# US 11,994,519 B2 · Biomarkers for multiple sclerosis > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,994,519 B2 - Document ID: US11994519B2 - Status: Granted - Kind: B2 - Application number: 16/772,034 - Filed: 2018-12-13 - Published: 2021-07-15 - Granted: 2024-05-28 - Earliest claimed: 2017-12-13 - Assignees: GLX Analytix ApS - Applicants: GLX Analytix ApS - Inventors: Brian William Della Valle; Casper Hempel; Marie Agnete Larsen - Web page: https://patentgrep.com/patent/US11994519B2 ## CPC - G01N 33/564: for pre-existing immune complex or autoimmune disease {, i.e. systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, rheumatoid factors or complement components C1-C9} - C08L 5/08: Chitin; Chondroitin sulfate; Hyaluronic acid; Derivatives thereof - G01N 2400/40: Glycosaminoglycans, i.e. GAG or mucopolysaccharides, e.g. chondroitin sulfate, dermatan sulfate, hyaluronic acid, heparin, heparan sulfate, and related sulfated polysaccharides - G01N 2800/285: Demyelinating diseases; Multipel sclerosis - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - C08L: COMPOSITIONS OF MACROMOLECULAR COMPOUNDS (compositions based on polymerisable monomers C08F, C08G; artificial filaments or fibres D01F; textile treating compositions D06) ## Abstract The present invention relates to biomarkers associated with multiple sclerosis (MS), particular GLX molecules, and teven more particular GLX-related glycosaminoglycans (GAGs) and GLX-related proteoglycans (PGs). ## Claims ### Claim 1 A method for monitoring disease progression or regression of multiple sclerosis (MS) in a subject, the method comprising: detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one biomarker is selected from GLX-related glycosaminoglycans (GAGs), and at least one biomarker is selected from GLX-related proteoglycans (PGs); detecting the level of the same at least two biomarkers in a second biological sample from the subject, wherein said second biological sample has been obtained at a different time point than said first biological sample from said subject; comparing the levels of the at least two biomarkers detected in the second biological sample; determining a cut-off level using a multivariate statistical analysis comprising machine learning, wherein the machine learning transforms the levels of the at least two biomarkers detected in the first and/or second samples to the cut-off level; and determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the levels of the at least two biomarkers detected in the second biological sample with respect to the cut-off level, wherein the cut-off level indicates the progression or regression of MS, and wherein the at least two biomarkers in the first biological sample and the same at least two biomarkers in the second biological sample are detected by a method selected from the group consisting of: binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy. ### Claim 2 (depends on 1) The method according to claim 1, wherein the at least one biomarker of GLX-related glycosaminoglycans (GAGs) comprises at least one biomarker selected from keratan sulfate (KS), chondroitin sulfate (CS), hyaluronic acid (HA), heparan sulfate (HS), or dermatan sulfate. ### Claim 3 (depends on 1) The method according to claim 1, wherein the at least one biomarker of GLX-related proteoglycans (PGs) comprises at least one biomarker selected from CD44, Syndecans, Glypicans, Biglycan, Perlecan, Mimecan, Decorin, or Versican. ### Claim 4 (depends on 3) The method according to claim 3, wherein the Syndecans are selected from the group consisting of Syndecan-4, Syndecan-1, Syndecan-3 and Syndecan-2. ### Claim 5 (depends on 3) The method according to claim 3, wherein the Glypicans are selected from the group consisting of Glypican-1, Glypican-2, Glypican-3, Glypican-4, Glypican-5, and Glypican-6. ### Claim 6 (depends on 1) The method according to claim 1, wherein the at least one biomarker of GLX-related proteoglycans (PGs) comprises CD44 or Perlecan. ### Claim 7 (depends on 1) The method according to claim 1, wherein the at least one biomarker of GLX-related glycosaminoglycans (GAGs) comprises HA. ### Claim 8 (depends on 1) The method according claim 1, wherein the at least one biomarker of GLX-related glycosaminoglycans (GAGs) comprises HA and the at least one biomarker of GLX-related proteoglycans (PGs) comprises CD44 or Perlecan. ### Claim 9 (depends on 1) The method according to claim 1, wherein the first and second biological samples comprise whole blood, serum, plasma, or cerebrospinal fluid. ### Claim 10 (depends on 1) The method according to claim 1, further comprising subgrouping or staging the disease, or providing a prognosis for said subject. ### Claim 11 (depends on 1) The method according to claim 1, where the subject is a human subject. ### Claim 12 (depends on 1) The method according to claim 1, further comprising administering or inducing one to administer to said subject a treatment for MS when said subject is determined to have a progression of the multiple sclerosis or when said subject is determined to have no regression or progression of the multiple sclerosis. ### Claim 13 (depends on 1) The method according to claim 1, further comprising administering or inducing one to administer to said subject a CD20 monoclonal antibody, a stem cell therapy, or an immune modulator when said subject is determined to have a progression of the multiple sclerosis or when said subject is determined to have no regression or progression of the multiple sclerosis. ### Claim 14 (depends on 1) The method according to claim 1, further comprising administering or inducing one to administer to said subject a dietary regime, a fecal transplantation regime, probiotics, or bone marrow transplantation when said subject is determined to have a progression of the multiple sclerosis or when said subject is determined to have no regression or progression of the multiple sclerosis. ### Claim 15 (depends on 1) The method according to claim 1, further comprising administering or inducing one to administer to said subject ozanimod, laquinimod, a PEGylated version of interferon-β-1a, alemtuzumab, daclizumab, rituximab, ocrelizumab, ofatumumab, interferon βB-1b, glatirameracetate, mitoxantrone, natalizumab, fingolimod, teriflunomide, dimethyl fumarate, glucagon-like peptide-1, or metformin when said subject is determined to have a progression of the multiple sclerosis or when said subject is determined to have no regression or progression of the multiple sclerosis. ### Claim 16 (depends on 1) The method according to claim 1, further comprising administering to said subject magnetic resonance imaging (MRI). ### Claim 17 (depends on 16) The method according to claim 16, wherein the MRI comprises a gadolinium-enhancement sequence. ### Claim 18 (depends on 16) The method according to claim 16, wherein the MRI comprises a fluid attenuation inversion recovery (FLAIR) sequence. ### Claim 19 (depends