WO2022030629A1 - Procédé de prédiction de symptôme, d'adéquation thérapeutique et/ou de résultat de traitement pour une infection virale - Google Patents

Procédé de prédiction de symptôme, d'adéquation thérapeutique et/ou de résultat de traitement pour une infection virale Download PDF

Info

Publication number
WO2022030629A1
WO2022030629A1 PCT/JP2021/029375 JP2021029375W WO2022030629A1 WO 2022030629 A1 WO2022030629 A1 WO 2022030629A1 JP 2021029375 W JP2021029375 W JP 2021029375W WO 2022030629 A1 WO2022030629 A1 WO 2022030629A1
Authority
WO
WIPO (PCT)
Prior art keywords
ifn
mmp
stnf
biological
parameters
Prior art date
Application number
PCT/JP2021/029375
Other languages
English (en)
Japanese (ja)
Inventor
和幸 吉崎
賀津子 宇野
仁 藤宮
Original Assignee
和幸 吉崎
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 和幸 吉崎 filed Critical 和幸 吉崎
Priority to JP2022541756A priority Critical patent/JPWO2022030629A1/ja
Publication of WO2022030629A1 publication Critical patent/WO2022030629A1/fr

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
    • C07K14/475—Growth factors; Growth regulators
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
    • C07K14/52—Cytokines; Lymphokines; Interferons
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
    • C07K14/705—Receptors; Cell surface antigens; Cell surface determinants
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
    • C07K14/705—Receptors; Cell surface antigens; Cell surface determinants
    • C07K14/715—Receptors; Cell surface antigens; Cell surface determinants for cytokines; for lymphokines; for interferons
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M1/00—Apparatus for enzymology or microbiology
    • C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N9/00—Enzymes; Proenzymes; Compositions thereof; Processes for preparing, activating, inhibiting, separating or purifying enzymes
    • C12N9/14—Hydrolases (3)
    • C12N9/24—Hydrolases (3) acting on glycosyl compounds (3.2)
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N9/00—Enzymes; Proenzymes; Compositions thereof; Processes for preparing, activating, inhibiting, separating or purifying enzymes
    • C12N9/14—Hydrolases (3)
    • C12N9/48—Hydrolases (3) acting on peptide bonds (3.4)
    • C12N9/50—Proteinases, e.g. Endopeptidases (3.4.21-3.4.25)
    • C12N9/64—Proteinases, e.g. Endopeptidases (3.4.21-3.4.25) derived from animal tissue
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/70—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving virus or bacteriophage
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
    • Y02A50/30—Against vector-borne diseases, e.g. mosquito-borne, fly-borne, tick-borne or waterborne diseases whose impact is exacerbated by climate change

