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WO2025202134A1 - Methods for diagnosing advanced liver fibrosis or liver cirrhosis - Google Patents

Methods for diagnosing advanced liver fibrosis or liver cirrhosis

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Publication number
WO2025202134A1
WO2025202134A1 PCT/EP2025/058007 EP2025058007W WO2025202134A1 WO 2025202134 A1 WO2025202134 A1 WO 2025202134A1 EP 2025058007 W EP2025058007 W EP 2025058007W WO 2025202134 A1 WO2025202134 A1 WO 2025202134A1
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WO
WIPO (PCT)
Prior art keywords
subject
score
liver fibrosis
age
comprised
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
PCT/EP2025/058007
Other languages
French (fr)
Inventor
Bérénice ALARD
Jérémy MAGNANENSI
Zouher Majd
Alexandra CARON
Morgane DEHORNOIS
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Genfit SA
Original Assignee
Genfit SA
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Application filed by Genfit SA filed Critical Genfit SA
Publication of WO2025202134A1 publication Critical patent/WO2025202134A1/en
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • 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
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A61P1/16—Drugs for disorders of the alimentary tract or the digestive system for liver or gallbladder disorders, e.g. hepatoprotective agents, cholagogues, litholytics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/46—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from vertebrates
    • G01N2333/47—Assays involving proteins of known structure or function as defined in the subgroups
    • G01N2333/4701—Details
    • G01N2333/4728—Details alpha-Glycoproteins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/705—Assays involving receptors, cell surface antigens or cell surface determinants
    • G01N2333/70503—Immunoglobulin superfamily, e.g. VCAMs, PECAM, LFA-3
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/81—Protease inhibitors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/08—Hepato-biliairy disorders other than hepatitis
    • G01N2800/085—Liver diseases, e.g. portal hypertension, fibrosis, cirrhosis, bilirubin

Definitions

  • Liver fibrosis common to liver damage and liver diseases, may have many, chronic or not, etiologies including viral Hepatitis B and C (HBV and HCV), Human Immunodeficiency Virus (HIV) and Hepatitis C Virus (HCV) co-infection, Drug-Induced Liver Injury (DILI), cholestatic liver diseases including Primary Biliary Cholangitis (PBC) and Primary Sclerosing Cholangitis (PSC), AIH (Autoimmune Hepatitis), biliary atresia, acute liver disease (ALD), Acute Liver Failure (ALF), cirrhosis, Acute on Chronic Liver Failure (ACLF), Wilson disease, Metabolic dysfunction-associated fatty liver disease (MAFLD), Metabolic dysfunction-associated steatohepatitis (MASH), Alcohol Related Liver Disease (ARLD), alcoholic liver disease and hemochromatosis.
  • HBV and HCV Human Immunodeficiency Virus
  • HCV Hepatit
  • Liver fibrosis is an abnormal wound repair process and is characterized by an excessive accumulation of extracellular matrix protein. It is stimulated by chronic inflammation and occurs as a result of the liver healing process when the liver becomes scarred.
  • liver diseases are without symptoms until the disease has progressed to a later stage. The early detection of the disease is therefore challenging.
  • the risk of liver-related mortality increases exponentially with increase in fibrosis stage and mortality and morbidity rates increase exponentially once cirrhosis develops.
  • Cirrhosis is the point where the liver is completely scarred and is beyond the self-healing ability.
  • Cirrhosis is a leading cause of mortality and morbidity across the world. It is the 11 th leading cause of death and 15 th leading cause of morbidity, accounting for 2.2% of deaths and 1.5% of disability- adjusted life years worldwide in 2016.
  • Chronic liver diseases caused 1.32 million deaths in 2017 in the world (Sepanlou, et al.
  • Cirrhosis is said compensated when patients do not have any visible symptoms of the disease, and cirrhosis is decompensated when cirrhosis has progressed to the point that the liver is having trouble functioning and with the occurrence of symptoms of the disease.
  • F2 refers to a subject with portal/periportal and perisinusoidal fibrosis
  • liver biopsy has several recognized limitations including sampling errors, inter-observer variability, and hospitalization.
  • the main disadvantage is the significant risk of complications including bleeding, pain and even death.
  • a biopsy does not reflect the changes in the whole liver and does not differentiate early cirrhosis from progressed cirrhosis and therefore does not constitute a reliable prognostic predictor (Sumida Y et al. Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. World J Gastroenterol 2014; 20: 475-485).
  • obesity ascites, acute inflammation, liver congestion, and elevated portal vein pressure may for example reduce ultrasound TE (Fibroscan) accuracy by influencing the velocity of shear wave.
  • ultrasound TE Fibroscan
  • a falsely increased liver stiffness due to postprandial increase in portal vein pressure, has been observed with this method.
  • a first aspect of the invention relates to an in vitro method for diagnosing advanced liver fibrosis (F3 or F4) or liver cirrhosis (F4) in a subject, comprising: a) providing (i) the circulating levels of Soluble Vascular Cell Adhesion Molecule-1 (sVCAM), Thrombospondin 2 (TSP-2) and alpha 2 Macroglobulin (A2M) in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score; c) comparing said score with cut-off values to diagnose or prognose advanced liver fibrosis or liver cirrhosis in said subject.
  • sVCAM Soluble Vascular Cell Adhesion Molecule-1
  • TSP-2 Thrombospondin 2
  • A2M alpha 2 Macroglobulin
  • P3 is comprised between 0,01 and 2.5, in particular between 0.1 and 1.5;
  • P4 is comprised between -8 e 06 and - 0.5 e -7 , in particular between -6.5 e -6 and -1.0 e -6 .
  • a score SA higher than a cut-off value col is indicative of advanced liver fibrosis, particularly col being comprised between 0.2 and 0.7, more particularly col being comprised between 0.25 and 0.63.
  • Another aspect of the invention relates to an in vitro method for monitoring the progression of liver fibrosis in a subject, comprising the steps of: a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SC; and c) comparing the score SC with a score SB, which is obtained by combining levels of sVCAM, TSP-2, A2M and the age previously measured in the same subject and with the same mathematical function.
  • one event linked to the evolution of pathological state occurs during the monitoring, between SB and SC.
  • said event is selected from liver transplantation, acute on chronic liver fibrosis, compensated cirrhosis, decompensated cirrhosis, episode of ascites and presence of esophageal varices at endoscopy.
  • a decrease of SE compared to SD indicates the efficacy of the anti-fibrotic agent; an increase of SE compared to SD indicates the non-efficacy of the anti-fibrotic agent, and/or the nonresponsiveness of the patient.
  • the term "advanced fibrosis” or “advanced liver fibrosis” refers to a fibrosis stage of F>3, i.e. a fibrosis stage F3 or a fibrosis stage F4.
  • cirrhosis or “liver cirrhosis” refers to a fibrosis stage of F4.
  • subject and “patient” may be used interchangeably herein and refer to a human subject.
  • any range must be considered as including the upper and lower limits.
  • Fibrosis-4 (FIB-4) index is calculated as age (years) x AST (U/L) /platelet (x 10 9 /L) /VALT(U/L), where AST is aspartate aminotransferase and ALT is alanine aminotransferase.
  • the Enhanced Liver Fibrosis panel (ELF, Siemens Healthcare GmbH, Eriangen, Germany) is a test to predict fibrosis based on three fibrosis biomarkers: hyaluronic acid (HA), tissue inhibitor of matrix metalloproteinases-1 (TIMP-l) and amino-terminal propeptide of procollagen type III (PH IN P).
  • This test uses the following equation to calculate the ELF score (2.278 + 0.851 In(cHA) + 0.751 In(cpniNp) + 0.394 In(cnivip-i)), wherein C is the concentration of the biomarker.
  • Specificity is the ability of a test to correctly identify those who do not have the disease.
  • the specificity measures the proportion of negative ("healthy") cases in a population of healthy patients evaluated using a reference method ("gold standard”).