on 17) The method according to claim 17, wherein the MRI further comprises a fluid attenuation inversion recovery (FLAIR) sequence. ### Claim 20 (depends on 1) The method according to claim 1, wherein the multivariate statistical analyses comprises a partial least squares discriminate analysis. ### Claim 21 (depends on 1) The method according to claim 1, wherein the multivariate statistical analyses comprises a random forest analysis. ### Claim 22 (depends on 1) The method according to claim 1, wherein the multivariate statistical analyses comprises a support vector machine. ### Claim 23 (depends on 1) The method according to claim 1, wherein the different time point of obtaining the second biological sample is a later time point than said first biological sample from said subject. ### Claim 24 (depends on 1) The method according to claim 1, wherein the different time point of obtaining the second biological sample is a prior time point than said first biological sample from said subject. ### Claim 25 A method for monitoring disease risk, progression, or regression of multiple sclerosis (MS) in a subject, the method comprising: detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one biomarker is selected from GLX-related glycosaminoglycans (GAGs), and at least one biomarker is selected from GLX-related proteoglycans (PGs); comparing the levels of the at least two biomarkers in the first biological sample to reference levels for the at least two biomarkers; determining a cut-off level using a multivariate statistical analysis comprising machine learning, which transforms the levels of the at least two biomarkers detected in the first biological sample to the cut-off level; and determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the reference levels for the at least two biomarkers with respect to the cut-off level, wherein the cut-off level indicates a risk of MS or the progression or regression of MS, and wherein the at least two biomarkers in the first biological sample are detected by a method selected from the group consisting of: binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy. ### Claim 26 A method for monitoring disease progression or regression of multiple sclerosis (MS) in a subject, the method comprising: detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one of the biomarkers is selected from GLX-related glycosaminoglycans (GAGs); detecting the level of the same at least two biomarkers in a second biological sample from the subject, wherein said second biological sample has been obtained at a different time point than said first biological sample from said subject; comparing the levels of the at least two biomarkers detected in the second biological sample; determining a cut-off level using a multivariate statistical analysis comprising machine learning, wherein the machine learning transforms the levels of the at least two biomarkers detected in the first and/or second samples to the cut-off level; and determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the levels of the at least two biomarkers detected in the second biological sample with respect to the cut-off level, wherein the cut-off level indicates the progression or regression of MS, and wherein the at least two biomarkers in the first biological sample and the same at least two biomarkers in the second biological sample are detected by a method selected from the group consisting of: binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy. ### Claim 27 A method for monitoring disease progression or regression of multiple sclerosis (MS) in a subject, the method comprising: detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one of the biomarkers is selected from GLX-related proteoglycans (PGs); detecting the level of the same at least two biomarkers in a second biological sample from the subject, wherein said second biological sample has been obtained at a different time point than said first biological sample from said subject; comparing the levels of the at least two biomarkers detected in the second biological sample; determining a cut-off level using a multivariate statistical analysis comprising machine learning, wherein the machine learning transforms the levels of the at least two biomarkers detected in the first and/or second samples to the cut-off level; and determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the levels of the at least two biomarkers detected in the second biological sample with respect to the cut-off level, wherein the cut-off level indicates the progression or regression of MS, and wherein the at least two biomarkers in the first biological sample and the same at least two biomarkers in the second biological sample are detected by a method selected from the group consisting of: binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy. ### Claim 28 A method for monitoring disease risk, progression, or regression of multiple sclerosis (MS) in a subject, the method comprising: detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one of the biomarkers is selected from GLX-related glycosaminoglycans (GAGs); comparing the levels of the at least two biomarkers in the first biological sample to reference levels for the at least two biomarkers; determining a cut-off level using a multivariate statistical analysis comprising machine learning, which transforms the levels of the at least two biomarkers detected in the first biological sample to the cut-off level; and determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the reference levels for the at least two biomarkers with respect to the cut-off level, wherein the cut-off level indicates a risk of MS or the progression or regression of MS, and wherein the at least two biomarkers in the first biological sample are detected by a method selected from the group consisting of: binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy. ### Claim 29 A method for monitoring disease risk, progression, or regression of multiple sclerosis (MS) in a subject, the method comprising: detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one of the biomarkers is selected from GLX-related proteoglycans (PGs); comparing the levels of the at least two biomarkers in the first biological sample to reference levels for the at least two biomarkers; determining a cut-off level using a multivariate statistical analysis comprising machine learning, which transforms the levels of the at least two biomarkers detected in the first biological sample to the cut-off level; and determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the reference levels for the at least two biomarkers with respect to the cut-off level, wherein the cut-off level indicates a risk of MS or the progression or regression of MS, and wherein the at least two biomarkers in the first biological sample are detected by a method selected from the group consisting of: binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy.