold value corresponding to CXCL9, it can be determined that the subject has a high likelihood of developing severe respiratory infection. When the measured value of CXCL9 is lower than the threshold value corresponding to CXCL9, it can be determined that the subject has a low likelihood of developing severe respiratory infection.

[0163] In one embodiment, the biomarker includes CCL17, and when the measured value of CCL17 is lower than a threshold value corresponding to CCL17, it can be determined that the subject has a high likelihood of developing severe respiratory infection. When the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it can be determined that the subject has a low likelihood of developing severe respiratory infection.

[0164] In one embodiment, the biomarkers may include at least two selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and the risk of severe respiratory infection in a subject may be determined based on their measured values. Specifically, if the measured value of at least one of the biomarkers selected from the above group exceeds a threshold value corresponding to that biomarker, the subject may be determined to have a high likelihood of severe respiratory infection. If the measured values of all the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker, the subject may be determined to have a low likelihood of severe respiratory infection.

[0165] As an example, the biomarkers are 2 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when any one or both of them are above the corresponding threshold value, it is determined that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 3 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when at least one of them is above the corresponding threshold value, it is determined that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 4 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when at least one of them is above the corresponding threshold value, it is determined that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 5 consisting of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when at least one of them is above the corresponding threshold value, it is determined that the subject has a high probability of severe respiratory infection.

[0166] In another embodiment, the biomarkers may include at least two selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and based on their measured values, the risk of severe respiratory infection in the subject may be determined to be categorized into three stages. Specifically:

[0167] When all the measured values of the biomarkers selected from the above group are equal to or higher than the threshold value corresponding to each biomarker, it is determined that there is a high possibility that the respiratory infection has become severe;

[0168] When at least one of the measured values of the biomarkers selected from the above group is equal to or higher than a threshold value corresponding to the biomarker, the possibility of the respiratory infection becoming severe is determined to be moderate;

[0169] When all the measured values of the biomarkers selected from the above group are lower than the threshold value corresponding to each biomarker, it can be determined that the possibility of the respiratory infection becoming severe is low.

[0170] As an example, the biomarkers are 2 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when both of them are above the corresponding threshold value, it is determined that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 3 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when all of them are above the corresponding threshold value, it is determined that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 4 selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when all of them are above the corresponding threshold value, it is determined that the subject has a high probability of severe respiratory infection. In another example, the biomarkers are 5 consisting of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and when all of them are above the corresponding threshold value, it is determined that the subject has a high probability of severe respiratory infection.

[0171] In a further embodiment, the biomarkers may include at least one selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and CCL17, and the risk of severe respiratory infection in a subject may be determined based on their measured values. Specifically, if the measured value of at least one of the biomarkers selected from the above group is above the threshold value corresponding to that biomarker, and / or the measured value of CCL17 is below the threshold value corresponding to CCL17, the subject may be determined to have a high likelihood of severe respiratory infection. If the measured values of all the biomarkers selected from the above group are below the threshold value corresponding to each biomarker, and the measured value of CCL17 is above the threshold value corresponding to CCL17, the subject may be determined to have a low likelihood of severe respiratory infection.

[0172] In another embodiment, the biomarkers may include at least one selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and CCL17, and based on their measured values, the risk of severe respiratory infection in the subject may be determined to be categorized into three stages. Specifically:

[0173] When all of the measured values of the biomarkers selected from the above group are above the threshold values corresponding to the respective biomarkers, and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it is determined that the subject has a high possibility of developing a severe respiratory infection;

[0174] When at least one of the measured values of the biomarkers selected from the above group is equal to or higher than the threshold value corresponding to the biomarker, and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, the possibility of the subject's respiratory infection becoming severe is determined to be moderate;

[0175] If at least one of the measured values of the biomarkers selected from the above group is lower than the threshold value corresponding to the biomarker, and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, the possibility of the subject's respiratory infection becoming severe is determined to be moderate;

[0176] When all the measured values of the biomarkers selected from the above group are lower than the threshold values corresponding to the respective biomarkers and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it can be determined that the possibility of the respiratory infection in the subject becoming severe is low.

[0177] In a more specific example, the biomarkers include IFNλ3 and CCL17,

[0178] When the measured value of IFNλ3 is above the threshold value corresponding to IFNλ3 and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it is determined that the possibility of the subject's respiratory infection becoming severe is high; When the measured value of IFNλ3 is above the threshold value corresponding to IFNλ3 and the measured value of CCL17 is above the threshold value corresponding to CCL17, it is determined that the possibility of the subject's respiratory infection becoming severe is moderate;

[0179] If the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3, and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, the possibility of the subject's respiratory infection becoming severe is determined to be moderate;

[0180] When the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17, it can be determined that the possibility of the respiratory infection in the subject becoming severe is low.

[0181] One embodiment of the present invention is a method for monitoring the measured values of biomarkers in a sample collected from a subject (hereinafter also referred to as a "monitoring method"). In the monitoring method of this embodiment, the measured values of the biomarkers are obtained using samples collected from the subject at multiple time points. The details of the subject, the sample, the biomarkers, and the acquisition of their measured values are the same as those described for the prediction method of this embodiment above.

[0182] In this embodiment, the multiple time points can be any time point that is different from each other as long as they are more than two. For example, the multiple time points include a first time point and a second time point that is different from the first time point. The first time point is not particularly limited and is an arbitrary time point. For example, the first time point can also be the time point at which the subject is confirmed to have a respiratory infection, the time point at which the subject presents symptoms of a respiratory infection, the time point at which the subject is hospitalized, etc. The second time point is not particularly limited as long as it is different from the first time point. Preferably, the second time point is a time point that is within 1 month from the first time point. Specifically, the second time point is a time point that is 0.5 hours, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 12 hours, 15 hours, 18 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 8 days, 9 days, 10 days, 12 days, 2 weeks, 3 weeks, 4 weeks or 1 month from the first time point.

[0183] In this embodiment, "samples collected from a subject at multiple time points" refers to samples collected from the same subject at each of the multiple time points. For example, this includes a first sample collected from a subject at a first time point and a second sample collected from the same subject at a second time point different from the first time point. In the monitoring method of this embodiment, biomarkers can be measured at the time of sample collection, or the collected samples can be stored and measured collectively.