Definitions

  • the present disclosure relates to methods for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes. More specifically, the present disclosure relates to a method of predicting the degree of symptom of a subject infected with a virus and a method of predicting therapeutic suitability and treatment result thereof. More specifically, the present disclosure relates to a technique for predicting the symptoms of a disease caused by a coronavirus or the like in advance, and appropriately providing a preventive method or a therapeutic agent thereof to a patient.
  • Viral infectious diseases are still influential enough to pose a threat to humankind even in modern times, and viral diseases such as AIDS (HIV) and Ebola hemorrhagic fever continue to take time to control. .. Furthermore, in recent years, infectious diseases such as SARS (SARS-CoV), MERS (MERS coronavirus), and COVID-19 (SARS-CoV-2) have an influence that changes daily life.
  • SARS SARS-CoV
  • MERS MERS
  • COVID-19 SARS-CoV-2
  • the present disclosure provides: (Item 1) A method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment results. The step of obtaining one or more biological parameters in the subject, and A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters. Including, how. (Item 2) The virus infection symptom, treatment suitability, and / or treatment result is a virus infection symptom.
  • the method according to item 1, wherein the prediction step includes a step of analyzing the biological parameter by comparing the biological parameter with a reference value and using it as an index for predicting a virus infection symptom.
  • (Item 3) The method according to item 1 or 2, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  • (Item 4) The method according to any one of items 1 to 3, wherein the biological parameter contains at least one marker that is not an inflammatory cytokine.
  • (Item 5) The method according to any one of items 1 to 4, wherein the biological parameter is a gene product (protein).
  • (Item 6) The method according to any one of items 1 to 5, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item 7) Any one of items 1 to 6, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item 8) The method according to any one of items 1 to 7, wherein the virus infection is SARS-CoV-2 infection.
  • (Item 9) The method according to any one of items 1 to 8, wherein the prediction of the symptom includes the prediction of the degree of symptom.
  • (Item 10) The method according to any one of items 1 to 9, wherein the prediction of the symptom includes a prediction of whether or not the virus infection symptom of the subject becomes severe.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL.
  • the method according to any one of items 1 to 10. The one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method of any one of items 1-12, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Items 1-11, including parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin,
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , The method of any one of items 1-11, comprising a parameter selected from the group consisting of Leptin, OncostatinM, and VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • MCP-1 MCP-1
  • M-CSF MIG
  • b-NGF b-NGF
  • PDGF-bb VEGF
  • APRIL BAFF
  • sCD30 IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item 17C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising parameters selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • Item 17E From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • (Item 19) The method according to any one of items 1 to 18, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  • (Item A1) A method for preventing or treating a virus infection in a subject. The step of obtaining one or more biological parameters in the subject, and A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters. With the step of preventing or treating the subject based on the prediction of viral infection symptoms, treatment suitability, and / or treatment outcome. Including, how.
  • the prediction step is an index for predicting the virus infection symptom, the treatment suitability, and / or the treatment result by analyzing the biological parameter by comparing the biological parameter with a reference value.
  • the step of preventing or treating includes a step of administering a therapeutic agent to the subject when the subject is predicted to be a seriously ill patient.
  • the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  • the inflammatory marker comprises at least one marker that is not an inflammatory cytokine.
  • the biological parameter is a gene product (protein).
  • (Item A6) The method according to any one of items A1 to A5, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item A7) Any one of items A1 to A6, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item A8) The method according to any one of items A1 to A7, wherein the virus infection is SARS-CoV-2 infection.
  • the therapeutic agents include remdesivir, fabipiravir, cyclesonide, nafamostat, camostat, ivermectin, steroids, tocilizumab, sarilumab, tofacitinib, baricitinib, luxolitinib, acalaburtinib, luxolitinib, acalaburtinib, luxolitinib, acalaburtinib, rubrismab, -001, dexamethasone, casiribimab / imdebimab, baricitinib / etecebimab, sotrovimab, VIR-7832, AZD7442, molnupiravir, AT-527, BI767551, PF-07304814, PF-07321332, VIR-2703, anakinra
  • the method according to any one of items A1 to A8, which
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basic FGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP- 1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40) , IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, M
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • IL-12 IL-13, IL-15, IL-17, basic FGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP- 1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40) , IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13,
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items A1 to A11, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Items A1 to A10 comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items A10, comprising a parameter selected from the group consisting of VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. (Item A16C)
  • the one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item A16C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising parameters selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • Item A16E From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • the reference value is a value of the biological parameter in a healthy person.
  • (Item A18) The method according to any one of items A1 to A17, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  • (Item B1) A method in which one or more biological parameters in a subject are used as an index for predicting the viral infection symptom, treatment suitability, and / or treatment result of the subject.
  • (Item B2) The method according to item B1, which comprises the step of obtaining one or more biological parameters in the subject.
  • the one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.).
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • the biological parameter comprises at least one marker that is not an inflammatory cytokine.
  • the biological parameter is a gene product (protein).
  • (Item B9) The method according to any one of items B1 to B8, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item B10) Any one of items B1 to B9, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item B11) The method according to any one of items B1 to B10, wherein the virus infection is SARS-CoV-2 infection.
  • (Item B12) The method according to any one of items B1 to B11, wherein the symptom prediction includes a symptom degree prediction.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items B1 to B14, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Item B1-B13, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items B13, comprising a parameter selected from the group consisting of VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL. -10, IL-12 (p40), IL-16, IL-17A, IL-18, IP-10, MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF -Bb, SCGF-b, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL -11, IL-20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item B19C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • IL-12 p40
  • IL-20 IL-32
  • IL-35 MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • (Item B21) The method according to any one of items B1 to B20, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  • (Item C1) An in vitro method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome. The step of obtaining one or more biological parameters in the subject, and A step of predicting the viral infection symptom, treatment suitability, and / or treatment result based on the biological parameters. Including, how. (Item C2)
  • the virus infection symptom, the treatment suitability, and / or the prediction of the treatment result are the virus infection symptom.
  • the prediction of the symptom is the prediction of the degree of symptom.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb , RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 ( p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP -2, M
  • the virus infection symptom, the treatment suitability, and / or the prediction of the treatment result is the virus infection symptom.
  • the prediction of the symptom is the prediction of the degree of symptom.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL.
  • the method according to item C1. The step according to item C1 or C2, wherein the prediction step includes a step of analyzing the biological parameter by comparing the biological parameter with a reference value and using it as an index for predicting a virus infection symptom.
  • Method. The method according to any one of items C1 to C3, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  • the inflammatory marker comprises at least one marker that is not an inflammatory cytokine.
  • the biological parameter is a gene product (protein).
  • (Item C7) The method according to any one of items C1 to C6, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item C8) Any one of items C1 to C7, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item C9) The method according to any one of items C1 to C8, wherein the virus infection is SARS-CoV-2 infection.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL. -12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF- ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT,
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Item C1-C9, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items C9, comprising a parameter selected from the group consisting of VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item C15C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • Item C15E From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • (Item C17) The method according to any one of items C1 to C16, wherein the one or more biological parameters are derived from the peripheral blood of the subject.
  • (Item D1) An in vitro method in which one or more biological parameters in a subject are used as indicators for predicting the subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
  • (Item D2) The method according to item D1, which comprises the step of obtaining one or more biological parameters in the subject.
  • the one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.). Included), according to item D1 or D2.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the method according to any one of items D1 to D3, which comprises the parameters to be added.
  • Method. (Item D4A)
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • Method. (Item D5) The method according to any one of items D1 to D4, wherein the biological parameter comprises at least one cytokine and at least one inflammatory marker.
  • the biological parameter comprises at least one marker that is not an inflammatory cytokine.
  • the biological parameter is a gene product (protein).
  • Item D8 The method according to any one of items D1 to D7, further comprising a step of analyzing the biological parameter by comparing the biological parameter with a reference value.
  • (Item D9) The method according to any one of items D1 to D8, wherein the virus infection is a virus infection belonging to the Coronaviridae family.
  • (Item D10) Any one of items D1 to D9, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The method described in the section.
  • (Item D11) The method according to any one of items D1 to D10, wherein the virus infection is SARS-CoV-2 infection.
  • (Item D12) The method according to any one of items D1 to D11, wherein the symptom prediction includes a symptom degree prediction.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35, the method according to any one of items D1 to D14, comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Item D1 to D13, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT. The method described in any one of the items.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and the method according to any one of items D13, comprising a parameter selected from the group consisting of VCAM-1.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. The one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item D19C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • IL-12 p40
  • IL-20 IL-32
  • IL-35 MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • the one or more biological parameters include biomolecular parameters, clinical data, and the amount and / or type of virus in the body (the amount of virus in blood, the type of virus variant (specified by SNP, etc.), etc.). Included), according to item E1.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the biomarker according to item E1 or E2. (Item E3A)
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • (Item E8) Any one of items E1 to E7, wherein the virus infection is a virus infection selected from the group consisting of HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2. The biomarker described in the section.
  • (Item E9) The biological marker according to any one of items E1 to E8, wherein the virus infection is SARS-CoV-2 infection.
  • (Item E10) The biological marker according to any one of items E1 to E9, wherein the prediction of the symptom includes a prediction of the degree of symptom.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-13 IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF , APLIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL- 27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP.
  • the biomarker according to any one of items E1 to E11 comprising a parameter selected from the group.
  • the one or more biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF-. ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, IL- 35.
  • the biomarker according to any one of items E1 to E12 comprising a parameter selected from the group consisting of LIGHT, MMP-1, and Pentraxin-3.
  • the one or more biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 (p70). , IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL Item E1-E11, comprising parameters selected from the group consisting of -28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, and LIGHT.
  • the biomarker according to any one of the above.
  • the one or more biological parameters are IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130.
  • the one or more biological parameters are IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163. , Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL -27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF -R2, TSLP, TWEAK, ADAMATS13, Angiopoietin-2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin
  • the one or more biological parameters are APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8. , IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29.
  • IL-32, IL-34, IL-35 LIGHT, MMP-1, MMP-2, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK, ADAMTS13, Angiopoietin -2, BMP-2, CD40Grid, CX3CL1, HGF, IFN- ⁇ R1, L-Selectin, LIF, TRAIL, VEGFR2 / KDR, Aggrecan, B7H1 / PDL1, CD40, CD44, E-Selectin, ICAM-1, IL-18 , Leptin, OncostatinM, and VCAM-1, the biomarker according to any one of items E1 to E11 selected from the group.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL.
  • IL-16 IL-17A, IL-18, IP-10
  • MCP-1 MCP-1
  • MCP-3 M-CSF
  • MIF MIG
  • b-NGF MIG
  • PDGF-bb PDGF-bb
  • SCGF -B SDF-1a
  • TRAIL VEGF
  • APRIL BAFF
  • sCD30 sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL -20, IL-22, IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP , And the method according to any one of the above items, comprising parameters selected from the group consisting of TWEAK.
  • the one or more biological parameters are CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, IL-. 17A, MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL-6Ra, IL Includes parameters selected from the group consisting of -20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK.
  • the one or more biological parameters are HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL
  • the one or more biological parameters are HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16, MCP-1 (MCAF), MIG. , B-NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2.
  • Method. (Item E17C)
  • the one or more bioparameters are MCP-3, IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items comprising parameters selected from the group consisting of. (Item E17C2) From the group consisting of IFN-g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the one or more biological parameters are IFN-g, IL-1ra, IL-6, IP-10, MCP-1 (MCAF), TNF-b, APRIL, BAFF, sCD30, IFN-a2, IFN-b, The method according to any one of the above items, comprising a parameter selected from the group consisting of IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, and IL-35.
  • IL-12 p40
  • IL-20 IL-32
  • IL-35 MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the method according to any one of the above items including the parameters selected.
  • the one or more biological parameters are IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF.
  • the method according to any one of the above items comprising a parameter selected from the group consisting of -a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8.
  • Item F1 A reagent, a kit, or a device for use in the method according to any one of the above items, or a combination thereof.
  • (Item F2) Reagents, kits, or devices, or combinations thereof, for use in predicting viral infection symptoms, treatment suitability, and / or treatment outcomes from one or more biological parameters of a subject.
  • Reagents, kits, or devices, or combinations thereof are examples of the virus infection symptoms, treatment suitability, and / or treatment outcomes from one or more biological parameters of a subject.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL. -10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, Chitinase-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP -1, MMP
  • the reagent, kit, or device according to any one of items F1 to F4, or a combination thereof.
  • the one or more biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL.
  • the reagent, kit, or device according to any one of items F1 to F4, or a combination thereof.
  • the step according to item 1 includes the step of applying the biological parameter to a predetermined prediction model and outputting the probability regarding the prediction of the viral infection symptom, the treatment suitability, and / or the treatment result. the method of.
  • the bioparameters are based on biomolecular parameters, clinical data, and the amount and / or type of virus in the body (including blood viral load, type of virus variant (specified by SNP, etc.), etc.).
  • the method according to item G1 Contains information about at least one selected from the group of The method according to item G1, wherein the predictive model is created based on at least one selected from the group consisting of biomolecular parameters, clinical data, historical information and treatment information.
  • the biological parameter comprises information on at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, blood viral load, and ACR-R1 isotypes.
  • the predictive model was created based on at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, treatment history, treatment effect information, safety information, and status history information.
  • the method according to item G1 or G2 which is a thing.
  • (Item G4) The method according to any one of items G1 to G3, wherein the prediction model is generated based on reinforcement learning or a neural network.
  • (Item G5) A computer program that causes a computer to perform processing of a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, wherein the method is: A step of causing the computer to obtain one or more biological parameters in the subject. A step of causing the computer to apply the biological parameters to a predetermined predictive model and output probabilities regarding viral infection symptoms, treatment suitability, and / or treatment results. Including the program.
  • (Item G6) A recording medium that stores a computer program that causes a computer to perform processing of a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
  • Recording medium including.
  • (Item G7) A system for predicting viral infection symptoms, treatment suitability, and / or treatment results of subjects.
  • a means for obtaining one or more biological parameters in the subject As a means of applying the biological parameters to a predetermined predictive model and outputting probabilities regarding viral infection symptoms, treatment suitability, and / or treatment results.
  • Including the system. (Item G8) A computer program that causes a computer to execute a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment result. The step of inputting one or more biological parameters in the subject into the computer, A step of causing the computer to perform calculations to predict the viral infection symptoms, treatment suitability, and / or treatment outcomes based on the biological parameters. Including the program.
  • Recording medium including.
  • (Item G10) A system for predicting viral infection symptoms, treatment suitability, and / or treatment results of a subject.
  • a means for inputting one or more biological parameters in the subject As a means of performing calculations that predict the viral infection symptoms, treatment suitability, and / or treatment outcomes based on the biological parameters.
  • the methods, programs, storage media, and systems of the present disclosure as described above may include any of the features described elsewhere herein.
  • the optimal treatment method and / or the treatment result when the subject is infected with the virus so that the optimal treatment according to the degree of the symptoms of each individual patient can be performed. Can be applied.
  • Such a method is not an ad hoc treatment, but can maximize the therapeutic effect for each individual, and it is also possible to formulate the most effective treatment policy for each patient. Even if a pandemic is caused by a virus, it can greatly contribute to avoiding the collapse of medical care.
  • FIG. 1 is a schematic diagram showing the relationship between the treatment route model for constructing a predictive model for predicting the symptom degree and the treatment suitability assuming the patient's condition and the prediction of the symptom degree and the treatment suitability. ..
  • FIG. 2 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a healthy subject and statistically processing the expression level for each measured biological parameter.
  • FIG. 3 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a COVID-19 severely ill patient and statistically processing the expression level for each measured biological parameter.
  • FIG. 1 is a schematic diagram showing the relationship between the treatment route model for constructing a predictive model for predicting the symptom degree and the treatment suitability assuming the patient's condition and the prediction of the symptom degree and the treatment suitability. ..
  • FIG. 2 shows the results of performing a cytokine / chemokine /
  • FIG. 4 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a rheumatism patient and statistically processing the expression level for each measured biological parameter.
  • FIG. 5 shows the results of performing a cytokine / chemokine / soluble receptor assay using a sample obtained from a severely ill COVID-19 patient and statistically processing the expression level for each measured biological parameter.
  • "COVID-19-0" is a patient before Actemra treatment
  • COVID-19-1w is a patient about 1 to 5 days after Actemra treatment
  • COVID-19-1w is a patient about 7 days after Actemra treatment.
  • FIG. 6 is a diagram showing changes in IL-6 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 7 is a diagram showing changes in IL-1Ra before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 8 is a diagram showing changes in IP-10 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 9 is a diagram showing changes in BAFF before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 10 is a diagram showing changes in APRIL before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 11 is a diagram showing changes in VCAM-1 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 12 is a diagram showing changes in IFN-28A before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 10 is a diagram showing changes in APRIL before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 11 is a diagram showing changes in VCAM-1 before and after treatment with tocilizumab
  • FIG. 13 is a diagram showing changes in IL-29 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 14 is a diagram showing changes in IFN-a2 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 15 is a diagram showing changes in IFN-b before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 16 is a diagram showing changes in IFN-g before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 17 is a diagram showing changes in TNF-a before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 18 is a diagram showing changes in sgp130 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 19 is a diagram showing changes in IL12 (p40) before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 20 is a diagram showing changes in IL-6Ra before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 21 is a diagram showing changes in IL-10 before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • FIG. 22 is a diagram showing changes in TWEAK before and after treatment with tocilizumab or dexmedetomidine using samples obtained from COVID-19 infected patients.
  • coronaviridae is the largest coronaviridae belonging to Noroviruses, and includes coronaviridae, Arteriviridae, Mesoniviridae, Roniviridae and the like. Coronavirus is further divided into four genera: ⁇ , ⁇ , ⁇ , coronavirus, and ⁇ , ⁇ coronavirus causes infection in 10% to 30% of the human airway / intestinal tract.
  • Viruses of the genus Coronavirus exist as RNA viruses wrapped in an envelope. Its diameter is about 80-120 nm and the genetic material is the largest of all RNA viruses. There are three glycoproteins on the surface of the virus particle, spine glycoprotein (S, Spike Protein, receptor binding site, cell lysis and antigen); membrane glycoprotein (E, Envelope Protein, Envelope Protein, small, binding to cell membrane). Protein); Membrane Protein (M, Membrane Protein, membrane transporter for nutrients, aggregates the emergence of new viruses and the formation of viral envelopes). Hemagglutinin Glycoprotein (HE protein, Hemagglutinin- There is esterase).
  • the virus determines the tissue or host of the virus, primarily through the binding of the Spike protein (S protein) to the host cell receptor through viral entry.
  • S protein Spike protein
  • Both the N-terminal domain (S1-NTD) and the C-terminal domain (S1-CTD) of the coronavirus S protein S1 subunit can be a receptor binding domain (RBD). It is believed that S1-NTD binds to glycosyl receptors and S1-CTD binds to protein receptors.
  • Coronaviruses known to cause cold-like syndromes that infect humans include: HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV and SARS-CoV-2. Be done.
  • biological parameter refers to any parameter related to the living body, such as a factor related to the living body (for example, a cytokine or a receptor (ACE-R1 or the like), a signaling molecule, etc.), a protein, a polynucleotide or the like.