  • True Negative (TN) subjects are subjects without the disease with the value of a parameter of interest below a cut-off.
  • Likelihood ratio for negative test result represents the ratio of the probability that a negative result will occur in subjects with the disease to the probability that the same result will occur in subjects without the disease.
  • LR- (1-sensitivity) / specificity.
  • LR- is a good indicator for ruling-out the diagnosis.
  • LR- between 0.1 and 0.2 indicates a moderate probability to well diagnose patients without the studied condition.
  • Good diagnostic test of patients that are without the studied condition has LR- ⁇ 0,1.
  • the "mean score” is the average of the results of the test calculated in a group.
  • the circulating levels of sVCAM, TSP-2 and A2M are measured from multiple blood-derived samples isolated from a subject on the same time or at different time-points during a period of from some months to some years.
  • the same kind of sample is used each time when a measure has to be done.
  • the levels of the three biomarkers measured in a biological fluid sample isolated from a subject and the age of the subject are combined in a mathematical function to assign a score.
  • the age of the subject to be used in the mathematical function is the age when the biological fluid sample was isolated from said subject for the measurement of the levels of said three biomarkers.
  • the score obtained can be used to discriminate subjects having advanced liver fibrosis or liver cirrhosis from subjects not having advanced liver fibrosis, or not having liver cirrhosis, or be used to predict the evolution of a subject's liver fibrosis.
  • the invention relates to an in vitro method for diagnosing advanced liver fibrosis in a human subject, comprising: a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject, b) calculating a score SA based on step (a) by using a mathematical function, c) comparing this SA to a cut-off value col, d) wherein a SA greater than the cut-off value col is indicative of a subject with advanced liver fibrosis.
  • a calculated SA value lower than the cut-off value col is indicative of a subject not having advanced liver fibrosis.
  • the invention relates to an in vitro method for diagnosing liver cirrhosis in a human subject, comprising a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject, b) calculating a score SA based on step (a) by using a mathematical function, c) comparing this SA to a cut-off value co2, d) wherein a SA greater than the cut-off value co2 is indicative of a subject with liver cirrhosis. In contrast, a calculated SA value lower than the cut-off value co2 is indicative of a subject not having liver cirrhosis.
  • the score A (SA), the cut-off value col and the cut-off value co2 used for the diagnosis of advanced liver fibrosis or liver cirrhosis are calculated according to above defined mathematical function on a training population.
  • SA is compared with the cut-off value col, which is indicative of an advanced liver fibrosis.
  • col is comprised between 0.2 and 0.7, more particularly between 0.25 and 0.63, in particular equal to 0.5801.
  • the cut-off value to be used in this method depends on the liver fibrosis stage of the subject.
  • the SF score of a subject with biopsy-proven stage 1 liver fibrosis is compared with a cut-off value CO-F1;
  • SF score of a subject with biopsy-proven stage 2 liver fibrosis is compared with a cut-off value CO-F2;
  • SF score of a subject with biopsy-proven stage 3 liver fibrosis is compared with a cut-off value CO-F3.
  • the cut-off values CO-F1, CO-F2 and CO-F3 may be determined in training populations with stable biopsy-proven stage 1, 2 or 3 liver fibrosis, respectively.
  • the cut-off value CO-F1 is determined in a training population with stable biopsy-proven stage 1 liver fibrosis.
  • a patient is considered to have stable liver fibrosis if the patient's liver fibrosis stage remained unchanged at the screening visit and 18 months after the screening visit.
  • a patient with biopsy-proven stage 1 liver fibrosis is considered to have stable liver fibrosis if the patient's liver fibrosis stage remained at stage 1 18 months after the screening visit.
  • the cut-off values of the SF score may be a range of values.
  • the cut-off value CO-F1 is a range comprised between 0.06 and 0.20; the cutoff value CO-F2 is a range comprised between 0.25 and 0.44; and the cut-off value CO-F3 is a range comprised between 0.47 and 0.86.
  • SF score of a subject with biopsy-proven stage 1 liver fibrosis when the SF score of a subject with biopsy-proven stage 1 liver fibrosis is higher than a range comprised between 0.06 and 0.20, this indicates that the subject's liver fibrosis will progress; when the SF score of the subject is lower than said range, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is comprised in said range, this indicates that the subject's liver fibrosis will be stable.
  • SF score of a subject with biopsy-proven stage 2 liver fibrosis when the SF score of a subject with biopsy-proven stage 2 liver fibrosis is higher than a range comprised between 0.25 and 0.44, this indicates that the subject's liver fibrosis will progress; when the score SF of the subject is lower than said range, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is comprised in said range, this indicates that the subject's liver fibrosis will be stable.
  • SF score of a subject with biopsy-proven stage 3 liver fibrosis when the SF score of a subject with biopsy-proven stage 3 liver fibrosis is higher than a range comprised between 0.47 and 0.86, this indicates that the subject's liver fibrosis will progress; when the score SF of the subject is lower than said range, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is comprised in said range, this indicates that the subject's liver fibrosis will be stable.
  • SF score of a subject with biopsy-proven stage 1 liver fibrosis when the SF score of a subject with biopsy-proven stage 1 liver fibrosis is higher than a CO-F1 value selected in a range comprised between 0.06 and 0.20, this indicates that the subject's liver fibrosis will progress; when the SF score of the subject is lower than said selected CO-F1 value, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is identical to the selected CO-F1 value, this indicates that the subject's liver fibrosis will be stable.
  • SF score of a subject with biopsy-proven stage 3 liver fibrosis when the SF score of a subject with biopsy-proven stage 3 liver fibrosis is higher than a CO-F3 value selected in a range comprised between 0.47 and 0.86, this indicates that the subject's liver fibrosis will progress; when the SF score of the subject is lower than said selected CO-F3 value, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is identical to the selected CO-F3 value, this indicates that the subject's liver fibrosis will be stable.
  • the invention also relates to a computer program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
  • the subject assign the subject as: a subject whose liver fibrosis will progress, when the score SF is higher than the cutoff value; a subject whose liver fibrosis will regress when the score SF is lower than the cutoff value; a subject whose liver fibrosis will be stable when the SF score is neither higher nor lower than the cutoff value.
  • the present invention also provides an in vitro method for monitoring the progression of liver fibrosis in a subject by measuring the levels of three circulating markers, i.e. sVCAM, TSP-2 and A2M.
  • monitoring refers to the tracking of the evolution of a disease or condition. Monitoring is the ongoing, systematic collection and analysis of data as a protocol or condition progresses, for example during a clinical study or a treatment protocol.
  • the score SC is calculated by using a non-invasive method and correlated with the evolution of liver fibrosis, it allows not only to diagnose but also to monitor easily liver fibrosis progression, by repeated measures.
  • SC is higher than SB, it means that liver fibrosis worsens, whereas if the SC decreases with time in the same subject, i.e. SC is lower than SB, it means that liver fibrosis decreases. No significant change between SC and SB measured in a certain lapse of time in a same subject means that liver fibrosis is stable.
  • the method of the invention also allows to determine the likelihood of a subject with advanced liver fibrosis to progress towards cirrhosis or towards worsening cirrhosis. During a follow-up, the change in the value between SC and SB is therefore an indicator of liver fibrosis progression or liver fibrosis regression.
  • SC is measured while an event linked to the evolution of pathological state occurs.
  • Said events comprise liver transplantation, acute on chronic liver fibrosis (ACLF), compensated cirrhosis and decompensated cirrhosis, episode of ascites and presence of esophageal varices at endoscopy.
  • ACLF chronic liver fibrosis
  • the events are acute on chronic liver fibrosis, compensated and decompensated cirrhosis.
  • SC is measured at least 3 months afterthe measurement of SB, particularly in a period between 3 months and 10 years, preferably in a period between 3 months and 2 years, more preferably between 1 and 2 years, after the measurement of SB.