[0184] In the monitoring method of this embodiment, the biomarker values measured in the same subject are monitored to form an indicator for predicting the severity of respiratory infection. In a preferred embodiment, the values of the same biomarker are obtained at multiple time points. The biomarker values measured in each subject can also be compared with the threshold value corresponding to the biomarker, and based on the comparison results, the probability of the subject's respiratory infection becoming severe can be indicated as high or low. The details of the threshold value corresponding to the biomarker are the same as those described for the prediction method of this embodiment above.

[0185] In one embodiment, the biomarker includes IFNλ3, and when the measured value of IFNλ3 at at least one of a plurality of time points is above a threshold value corresponding to IFNλ3, it indicates that the subject has a high likelihood of developing severe respiratory infection. When the measured value of IFNλ3 at any of the plurality of time points is below the threshold value corresponding to IFNλ3, it indicates that the subject has a low likelihood of developing severe respiratory infection.

[0186] In one embodiment, the biomarker includes CXCL11, and when the measured value of CXCL11 at at least any one of a plurality of time points is above a threshold value corresponding to CXCL11, it indicates that the subject has a high likelihood of developing severe respiratory infection. When the measured value of CXCL11 at any one of the plurality of time points is below the threshold value corresponding to CXCL11, it indicates that the subject has a low likelihood of developing severe respiratory infection.

[0187] In one embodiment, the biomarker includes IP-10, and when the measured value of IP-10 at at least any one of a plurality of time points is above a threshold value corresponding to IP-10, it indicates that the subject has a high likelihood of developing a severe respiratory infection. When the measured value of IP-10 at any one of the plurality of time points is below the threshold value corresponding to IP-10, it indicates that the subject has a low likelihood of developing a severe respiratory infection.

[0188] In one embodiment, the biomarker includes IL-6, and when the measured value of IL-6 at at least any one of a plurality of time points is above a threshold value corresponding to IL-6, it indicates that the subject has a high likelihood of developing severe respiratory infection. When the measured value of IL-6 at any one of the plurality of time points is below the threshold value corresponding to IL-6, it indicates that the subject has a low likelihood of developing severe respiratory infection.

[0189] In one embodiment, the biomarker includes CXCL9, and when the measured value of CXCL9 at at least any one of a plurality of time points is above a threshold value corresponding to CXCL9, it indicates that the subject has a high likelihood of developing severe respiratory infection. When the measured value of CXCL9 at any one of the plurality of time points is below the threshold value corresponding to CXCL9, it indicates that the subject has a low likelihood of developing severe respiratory infection.

[0190] In one embodiment, the biomarkers may include at least two selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and the measured values of the biomarkers at multiple time points may indicate a subject's risk of developing a severe respiratory infection. Specifically, if at least one of the measured values of the biomarkers selected from the above group is above a threshold value corresponding to the biomarker at at least any one of the multiple time points, the subject is indicated to have a high likelihood of developing a severe respiratory infection. If at any one of the multiple time points, all of the measured values of the biomarkers selected from the above group are below the threshold value corresponding to each biomarker, the subject is indicated to have a low likelihood of developing a severe respiratory infection.

[0191] The measured value of CCL17 can also be obtained for each of the aforementioned subjects. In one embodiment, the biomarker includes CCL17, and if the measured value of CCL17 is lower than a threshold value corresponding to CCL17 at at least one of the multiple time points, it indicates that the subject has a high likelihood of developing a severe respiratory infection. If the measured value of CCL17 is above the threshold value corresponding to CCL17 at any of the multiple time points, it indicates that the subject has a low likelihood of developing a severe respiratory infection.

[0192] In a further embodiment, the biomarkers may include at least one selected from IFNλ3, CXCL11, IP-10, IL-6, and CXCL9, and CCL17, and the biomarker values measured at multiple time points may indicate a subject's risk of developing a severe respiratory infection. Specifically, if at least one of the biomarker values selected from the group is above a threshold value corresponding to the biomarker at at least any one of the multiple time points, and / or if the CCL17 value is below the threshold value corresponding to CCL17 at at least any one of the multiple time points, then the subject is likely to develop a severe respiratory infection. If at any one of the multiple time points, all of the biomarker values selected from the group are below the threshold value corresponding to each biomarker, and if the CCL17 value is above the threshold value corresponding to CCL17 at any one of the multiple time points, then the subject is likely to develop a severe respiratory infection.

[0193] The conditions for terminating the monitoring method of this embodiment are not particularly limited and can be determined appropriately by a medical practitioner, such as a physician. For example, if the measured values of biomarkers obtained from samples collected from a subject at multiple time points indicate a high likelihood of severe respiratory infection in the subject, the monitoring method of this embodiment can be terminated. In this case, medical intervention to address the severity of the respiratory infection is preferably performed in the subject. Details of the medical intervention are described above.

[0194] In each of the above embodiments, when the measured values of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 are equal to the threshold values corresponding to the respective biomarkers, it can be indicated that the subject has a high or low likelihood of developing a severe respiratory infection. Furthermore, in each of the above embodiments, when the measured value of CCL17 is equal to the threshold value corresponding to CCL17, it can be indicated that the subject has a low or high likelihood of developing a severe respiratory infection.

[0195] One embodiment of the present invention is a kit for use in the prediction and / or monitoring methods of the present embodiment described above. The kit of this embodiment contains at least one reagent selected from the group consisting of a reagent containing a substance that specifically binds to IFNλ3, a reagent containing a substance that specifically binds to CCL17, a reagent containing a substance that specifically binds to CXCL11, a reagent containing a substance that specifically binds to IP-10, a reagent containing a substance that specifically binds to IL-6, and a reagent containing a substance that specifically binds to CXCL9. In a further embodiment, the kit may also include at least one reagent selected from the group consisting of a reagent containing a substance that specifically binds to IFNλ3, a reagent containing a substance that specifically binds to CXCL11, a reagent containing a substance that specifically binds to IP-10, a reagent containing a substance that specifically binds to IL-6, and a reagent containing a substance that specifically binds to CXCL9, and a reagent containing a substance that specifically binds to CCL17. In a preferred embodiment, the kit includes a reagent containing a substance that specifically binds to IFNλ3 and a reagent containing a substance that specifically binds to CCL17. Examples of substances that specifically bind to each biomarker include antibodies and aptamers. Among these, antibodies are particularly preferred.

[0196] An example of the kit of this embodiment is shown in Figure 1A .exist Figure 1A In the figure, 11 represents a reagent kit, 12 represents a first container containing a reagent containing a substance that specifically binds to IFNλ3, 13 represents a second container containing a reagent containing a substance that specifically binds to CCL17, 14 represents a packaging box, and 15 represents accompanying documents. The accompanying documents may also describe the composition, usage, and storage methods of each reagent. While the kit in this example includes a reagent containing a substance that specifically binds to IFNλ3 and a reagent containing a substance that specifically binds to CCL17, these reagents may also be replaced with a reagent containing a substance that specifically binds to another biomarker.

[0197] In a preferred embodiment, the kit of this embodiment contains a capture antibody and a detection antibody for the biomarker. The detection antibody may also be labeled with a labeling substance. The details of the capture antibody, detection antibody, and labeling substance are the same as those described for the prediction method of this embodiment above. Furthermore, the kit may also contain a solid phase and a substrate. The details of the solid phase and substrate are the same as those described for the prediction method of this embodiment above.

[0198] An example of a kit according to a further embodiment is shown in Figure 1B .exist Figure 1BIn the figure, 21 represents a reagent kit, 22 represents a first container containing a reagent comprising a capture antibody against IFNλ3, 23 represents a second container containing a reagent comprising a labeled antibody for detecting IFNλ3, 24 represents a third container containing a reagent comprising a capture antibody against CCL17, 25 represents a fourth container containing a reagent comprising a labeled antibody for detecting CCL17, 26 represents accompanying documentation, and 27 represents a packaging box. The kit in this example includes reagents comprising a capture antibody against IFNλ3 and a labeled antibody for detecting IFNλ3, and reagents comprising a capture antibody against CCL17 and a labeled antibody for detecting IFNλ3. However, the kit may also include reagents comprising a capture antibody against other biomarkers and a labeled antibody for detecting IFNλ3.

[0199] In any of the above kits, a calibrator is also preferably included. Examples of calibrators include calibrators for the quantification of IFNλ3 (calibrators for IFNλ3), calibrators for the quantification of CXCL11 (calibrators for CXCL11), calibrators for the quantification of IP-10 (calibrators for IP-10), calibrators for the quantification of IL-6 (calibrators for IL-6), calibrators for the quantification of CXCL9 (calibrators for CXCL9), and calibrators for the quantification of CCL17 (calibrators for CCL17). For example, the IFNλ3 calibrator may include a buffer solution containing no IFNλ3 (negative control) and a buffer solution containing IFNλ3 at a known concentration. For example, the CXCL11 calibrator may include a buffer solution containing no CXCL11 (negative control) and a buffer solution containing CXCL11 at a known concentration. For example, the IP-10 calibrator may include a buffer solution containing no IP-10 (negative control) and a buffer solution containing IP-10 at a known concentration. For example, IL-6 calibrators may include a buffer solution containing no IL-6 (negative control) and a buffer solution containing IL-6 at a known concentration. For example, CXCL9 calibrators may include a buffer solution containing no CXCL9 (negative control) and a buffer solution containing CXCL9 at a known concentration. For example, CCL17 calibrators may include a buffer solution containing no CCL17 (negative control) and a buffer solution containing CCL17 at a known concentration.

[0200] An example of a kit according to a further embodiment is shown in Figure 1C .exist Figure 1CIn the figure, 31 represents a reagent kit, 32 represents a first container containing a reagent containing an antibody for capturing IFNλ3, 33 represents a second container containing a reagent containing a labeled antibody for detecting IFNλ3, 34 represents a third container containing a reagent containing an antibody for capturing CCL17, 35 represents a fourth container containing a reagent containing a labeled antibody for detecting CCL17, 36 represents a fifth container containing a buffer solution containing neither IFNλ3 nor CCL17, 37 represents a sixth container containing a buffer solution containing both IFNλ3 and CCL17 at their respective specified concentrations, 38 represents a packaging box, and 39 represents accompanying documentation. The buffer solution containing neither IFNλ3 nor CCL17 and the buffer solution containing both IFNλ3 and CCL17 at their respective specified concentrations can be used as calibrants for the quantitative determination of IFNλ3 and CCL17. The kit in this example includes reagents for capturing antibodies and labeled antibodies for detecting IFNλ3, reagents for capturing antibodies and labeled antibodies for detecting CCL17, and calibrators for quantifying IFNλ3 and CCL17. However, instead of these reagents, the kit may include reagents for capturing antibodies and labeled antibodies for detecting other biomarkers and calibrators.

[0201] In a further embodiment, a container containing a reagent selected from a reagent containing a substance that specifically binds to IFNλ3, a reagent containing a substance that specifically binds to CCL17, a reagent containing a substance that specifically binds to CXCL11, a reagent containing a substance that specifically binds to IP-10, a reagent containing a substance that specifically binds to IL-6, and a reagent containing a substance that specifically binds to CXCL9 may be packaged in a box and provided to the user as a kit. The box may also contain accompanying documents. The accompanying documents may also describe the composition, usage, storage method, etc. of the reagent. An example of a kit is shown in Figure 1D .exist Figure 1D In the figure, 41 represents a reagent kit, 42 represents a container containing a reagent containing a substance that specifically binds to IFNλ3, 43 represents a packaging box, and 44 represents accompanying documents. While the kit in this example includes a reagent containing a substance that specifically binds to IFNλ3, it may also include a reagent containing a substance that specifically binds to another biomarker.

[0202] One embodiment of the present invention is a device for implementing the prediction method and / or monitoring method of the present embodiment described above. Such a device is a device for assisting in the prediction of the severity of respiratory infections (hereinafter also referred to as a "device"). In addition, another embodiment of the present invention is a computer program for causing a computer to execute the prediction method and / or monitoring method of the present embodiment described above. Such a computer program is a computer program for assisting in the prediction of the severity of respiratory infections.

[0203] Next, an example of a device for implementing the above-described prediction method according to the present embodiment will be described with reference to the drawings. Figure 2 This is a schematic diagram of the device of this embodiment. Figure 2 The device 10 shown in FIG. 1 comprises an immunoassay device 20 and a computer system 30 connected to the immunoassay device 20 .

