US 11,994,519 B2
Biomarkers for multiple sclerosis
Brian William Della Valle, København V (DK); Casper Hempel, Dyssegård (DK); and Marie Agnete Larsen, Hinnerup (DK)
Assigned to GLX Analytix ApS, København (DK)
Appl. No. 16/772,034
Filed by GLX Analytix ApS, København N (DK)
PCT Filed Dec. 13, 2018, PCT No. PCT/EP2018/084825
§ 371(c)(1), (2) Date Jun. 11, 2020,
PCT Pub. No. WO2019/115724, PCT Pub. Date Jun. 20, 2019.
Claims priority of application No. 17207028 (EP), filed on Dec. 13, 2017.
Prior Publication US 2021/0215691 A1, Jul. 15, 2021
Int. Cl. B01L 3/00 (2006.01); B01F 23/00 (2022.01); B01F 23/41 (2022.01); B01F 101/23 (2022.01); B23Q 17/24 (2006.01); C08L 5/08 (2006.01); C12M 1/34 (2006.01); C12Q 1/04 (2006.01); C12Q 1/18 (2006.01); G01N 21/3577 (2014.01); G01N 21/359 (2014.01); G01N 21/39 (2006.01); G01N 21/45 (2006.01); G01N 21/64 (2006.01); G01N 30/12 (2006.01); G01N 30/68 (2006.01); G01N 30/70 (2006.01); G01N 30/72 (2006.01); G01N 30/88 (2006.01); G01N 33/00 (2006.01); G01N 33/18 (2006.01); G01N 33/50 (2006.01); G01N 33/564 (2006.01); G01N 33/68 (2006.01)
CPC G01N 33/564 (2013.01) [C08L 5/08 (2013.01); G01N 2400/40 (2013.01); G01N 2800/285 (2013.01)] 29 Claims
OG exemplary drawing
 
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.