  • SC is measured in a period between 3 months and 2 years, and preferentially 3 months, after the measurement of SB.
  • the suitable moment for the measurement of SC may depend on comorbidities.
  • a decision may be taken to give life-style recommendations to a subject (such as a food regimen or providing physical activity recommendations), to medically take care of a subject (e.g. by setting regular visits to a physician or regular examinations, for example for regularly monitoring markers of liver damage), or to administer at least one liver fibrosis therapy to the patient, to treat advanced liver fibrosis or liver cirrhosis.
  • a decision may be taken to give life-style recommendations to a subject or to administer at least one liver fibrosis therapy.
  • the invention thus further relates to an anti-fibrotic compound for use in a method for treating advanced liver fibrosis or liver cirrhosis in a subject in need thereof, wherein the subject has been identified thanks to a method according to the invention.
  • the invention thus further relates to an anti-fibrotic compound for use in a method for treating advanced liver fibrosis or liver cirrhosis in a subject in need thereof, wherein the subject has been identified thanks to a method according to the invention.
  • treatment relates to both therapeutic measures and prophylactic or preventive measures, wherein the goal is to prevent or slow down (lessen) an undesired physiological change or disorder.
  • Beneficial or desired clinical results include, but are not limited to, alleviation of symptoms, stabilizing pathological state (specifically not worsening), slowing down or stopping the progression of the disease, improving or mitigating the pathological condition.
  • treatment is directed to slow the progression of fibrosis and reduce the risk of further complications. It can also involve prolonging survival in comparison with the expected survival if the treatment is not received.
  • the anti-fibrotic agent is administered in a therapeutically effective amount.
  • therapeutically effective amount refers to an amount of the drug effective to achieve a desired therapeutic result.
  • a therapeutically effective amount of a drug may vary according to factors such as the disease state, age, sex, and weight of the individual, and the ability of drug to elicit a desired response in the individual.
  • a therapeutically effective amount is also one in which any toxic or detrimental effects of agent are outweighed by the therapeutically beneficial effects.
  • the effective dosages and dosage regimens for drug depend on the disease or condition to be treated and may be determined by the persons skilled in the art. A physician having ordinary skill in the art may readily determine and prescribe the effective amount of the pharmaceutical composition required.
  • a suitable dose of a composition of the present invention will be that amount of the compound which is the lowest dose effective to produce a therapeutic effect according to a particular dosage regimen. Such an effective dose will generally depend upon the factors described above.
  • the invention further relates to an anti-fibrotic compound for use in a method for treating liver fibrosis in a F3 or F4 patient, wherein the patient is classified as having advanced liver fibrosis or liver cirrhosis according to the method of the invention.
  • the invention also relates to an anti-fibrotic compound for use in a method for treating liver fibrosis, wherein the subject, diagnosed or classified as having advanced liver fibrosis or liver cirrhosis, is treated with an anti-fibrotic compound as defined here below, thanks to a method according to the invention.
  • XI represents a halogen atom, a R1 group or Gl-Rl group
  • X2 represents a G2-R2 group
  • G1 represents an atom of oxygen
  • G2 represents an atom of oxygen or sulfur
  • R1 represents a hydrogen atom, an unsubstituted alkyl group, an aryl group or an alkyl group that is substituted by one or more substituents selected from halogen atoms, alkoxy groups, alkylthio groups, cycloalkyl groups, cycloalkylthio groups and heterocyclic groups;
  • R2 represents an alkyl group substituted by a -COOR3 group, wherein R3 represents a hydrogen atom or an alkyl group that is substituted or not by one or more substituents selected from halogen atoms, cycloalkyl groups and heterocyclic groups.
  • R4 and R5 identical or different, represent an alkyl group that is substituted or not by one or more substituent selected from halogen atoms, cycloalkyl groups and heterocyclic groups;
  • - AMP activated protein kinase stimulators such as PXL-770, MB-11055, Debio-0930B, metformin, CNX- 012, 0-304, mangiferin calcium salt, eltrombopag, carotuximab, and imeglimin;
  • CCR antagonists such as cenicriviroc (CCR2/5 antagonist), PG-092, RAP-310, INCB-10820, RAP-103, PF-04634817, and CCX-872;
  • FXR Farnesoid X receptor
  • OCA obeticholic acid
  • LJN452 tropifexor
  • GS9674 cilofexor
  • LMB763 Nidufexor
  • EDP-305 AKN-083, INT-767
  • GNF-5120 LY2562175
  • INV-33 INV-33
  • NTX-023- 1 EP-024297
  • Px-103 SR-45023
  • TERN-101 (6- ⁇ 4-[5-Cyclopropyl-3-(2,6-dichloro-phenyl)-isoxazol-4- ylmethoxy]-piperidin-l-yl ⁇ -l-methyl-lH-indole-3 carboxylic acid), TERN-201, TERN-501 and TERN-301; - Fibroblast Growth Factor 19 (FGF-19) receptor ligand or functional engineered variant of FGF-19;
  • FGF-19 Fibroblast Growth Factor 19
  • FGF-19 Fibroblast Growth Factor 19 analogues such as NGM-282 (aldafermin);
  • GLP-1 Glucagon-like peptide-1
  • GLP-1 analogs such as semaglutide, liraglutide, exenatide, albiglutide, dulaglutide, lixisenatide, loxenatide, efpeglenatide, taspoglutide, MKC-253, DLP-205, and ORMD-0901;
  • NTZ N- - nitazoxanide
  • TZ active metabolite tizoxanide
  • other prodrugs of TZ such as RM-5061;
  • - PPAR alpha agonists such as fenofibrate, ciprofibrate, pemafibrate, gemfibrozil, clofibrate, binifibrate, clinofibrate, clofibric acid, nicofibrate, pirifibrate, plafibride, ronifibrate, theofibrate, tocofibrate, and SR10171;
  • - PPAR gamma agonists such as pioglitazone, deuterated pioglitazone, rosiglitazone, efatutazone, ATx08-001, OMS-405, CHS-131, THR-0921, SER-150-DN, KDT-501, GED-0507-34-Levo, CLC-3001, and ALL-4;
  • GW501516 Endurabol or ( ⁇ 4-[( ⁇ 4-methyl-2-[4-(trifluoromethyl)phenyl]- l,3-thiazol-5-yl ⁇ methyl)sulfanyl]-2-methylphenoxy ⁇ acetic acid)
  • MBX8025 Seladelpar or ⁇ 2-methyl-4- [5-methyl-2-(4-trifluoromethyl- phenyl)-2H-[l,2,3]triazol-4-ylmethylsylfanyl]-phenoxy ⁇ -acetic acid
  • GW0742 [4-[[[2-[3-fluoro-4-(trifluoromethyl)phenyl]-4-methyl-5-thiazolyl]methyl]thio]-2-methyl phenoxy]acetic acid), L165041, HPP-593, and NCP-1046;
  • CLA conjugated linoleic acid
  • T3D-959 conjugated linoleic acid
  • SGLT Sodium-glucose transport 2 inhibitors
  • licoglifozin remogliflozin, dapagliflozin, empagliflozin, ertugliflozin, sotagliflozin, ipragliflozin, tianagliflozin, canagliflozin, tofogliflozin, janagliflozin, bexagliflozin, luseogliflozin, sergliflozin, HEC-44616, AST-1935, and PLD-101.
  • SGLT Sodium-glucose transport
  • stearoyl CoA desaturase-1 inhibitors/fatty acid bile acid conjugates such as aramchol, GRC-9332, steamchol, TSN-2998, GSK-1940029, and XEN-801;
  • TRR thyroid receptor P agonists
  • VK-2809 resmetirom
  • MGL-3745 MGL-3745
  • SKL-14763 sobetirome
  • BCT-304 ZYT-1
  • MB-07811 eprotirome
  • anti-fibrotic compounds are preferably selected in the group consisting of pegbelfermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, seladelpar, semaglutide, sitagliptin, TERN-101, TERN-201, tropifexor, ambrisentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642, and orlistat (Xenical).