[0204] In this embodiment, the type of immunoassay device is not particularly limited and can be appropriately selected according to the method for measuring biomarkers. Figure 2 In the example shown, immunoassay device 20 is a commercially available automated immunoassay device capable of detecting chemiluminescent signals generated by a sandwich ELISA method using magnetic particles immobilized with a capture antibody and an enzyme-labeled detection antibody. Immunoassay device 20 is not particularly limited as long as it can detect signals based on the labeling substance used, and can be appropriately selected depending on the type of labeling substance used.

[0205] When a reagent containing magnetic particles with fixed capture antibodies, a reagent containing enzyme-labeled detection antibodies, and a test body collected from a subject are placed in an immunoassay device 20, the immunoassay device 20 uses each reagent to perform an antigen-antibody reaction, obtains a chemiluminescence signal as optical information based on the enzyme-labeled antibody specifically bound to the biomarker, and sends the obtained optical information to a computer system 30.

[0206] The computer system 30 includes a computer body 300, an input unit 301, and a display unit 302 for displaying information about the test subject or the determination result. The computer system 30 receives optical information from the immunoassay device 20. Furthermore, the processor of the computer system 30 executes a computer program for assisting in the prediction of the severity of respiratory infection installed on the hard disk 313 based on the optical information. Figure 2 As shown, the computer system 30 can be a separate device from the immunoassay device 20, or it can be a device that incorporates the immunoassay device 20. In the latter case, the computer system 30 itself can serve as the prediction support device 10. A computer program for supporting the prediction of severe respiratory infections can also be installed in a commercially available automated immunoassay device. The device 10 can also be a device that integrates the immunoassay device 20 and the computer system 30.

[0207] Reference Figure 3The computer system 300 includes a CPU (Central Processing Unit) 310, a ROM (Read Only Memory) 311, a RAM (Random Access Memory) 312, a hard disk 313, an input / output interface 314, a reader 315, a communication interface 316, and an image output interface 317. The CPU 310, the ROM 311, the RAM 312, the hard disk 313, the input / output interface 314, the reader 315, the communication interface 316, and the image output interface 317 are connected to each other via a bus 318 for data communication. Furthermore, the immunoassay device 20 is communicatively connected to the computer system 30 via the communication interface 316.

[0208] The CPU 310 can execute programs stored in the ROM 311 or the hard disk 313 and programs loaded into the RAM 312 . The CPU 310 calculates the measurement value of the biomarker and displays it on the display unit 302 .

[0209] ROM 311 is composed of a mask ROM, PROM, EPROM, EEPROM, etc. As mentioned above, computer programs executed by CPU 310 and data used in executing the computer programs are recorded in ROM 311. The computer programs recorded in ROM 311 include BIOS (Basic Input Output System).

[0210] The RAM 312 is composed of an SRAM, a DRAM, etc. The RAM 312 is used to read the programs recorded in the ROM 311 and the hard disk 313. When executing these programs, the RAM 312 is used as a work area of the CPU 310.

[0211] Computer programs such as an operating system and application programs to be executed by the CPU 310 and data used in the execution of the computer programs are installed in the hard disk 313 .

[0212] The reading device 315 is composed of a floppy disk drive, a CD-ROM drive, a DVD-ROM drive, a USB port, an SD card reader, a CF card reader, a memory stick reader, a solid state drive, etc. The reading device 315 can read programs or data recorded on the removable recording medium 400 .

[0213] The input / output interface 314 is composed of, for example, serial interfaces such as USB, IEEE1394, and RS-232C, parallel interfaces such as SCSI, IDE, and IEEE1284, and analog interfaces such as D / A converters and A / D converters. An input unit 301 such as a keyboard and mouse is connected to the input / output interface 314. The operator can input various commands to the computer 300 through this input unit 301.

[0214] The communication interface 316 is, for example, an Ethernet (registered trademark) interface, etc. The computer body 300 can also send print data to a printer or the like via the communication interface 316 .

[0215] The image output interface 317 is connected to the display unit 302 composed of an LCD, CRT, etc. Thus, the display unit 302 can output a video signal corresponding to the image data supplied from the CPU 310. The display unit 302 displays an image (screen) based on the input video signal.

[0216] Reference Figure 4A The processing program executed by the apparatus 10 of this embodiment will be described. This description will take as an example the case where the IFNλ3 measurement value is obtained and output from the chemiluminescent signal generated by a sandwich ELISA method using magnetic particles immobilized with a capture antibody and an enzyme-labeled detection antibody. Alternatively, the measurement value of CCL17, CXCL11, IP-10, IL-6, or CXCL9 may be obtained instead of the IFNλ3 measurement value.

[0217] In step S101, CPU 310 acquires optical information (chemiluminescent signal) from immunoassay device 20. In step S102, CPU 310 calculates the IFNλ3 measurement value from the acquired optical information and stores it on hard disk 313. In step S103, CPU 310 outputs the IFNλ3 measurement value and displays it on display unit 302 or prints it to a printer. When outputting the IFNλ3 measurement value, a threshold value corresponding to IFNλ3 may also be displayed on display unit 302 as reference information. This allows physicians and others to be provided with indicators that can assist in predicting the severity of respiratory infections.

[0218] Reference Figure 4B , other processing procedures executed by the device 10 of this embodiment are described. Among them, the case of obtaining the measured values of IFNλ3 and CCL17 and outputting them is described as an example. In step S201, the CPU 310 obtains optical information (chemiluminescence signal) from the immunoassay device 20. In step S202, the CPU 310 calculates the measured values of IFNλ3 and CCL17 from the obtained optical information and stores them in the hard disk 313. In step S203, the CPU 310 outputs the measured values and displays them on the display unit 302 or prints them out using a printer. When outputting the measured values of IFNλ3 and CCL17, the threshold values corresponding to each of IFNλ3 and CCL17 may also be displayed on the display unit 302 as reference information. In this way, an indicator for assisting in the prediction of the severity of respiratory infections can be provided to physicians and the like.

[0219] Reference Figure 4CThe following describes the process for predicting the severity of a respiratory infection based on the measured value of IFNλ3. In step S301, CPU 310 obtains optical information (chemiluminescent signal) from the immunoassay device 20. In step S302, CPU 310 calculates the measured value of IFNλ3 from the obtained optical information and stores it on hard disk 313. In step S303, CPU 310 compares the calculated measured value of IFNλ3 with the threshold value corresponding to IFNλ3 stored on hard disk 313. If the measured value of IFNλ3 is above the threshold value, the process proceeds to step S304. In step S304, CPU 310 stores the result of the determination that the subject's respiratory infection is likely to become severe on hard disk 313.