  • Biological parameters absolute, relative, variable, etc.
  • clinical data of the organism two-dimensional or three-dimensional images of the organism (eg, X-ray CT image), parameters related to the pathogen in the organism (eg, infection). It includes, but is not limited to, factors (eg, amounts or levels of bacteria, viruses, etc.), treatment history, treatment efficacy information, safety information, state history information, and the like.
  • Clinical data is used interchangeably with clinical information and includes any clinically available data or information, including laboratory data, clinical symptoms, findings, and the like.
  • the biomolecular parameter includes a biomolecular parameter
  • the biomolecule parameter includes a biological parameter such as a protein or a polynucleotide expressed in a sample collected from a subject.
  • Bioparameters can be nucleic acids, nucleic acid fragments, polynucleotides, oligonucleotides, polypeptides, peptide fragments or proteins that can be detected or quantified, or their values or variations in their values.
  • Bioparameters include IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL.
  • sample may be arbitrary, and includes, for example, serum, plasma, peripheral blood, blood, urine, saliva, and the like, but is not limited thereto.
  • serum is used for biomolecular parameters and the like. Therefore, in certain embodiments of the present disclosure, the methods of the present disclosure are carried out using parameters obtained from measurements in serum collected from a subject. In terms of viral load, nasal mucus and saliva can be used instead of serum.
  • symptom refers to the deviation of normal function or sensation in a patient, reflecting the presence of an abnormal condition or disease. Includes subjective or objective ones.
  • symptoms can be expressed as symptomatic (or severe), for example, severe, moderate (may be divided into levels, eg, moderate (I), moderate (II), etc.). It may be classified into mild, asymptomatic (invisible), etc.).
  • a virus infection symptom refers to a symptom at the time of virus infection. The following classifications may be typically adopted for the viral infection symptoms of COVID-19.
  • the term "predictive model” refers to a model for predicting one event (eg, a medical event such as viral infection symptoms, treatment suitability, and / or treatment result) from another factor. It can be expressed using a prediction formula, a coefficient, or the like. For example, based on the present disclosure, early asymptomatic, mild, based on post-infection symptom severity (which is interchangeably used herein as “symptomatic” or “severity”, they have the same meaning). It is classified into moderate (I), moderate (II), severe, etc., and the initial information such as blood virus amount, biomolecular weight, antibody presence and absence, clinical findings, treatment history, etc. of the patient according to the degree of symptom and over time.
  • a stochastic transition model for example, in the case of n ⁇ 200
  • deep neural network AI analysis
  • the present disclosure provides a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
  • Such a method comprises obtaining one or more biological parameters in the subject and predicting the viral infection symptom, therapeutic suitability, and / or treatment outcome based on the biological parameters.
  • the present disclosure provides a method for predicting a subject's viral infection symptoms.
  • the methods of the present disclosure can predict the degree of symptom of a viral infection in a subject, or whether it will be aggravated.
  • the method of the present disclosure is to obtain biological parameters from a sample taken from a subject and to predict viral infection symptoms by comparing the obtained parameters with parameters of the same type in a healthy subject. Indicators can be provided.
  • the bioparameters used can be, but are not limited to, biomolecular parameters. Even when the biomolecule parameter is used, other biomolecule parameters (clinical data, image data, etc.) may be used in combination in addition to the biomolecule parameter.
  • virus infection symptoms can be predicted based on biological parameters obtained from a sample collected from a subject. For example, for severe, moderate, and mild virus-infected patients, the above-mentioned biological parameters are measured in advance, and regression analysis is performed to calculate the degree of symptoms (objective variable) and the measured values (explanatory variables) of the above parameters. Then, a method of applying the measured values of the parameters of the subject to be predicted to the regression equation can be mentioned.
  • the probability of severe illness is determined in advance or based on experience as to whether or not it is applied to each therapeutic drug, and when the actually calculated probability of severe illness is equal to or higher than a predetermined standard, the specific treatment is performed. It can also be determined to administer the drug. For example, if the predicted probability of severe illness is 50% or higher, 60% or higher, 70% or higher, 80% or higher, 85% or higher, 90% or higher, or 95% or higher, it is determined to administer the specific therapeutic agent. can do. Specific values of such criteria can be appropriately set based on experience, etc., in addition to the specific numerical values described in the present specification, and specific values described in the present specification. It can be adopted even if it is not.
  • predictions are made using bioparameters in a sample taken from a subject, so the method of the present disclosure is the amount of bioparameters in the sample (eg, in serum or peripheral blood) or It may include the step of measuring the concentration.
  • the sample can be obtained from a subject by collecting blood from a subject by a means well known in the art such as a blood collection tube.
  • the serum may be separated and then dispensed and stored frozen.
  • the amount of virus in a sample collected from a subject may be quantified by means such as virus-specific PCR.
  • the method of the present disclosure predicts whether the symptom becomes severe when the subject is infected with the virus, or whether the symptom is cured by mild or moderate illness.
  • the subject to be predicted is not particularly limited, and may be a person who does not have the virus or a patient who has already suffered from the virus. Further, in the method of the present disclosure, the subject to be predicted is not limited to medication history, medical history, gender, age, and the like.
  • the causative virus of the symptom predicted by the method of the present disclosure is not particularly limited, but includes, for example, a virus that infects an animal (for example, a human) (including a DNA virus or an RNA virus).
  • Adenovirus family virus for example, herpes virus family virus (for example, simple herpes virus, varicella / herpes zoster virus, cytomegalovirus, EB virus), hepatitis, which are viruses that affect humans and show cold-like syndrome.
  • Viruses hepatitis C virus, hepatitis B virus
  • immunodeficiency viruses such as HIV
  • coronaviruses such as HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV and SARS-CoV-2 are included.
  • coronaviruses there are four types of coronaviruses that infect humans: human coronavirus 229E, OC43, NL63, and HKU-1 as the causative viruses of colds, and severe acute respiratory syndrome (SARS) that occurred in 2002, which causes severe pneumonia.
  • coronavirus coronavirus
  • MERS Middle East Respiratory Syndrome
  • new coronavirus 2019-nCoV, SARS-CoV-2
  • the new coronavirus is classified into the same beta coronavirus genus as the SARS coronavirus, and the gene of the new coronavirus is highly homologous to the gene of the SARS coronavirus (about 80%), and further, the acceptance similar to the SARS coronavirus.
  • ACE1 or ACE2 the body
  • ACE1 or ACE2 the body
  • differences in ACE1 or ACE2 subtypes manifest in differences in infectivity.
  • the clinical symptoms of the new coronavirus are said to progress from influenza-like symptoms such as headache, high fever, malaise, and cough to pneumonia, which is the main complaint of dyspnea in severe cases.
  • Symptomatic treatments such as fever reduction and respiratory support are taken, and diversion or new development of vaccines, antiviral drugs (favipiravir, remdesivir, etc.) and immunotherapeutic agents is underway.
  • methods for measuring viral genes such as the PCR method and other rapid diagnostic methods, are being developed. Since the gene sequence of the new coronavirus is close to that of the SARS coronavirus and is also homologous to the SARS-like coronavirus derived from the bat, it is likely that the bat carries the virus from which the new coronavirus originated. Conceivable.
  • the virus has typical characteristics of the coronavirus family and belongs to the beta coronavirus 2B strain. Comparing the entire genome sequence of COVID-19 virus with the other available genome sequences of ⁇ coronavirus, the closest relationship is the coronavirus strain BatCov RaTG13, such as bat SARS, with identity 96. It was shown to be%. Virus isolation is from human airway epithelial cells, Vero It has been carried out on various cell lines such as E6 and Huh-7, and these samples can also be used. Most people infected with the COVID-19 virus recover with mild illness.
  • the degree of symptom predicted by the method of the present disclosure is not particularly limited and can be appropriately set depending on the type of virus.
  • Severe (dyspnea, septic shock, and / or tachypnea / disorder), and other mild to moderate.
  • the methods of the present disclosure include IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, as parameters used to predict viral infection symptoms.
  • the Bio-PlexHman Cytokine 27-Plex Panel (27 items) (IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL- 6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF), Inframation1 kit (37 items) (APRIL, BAFF, CD30, CD163, Cytokine-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22,
  • the methods of the present disclosure include bioparameters such as IL-4, IL-5, IL-12, IL-15, basicFGF, MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30, CD163, Chitinase-3.
  • the methods of the present disclosure include bioparameters such as IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30. , CD163, Chitinase-3, gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-22.
  • bioparameters such as IL-2, IL-4, IL-5, IL-12, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , RANTES, APRIL, BAFF, CD30.
  • CD163, Chitinase-3 gp130, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-22.
  • the biological parameters are IL-2, IL-5, IL-6, IL-7, IL-10, IL-11, IL-12 (p40), IL-12 ( p70), IL-13, IL-15, IL-17, IL-22, IL-32, IL-34, IL-35, TNF- ⁇ , GM-CSF, IL-1Ra, IFN- ⁇ 2, IFN- ⁇ , IL-28A, IL-8, IP-10, MCP-1, basic FGF, VEGF, VCAM-1, CD30, BAFF, Pentraxin-3, LIGHT.
  • the methods of the present disclosure include bioparameters such as IL-1Ra, IL-13, IP-10, MCP-1, PDGF-bb, APRIL, BAFF, CD30, IL-11, IL-28A, IL. Parameters selected from the group consisting of -29, Pentraxin-3, and sTNF-R1 are preferred.
  • the biological parameters are selected from the group consisting of IP-10, MCP-1, PDGF-bb, APRIL, BAFF, CD30, IL-11, Pentraxin-3, sTNF-R1. Parameters are preferred.
  • the biological parameters are IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10. , IL-12, IL-13, IL-15, IL-17, basicFGF, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , PDGF-bb, TNF- ⁇ , VEGF, APRIL, BAFF, CD30, CD163, IFN- ⁇ 2, IFN- ⁇ , IL-6R ⁇ , IL-11, IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-2, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, and TSLP Parameters
  • the biological parameters are IL-1Ra, IL-2, IL-7, IL-8, IL-10, IL-12, IL-15, basic FGF, IP-10, TNF- ⁇ , BAFF, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-11, IL-19, IL-20, IL-27, IL-28A, IL-29, IL-32, IL-34, Parameters selected from the group consisting of IL-35, LIGHT, MMP-1, and Pentraxin-3 are preferred.
  • a group of parameters whose numerical values change in any of mild, moderate I, moderate II, and severe can be selected in Table 3.
  • CTACK GM-CSF, HGF, IFN-a2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL.
  • IL-17A, IL-18, IP-10 MCP-1 (MCAF), MCP-3, M-CSF, MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, TRAIL, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-a2, IFN-g, IL-2, IL-6Ra, IL-10, IL-11, IL-20, IL-22, From the group consisting of IL-28A, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, Osteopontin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, and TWEAK. It can be the parameter of choice. Since the above biological parameters change gradually or gradually from mild to moderate I, moderate II, and severe, they can be used as parameters to indicate the severity of symptoms, and the severity
  • the biological parameters are a group of parameters whose numerical values are changed in any of mild, moderate I, moderate II, and severe in Table 3, and are changed in influenza. Can be selected without, for example, CTACK, GM-CSF, HGF, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-12 (p40), IL-16.
  • IL-17A MCP-1 (MCAF), MIF, MIG, b-NGF, PDGF-bb, SCGF-b, SDF-1a, BAFF, sCD30, sCD163, Chitinase 3-like 1, IFN-g, IL- Parameters selected from the group consisting of 6Ra, IL-20, IL-32, IL-34, IL-35, LIGHT, MMP-1, MMP-3, Osteocalcin, sTNF-R1, sTNF-R2, TSLP, and TWEAK. Can be. Since the above biological parameters change gradually or gradually in mild, moderate I, moderate II, and severe cases, they can be used as parameters indicating the severity of symptoms, and are further distinguished from influenza virus. It is possible to predict the severity of the disease.
  • a group of parameters whose numerical values are changing in any of moderate I or II and severe can be selected, for example, HGF, IFN. -A2, IL-1ra, IL-2Ra, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-16, IL-18, IP-10, MCP-1 (MCAF), M-CSF, MIG, b-NGF, PDGF-bb, VEGF, APRIL, BAFF, sCD30, IFN-a2, IL-2, IL-6Ra, IL-10, IL-11, IL It can be a parameter selected from the group consisting of -28A, IL-34, Pentraxin-3, sTNF-R1 and sTNF-R2. Since the numerical values of the above biological parameters change stepwise or gradually from moderate to severe, they can be used as parameters indicating the severity of symptoms, and it is possible