  • the anti-fibrotic agent is resmetirom.
  • the invention further relates to a method for assessing the efficacy of an anti-fibrotic agent in a subject suffering from advanced liver fibrosis or liver cirrhosis, comprising: a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from a subject suffering from advanced liver fibrosis, wherein said subject has been administered an anti- fibrotic agent before the isolation of the biological fluid sample, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SE; and c) comparing the score SE with a score SD, which is obtained by combining the levels of sVCAM, TSP- 2, A2M and the age, previously measured before administration of the anti-fibrotic agent to the same subject and with the same mathematical function.
  • Said SD and SE may be obtained through a same mathematical function, especially the mathematical function as defined above.
  • liver fibrosis worsens, meaning either that the subject does not respond to the treatment with the anti-fibrotic agent or the treatment is not effective. If SE is lower than SD, then liver fibrosis regresses, meaning that the subject responds to the treatment with the anti-fibrotic agent and the treatment is effective.
  • the invention also relates to a computer program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
  • - assign the subject as: a subject who does not respond to the treatment with the anti-fibrotic agent, when the score SE is higher than SD; a subject who responds to the treatment with the anti-fibrotic agent, when the score SE is lower than SD ;.or having stable liver fibrosis when there is no significant change between SE and SD.
  • the present invention provides also a computer program product or a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out one of the methods of the present invention as described above.
  • the study population consists of patients that were screened for potential inclusion in the Resolve-lt phase3 clinical trial. All patients with biopsy results available, and blood samples used for biomarkers measurements with less than 90days between biopsies and blood collections dates were selected as of potential utility for this study.
  • Selected patients are those with full data on numerous biomarkers, as well as on the usual demographical and clinical parameters (age, sex, Type-2 Diab, Dyslipidemia, Arterial Hypertension [HT], BMI) to maximize both the number of patients and of biomarkers.
  • AUROC values The overall diagnostic performance of non-invasive tests and biomarkers are estimated using AUROC values, and these statistics are tested for significant differences using Delong tests. AUROC values are reported with 95%CI estimated using 1000 bootstrap samples.
  • T2D, HT, BMI and Dyslipidemia we extracted a total of 1124 patients (562 in each category), 678 (339 in each category), 1048 (524 in each category), 1084 patients (542 in each category), 750 patients (375 in each category) and 1052 patients (526 in each category) respectively.
  • age was the only one which had a significant impact on the mean scores of the modelization STA, with a significantly higher score for patient older than 60 (p values ⁇ 0.0001, see Fig. 1A).
  • Table 8 AUROC values for cirrhosis detection

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Abstract

Provided herein is a method of diagnosing or prognosing advanced liver fibrosis or liver cirrhosis in a patient. The method is accomplished by determining a score based on the circulating levels in serum of three markers.

Description

METHODS FOR DIAGNOSING ADVANCED LIVER FIBROSIS OR LIVER CIRRHOSIS
The present invention relates to a method for the diagnosis of advanced liver fibrosis or liver cirrhosis, for prognosing or monitoring the progression of liver fibrosis in a subject or for assessing the efficacy of an anti-fibrotic agent. The invention also relates to the compounds for use in the treatment of liver fibrosis, wherein the subject to be treated is identified according to the method of the invention.
BACKGROUND
Liver fibrosis, common to liver damage and liver diseases, may have many, chronic or not, etiologies including viral Hepatitis B and C (HBV and HCV), Human Immunodeficiency Virus (HIV) and Hepatitis C Virus (HCV) co-infection, Drug-Induced Liver Injury (DILI), cholestatic liver diseases including Primary Biliary Cholangitis (PBC) and Primary Sclerosing Cholangitis (PSC), AIH (Autoimmune Hepatitis), biliary atresia, acute liver disease (ALD), Acute Liver Failure (ALF), cirrhosis, Acute on Chronic Liver Failure (ACLF), Wilson disease, Metabolic dysfunction-associated fatty liver disease (MAFLD), Metabolic dysfunction-associated steatohepatitis (MASH), Alcohol Related Liver Disease (ARLD), alcoholic liver disease and hemochromatosis.
Liver problems can be caused by a variety of factors that damage the liver, such as viruses, immune system abnormality, inherited abnormal genes, cancer, alcohol use and obesity. Over time, conditions that damage the liver can lead to scarring (cirrhosis), which can lead to liver failure, a life-threatening condition that demands urgent medical care. Liver failure occurs when large parts of the liver become damaged beyond repair.
Liver fibrosis is an abnormal wound repair process and is characterized by an excessive accumulation of extracellular matrix protein. It is stimulated by chronic inflammation and occurs as a result of the liver healing process when the liver becomes scarred.
The prediction of liver fibrosis is a key step in the assessment and management of patients with liver damage and/or liver disease. Therefore, since the early and precise evaluation of severity and status of liver fibrosis is essential for diagnosis, monitoring and prognosis, a quantitative measurement is crucial to assess disease progression.
Most forms of liver diseases are without symptoms until the disease has progressed to a later stage. The early detection of the disease is therefore challenging. The risk of liver-related mortality increases exponentially with increase in fibrosis stage and mortality and morbidity rates increase exponentially once cirrhosis develops. Cirrhosis is the point where the liver is completely scarred and is beyond the self-healing ability. Cirrhosis is a leading cause of mortality and morbidity across the world. It is the 11th leading cause of death and 15th leading cause of morbidity, accounting for 2.2% of deaths and 1.5% of disability- adjusted life years worldwide in 2016. Chronic liver diseases caused 1.32 million deaths in 2017 in the world (Sepanlou, et al. The global, regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol 2020;5:245-266.). Cirrhosis is said compensated when patients do not have any visible symptoms of the disease, and cirrhosis is decompensated when cirrhosis has progressed to the point that the liver is having trouble functioning and with the occurrence of symptoms of the disease. Although the clinical features of cirrhosis decompensation are well described (i.e., ascites, spider naevi, jaundice, signs of hepatic encephalopathy), patients who have compensated cirrhosis often have no clinical signs and might be entirely asymptomatic.
Hepatologists and healthcare providers have proposed scoring systems for staging liver fibrosis like the BRUNT/KLEINER system, wherein:
F0 refers to a subject with an absence of liver fibrosis;
Fl refers to a subject with portal or perisinusoidal fibrosis;
F2 refers to a subject with portal/periportal and perisinusoidal fibrosis;
F3 refers to a subject with septal or bridging liver fibrosis;
F4 refers to a subject with liver cirrhosis.
Since severe (fibrosis stage F>2) liver diseases can progress to hepatocellular carcinoma, the accurate staging of liver fibrosis in these liver diseases, especially the early diagnosis of advanced liver fibrosis (Fibrosis stage F3 or F4) and liver cirrhosis (Fibrosis stage F4) is crucial.
Furthermore, the rate of fibrosis progression evolves over time and the diagnostic assay has to be performed several times. In consequence, this test must be repeatable and without risk for the patients, reliable and accurate. Therefore, non-invasive assays are needed for the diagnosis of advanced liver fibrosis (F3 or F4) and liver cirrhosis (F4). It is also important to specifically distinguish patients with cirrhosis (F= 4) among patients with severe liver disease, because these F4 patients require an emergency treatment.
So far, invasive liver biopsy remains the Gold Standard for the assessment of liver fibrosis.
Nevertheless, liver biopsy has several recognized limitations including sampling errors, inter-observer variability, and hospitalization. The main disadvantage is the significant risk of complications including bleeding, pain and even death. Furthermore, a biopsy does not reflect the changes in the whole liver and does not differentiate early cirrhosis from progressed cirrhosis and therefore does not constitute a reliable prognostic predictor (Sumida Y et al. Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. World J Gastroenterol 2014; 20: 475-485).