[0220] On the other hand, if the measured IFNλ3 value is lower than the threshold value in step S303, the process proceeds to step S305. In step S305, CPU 310 stores the result of the determination that the subject's respiratory infection has a low likelihood of becoming seriously ill in hard disk 313. In step S306, CPU 310 outputs the determination result, displaying it on display unit 302 or printing it to a printer. This provides physicians and others with indicators that aid in predicting the severity of respiratory infection.

[0221] Reference Figure 4D The following describes the process of predicting the severity of respiratory infection based on the measured values of IFNλ3 and CXCL11. Figure 4D In FIG, the threshold value corresponding to the measured value of IFNλ3 is expressed as a “first threshold value”, and the threshold value corresponding to the measured value of CXCL11 is expressed as a “second threshold value”.

[0222] In step S401, CPU 310 acquires optical information (chemiluminescent signal) from immunoassay device 20. In step S402, CPU 310 calculates the measured values of IFNλ3 and CXCL11 from the acquired optical information and stores them in hard disk 313. In step S403, CPU 310 compares the calculated measured value of IFNλ3 with the first threshold value stored in hard disk 313. If the measured value of IFNλ3 is above the first threshold value, the process proceeds to step S404. In step S404, CPU 310 stores the result of the determination that the subject's respiratory infection is likely to become severe in hard disk 313.

[0223] If the IFNλ3 measured value is lower than the first threshold value in step S403, the process proceeds to step S405. In step S405, CPU 310 compares the calculated CXCL11 measured value with the second threshold value stored in hard disk 313. If the CXCL11 measured value is higher than the second threshold value, the process proceeds to step S404, where CPU 310 stores the result of the determination that the subject's respiratory infection is likely to become severe in hard disk 313. If the CXCL11 measured value is lower than the second threshold value in step S405, the process proceeds to step S406. In step S406, CPU 310 stores the result of the determination that the subject's respiratory infection is likely to become severe in hard disk 313. In step S407, CPU 310 outputs the determination result, displaying it on display unit 302 or printing it to a printer. This makes it possible to provide physicians and others with indicators that assist in predicting the severity of respiratory infections.

[0224] Reference Figure 4E The following describes the process of predicting the severity of respiratory infection based on the measured values of IFNλ3 and CCL17. Figure 4E In FIG, the threshold value corresponding to the measured value of IFNλ3 is expressed as a “first threshold value”, and the threshold value corresponding to the measured value of CCL17 is expressed as a “third threshold value”.

[0225] In step S501, CPU 310 acquires optical information (chemiluminescent signal) from the immunoassay device 20. In step S502, CPU 310 calculates the measured values of IFNλ3 and CCL17 from the acquired optical information and stores them in hard disk 313. In step S503, CPU 310 compares the calculated measured value of IFNλ3 with the first threshold stored in hard disk 313. If the measured value of IFNλ3 is above the first threshold, the process proceeds to step S504. In step S504, CPU 310 stores the result of the determination that the subject's respiratory infection is likely to become severe in hard disk 313. In step S503, if the measured value of IFNλ3 is below the first threshold, the process proceeds to step S505. In step S505, CPU 310 compares the calculated measured value of CCL17 with the third threshold stored in hard disk 313. If the measured value of CCL17 is above the third threshold, the process proceeds to step S506. In step S506 , the CPU 310 stores the determination result that the possibility of the test subject's respiratory infection becoming severe is low in the hard disk 313 .

[0226] If, in step S505, the measured value of CCL17 is lower than the third threshold, processing proceeds to step S504, where CPU 310 stores the result of the determination that the subject's respiratory infection is likely to become severe in hard disk 313. In step S507, CPU 310 outputs the determination result, displaying it on display unit 302 or printing it to a printer. This provides physicians and others with indicators that aid in predicting the severity of respiratory infection.

[0227] Reference Figure 4F , the process for predicting the severity of respiratory infection based on the measured value of CCL17 is described. In step S601, CPU310 obtains optical information (chemiluminescent signal) from the immunoassay device 20. In step S602, CPU310 calculates the measured value of CCL17 from the obtained optical information and stores it on the hard disk 313. In step S603, CPU310 compares the calculated measured value of CCL17 with the threshold value corresponding to CCL17 stored on the hard disk 313. When the measured value of CCL17 is lower than the threshold value, the process proceeds to step S604. In step S604, CPU310 stores the judgment result that the subject's respiratory infection is likely to become severe on the hard disk 313.

[0228] On the other hand, if the measured value of CCL17 is above the threshold in step S603, the process proceeds to step S605. In step S605, CPU 310 stores the result of the determination that the subject's respiratory infection has a low likelihood of becoming severe in hard disk 313. In step S606, CPU 310 outputs the determination result, displaying it on display unit 302 or printing it to a printer. This provides physicians and others with an indicator that assists in predicting the severity of respiratory infection.

[0229] In another embodiment, three selected from IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used as biomarkers. At this time, CPU310 obtains optical information (chemiluminescent signal) for these three biomarkers from the immunoassay device 20. CPU310 calculates the measured value of each biomarker from the obtained optical information and stores it in hard disk 313. CPU310 compares the measured value of each biomarker with the corresponding threshold value. When the measured value of at least one biomarker becomes above the threshold value, the judgment result that the possibility of the subject's respiratory infection becoming severe is high is stored in hard disk 313. When the measured values of all biomarkers are lower than the threshold value, the judgment result that the possibility of the subject's respiratory infection becoming severe is low is stored in hard disk 313. CPU310 outputs the judgment result and displays it on the display unit 302 or prints it by a printer.

[0230] In another embodiment, three selected from IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used as biomarkers. At this time, CPU310 obtains optical information (chemiluminescent signal) for these three biomarkers from the immunoassay device 20. CPU310 calculates the measured value of each biomarker from the obtained optical information and stores it in hard disk 313. CPU310 compares the measured value of each biomarker with the corresponding threshold value. When the measured value of all biomarkers becomes above the threshold value, the judgment result that the possibility of severe respiratory infection of the subject is high is stored in hard disk 313. When the measured value of at least one biomarker is lower than the threshold value, the judgment result that the possibility of severe respiratory infection of the subject is low is stored in hard disk 313. CPU310 outputs the judgment result and displays it on the display unit 302 or prints it by a printer.