  • a parameter group in which the numerical values are changed in any of moderate I or II and severe, and which is not changed in influenza is selected.
  • It can be a parameter selected from the group consisting of -NGF, PDGF-bb, BAFF, sCD30, IL-6Ra, IL-34, sTNF-R1 and sTNF-R2. Since the above biological parameters change gradually or gradually from moderate to severe, they can be used as parameters indicating the severity of symptoms, and can be further distinguished from influenza virus, and the severity is predicted. Is also possible in part.
  • a parameter group whose numerical value changes only in severe cases can be selected in Table 3, for example, MCP-3, IFN-g, IL-12. It can be a parameter selected from the group consisting of (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the above biological parameters are markers peculiar to severely ill patients and can be used as parameters for identifying severely ill patients.
  • a parameter group whose numerical value changes only in severe cases and whose numerical value does not change in influenza can be selected, for example, IFN. It can be a parameter selected from the group consisting of ⁇ g, IL-12 (p40), IL-20, IL-32, IL-35, MMP-1, MMP-3, Osteocalcin, TSLP, and TWEAK.
  • the above biological parameters are markers peculiar to severely ill patients, can be used as parameters for identifying severely ill patients, and can be further distinguished from influenza virus.
  • a group of parameters whose expression is altered (increased or decreased) by tosirizumab treatment and / or dexmedetomidin treatment can be selected, for example, Eotaxin.
  • G-CSF IFN-g, IL-2, IL-4, IL-1ra, IL-6, IL-8, IL-17A, IP-10, MCP-1 (MCAF), MIP-1b, TNF-b , APRIL, BAFF, sCD30, sCD163, gp130, IFN-a2, IFN-b, IL-6Ra, IL-12 (p40), IL-19, IL-20, IL-28A, IL-29, IL-35, It can be a parameter selected from the group consisting of MMP-1 and TWEAK.
  • the above biological parameters can be used as parameters that can be used as an index of the degree of alleviation or cure by treatment in addition to the degree of severity, and in that sense, some prediction of aggravation may be possible.
  • a group of parameters whose expression is changed by tocilizumab treatment but not by dexmedetomidine treatment can be selected in Table 3, for example, IL-1ra, TNF. It can be a parameter selected from the group consisting of ⁇ b, IL-12 (p40), IL-20, IL-28A, IL-29, and IL-35.
  • the above biological parameters can be used as parameters that can be used as an index of the degree of alleviation or cure by treatment in addition to the degree of severity, and in that sense, some prediction of aggravation may be possible.
  • a parameter group whose numerical value changes only in severe cases in Table 3 and which is also judged as “+” in Table 2 is selected.
  • the above biological parameters are markers peculiar to severely ill patients, and are markers commonly found in various cohorts, and can be more preferably used as parameters for identifying severely ill patients.
  • the parameters individually evaluated in FIGS. 6 to 22 can be selected as the biological parameters, for example, IL-6, IL-1Ra, IP-10, BAFF, APRIL, and the like. From VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF-a, sgp130, IL12 (p40), IL-6Ra, IL-10, TWEAK, and IL-8 It can be a parameter selected from the group of. As a result of various analyzes, the above biological parameters are examples that can be advantageously used as severity levels and various diagnostic markers.
  • one of the above-mentioned parameters may be used alone as a symptom prediction parameter, but from the viewpoint of predicting a virus infection symptom with higher accuracy, among these. It is preferable to use two or more kinds.
  • the inflammatory marker is at least not an inflammatory cytokine.
  • It can contain one type of marker. That is, for example, by using chemokines and markers other than inflammatory markers such as markers involved in angiogenesis and alveolar formation, not only the inflammatory state due to virus infection but also lung symptoms associated with virus infection and blood vessels can be used. At the same time, the involvement of thrombosis due to the activity of the endothelium can be investigated, and viral infection can be predicted with high probability.
  • cytokines, chemokines and soluble receptors can be measured by known methods, known measurement kits.
  • Reagents used in a measurement system utilizing such an antigen-antibody reaction can be provided individually or as a set as a diagnostic agent for determining each biologic.
  • the biological parameters as described above can be used as biomarkers because they can predict the degree of viral infection symptoms in the subject. Therefore, in one aspect of the present disclosure, one or more molecular markers for predicting viral infection symptoms of a subject can be provided, and as the molecular marker, the above-mentioned biological parameters can be adopted. ..
  • the disclosure is a method of treating a virus-infected patient, The step of obtaining one or more biological parameters in the patient and A step of analyzing the biological parameter by comparing the biological parameter with a reference value to predict the viral infection symptom of the patient and a step of administering a therapeutic agent to the patient when the patient is predicted to be a seriously ill patient.
  • the therapeutic agent is not particularly limited, and prevention of a protein subunit, a live attenuated virus, a vaccine using an inactivated virus, a DNA vaccine, an RNA vaccine, a vaccine using a non-replicating virus vector, and the like.
  • remdecibir In addition to vaccines, remdecibir, fabipiravir, cyclesonide, nafamostat, camostat, ivermectin, steroids, corticosteroids, tosirizumab, salilmab, tofacitinib, varicitinib, luxolitinib, acarabrutinib, luxolitinib, acarabrutinib, luxolitinib, acarabrutinib, rubrismab, erythran 001, dexamethasone, cashiribimab / imdebimab, bamuranibimab / etesebimab, sotrobimab, VIR-7832, AZD7442, molnupiravir, AT-527, BI767551, PF-07304814, PF-07321332, VIR-2703, anakin Stage patient plasma
  • Oxygen inhalation Extracorporeal membrane oxygenation Intravenous drip: Steroid agent IL-6 agent such as Actemra (tocilizumab) Oral: Antiviral agent (favipiravir (Abigan), remdesivir, etc.) Antithrombotic drugs (eg, heparin, warfarin, aspirin).
  • ECMO Oxygen inhalation Extracorporeal membrane oxygenation
  • prophylactic or therapeutic agents such as inhibitors of the biomolecule can be developed, such inhibitors.
  • Such prophylactic or therapeutic agents are also within the scope of this disclosure.
  • any available vaccine such as mRNA type vaccine, DNA type vaccine, inactivated protein vaccine, inactivated vaccine, attenuated vaccine, live vaccine and the like can be used, and for example, Tojnameran (Pfizer-BioNTech) can be used.
  • S Janssen COVID-19 Vaccine
  • NVX-CoV2373 Novavax COVID-19 Vaccine
  • the present disclosure can further provide a diagnostic agent or test agent for carrying out the predictive method of the present disclosure, and specifically, the diagnostic agent or test agent of the present disclosure is for predicting a viral infection symptom. It is a diagnostic agent or a test agent and is characterized by containing a reagent capable of detecting at least one parameter selected from the above-mentioned biological parameters.
  • the diagnostic agent or test agent of the present disclosure is a diagnostic agent or test agent used in a method for predicting viral infection symptoms by measuring the amount or concentration of biological parameters in a sample taken from a subject.
  • the diagnostic agent comprises a reagent for detecting the biological parameter. After predicting the symptoms of infection before the actual infection, by setting an appropriate threshold for the degree or probability of the infection, administration of a prophylactic drug such as a vaccine to prevent viral infection, or the actual virus. It can be used to design treatment plans in the event of infection.
  • reagents for the required bioparameters can be selected and used based on the information described herein. When there are a plurality of such reagents, they may be provided separately, may be provided together as a set, or may be provided as a kit together with other necessary reagents (for example, a color former).
  • Such biological parameters can be measured by a measurement system using an antigen-antibody reaction such as ELISA, and as reagents capable of detecting biological parameters, specifically, an antibody that can specifically bind to the biological parameter, and The fragment is mentioned. Further, an antibody that can specifically bind to a biological parameter may be bound on an appropriate support and provided as an antibody array.
  • an antigen-antibody reaction such as ELISA
  • the method of the present disclosure is to compare biological parameters obtained from a sample taken from a subject with, if necessary, parameters of the same type in a healthy subject to indicate viral infection symptoms, therapeutic suitability, and the like. And / or treatment outcomes can be predicted.
  • the values of the biological parameters for each subject obtained as described above can be logarithmically converted. This makes it possible to get closer to the normal distribution.
  • each of the biological parameters obtained from a severely ill person with the value of a healthy person, it is possible to find a biological parameter whose expression changes specifically for the severely ill person, and the biological parameter thereof.
  • Subject viral infection symptoms, treatment suitability, and / or treatment outcomes can be predicted on the basis of increased or decreased expression of.
  • the present disclosure is, in another aspect, a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome.
  • the step of obtaining one or more biological parameters in the subject, and A method may be provided that comprises applying the biological parameters to a predetermined predictive model and outputting probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes.
  • prediction models generated based on reinforcement learning or neural networks may provide methods for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes of a subject.
  • the biomolecular parameter includes a biomolecular parameter (eg, a factor indicating an in vivo response such as the amount of cytokine or its fluctuation, a factor determining the compatibility of a virus on the biological side such as ACR-R1 isotype).
  • a biomolecular parameter eg, a factor indicating an in vivo response such as the amount of cytokine or its fluctuation, a factor determining the compatibility of a virus on the biological side such as ACR-R1 isotype.
  • Clinical data including clinical findings, X-ray CT images), and amount and / or type of virus in the body (including blood viral load, type of virus variant (identifiable by SNP, etc.), etc.) It can contain information about at least one.
  • the biological parameter comprises information about at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, blood viral load, and ACR-R1 isotype. Can be done.
  • Predictive models include biomolecular parameters (such as the amount of cytokines or their fluctuations), clinical data (clinical findings, X-ray CT images, etc.), historical information (treatment history, state history information, etc.) and treatment method information (therapeutic effect information, safety). Factors selected from (sex information, etc.) may be used.
  • the predictive model is at least one selected from the group consisting of cytokine levels or variations thereof, clinical data, X-ray CT images, treatment history, treatment efficacy information, safety information, and status history information. Can be generated based on.
  • the clinical endpoints used to generate the predictive model are not particularly limited and include various treatments treated according to the patient's symptoms and their effects, as well as clinical data over time in the patient. Any clinical information can be used.
  • the clinical data is not particularly limited and can be appropriately selected depending on the virus for which the symptom is predicted.
  • clinical data that is known to fluctuate particularly in a target virus-infected patient can be used, specifically, leukocyte count (per ⁇ l), erythrocyte count (per ⁇ l), and absolute neutrophil count.
  • clinical data includes clinical symptoms including fever, nasal juice, cough, malaise, nausea, vomiting, diarrhea, abdominal symptoms, lung X-ray photography, lung CT findings, pO 2 , nasal mucosa virus test, oral cavity.
  • Test values including mucosal virus test, blood virus test, leukocyte (WBC), neutrophil (Neu), lymphocyte (Lym), platelet (Plt), blood pigment (Hb), CRPvFib, albumin (Alb), AST , ALT, lactate dehydrogenase (LDH), AL-ph, creatinine, procalcitonin, prothrombin time, FDP, blood test values such as DD dimer, and antibody test values including IgG antibody and IgM antibody. It is preferable to use clinical evaluation variables such as one or more clinical symptoms, findings, or laboratory values.
  • clinical data include fever, chest CT, FDP, ferritin, absolute lymphocyte count, CRP, and / or antibody test values, and biomolecular parameters IL-6, IL-8, FGF, IFN ⁇ , It is preferred to use IFN ⁇ , IFN ⁇ , IP-10, BAFF, IL-11, and / or Ferraxin.
  • the treatment history for generating a predictive model includes a history of past treatments (eg, administration history of antiviral drugs, etc.), and a history of arbitrary data of the subject.
  • treatment history includes presence / absence of oxygen inhalation, inhalation volume, duration and / or effect, presence / absence of ECMO, duration and / or effect, infusion of steroids and Actemra.
  • Presence / absence, dosage, duration, and / or effect, presence / absence of oral medications such as antiviral drugs such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin, dosage, administration period, and / or time-dependent information on the effect.
  • antiviral drugs such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin
  • the therapeutic effect information or safety information for generating a predictive model includes the presence or absence of oxygen inhalation, the amount of inhalation, the period and / or the effect, the presence or absence of ECMO, the period and / or the effect.
  • Presence / absence, dosage, duration, and / or effect of infusion of steroids and Actemra, presence / absence of oral medications such as antiviral drugs such as Avigan and antithrombotic drugs such as heparin, warfarin, and aspirin, dosage, administration period, and / or Efficacy and / or safety information can be used.
  • FIG. 1 The treatment route model on the left side of FIG. 1 summarizes the patient's condition so far. Since hospitals are not always hospitalized from the mild stage, the routes are divided according to the stage at the time of admission. In the case of continuation from mild illness, it is a model that predicts therapeutic demand while improving the system of symptom degree prediction model in which each step is connected.
  • the right side of FIG. 1 is the established state transition model, and each step is a combination model based on the conventional machine learning model.
  • Data such as about 70 kinds of biomolecular weight fluctuations, clinical data, extra-trace blood viral load from X-ray CT images, and ACE-R1 isotype are input as nodal variables in the symptom degree model as initial test data.
  • the degree of symptom is divided into ranks such as "asymptomatic”, “mild”, “moderate (I)”, “moderate (II)", and "severe”, and the result is obtained as a probability value corresponding to each rank. Can be done.
  • the predictive model is that the target patients are asymptomatic group (40 patients), mild (40 patients), moderate (I) (40 patients), moderate (II) (20 patients), severe (20 patients). It is possible to acquire and generate the symptoms and biological parameters of the name). For less than 200 people, a statistically established transition model can be used, and for more than 200 people, a therapeutic effect prediction model based on AI's deep manual network can be used.
  • patient clinical findings, imaging findings, past treatment history, presence / absence of antibody, blood viral load, blood biomolecules, etc. can be used as evaluation candidate items, and each of them mainly consists of respiratory condition.
  • the stage symptom classification can be the objective variable.
  • the virus in blood can be quantified using the PCR method, and 90 items of biomolecular measurement can be quantified using Bio-Plex.
  • Evaluation candidate items can be specified for each symptom degree stage, and the concordance rate of restage prediction and the establishment of symptom degree prediction can be calculated.
  • treatment for each stage is assumed, optimal treatment prediction is performed, and optimal treatment is applied according to the predicted symptom degree.
  • the degree of symptom is predicted for each stage such as asymptomatic, mild, moderate (I), moderate (II), and severe, the suitability of the treatment method is predicted, and the optimum treatment can be performed.
  • multivariate analysis can be used in generating predictive models, and multivariate analysis can use LASSO (Least Absolute Shrinkage and Selection Operator) (Trevor et. Al .: Statistical). Learning with Sparsity: The Lasso and Generalizations, CRC Press, 2015).
  • LASSO Least Absolute Shrinkage and Selection Operator
  • This is a method of performing parameter estimation and variable selection at the same time (a technology that combines regression estimation and optimization theory of system engineering), and it is possible to calculate even if the explanatory variables are larger than the number of samples.
  • the background is the assumption that few variables are really valid. For example, in gene information analysis, the number of explanatory variables is 20,000, and the number of samples can be several hundred. You can get the regression coefficient while selecting the explanatory variables. Since the representative explanatory variable is automatically selected, the problem of multicollinearity does not occur. Therefore, the explanatory variables can be determined while numerically evaluating the degree of overfitting as much as possible in the limited data.
  • the performance of the model is the prediction obtained for the remaining 20% of the data obtained by constructing the regression model by the LASSO using 80% of the data of the data set divided into 8 to 2 in advance. It can be evaluated using Cohen's d from the distribution of values (J. Cohen: Statistical Power Analysis for the Behavioral Sciences, second edition, Statistics Press. ). Cohen's d is a value that does not depend on the sample size. Cohen's d is the difference between the mean values of the two groups divided by the combined (pooled) standard deviation of the two groups. The performance of the predictive model can be evaluated using Cohen's d between the verification group and the learning group, and in the medical field, a model with Cohen's d of 1.2 or more is preferable. ..
  • the techniques provided in the present disclosure allow the prediction of viral infection symptoms, treatment suitability, and / or treatment outcomes by appropriately selecting biological parameters and applying them to predictive models.
  • the present disclosure obtains biological parameter data from a subject, determines the current viral infection status and symptoms, predicts how severe it will be in the near future, and how the degree of symptoms will be. It can be predicted whether it will change. Furthermore, since it is possible to determine the current virus infection status and symptoms, predict how severe the disease will be in the near future, and predict how the degree of symptoms will change, what kind of treatment will be performed at this time. It is possible to determine whether it is appropriate or optimal, or which of the available treatments is optimal, or to what extent the symptoms are in the course of treatment. It is possible to predict how much the symptoms will worsen and the degree of symptoms can be suppressed.
  • the prognosis is better for the subject tolerable to moderate (II).
  • the present disclosure can improve its accuracy as it is used. It can be expressed using a certain prediction formula, coefficient, and so on. For example, based on the present disclosure, it is classified into early asymptomatic, mild, moderate (I), moderate (II), severe, etc. based on the degree of post-infection symptom, and the amount of virus in the blood of the patient according to the degree of symptom. , Biomolecular weight, presence / absence of antibody, clinical findings, initial information such as treatment history and time-dependent information are used as evaluation items, and the degree of symptom at each stage is used as the objective variable. Alternatively, deep neural networks (AI analysis) can be used to further improve the predictive model and use it sequentially.
  • AI analysis can be used to further improve the predictive model and use it sequentially.
  • the determination method or analysis method can be executed by a program. That is, it is a computer program that causes a computer to execute a process of a method for predicting a virus infection symptom of a subject, wherein the method is a step of causing the computer to obtain one or a plurality of biological parameters in the subject.
  • a program may be provided to the computer comprising a step of analyzing the bioparameters by comparing them with reference values and using them as indicators for predicting viral infection symptoms.
  • a computer program that causes a computer to perform processing of a method in which one or more biological parameters in a subject are used as an index for predicting a viral infection symptom of the subject, wherein the method causes the computer to perform the subject.
  • a computer program that causes a computer to process a method for predicting a subject's viral infection symptoms, treatment suitability, and / or treatment outcome, wherein the method causes the computer to perform one or more of the subjects. It comprises the steps of obtaining a plurality of bioparameters and causing the computer to apply the bioparameters to a predetermined predictive model to output probabilities for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes.
  • the program may be offered.
  • a recording medium containing such a program may also be provided.
  • the recording medium can be a non-temporary recording medium.
  • the system may include a program for causing a computer to perform the methods described herein, eg, may include a recording medium containing such a program. Further, a computer (for example, a computer) for executing the instruction instructed by the program may be provided.
  • the arithmetic unit may be physically integrated or may consist of a plurality of physically separated components. Inside these arithmetic units, functions corresponding to a learning unit, a calculation unit, an acquisition unit, and the like can be provided as needed.
  • the system of the present disclosure can be realized as an ultra-multifunctional LSI manufactured by integrating a plurality of components on one chip, and specifically, a microprocessor, a ROM (Read Only Memory), and a RAM. It may be a computer system configured to include (Random Access Memory) and the like. A computer program is stored in the ROM. When the microprocessor operates according to a computer program, the system LSI achieves its function.
  • a system LSI it may be referred to as an IC, an LSI, a super LSI, or an ultra LSI due to the difference in the degree of integration.
  • the method of making an integrated circuit is not limited to the LSI, and may be realized by a dedicated circuit or a general-purpose processor.
  • An FPGA Field Programmable Gate Array
  • a reconfigurable processor that can reconfigure the connection and settings of the circuit cells inside the LSI may be used.
  • an integrated circuit technology that replaces an LSI appears due to advances in semiconductor technology or another technology derived from it, it is naturally possible to integrate functional blocks using that technology. The application of biotechnology may be possible.
  • one aspect of the present disclosure is for such viral infection symptoms, treatment suitability, and / or prediction of treatment outcomes or prediction of viral infection symptoms, treatment suitability, and / or treatment outcomes.
  • the system for identifying indicators but also the prediction of virus infection symptoms or the prediction of virus infection symptoms, or the prediction of virus infection symptoms, which steps through the characteristic components included in the system for identifying indicators for predicting virus infection symptoms. It may be an index for predicting a virus infection symptom, a value generation, a discrimination / classification method.
  • the present disclosure may be a computer program that causes a computer to execute each characteristic step included in feature amount reduction, feature amount extraction, pain discrimination / estimation model generation, and pain discrimination / estimation.
  • one embodiment of the present disclosure identifies indicators for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes or for predicting viral infection symptoms, treatment suitability, and / or treatment outcomes. It may be a computer program that causes the computer to perform each of the characteristic steps included in the method. Also, one aspect of the present disclosure may be a computer-readable, non-temporary recording medium on which such a computer program is recorded.
  • the present disclosure may be realized as an embodiment using the cloud, IoT and AI, and the prediction technology of the present disclosure may be provided as an all-inclusive system or device.
  • the biological parameters are mainly measured and the results are displayed as a prediction device, and the calculation and the calculation of the discrimination model are performed by a server or the cloud.
  • Some or all of these can be performed using IoT (Internet of Things) and / or artificial intelligence (AI).
  • the apparatus of the present disclosure also stores a prediction model and makes a discrimination on the spot, but a semi-standalone type, which is a form in which the main calculation such as the calculation of the discrimination model is performed on a server or the cloud, can be assumed. .. Since it is not always possible to send and receive at some places such as hospitals, a model that can be used even when shielded is assumed.
  • the present disclosure describes a subject's viral infection symptoms, therapeutic suitability, And / or a program that causes a computer to execute a method of predicting a treatment result, the method of which is a) a step of obtaining a biological parameter from a model subject, and b) a step of generating a predictive model based on the biological parameter.
  • a program, and recording media, systems and equipment containing it, including steps to apply biological parameters from the subject to a predictive model to predict viral infection symptoms, treatment suitability, and / or treatment outcomes. offer.
  • the generation of the prediction model is realized on the server located away from the clinical site, and only the acquisition of biometric parameters is input at the clinical site or the clinical test site near it. It may be in the form of.
  • a simple biological parameter acquisition device may be provided, whereby the calculation result may be instantly converted into a clinical setting so that an instant judgment can be made.
  • SaaS Software as a service
  • a function for improving the prediction model may be provided.
  • selecting the optimum parameter with the highest prediction accuracy can be realized by utilizing any known in the art, such as the MATLAB LASSO function, SVM function, and the like. can.
  • This function may be in the part that generates the predictive model, or it may be provided as a separate module.
  • This predictive model improvement function is, for example, option 1 (period 1 year, once or twice a year), option 2 (period 1 year, once every 1 or 2 months), option 3 (period extension, once or twice a year). ), Option 4 (extension of period +1, once every two months) and other options may be provided.
  • Data can be saved as needed.
  • Data storage is usually provided on the server side, but it may be on the terminal side as well as the cloud type as well as the all-equipped type.
  • data storage is standard (for example, up to 10 GB in the cloud), option 1 (for example, 1 TB increase in the cloud), option 2 (setting parameters in the cloud and split storage), option 3. It may offer the option of (save in the cloud by discriminant model). Save data, download data from all sold devices to create big data, update discriminant models over time, build new models, for example, new ones other than COVID-19 It is possible to provide new discrimination model software such as pandemics. It may have a data analysis option. Here, it is possible to provide patient pattern classification (searching for patient clusters based on discrimination accuracy and pattern change of feature amount).
  • each component may be configured by dedicated hardware or may be realized by executing a software program suitable for each component.
  • Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory.
  • program is used in the usual meaning used in the relevant field, and describes the processes to be performed by a computer in order, and is treated as a "thing" by law. All computers operate according to the program. In modern computers, programs are represented as data and stored in recording media or storage devices.
  • the "recording medium” is a recording medium in which the program for executing the present disclosure is stored, and the recording medium may be any as long as the program can be recorded.
  • the recording medium may be an external storage device such as a ROM, an HDD, a magnetic disk, or a flash memory such as a USB memory that can be stored internally, but the present invention is not limited thereto.
  • system refers to a configuration for executing the method or program of the present invention, and originally means a system or organization for accomplishing an object, and a plurality of elements are systematically configured. In the field of computers, it refers to the entire configuration of hardware, software, OS, network, etc.
  • Example 1-1 Cytokine / chemokine / soluble receptor assay (1)
  • Cytokines and bimarkers were quantified using the multiplex cytokine array system Bio-Plex 200 (Bio-Rad Laboratories, CA, USA) according to the manufacturer's instructions. Serum was collected from 13 severely ill patients with COVID-19 and 42 healthy subjects before treatment and centrifuged at 1600 xg for 10 minutes. Serum samples were frozen at ⁇ 80 ° C. until analysis. Cytokines / chemokines / soluble receptors in the patient were simultaneously quantified.
  • the Bio-PlexHman Cytokine 27-Plex Panel (27 items) (IL-1 ⁇ , IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL- 8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN- ⁇ , IP-10, MCP-1, MIP-1 ⁇ , MIP-1 ⁇ , PDGF-bb, RANTES, TNF- ⁇ , VEGF), Information1 kit (37 items) (APRIL, BAFF, CD30, CD163, Cytokine-3, gp130, IFN- ⁇ 2, IFN- ⁇ , IFN- ⁇ , IL-2, IL-6R ⁇ , IL-8, IL-10, IL-11, IL-12 (p40), IL-12 (p70), IL-19, IL-20, IL-22, IL-26, IL-27,
  • Example 1-2 Cytokine / chemokine / soluble receptor assay (2)
  • each cytokine / chemokine / soluble receptor shown was quantified using the Bio-Plex Pro Human Cytokine Screening 48-Plex Panel and the Bio-Plex Pro Human Inflammation 1,37-Plex Panel. These multiplex cytokine arrays were measured according to the manufacturer's instructions. Data acquisition and analysis were performed using Bio-Plex Manager software version 5.0.
  • Example 2 statistical analysis
  • the expression levels of biological parameters for each subject obtained using Bio-Plex 200 were compared.
  • cytokines / chemokines / soluble receptors were simultaneously quantified using sera obtained from rheumatoid arthritis patients in addition to healthy subjects and COVID-19 severely ill patients. All parameters were logarithmically converted and used because the distribution is more normalized. After that, the values were classified into values (green) included in the range of 10 to 90% of the values appearing in healthy subjects, higher values (red), and lower values (light green). The results are shown in FIGS. 2 to 5. In FIGS.
  • COVID-19-0 is a patient before Actemra treatment
  • COVID-19-1w is a patient about 1 to 5 days after Actemra treatment
  • COVID-19-1w is Actemra treatment.
  • Patients about 7 days after, and “COVID-19-2w” represent patients about 10 to 14 days after Actemra treatment, respectively.
  • COVID-19 infection symptoms can be predicted.
  • Pentraxin-3 it is considered that severe COVID-19 infection can be predicted with extremely high sensitivity by using Pentraxin-3.
  • Example 3 Prediction of asymptomatic, mild, and moderately ill persons
  • Informed consent will be obtained for those who are positive for nasal mucosal juice COVID-19 and whose stage is confirmed by the degree of signification of respiratory symptoms that have been tested for antibodies.
  • Blood is collected from the specified blood collection tube, and after serum separation, it is dispensed and stored frozen.
  • the amount of virus in the blood is quantified by COVID-19-specific PCR.
  • 90 kinds of biomolecules are quantitatively measured using BioPlex, Millipore, and R & D cytokine measurement kits of BioRad.
  • the viral load, biomolecular weight, presence / absence of antibody, clinical findings, and past treatment methods are used as evaluation items, and the evaluation items for predicting the degree of symptoms are specified. Based on the above, it is possible to acquire biological parameters for predicting asymptomatic, mild, and moderately ill patients in addition to severely ill patients, and it is considered that COVID-19 infection symptoms can be predicted using these parameters. Be done.
  • Example 4 Generation of prediction model
  • each symptom level is re-determined, and a machine learning probabilistic transition model or deep neural network statistical analysis method using AI is used. Select the most suitable therapeutic agent and treatment method.
  • the analysis model is shown in Fig. 1.
  • the patient's symptoms and clinical data over time are learned.
  • the model is to predict the therapeutic request while improving the system of the symptom degree prediction model in which each step is connected.
  • the right side of FIG. 1 is an established state transition model, and each step is a combination model based on a conventional machine learning model.
  • Data such as about 70 kinds of biomolecular weight fluctuations, clinical data, extra-trace blood viral load from X-ray CT images, and ACE-R1 isotype are input as nodal variables in the symptom degree model as initial test data.
  • the degree of symptom is divided into ranks such as "asymptomatic”, “mild”, “moderate (I)”, “moderate (II)", and "severe”, and the result is obtained as a probability value corresponding to each rank. Can be done.
  • Target patients include asymptomatic group (40 patients), mild (40 patients), moderate (I) (40 patients), moderate (II) (20 patients), severe (20 patients) symptoms and biological parameters. get.
  • a statistically established transition model is used, and for more than 200 people, a therapeutic effect prediction model based on AI's deep manual network is used.
  • Patient clinical findings, imaging findings, past treatment history, presence / absence of antibody, blood viral load, blood biomolecules, etc. are used as evaluation candidate items.
  • the objective variable is the symptom classification of each stage, mainly the respiratory condition.
  • the virus in blood is quantified using the PCR method, and 90 items of biomolecule measurement are quantified using Bio-Plex as in Example 1.
  • the evaluation candidate items are specified for each symptom degree stage, and the concordance rate of the restage prediction and the establishment of the symptom degree prediction are calculated.
  • the predicted symptom degree treatment for each stage is assumed, optimal treatment is predicted, and optimal treatment is applied. This makes it possible to predict the suitability of the treatment method and perform optimal treatment.
  • Clinical data includes clinical symptoms including fever, nasal discharge, cough, malaise, nausea, vomiting, diarrhea, abdominal symptoms, lung X-ray photography, lung CT findings, pO 2 , nasal mucosa virus test, oral mucosa virus test, blood.
  • Test values including medium virus test, WBC, Neu, Lym, Plt, Hb, CRP, Flb, Alb, AST, ALT, LDH, AL-ph, Creatinin, Procalcitonin, Prothrombin time, FDP, D-D dimer, etc.
  • Use clinical endpoints such as blood test values and one or more clinical symptoms, findings, or test values selected from antibody test values including IgG and IgM antibodies.
  • the treatment history and status history information for generating a predictive model include past treatment history (for example, administration history of antiviral drugs and the like), and history of arbitrary data of the target subject (for example, clinical data).
  • the treatment history includes the presence / absence of oxygen inhalation, inhalation amount, duration and / or effect, presence / absence of ECMO, duration and / or effect, presence / absence of infusion of steroids and Actemra, dosage, etc.
  • Example 5 Use of prediction model: virus infectivity / symptom degree prediction
  • virus infectivity / symptom degree prediction is performed using the prediction model generated in Example 4.
  • the selected biological parameters obtained from the subject are applied to the obtained predictive model, and the obtained probabilities are used for each stage such as asymptomatic, mild, moderate (I), moderate (II), and severe.
  • the degree of symptom can be classified into the degree of symptom, or the degree of symptom can be displayed as a numerical value (for example, displayed on a 100% or 100-point scale).
  • an action plan that should be taken in the future. For example, even if the virus infection is positive (for example, PCR, LAMP method, or antigen method), if it is judged to be asymptomatic or mild by the method of the present disclosure, it is not in the hospital but in a simple accommodation facility or at home. It is possible to make a decision to perform both isolation, and for subjects who are at risk of becoming severe or have a high probability of becoming severe, they can be placed in facilities for the severely ill (hospitals and facilities including ICU). Alternatively, those who are determined to remain in moderate illness can be assigned to a hospital that can accommodate moderate illness.
  • the virus infection for example, PCR, LAMP method, or antigen method
  • the attending physician can select and administer appropriate treatment that is known or available at that time to the subject.
  • Example 6 Use of prediction model: treatment suitability and treatment result prediction
  • treatment suitability and treatment result prediction are performed in this example.
  • the selected bioparameters obtained from the subject can be applied to the obtained prediction model to calculate items related to treatment suitability and / or treatment result prediction.
  • the compatibility of a certain therapeutic agent eg, remdesivir, actemra, steroid agent, etc.
  • therapeutic method ECMO, oxygen inhalation therapy, etc.
  • Such therapeutic suitability can be expressed numerically or verbal.
  • the treatment result can be calculated for a therapeutic agent (for example, remdesivir, actemra, steroid agent, etc.) or a therapeutic method (ECMO, oxygen inhalation therapy, etc.).
  • the treatment result is, for example, YES-NO, that is, whether it responds or not, or the degree of improvement (for example, how the degree of symptom changes from before treatment to after treatment, or how it takes) is calculated. can do.
  • Predictive models can be applied to these on hand or available treatments or combinations thereof to compare improvements and select the optimal method from among them.
  • Example 7 Self-study model
  • the prediction model once generated in Example 4 can be improved while actually using it.
  • These models may be performed in a closed environment in a specific hospital, or may be learned by taking in data outside the facility as open and taking in as big data. In that case, the data to be captured can be standardized and machine-learned.
  • selecting the optimum parameter with the highest prediction accuracy can be realized by utilizing any known in the art, such as the MATLAB LASSO function, SVM function, and the like. can.
  • Example 8 Changes in biological parameters in Actemra and Avigan treatment
  • serum obtained from severely infected COVIDEO-19 infected patients was used before and after treatment of the biological parameters shown in the table below. I investigated the fluctuation.
  • Treatment with Actemra and Avigan reduced the expression levels of IL-6, IP-10, IL-19, Pentraxin-3, BAFF, APRIL, IL-1 ⁇ , Osteopontin, and MCP-1, and APRIL, TNF- ⁇ , MMP-1, IL-1Ra, IL-2, IL-17, IL-10, IL-12, IL-13, IL-15, FGF, IFN- ⁇ , PDGF, IFN- ⁇ 2, VEGF, CD30, IFN- ⁇ , IL-11, IL-20, IL-22, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT, TNF- ⁇ R1, TSLP, IL- 7,27p IFN- ⁇ , 27p IL-2, 27p IL-8, 27p IL-10, IL-26, MIP-1 ⁇ , Osteocalcin, eotaxin, MMP-2, MMP-3, IL-4, IL-5, The
  • Example 9 Changes in biological parameters in treatment with tocilizumab or dexmedetomidine
  • TZ tocilizumab
  • DX dexmedetomidine
  • various serums obtained from severely infected COVID-19 infected patients were used.
  • the changes in the biological parameters of Tocilizumab before and after treatment were investigated.
  • the results are shown in FIGS. 6 to 22.
  • Bioparameters include IL-6, IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-28A, IL-29, IFN-a2, IFN-b, IFN-g, TNF-a, sgp130.
  • the expression levels of IL-1Ra, IP-10, BAFF, APRIL, VCAM-1, IFN-a2, etc. decreased after treatment, and the expression levels of sgp130, IL-6Ra, TWEAK, etc. decreased after treatment. The amount has risen.
  • This disclosure provides a technique for individually predicting the degree of symptom of a virus-infected patient and applying an optimal treatment method according to the degree of symptom to eliminate severely ill patients, and is available in the pharmaceutical industry.

Abstract

La présente divulgation concerne un procédé de prédiction d'un symptôme d'une infection virale.　La présente divulgation concerne un procédé de prédiction d'un symptôme d'une infection virale chez un sujet, le procédé comprenant une étape d'acquisition d'un paramètre biologique ou d'une pluralité de paramètres biologiques chez le sujet et une étape de comparaison du paramètre biologique ou de chacun des paramètres biologiques avec une valeur de référence pour analyser le paramètre biologique ou les paramètres biologiques, et l'utilisation du paramètre biologique ou de chacun des paramètres biologiques en tant que mesure pour la prédiction du symptôme de l'infection virale. Lorsqu'un sujet est infecté par un virus, le sujet peut connaître un symptôme de la maladie à l'avance. Par conséquent, le traitement le plus approprié peut être appliqué en fonction du niveau du symptôme chez chaque patient individuellement.
PCT/JP2021/029375 2020-08-07 2021-08-06 Procédé de prédiction de symptôme, d'adéquation thérapeutique et/ou de résultat de traitement pour une infection virale WO2022030629A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2022541756A JPWO2022030629A1 (fr) 2020-08-07 2021-08-06

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2020135178 2020-08-07
JP2020-135178 2020-08-07

Publications (1)

Publication Number Publication Date
WO2022030629A1 true WO2022030629A1 (fr) 2022-02-10

Family

ID=80118181

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2021/029375 WO2022030629A1 (fr) 2020-08-07 2021-08-06 Procédé de prédiction de symptôme, d'adéquation thérapeutique et/ou de résultat de traitement pour une infection virale

Country Status (2)

Country Link
JP (1) JPWO2022030629A1 (fr)
WO (1) WO2022030629A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006524996A (ja) * 2003-05-02 2006-11-09 ホンコン・ディエヌエイ・チップス・リミテッド 核酸検出
JP2013516400A (ja) * 2009-12-30 2013-05-13 パラファーム カンパニー リミテッド ウイルス感染の予防および処置のための材料および方法
JP2020507746A (ja) * 2017-01-08 2020-03-12 ザ ヘンリー エム．ジャクソン ファンデーション フォー ザ アドバンスメント オブ ミリタリー メディシン，インコーポレーテッド 対象特有の菌血症転記を予測するための教師付き学習を使用するためのシステムおよび方法
JP6933834B1 (ja) * 2020-06-18 2021-09-08 国立研究開発法人国立国際医療研究センター 新型コロナウイルス感染者の重症化リスクの検査方法、その検査キット、コンパニオン診断薬及びその重症化リスクマーカー

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006524996A (ja) * 2003-05-02 2006-11-09 ホンコン・ディエヌエイ・チップス・リミテッド 核酸検出
JP2013516400A (ja) * 2009-12-30 2013-05-13 パラファーム カンパニー リミテッド ウイルス感染の予防および処置のための材料および方法
JP2020507746A (ja) * 2017-01-08 2020-03-12 ザ ヘンリー エム．ジャクソン ファンデーション フォー ザ アドバンスメント オブ ミリタリー メディシン，インコーポレーテッド 対象特有の菌血症転記を予測するための教師付き学習を使用するためのシステムおよび方法
JP6933834B1 (ja) * 2020-06-18 2021-09-08 国立研究開発法人国立国際医療研究センター 新型コロナウイルス感染者の重症化リスクの検査方法、その検査キット、コンパニオン診断薬及びその重症化リスクマーカー

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
MANDEL MATHILDA; HARARI GIL; GUREVICH MICHAEL; ACHIRON ANAT: "Cytokine prediction of mortality in COVID19 patients", CYTOKINE, ACADEMIC PRESS LTD, PHILADELPHIA, PA, US, vol. 134, 10 July 2020 (2020-07-10), US , XP086244100, ISSN: 1043-4666, DOI: 10.1016/j.cyto.2020.155190 *
RUSINOVICH O., MORA A., MUÑEZ E., DELGADO TELLEZ DE CEPEDA L., DE LA TORRE N., PAVÍA M., SANZ J., ESPINOSA M., ANDRÉU SÁNCHEZ J. L: "POS1218 SAFETY AND EFFICACY OF ANAKINRA IN SEVERE SARS-COV2 INFECTION (COVID19) AT A TERTIARY HOSPITAL", ANNALS OF THE RHEUMATIC DISEASES, BRITISH MEDICAL ASSOCIATION, GB, vol. 80, no. Suppl 1, 1 June 2021 (2021-06-01), GB , pages 892.2 - 893, XP055894664, ISSN: 0003-4967, DOI: 10.1136/annrheumdis-2021-eular.2590 *
SKEVAKI CHRYSANTHI; FRAGKOU PARASKEVI C.; CHENG CHONGSHENG; XIE MIN; RENZ HARALD: "Laboratory characteristics of patients infected with the novel SARS-CoV-2 virus", JOURNAL OF INFECTION., ACADEMIC PRESS, LONDON., GB, vol. 81, no. 2, 21 June 2020 (2020-06-21), GB , pages 205 - 212, XP086228509, ISSN: 0163-4453, DOI: 10.1016/j.jinf.2020.06.039 *

Also Published As

Publication number Publication date
JPWO2022030629A1 (fr) 2022-02-10

Similar Documents

Publication Publication Date Title
Banoei et al. Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying
Gisby et al. Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death
Metcalf et al. Serum C-reactive protein in adolescence and risk of schizophrenia in adulthood: a prospective birth cohort study
Kwon et al. Cerebrospinal fluid biomarkers to stratify injury severity and predict outcome in human traumatic spinal cord injury
US8961965B2 (en) Methods of diagnosing and treating pulmonary diseases or disorders
Tramontini Gomes de Sousa Cardozo et al. Serum from dengue virus-infected patients with and without plasma leakage differentially affects endothelial cells barrier function in vitro
JP2020190565A (ja) 発作および癲癇の検出のためのバイオマーカーおよび方法
US20220187295A1 (en) Biomarkers for Predicting Multiple Sclerosis Disease Activity
Kozak et al. Cytokines and their genetic polymorphisms related to periodontal disease
CN108034708B (zh) 通过多个mRNA的表达量确定雷公藤多苷片治疗类风湿性关节炎的个体有效性的系统
US20240094217A1 (en) Methods of treating sars-cov-2 infections
Guo et al. Predicting difficult-to-treat chronic rhinosinusitis by noninvasive biological markers
JP2024001063A (ja) 非てんかん性発作／発作なし／心因性非てんかん性発作に対するてんかん性発作の評価および処置のためのバイオマーカーおよび方法
JP2023153888A (ja) 発作およびてんかんの検出のためのバイオマーカーおよび方法
Thair et al. Gene Expression–based diagnosis of infections in critically ill patients—prospective validation of the sepsismetascore in a longitudinal severe trauma cohort
JP2024509576A (ja) 明細胞腎細胞がんを有する患者における治療に対する応答の予測
Campello et al. Evaluation of IL‐6 levels and+ 3954 polymorphism of IL‐1β in burning mouth syndrome: A systematic review and meta‐analysis
WO2022030629A1 (fr) Procédé de prédiction de symptôme, d'adéquation thérapeutique et/ou de résultat de traitement pour une infection virale
Sims et al. Relationship between gene expression patterns from nasopharyngeal swabs and serum biomarkers in patients hospitalized with COVID-19, following treatment with the neutralizing monoclonal antibody bamlanivimab
Goncalves et al. Timeline kinetics of systemic and airway immune mediator storm for comprehensive analysis of disease outcome in critically ill COVID-19 patients
Abiri et al. Obesity and risk of COVID-19 infection and severity: available evidence and mechanisms
US20230266342A1 (en) Biomarkers for Predicting Multiple Sclerosis Disease Progression
Murphy et al. Clinical outcomes following burn injury across the continuum of chronic glycemic control
Kugler et al. Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units
Yevgi Relationship between acute phase reactants and disability in Guillian-Barré syndrome during the COVID-19 pandemic

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21852778

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2022541756

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21852778

Country of ref document: EP

Kind code of ref document: A1