[0231] In another embodiment, four of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 are used as biomarkers. At this time, CPU 310 obtains optical information (chemiluminescent signal) for these four biomarkers from the immunoassay device 20. CPU 310 calculates the measured value of each biomarker from the obtained optical information and stores it in hard disk 313. CPU 310 compares the measured value of each biomarker with the corresponding threshold value. When the measured value of at least one biomarker becomes above the threshold value, the result of the determination that the possibility of the subject's respiratory infection becoming severe is high is stored in hard disk 313. When the measured values of all biomarkers are lower than the threshold value, the result of the determination that the possibility of the subject's respiratory infection becoming severe is low is stored in hard disk 313. CPU 310 outputs the determination result and displays it on the display unit 302 or prints it by a printer.

[0232] In another embodiment, four of IFNλ3, CXCL11, IP-10, IL-6 and CXCL9 are used as biomarkers. At this time, CPU310 obtains optical information (chemiluminescent signal) for these four biomarkers from the immunoassay device 20. CPU310 calculates the measured value of each biomarker from the obtained optical information and stores it in hard disk 313. CPU310 compares the measured value of each biomarker with the corresponding threshold value. When the measured value of all biomarkers becomes above the threshold value, the judgment result that the possibility of severe respiratory infection of the subject is high is stored in hard disk 313. When the measured value of at least one biomarker is lower than the threshold value, the judgment result that the possibility of severe respiratory infection of the subject is low is stored in hard disk 313. CPU310 outputs the judgment result and displays it on the display unit 302 or prints it by a printer.

[0233] In another embodiment, five biomarkers consisting of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 are used as biomarkers. At this time, CPU310 obtains optical information (chemiluminescent signal) for these five biomarkers from the immunoassay device 20. CPU310 calculates the measured value of each biomarker from the obtained optical information and stores it in hard disk 313. CPU310 compares the measured value of each biomarker with the corresponding threshold value. When the measured value of at least one biomarker becomes above the threshold value, the result of the determination that the possibility of the subject's respiratory infection becoming severe is high is stored in hard disk 313. When the measured values of all biomarkers are lower than the threshold value, the result of the determination that the possibility of the subject's respiratory infection becoming severe is low is stored in hard disk 313. CPU310 outputs the determination result and displays it on the display unit 302 or prints it by a printer.

[0234] In another embodiment, five biomarkers consisting of IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 are used as biomarkers. At this time, CPU310 obtains optical information (chemiluminescent signal) for these five biomarkers from the immunoassay device 20. CPU310 calculates the measured value of each biomarker from the obtained optical information and stores it in hard disk 313. CPU310 compares the measured value of each biomarker with the corresponding threshold value. When the measured value of all biomarkers becomes above the threshold value, the judgment result that the possibility of severe respiratory infection of the subject is high is stored in hard disk 313. When the measured value of at least one biomarker is lower than the threshold value, the judgment result that the possibility of severe respiratory infection of the subject is low is stored in hard disk 313. CPU310 outputs the judgment result and displays it on the display unit 302 or prints it by a printer.

[0235] One embodiment of the present invention relates to a method for treating respiratory infections. The method for treating respiratory infections of this embodiment includes: a process of measuring biomarkers in a test subject collected from a subject suffering from respiratory infections or a subject suspected of having respiratory infections, a process of predicting the severity of respiratory infections based on the measured values of the biomarkers, and a process of performing medical intervention on the subject predicted to have severe respiratory infections in the prediction process. Furthermore, examples of "medical intervention" include the administration of drugs, surgery, immunotherapy, gene therapy, oxygen supply treatment, treatment using an artificial heart-lung device, etc. The drug can be appropriately selected from known therapeutic drugs for respiratory infections or drugs that are candidates therefor. For example, when the respiratory infection is SARS-CoV-2 infection, as drugs, drugs with antiviral effects, drugs that reduce inflammation, ACE inhibitors, etc. can be cited. Specific examples include Favipiravir, Lopinavir, Ritonavir, Nafamostat, Camostat, Remdesivir, Ribavirin, Ivermectin, Ciclesonide, Chloroquine, Hydroxychloroquine, Interferon, Tocilizumab, Sarilumab, Tofacitinib, Baricitinib, Ruxolitinib, Acalabrutinib, Ravulizumab, Eritoran, Ibudilast, HLCM051, and LY3127804.

[0236] Next, the present invention will be described in detail with reference to Examples. Hereinafter, "HISCL" refers to a registered trademark of Sysmex Corporation.

[0237] [Example]

[0238] [Example 1]

[0239] (1) Biological samples

[0240] Serum obtained from 8 patients whose infection with SARS-CoV-2 was confirmed by PCR was used as a biological sample. The serum was prepared from blood collected at multiple time points from the day the patient was hospitalized. The information of each patient is shown in Table 1. In the table, "onset date" indicates the day when cold symptoms such as fever and cough appeared. "Severity" indicates the final condition of each patient after hospitalization. "Mild" indicates cases without pneumonia. "Moderate" indicates cases with pneumonia that do not require oxygen. "Severe" indicates cases with pneumonia that require oxygen. "Critical" indicates cases that require intensive treatment management including artificial respiration management.

[0241]

Table 1

[0242] Case number Onset date Hospitalization days age gender Severity 4 2020 / 1 / 31 2020 / 1 / 31 41 M moderate 5 2020 / 1 / 30 2020 / 1 / 30 50 F severe 7 2020 / 2 / 5 2020 / 2 / 11 63 M critical 8 2020 / 2 / 8 2020 / 2 / 11 28 F mild 9 2020 / 1 / 27 2020 / 2 / 12 83 M severe 10 2020 / 2 / 7 2020 / 2 / 12 64 F mild 11 2020 / 2 / 6 2020 / 2 / 13 63 M critical 12 2020 / 2 / 13 2020 / 2 / 17 53 M severe

[0243] (2) Determination of biomarkers

[0244] (2.1) Determination of chemokines and cytokines

[0245] The concentrations of various chemokines and cytokines were measured using the Bio-Plex Pro (trademark) human chemokine 40-Plex panel (#171AK99MR2, BIO-RAD) and the Bio-Plex Pro (trademark) human cytokine screening 48-Plex panel (#12007283, BIO-RAD). As an assay device, the Bio-Plex MAGPIX system (BIO-RAD) was used. Specific operations were performed according to the accompanying documentation of the test kit and the accompanying documentation of the assay device.

[0246] (2.2) Determination of INFλ3 (IL-28B)

[0247] As cytokines not included in the assays of the above-mentioned kit, INFλ3 concentrations were measured using the following reagents R1 to R5 using the fully automated immunoassay system HISCL-5000 (Sysmex Corporation). The anti-hIL-28B antibody (clone name: Hyb-TA2650B) provided by the National Center for Global Health and Medicine was labeled with biotin by conventional methods and dissolved in a buffer containing 1% bovine serum albumin (BSA) and 0.5% casein to prepare reagent R1 (capture antibody reagent). As the R2 reagent (solid phase), HISCL R2 reagent (Sysmex Corporation) containing streptavidin-conjugated magnetic particles was used. The anti-rhIL-28B antibody (clone name: Hyb-TA2664) provided by the National Center for Global Health and Medicine was prepared as a Fab' fragment by conventional methods. This Fab' fragment was labeled with ALP by conventional methods and dissolved in a buffer containing 1% BSA and 0.5% casein to prepare reagent R3 (detection antibody reagent). As the R4 reagent (assay buffer), HISCL R4 reagent (Sysmex Corporation) was used. As the R5 reagent (ALP substrate solution), HISCL R5 reagent (Sysmex Corporation) was used. Methods for producing anti-hIL-28B antibodies and anti-rhIL-28B antibodies are described in Patent Document 1.

[0248] The measurement procedure of HISCL-5000 is as follows. After mixing serum (30 μL) and R1 reagent (100 μL), add R2 reagent (30 μL). The magnetic particles in the obtained mixed solution are collected and the supernatant is removed, and HISCL cleaning solution (300 μL) is added to wash the magnetic particles. The supernatant is removed, and R3 reagent (100 μL) is added to the magnetic particles and mixed. The magnetic particles in the obtained mixed solution are collected and the supernatant is removed, and HISCL cleaning solution (300 μL) is added to wash the magnetic particles. The supernatant is removed, and R4 reagent (50 μL) and R5 reagent (100 μL) are added to the magnetic particles, and the chemiluminescence intensity is measured. As a calibrator (antigen for preparing the calibration curve), hIL-28B (10-046) provided by the National Center for Global Health and Medicine is used. The calibrator is measured in the same way as the serum to prepare a calibration curve. The chemiluminescence intensity obtained in the measurement of each serum is applied to the calibration curve to determine the concentration of INFλ3.

[0249] (3) Measurement results

[0250] From the measurement results of various chemokines and cytokines, IFNλ3, CXCL11, IP-10, IL-6, CXCL9 and CCL17 were found as biomarkers that can predict the prognosis of SARS-CoV-2 infection. For IFNλ3, CXCL11, IP-10, IL-6, CXCL9 and CCL17, the coordinate diagrams plotting the measured values of each patient are shown in Figures 5 to 10. In the figure, "days passed" indicates the number of days from the day the patient was hospitalized (days passed 0). The arrows in the figure indicate the time points when the patient was treated using an oxygen inhalation device or an artificial heart and lung device. In the figure, "IFNL3" refers to IFNλ3.

[0251] As shown in Figures 5-9A-E, in patients with Cases 5, 7, 9, 11, and 12, IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 showed high values, indicating a tendency for the infection to become more severe. Furthermore, in Patient 9, IFNλ3 values were lower than in other patients with severe disease, while CXCL11, IP-10, IL-6, and CXCL9 values tended to be higher. On the other hand, as shown in Figures 5-9F, G, and H, in Patients 4, 8, and 10, IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 values tended to be lower than in patients with severe disease. These results suggest that IFNλ3, CXCL11, IP-10, IL-6, and CXCL9 can be used as biomarkers to predict the severity of respiratory infections.

[0252] As from Figure 10AFrom the results of the study, it was found that CCL17 levels tended to be higher in patients with milder symptoms than in those with more severe symptoms. These results suggest that CCL17, when used in combination with IFNλ3, CXCL11, IP-10, IL-6, or CXCL9, could be used as a biomarker to identify patients with a low risk of developing severe respiratory infections.

[0253] [Example 2]

[0254] (1) Biological samples

[0255] Serum from 20 patients confirmed to be infected with SARS-CoV-2 by PCR was used as biological samples. The serum was prepared from blood collected at various time points from the patient's hospitalization date. The severity of the 20 patients was classified as mild in two, moderate in 11, severe in two, and critical in five.

[0256] (2) Determination of biomarkers

[0257] The concentrations of IFNλ3 and CCL17 in the plasma of each patient were measured in the same manner as in Example 1. Graphs showing the measured values of IFNλ and CCL17 for 28 patients (the sum of 20 patients in Example 2 and 8 patients in Example 1) are plotted in Figures 11 and 12 . Figure 11A Displays the IFNλ3 measurement values of critical and severe patients. Figure 11B The IFNλ3 measurement values for patients with moderate and mild disease are shown. Figure 12A Displays the CCL17 measurement values of critical and severe patients. Figure 12B The CCL17 measurement values for patients with moderate and mild disease are shown. In the figure, "Days after hospitalization" indicates the number of days from the day the patient was hospitalized (day 0).

[0258] (3) Analysis of biomarker measurement values

[0259] The measured values of 28 patients, which were obtained by combining the 20 patients in Example 2 and the 8 patients in Example 1, were analyzed. Among the 28 patients, patients with mild or moderate severity were classified as a "low-risk group" (hereinafter referred to as "L group"), and patients with severe or critical severity were classified as a "high-risk group" (hereinafter referred to as "H group"), and the concentrations of IFNλ3 and CCL17 in each group were plotted. The results are shown in Figure 13A and B. The horizontal lines in the figure represent the first quartile, median, and third quartile of the biomarker concentration of each group.

[0260] ROC analysis was performed on the biomarker concentrations of 28 patients to set the optimal cutoff value (threshold) for distinguishing between group L and group H. The sensitivity, specificity, and area under the curve (AUC) were calculated for the 28 patients to determine whether the possibility of severe respiratory infection was high or not using the set cutoff value. The resulting ROC curve is shown in Figure 14A The cutoff values of IFNλ3 and CCL17, the sensitivity, specificity, AUC and p-value of the determination using the cutoff values are shown in Table 2. For the two patient groups classified based on the above cutoff values, the event-free survival rate (EFS) after hospitalization was studied by the Kaplan-Meier method. The obtained Kaplan-Meier curve is shown in Figure 15A and B.

[0261]

Table 2

[0262]

[0263] Table 2 shows that IFNλ3 and CCL17 are biomarkers that enable prediction of severe respiratory infections. Figure 15A It was shown that when the measured value of IFNλ3 was higher than the cutoff value, the possibility of severe respiratory infection was high, and when the measured value of IFNλ3 was lower than the cutoff value, the possibility of severe respiratory infection was low. Figure 15B It was shown that when the measured value of CCL17 was lower than the cutoff value, the possibility of severe respiratory infection was high, and when the measured value of CCL17 was higher than the cutoff value, the possibility of severe respiratory infection was low.

[0264]

Explanation of symbols

[0265] 11, 21, 31, 41: Test kit

[0266] 12, 22, 32, 42: Container 1

[0267] 13, 23, 33: Second container

[0268] 24, 34: Container 3

[0269] 25, 35: Container 4

[0270] 36: Container 5

[0271] 37: Container 6

[0272] 14, 27, 38, 43: Bale boxes

[0273] 15, 26, 39, 44: accompanying documents

[0274] 10: Judgment device

[0275] 20: Immunoassay device

[0276] 30: Computer Systems

[0277] 40: Recording medium

[0278] 300: Computer Body

[0279] 301: Input

[0280] 302: Display unit

[0281] 310: CPU

[0282] 311: ROM

[0283] 312: RAM

[0284] 313: Hard Drive

[0285] 314: Input and output interface

[0286] 315: Reading device

[0287] 316: Communication interface

[0288] 317: Image output interface

[0289] 318: Bus

Claims

1. Use of a substance that can specifically bind to at least one biomarker selected from IFNλ3 and CCL17 in the manufacture of a kit for assisting in the prediction of the severity of an infection caused by SARS-CoV-2 in a subject suffering from an infection caused by SARS-CoV-2 or a subject suspected of having such an infection.

2. The method of claim 1, wherein The biomarker contains IFNλ3, When the measured value of IFNλ3 is equal to or higher than a threshold value corresponding to IFNλ3, it indicates that the possibility of the infectious disease in the subject becoming severe is high.

3. The method of claim 1, wherein The biomarker contains IFNλ3, When the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3, it indicates that the possibility of the infectious disease in the subject becoming severe is low.

4. The method of claim 1, wherein The biomarker comprises CCL17, When the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it indicates that the possibility of the infectious disease in the subject becoming severe is high.

5. The method of claim 1, wherein The biomarker comprises CCL17, When the measured value of CCL17 is equal to or higher than a threshold value corresponding to CCL17, it indicates that the possibility of the infectious disease in the subject becoming severe is low.

6. The method of claim 1, wherein The biomarkers include IFNλ3 and CCL17, When the measured value of IFNλ3 is equal to or higher than the threshold value corresponding to IFNλ3 and the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it indicates that the possibility of the infection in the subject becoming severe is high.

7. The use according to claim 1, wherein the temporal change in the measured value of the biomarker in the sample collected from the subject is used as an indicator for predicting the severity of the infectious disease.

8. The use according to claim 7, wherein the subject is whole blood, plasma or serum.

9. Use of a substance that can specifically bind to at least one biomarker selected from IFNλ3 and CCL17 in the manufacture of a kit for monitoring the measurement value of a biomarker using samples collected at multiple time points from subjects suffering from an infection caused by SARS-CoV-2 or subjects suspected of having such an infection.

10. The use according to claim 9, wherein The biomarker contains IFNλ3, When the measured value of IFNλ3 is equal to or higher than a threshold value corresponding to IFNλ3 at at least one time point among the plurality of time points, it is suggested that the possibility of the infectious disease in the subject becoming severe is high.

11. The use according to claim 9, wherein The biomarker contains IFNλ3, If the measured value of IFNλ3 is lower than the threshold value corresponding to IFNλ3 at any of the plurality of time points, it indicates that the possibility of the infectious disease in the subject becoming severe is low.

12. The use according to claim 9, wherein The biomarker comprises CCL17, If the measured value of CCL17 is lower than a threshold value corresponding to CCL17 at at least one time point among the plurality of time points, it indicates that the possibility of the infectious disease in the subject becoming severe is high.

13. The use according to claim 9, wherein The biomarker comprises CCL17, If the measured value of CCL17 is equal to or higher than the threshold value corresponding to CCL17 at any of the plurality of time points, it indicates that the possibility of the infection in the subject becoming severe is low.

14. The use according to claim 9, wherein The biomarkers include IFNλ3 and CCL17, When, at at least one time point among the multiple time points, the measured value of IFNλ3 becomes above the threshold value corresponding to IFNλ3, and when, at at least one time point among the multiple time points, the measured value of CCL17 is lower than the threshold value corresponding to CCL17, it is indicated that the infectious disease of the subject is likely to become severe.

15. The use according to claim 9, wherein the subjects collected at the multiple time points contain The first subject collected at the first time point, and A second subject is collected at a second time point different from the first time point.

16. The use according to claim 9, wherein The plurality of time points include a first time point and a second time point different from the first time point, The second time point is a time point within one month from the first time point.

17. The use according to any one of claims 1 to 16, wherein the substance is an antibody.

18. A device for assisting in the prediction of the severity of an infectious disease caused by SARS-CoV-2, comprising a computer including a processor and a memory under the control of the processor. The memory stores a computer program for causing the computer to execute the following steps: a step of calculating a measured value of a biomarker in a sample collected from a subject suffering from an infection caused by SARS-CoV-2 or a subject suspected of having such an infection, and a step of outputting the measured value of the biomarker for predicting the severity of the infectious disease, The biomarkers include at least one selected from IFNλ3 and CCL17.

19. A computer-readable medium having recorded thereon a computer program for assisting in the prediction of the severity of an infection caused by SARS-CoV-2, wherein the computer program is a computer program for causing the computer to execute the following steps: a step of calculating a measured value of a biomarker in a sample collected from a subject suffering from an infection caused by SARS-CoV-2 or a subject suspected of having such an infection, and a step of outputting the measured value of the biomarker, The biomarkers include at least one selected from IFNλ3 and CCL17.

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