To avoid above detailed life-threatening risks and diagnostic weakness, in vitro non-invasive diagnostic methods using biomarkers, scores, and physical methods have been developed. By contrast to biopsy which cannot be repeated without inconvenience, these methods can capture the dynamic process of fibrosis resulting from progression and regression since the measures are repeatable. These methods are based on readily available biochemical data and clinical features, such as the FIB4 test (Sterling RK et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006; 43:1317-1325), or assays that directly measure markers of fibrogenesis and fibrolysis, such as the Enhanced Liver Fibrosis (ELF) test (Guha, LN., et al., Noninvasive markers of fibrosis in nonalcoholic fatty liver disease: Validating the European Liver Fibrosis Panel and exploring simple markers. Hepatology 2008; 47: 455-460).
However, the substitution of these methods for liver biopsy still remains controversial and is not generally accepted due to insufficient diagnostic performance. The main issue of these two biochemical assays is that they cannot reliably differentiate in individual patients the advanced stages of liver fibrosis. Moreover, ELF test is expensive, which constitutes a drawback in case of repetition of the tests. Additionally, FIB4 has a poor performance in patients of less than 35 years old and is less specific in patients over 65.
Physical methods include imaging based on high frequency sound waves (ultrasound and echocardiography), computed tomography (CT), magnetic resonance imaging (MRI), transient elastography (TE, FibroScan) as well as scintigraphy. The main drawbacks of physical measures are the high cost, the low availability of equipment and the complexity of the method, that limit the daily clinical practice.
Moreover, obesity, ascites, acute inflammation, liver congestion, and elevated portal vein pressure may for example reduce ultrasound TE (Fibroscan) accuracy by influencing the velocity of shear wave. Furthermore, a falsely increased liver stiffness, due to postprandial increase in portal vein pressure, has been observed with this method.
Therefore, there is still an unmet medical need to develop new non-invasive diagnostic methods having an optimal diagnostic accuracy compared to biopsy, but also useful in monitoring the time course of liver fibrosis and/or showing whether there is a response to a given medication. In addition, it is important to provide a new method giving the best predictivity at the lowest cost and the easiest feasibility. SUMMARY OF THE INVENTION
The Inventors have conducted several very fine and complete analysis of a cohort of 3875 patients with different stage of fibrosis to provide novel and highly sensitive non-invasive diagnostic and monitoring methods of advanced liver fibrosis (F3 or F4) and liver cirrhosis (F4). WO 2024/013228 described a non-invasive diagnostic method of these diseases by measuring the circulating levels of three biomarkers, i.e. Soluble Vascular Cell Adhesion Molecule-1 (sVCAM), Thrombospondin 2 (TSP-2) and alpha 2 Macroglobulin (A2M). Said method demonstrates an accurate diagnosis of advanced liver fibrosis and liver cirrhosis compared to liver biopsy and existing solutions. However, it was recently observed by the inventors that the diagnostic performance of this method may not be homogeneous among subpopulations of different ages. Working on this problematic, the inventors have successfully developed a new mathematical function which allows to correct the impact of age on diagnostic performance by combining, in a mathematic function, the circulating levels of these three biological markers measured in a biological fluid sample isolated from a subject with the age of said subject. This mathematical function provides an improved diagnosis of advanced liver fibrosis (F3 or F4) and liver cirrhosis (F4) compared to existing solutions . Most importantly, compared to the method only combining the levels of sVCAM, TSP-2 and A2M, the method of the invention provides consistent high clinical performances independently of the age of the patients, which allows a reliable follow up of the liver fibrosis.
Accordingly, a first aspect of the invention relates to an in vitro method for diagnosing advanced liver fibrosis (F3 or F4) or liver cirrhosis (F4) in a subject, comprising: a) providing (i) the circulating levels of Soluble Vascular Cell Adhesion Molecule-1 (sVCAM), Thrombospondin 2 (TSP-2) and alpha 2 Macroglobulin (A2M) in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score; c) comparing said score with cut-off values to diagnose or prognose advanced liver fibrosis or liver cirrhosis in said subject.
In a particular embodiment, the score is a score SA obtained through the following mathematical function: with y = P0 + pi*loglO(TSP2 (ng/mL)) + P2*logl0(sVCAM (ng/mL)) + 3 * (A2M(g/L)) + 4 *(Age (years)3) wherein: 0 is comprised between -37 and -12 in particular between -31 and -17; pi is comprised between 1.5 and 7.9, in particular between 2 and 7;
P2 is comprised between 1 and 11, in particular between 1.7 and 8.5;
P3 is comprised between 0,01 and 2.5, in particular between 0.1 and 1.5; and
P4 is comprised between -8 e 06 and - 0.5 e-7, in particular between -6.5 e-6 and -1.0 e-6.
In a more particular embodiment, a score SA higher than a cut-off value col is indicative of advanced liver fibrosis, particularly col being comprised between 0.2 and 0.7, more particularly col being comprised between 0.25 and 0.63.
In another particular embodiment, a score SA higher than a cut-off value co2 is indicative of a liver cirrhosis, particularly co2 being comprised between 0.4 and 1.1, more particularly co2 being comprised between 0.5 and 1.0.
Another aspect of the invention relates to an in vitro method for monitoring the progression of liver fibrosis in a subject, comprising the steps of: a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SC; and c) comparing the score SC with a score SB, which is obtained by combining levels of sVCAM, TSP-2, A2M and the age previously measured in the same subject and with the same mathematical function.
In a particular embodiment, an increase of SC compared to SB indicates the progression of liver fibrosis. In another particular embodiment, a decrease of SC compared to SB indicates the regression of liver fibrosis. In a specific embodiment, no significant change between SC and SB measured in a certain lapse of time in a same subject means that liver fibrosis is stable. In a particular embodiment of said method, step (a) of said method for monitoring the progression of liver fibrosis is implemented at least 3 months after a previous measurement of the levels of sVCAM, TSP-2 and A2M, particularly in a period between 3 months and 10 years, more particularly in a period between 3 months and 2 years, after a previous measurement of the levels of sVCAM, TSP-2 and A2M.
In a specific embodiment of said method, one event linked to the evolution of pathological state occurs during the monitoring, between SB and SC. In a particular embodiment, said event is selected from liver transplantation, acute on chronic liver fibrosis, compensated cirrhosis, decompensated cirrhosis, episode of ascites and presence of esophageal varices at endoscopy.
In a preferred embodiment, said SB and SC are calculated through the above defined mathematical function.
A third aspect of the invention concerns a method for assessing the efficacy of an anti-fibrotic agent in treating advanced liver fibrosis or liver cirrhosis, comprising: a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from a subject suffering from advanced liver fibrosis, wherein said subject has been administered an anti- fibrotic agent before the isolation of the biological fluid sample, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SE; and c) comparing the score SE with a score SD, which is obtained by combining the levels of sVCAM, TSP- 2, A2M and the age, previously measured before administration of the anti-fibrotic agent to the same subject and with the same mathematical function.
Particularly, a decrease of SE compared to SD indicates the efficacy of the anti-fibrotic agent; an increase of SE compared to SD indicates the non-efficacy of the anti-fibrotic agent, and/or the nonresponsiveness of the patient.
In a preferred embodiment, said SE and SD are calculated through the above defined mathematical function.
In a preferred embodiment, the biological fluid sample of the subject used in the methods of the present invention is an interstitial fluid, saliva, urine or whole blood sample. In a particular embodiment, the biological fluid sample of the subject used in the methods of the present invention is a blood sample, a plasma sample or a serum sample. Preferably, the biological fluid sample is cell- free. More preferably, the blood sample is a serum sample from a subject.
Another aspect of the invention concerns a method for prognosing the evolution of liver fibrosis in a subject with biopsy-proven stage 1, 2 or 3 liver fibrosis, comprising a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample of said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SF; and c) comparing said score with a cut-off value to prognose the evolution of liver fibrosis in said subject.
In a particular embodiment, the SF score higher than a cut-off value is indicative of that the subject's liver fibrosis will progress (i.e. the subject will be a liver fibrosis worsener), the SF score lower than a cut-off value is indicative of that the subject's liver fibrosis will regress (i.e. the subject will be a liver fibrosis improver); the SF score neither higher nor lower than the cut-off value is indicative of that the subject's liver fibrosis will be stable.
Another aspect of the invention is also to provide anti-fibrotic agents for use in the treatment of advanced liver fibrosis or liver cirrhosis in a subject, wherein said subject is diagnosed as suffering from advanced liver fibrosis or liver cirrhosis according to the method of the present invention, wherein said agent is selected in the group consisting of pegbelfermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, seladelpar, semaglutide, sitagliptin, TERN-101, TERN-201, tropifexor, ambrisentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642 and orlistat.
In a particular embodiment, said agent is resmetirom.
FIGURES
Figure 1A represents Mean scores of modelization STA for patients younger than 50 (0) and older than
60 years old (1) in a homogenized subpopulation (n=339 patients per category). ****: p < 0.0001. Figure IB represents Mean scores of modelization STAII for patients younger than 50 (0) and older than 60 years old (1) in a homogenized subpopulation (n=339 patients per category), "ns": no significant difference between the scores obtained from the two groups of patients.
Figure 2A represents clinical performances of modelization STA for the detection of F3. "Spe": specificity; "Sen": sensitivity.
Figure 2B represents clinical performances of modelization STAII for the detection of F3. "Spe": specificity; "Sen": sensitivity.
Figure 3 represents Roc curves for detection of advanced liver fibrosis of the modelization STAII and other usual non-invasive tests (FIB-4, ELF, NFS and APRI) in a validation cohort of 1850 subjects.
Figure 4 represents Roc curves for detection of liver cirrhosis of the modelization STAII and other usual non-invasive tests (FIB-4, ELF, NFS and APRI) in a validation cohort of 1850 subjects.
Figure 5 represents boxplot of the SF score calculated at screening visit (Vo) for the categories "improver", "stable", "worsener" of patients with biopsy-proven stage 1, 2 or 3 liver fibrosis at Vo. Improver: patients who had a lower fibrosis stage at visit 7 (81 weeks later) compared to Vo; Stable: patients who had the same fibrosis stage at visit 7 compared to Vo; Worsener: patients who had a higher fibrosis stage at visit 7 compared to Vo. The lines above the boxplots represent the p values (Student test) by pairs of boxplots values.
DETAILED DESCRIPTION OF THE INVENTION
Definitions
Fibrosis scoring/staging
According to the present invention, the term "fibrosis" or "liver fibrosis" denotes a pathological condition of excessive deposition of fibrous connective tissue in the liver. More specifically, fibrosis is a pathological process, which includes a persistent fibrotic scar formation and overproduction of extracellular matrix by the connective tissue, as a response to tissue damage. Physiologically, the deposit of connective tissue can obliterate the architecture and function of liver. The different stages of liver fibrosis are defined by the Kleiner scoring system (Kleiner et al, Hepatology 2005, Vol 41, Issue 6, 1313-1321) wherein:
F0 refers to a subject with an absence of liver fibrosis;
Fl refers to a subject with portal or perisinusoidal fibrosis;
F2 refers to a subject with portal/periportal and perisinusoidal fibrosis;
F3 refers to a subject with septal or bridging liver fibrosis;
F4 refers to a subject with liver cirrhosis.
The stage of F0-2 is assigned to subjects having early liver fibrosis, F3 or F4 is assigned to subjects having advanced liver fibrosis, and the stage F4 is assigned to subjects having liver cirrhosis.
Using this fibrosis staging system, patients with no or minimal fibrosis (F=0 or 1) are generally not considered at risk of cirrhosis, liver failure, HCC (hepatocellular carcinoma) or liver-related death. Patients with severe (F>2) liver fibrosis are at risk of developing cirrhosis, liver failure, HCC and liver- related death. Patients with compensated cirrhosis (F=4) are at high risk of liver failure (decompensated cirrhosis), HCC and liver-related deaths.
In the context of the present invention, the term "advanced fibrosis" or "advanced liver fibrosis" refers to a fibrosis stage of F>3, i.e. a fibrosis stage F3 or a fibrosis stage F4.
In the context of the present invention, the term "cirrhosis" or "liver cirrhosis" refers to a fibrosis stage of F4.
The terms "subject" and "patient" may be used interchangeably herein and refer to a human subject.
Within the context of the present invention, the terms "biomarker", "marker", "biological marker" are interchangeable.
Within the scope of the present invention, any range must be considered as including the upper and lower limits.
Concurrent Noninvasive tests The Fibrosis-4 (FIB-4) index is calculated as age (years) x AST (U/L) /platelet (x 109/L) /VALT(U/L), where AST is aspartate aminotransferase and ALT is alanine aminotransferase.
The Enhanced Liver Fibrosis panel (ELF, Siemens Healthcare GmbH, Eriangen, Germany) is a test to predict fibrosis based on three fibrosis biomarkers: hyaluronic acid (HA), tissue inhibitor of matrix metalloproteinases-1 (TIMP-l) and amino-terminal propeptide of procollagen type III (PH IN P). This test uses the following equation to calculate the ELF score (2.278 + 0.851 In(cHA) + 0.751 In(cpniNp) + 0.394 In(cnivip-i)), wherein C is the concentration of the biomarker.
NFS score is calculated as follows: NFS = - 1.675 + 0.037 x age (years) + 0.094 x BMI (kg/m2) + 1.13 * IGF/diabetes (yes = 1, no = 0) + 0.99 x AST/ALT ratio - 0.013 x platelet (x 109/l) - 0.66 x albumin (g/dl) APRI score is calculated as follows:
APRI=[(AST Level (IU/L)/upper limit of the normal AST range(IU/L)) X 100]/Platelet Count (x 109/L).
Sensitivity is the ability of a test to correctly identify those who have the disease. The sensitivity measures the proportion of positive ("disease") cases in a population of sick patients evaluated using a reference method ("gold standard"), or as reliable as possible considering biopsy profiling. Sensitivity (Se) is the proportion of positive results (True Positive = TP) divided by the total number of sick patients (TP + False Negative = FN): Se = TP / (TP + FN). Sensitivity is usually expressed as a percentage (%), from 0 to 100%.
True Positive (TP) subjects are subjects with the disease with the value of a parameter of interest above a cut-off.
False Negative (FN) subjects are subjects with the disease with the value of a parameter of interest below a cut-off.
Specificity is the ability of a test to correctly identify those who do not have the disease. The specificity measures the proportion of negative ("healthy") cases in a population of healthy patients evaluated using a reference method ("gold standard"). Specificity (Sp) is the proportion of negative results (True Negative = TN) divided by the total number of healthy patients (TN + False Positive = FP): Sp = TN / (TN + FP). True Negative (TN) subjects are subjects without the disease with the value of a parameter of interest below a cut-off.
False Positive (FP) subjects are subjects without the disease with the value of a parameter of interest above a cut-off.
The prevalence of a given population is the number of cases of the disease within the population.
The Positive Predictive Value (PPV) is the probability to have the disease when a test is positive. PPV = number of True Positives / (number of True Positives + number of False Positives). PPV = TP/ (TP+FP). The negative Predictive Value (NPV) is the probability of not having the disease when a test is negative. NPV = number of True Negatives / (number of True Negatives + number of False Negatives). NPV = TN/ (TN+FN).
Prevalence affects PPV and NPV differently. PPV is increasing, while NPV decreases with the increase of the prevalence of the disease in a population. Whereas the change in PPV is more substantial, NPV is somewhat less influenced by disease prevalence. For low prevalence (5-10%), PPV value is low. In parallel, NPV is high. For high prevalence (80-90 %), PPV value is high and vice versa for the NPV.
Likelihood ratio is defined as the ratio of expected test result in subjects with a certain state/disease to the subjects without the disease.
Likelihood ratio for positive test results (LR+) represents the ratio of the probability that the positive test result is to occur in subjects with the disease compared to those without the disease. LR+ = sensitivity / ( 1-specif icity ) . LR+ is the best indicator for ruling-in diagnosis. The higher the LR+, the more the test is indicative of a disease. LR+ > 5 indicates a moderate to large increase evidence that the disease is present. Good diagnostic tests have LR+ > 10 and their positive result has a significant contribution to the diagnosis.
Likelihood ratio for negative test result (LR-) represents the ratio of the probability that a negative result will occur in subjects with the disease to the probability that the same result will occur in subjects without the disease. LR- = (1-sensitivity) / specificity. LR- is a good indicator for ruling-out the diagnosis. LR- between 0.1 and 0.2 indicates a moderate probability to well diagnose patients without the studied condition. Good diagnostic test of patients that are without the studied condition has LR- < 0,1.
There is a pair of diagnostic sensitivity and specificity values for every individual cut-off. To construct a receiver operating characteristic (ROC) graph, these pairs of values are plotted on the graph with the 1-specificity on the x-axis and sensitivity on the y-axis. The shape of a ROC curve and the area under the receiver operating characteristic curve (AUROC) shows how high is the discriminative power of a test. The closer the curve is located to upper-left hand corner and the larger the area under the curve, the better the test is at discriminating between diseased and non-diseased subjects. The area under the curve can have any value between 0.5 and 1 and it is a good indicator of the goodness of the test. A perfect diagnostic test has an AUROC of 1.0. whereas a non-discriminating test has an AUROC of 0.5. AUROC is a global measure of diagnostic accuracy.
The "mean score" is the average of the results of the test calculated in a group.
METHOD OF THE INVENTION
As mentioned above, advanced liver fibrosis and cirrhosis are associated with liver-related death and easy detection of advanced fibrotic subjects and cirrhotic subjects is thus of outmost importance. The present invention provides a solution to these unmet needs.
In the methods of the present invention, the levels of three circulating markers measured from a biological fluid sample isolated from a subject and the age of the subject are combined in a mathematical function to assign a score. Said 3 circulating markers are: Soluble Vascular Cell Adhesion Molecule-1 (sVCAM), Thrombospondin 2 (TSP-2) and alpha 2 Macroglobulin (A2M). sVCAM is also known as VCAM 1, INCAM-100, CD106 and registered in database UniProt under the number P19320. TSP-2 is also known as THBS2 and registered in database UniProt under the number P35442. A2M is also known as C3, PZP-like alpha-2-macroglobulin domain-containing protein 5 and registered in database UniProt under the number P01023.
According to the invention, the circulating levels of sVCAM, TSP-2 and A2M are measured in a biological fluid sample of a subject. In all the methods and embodiments presented herein, the biological fluid sample may be a sample of blood or of a blood-derived fluid such as serum and plasma, of saliva, of interstitial fluid or of urine. In a particular embodiment, the biological fluid sample of the subject is a blood, serum, or plasma sample. In a more particular embodiment, the sample is a serum sample. In another particular embodiment, the biological fluid sample is a cell-free sample.
According to the present invention, a biological fluid sample may be analyzed immediately after collection or be stored in cold environments, for example in freezers, for further analysis.
The circulating levels of sVCAM, TSP-2 and A2M may be measured by any conventional methodology well known in the art, such as immunoassays (e.g. ELISA (enzyme-linked immunosorbent assay), immunoturbidimetry, immuno-nephelometry, immune cytometry, protein array). For example, the levels of sVCAM, TSP-2 and A2M can be determined by antibodies, aptamers or peptides respectively directed against said markers.
In some methods of the invention, the circulating levels of sVCAM, TSP-2 and A2M are measured from multiple blood-derived samples isolated from a subject on the same time or at different time-points during a period of from some months to some years. In that case, the same kind of sample is used each time when a measure has to be done. For the sake of clarity, this means that if a previous measure was done from a serum sample, the subsequent measures are done from serum samples of the same subject. Likewise, if the previous measure was done from a blood or plasma sample, the subsequent measures are done from blood or plasma samples, respectively, of the same subject.
According to the methods of the invention, the levels of the three biomarkers measured in a biological fluid sample isolated from a subject and the age of the subject are combined in a mathematical function to assign a score. For the sake of clarity, the age of the subject to be used in the mathematical function is the age when the biological fluid sample was isolated from said subject for the measurement of the levels of said three biomarkers.
Said score is correlated with fibrosis status of a subject. Said score can be used for the diagnosis or the prognosis of advanced liver fibrosis or liver cirrhosis in a subject. In addition, said score is also correlated with the evolution of liver fibrosis, which allows not only to diagnose but also to monitor liver fibrosis progression or to assess the efficacy of an anti-fibrotic agent in treating advanced liver fibrosis or liver cirrhosis. Within the context of the present invention, one skilled in the art is aware of numerous suitable methods for developing mathematical function, and all of these are within the scope of the present invention. In a particular embodiment, the mathematical function includes a logistic regression equation.
In a further embodiment, the score is assigned by the following mathematical function:
1
Score = - - — -
1 + exp (— ) with y = P0 + pi*logio(TSP2 (ng/mL)) + P2*logio(sVCAM (ng/mL)) + 3 * (A2M(g/L)) + 4 *(Age (years)3) wherein: 0 is comprised between -37 and -12 in particular between -31 and -17; pi is comprised between 1.5 and 7.9, in particular between 2 and 7;
P2 is comprised between 1 and 11, in particular between 1.7 and 8.5;
P3 is comprised between 0,01 and 2.5, in particular between 0.1 and 1.5; and
P4 is comprised between -8 e 06 and - 0.5 e-7, in particular between -6.5 e-6 and -1.0 e-6.
In a particular embodiment, the invention relates to an in vitro method for diagnosing or prognosing advanced liver fibrosis or liver cirrhosis in a subject, comprising: a)providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b)combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score; and c) comparing said score with cut-off values to diagnose or prognose advanced liver fibrosis or liver cirrhosis in said subject.
By being compared to a specific cut-off value, the score obtained can be used to discriminate subjects having advanced liver fibrosis or liver cirrhosis from subjects not having advanced liver fibrosis, or not having liver cirrhosis, or be used to predict the evolution of a subject's liver fibrosis.
According to a particular embodiment, the invention relates to an in vitro method for diagnosing advanced liver fibrosis or liver cirrhosis in a subject, wherein the score is a score SA obtained through the above-defined mathematical function.
According to an embodiment of the invention, SA value is compared to a cut-off value (co). Said value may be calculated from Youden' statistical analysis of SA scores which are obtained on a training population. "Training population" refers to a population consisted of a given number of subjects, wherein the fibrosis stage of each subject is already determined by a method of prior art, like liver biopsy.
Particularly, said SA and cut-off value are calculated using a mathematical function to differentiate patients having advanced liver fibrosis from patients without advanced liver fibrosis.
Alternatively, said SA and cut-off values are calculated using a mathematical function to differentiate patients having liver cirrhosis from patients without liver cirrhosis.
A cut-off value col may be determined to be used to indicate the presence or absence of advanced liver fibrosis. A cut-off value co2 may be determined to be used to indicate the presence or absence of liver cirrhosis.
In a more particular embodiment, the invention relates to an in vitro method for diagnosing advanced liver fibrosis in a human subject, comprising: a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject, b) calculating a score SA based on step (a) by using a mathematical function, c) comparing this SA to a cut-off value col, d) wherein a SA greater than the cut-off value col is indicative of a subject with advanced liver fibrosis.
In contrast, a calculated SA value lower than the cut-off value col is indicative of a subject not having advanced liver fibrosis.
In another more particular embodiment, the invention relates to an in vitro method for diagnosing liver cirrhosis in a human subject, comprising a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject, b) calculating a score SA based on step (a) by using a mathematical function, c) comparing this SA to a cut-off value co2, d) wherein a SA greater than the cut-off value co2 is indicative of a subject with liver cirrhosis. In contrast, a calculated SA value lower than the cut-off value co2 is indicative of a subject not having liver cirrhosis. In a particular embodiment, the score A (SA), the cut-off value col and the cut-off value co2 used for the diagnosis of advanced liver fibrosis or liver cirrhosis are calculated according to above defined mathematical function on a training population.
In a particular embodiment, SA is compared with the cut-off value col, which is indicative of an advanced liver fibrosis. Particularly, col is comprised between 0.2 and 0.7, more particularly between 0.25 and 0.63, in particular equal to 0.5801.
In another specific embodiment, SA is compared with the cut-off value co2, which is indicative of a liver cirrhosis. Particularly, co2 is comprised between 0.4 and 1.1, more particularly between 0.5 and 1.0, in particular equal to 0.6103.
According to another aspect, the invention relates to a computer program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
- receive levels of sVCAM, TSP-2 and A2M measured in a biological fluid sample isolated from a subject and the age of a subject;
- calculate SA from these measured levels and the age, from a mathematical function as described herein; and
- assign the subject into the group of subjects having advanced liver fibrosis or liver cirrhosis upon the calculated score compared to predetermined cut-off values.
The present invention further provides a computer readable medium comprising the computer program described therein. According to a particular embodiment, the computer readable medium is non-transitory medium or a storage medium.
The invention also relates to an in vitro method for prognosing the evolution of liver fibrosis in a subject with biopsy-proven stage 1, 2 or 3 liver fibrosis, comprising a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SF; and c) comparing said score with a cut-off value to prognose the evolution of liver fibrosis in said subject. According to this embodiment, the SF score of the subject higher than a cut-off value is indicative of that the subject's liver fibrosis will progress; the SF score of the subject lower than the cut-off value is indicative of that the subject's liver fibrosis will regress; and the SF score of the subject neither higher nor lower than the cut-off value is indicative of that the subject's liver fibrosis will be stable.
The cut-off value to be used in this method depends on the liver fibrosis stage of the subject. According to this embodiment, the SF score of a subject with biopsy-proven stage 1 liver fibrosis is compared with a cut-off value CO-F1; SF score of a subject with biopsy-proven stage 2 liver fibrosis is compared with a cut-off value CO-F2; SF score of a subject with biopsy-proven stage 3 liver fibrosis is compared with a cut-off value CO-F3.
The cut-off values CO-F1, CO-F2 and CO-F3 may be determined in training populations with stable biopsy-proven stage 1, 2 or 3 liver fibrosis, respectively. For instance, the cut-off value CO-F1 is determined in a training population with stable biopsy-proven stage 1 liver fibrosis. A patient is considered to have stable liver fibrosis if the patient's liver fibrosis stage remained unchanged at the screening visit and 18 months after the screening visit. For example, a patient with biopsy-proven stage 1 liver fibrosis is considered to have stable liver fibrosis if the patient's liver fibrosis stage remained at stage 1 18 months after the screening visit.
According to an embodiment, the cut-off values of the SF score may be a range of values. According to a particular embodiment, the cut-off value CO-F1 is a range comprised between 0.06 and 0.20; the cutoff value CO-F2 is a range comprised between 0.25 and 0.44; and the cut-off value CO-F3 is a range comprised between 0.47 and 0.86.
In a specific embodiment, when the SF score of a subject with biopsy-proven stage 1 liver fibrosis is higher than a range comprised between 0.06 and 0.20, this indicates that the subject's liver fibrosis will progress; when the SF score of the subject is lower than said range, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is comprised in said range, this indicates that the subject's liver fibrosis will be stable.
In another specific embodiment, when the SF score of a subject with biopsy-proven stage 2 liver fibrosis is higher than a range comprised between 0.25 and 0.44, this indicates that the subject's liver fibrosis will progress; when the score SF of the subject is lower than said range, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is comprised in said range, this indicates that the subject's liver fibrosis will be stable.
In another specific embodiment, when the SF score of a subject with biopsy-proven stage 3 liver fibrosis is higher than a range comprised between 0.47 and 0.86, this indicates that the subject's liver fibrosis will progress; when the score SF of the subject is lower than said range, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is comprised in said range, this indicates that the subject's liver fibrosis will be stable.
According to another particular embodiment, the cut-off values of the SF score may be specific values. According to a more particular embodiment, the cut-off value CO-F1 is a value selected in a range comprised between 0.06 and 0.20; the cut-off value CO-F2 is a value selected in a range comprised between 0.25 and 0.44; the cut-off value CO-F3 is a value selected in a range comprised between 0.47 and 0.86. More particularly, the cut-off value CO-F1 may be 0.15; the cut-off value CO-F2 may be 0.32; and the cut-off value CO-F3 may be 0.64.
In a specific embodiment, when the SF score of a subject with biopsy-proven stage 1 liver fibrosis is higher than a CO-F1 value selected in a range comprised between 0.06 and 0.20, this indicates that the subject's liver fibrosis will progress; when the SF score of the subject is lower than said selected CO-F1 value, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is identical to the selected CO-F1 value, this indicates that the subject's liver fibrosis will be stable.
In another specific embodiment, when the SF score of a subject with biopsy-proven stage 2 liver fibrosis is higher than a CO-F2 value selected in a range comprised between 0.25 and 0.44, this indicates that the subject's liver fibrosis will progress; when the SF score of the subject is lower than said selected CO-F2 value, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is identical to the selected CO-F2 value, this indicates that the subject's liver fibrosis will be stable.
In another specific embodiment, when the SF score of a subject with biopsy-proven stage 3 liver fibrosis is higher than a CO-F3 value selected in a range comprised between 0.47 and 0.86, this indicates that the subject's liver fibrosis will progress; when the SF score of the subject is lower than said selected CO-F3 value, this indicates that the subject's liver fibrosis will regress; when the SF score of the subject is identical to the selected CO-F3 value, this indicates that the subject's liver fibrosis will be stable.
The invention also relates to a computer program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
- receive (i) levels of sVCAM, TSP-2, A2M measured in a biological fluid sample of a subject with biopsy- proven stage 1, 2 or 3 liver fibrosis; and (ii) the age of the subject
- calculate a SF score from these measured levels, through a mathematical function;
- compare the score SF with a predetermined cutoff value of SF score, and
- assign the subject as: a subject whose liver fibrosis will progress, when the score SF is higher than the cutoff value; a subject whose liver fibrosis will regress when the score SF is lower than the cutoff value; a subject whose liver fibrosis will be stable when the SF score is neither higher nor lower than the cutoff value.
The present invention also provides an in vitro method for monitoring the progression of liver fibrosis in a subject by measuring the levels of three circulating markers, i.e. sVCAM, TSP-2 and A2M.
Particularly, said method comprises the steps of: a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SC; and c) comparing the score SC with a score SB, which is obtained by combining levels of sVCAM, TSP-2, A2M and the age previously measured in the same subject and with the same mathematical function.
As used herein, the term "monitoring" refers to the tracking of the evolution of a disease or condition. Monitoring is the ongoing, systematic collection and analysis of data as a protocol or condition progresses, for example during a clinical study or a treatment protocol.
Since the score SC is calculated by using a non-invasive method and correlated with the evolution of liver fibrosis, it allows not only to diagnose but also to monitor easily liver fibrosis progression, by repeated measures.
In a particular embodiment, the circulating levels of sVCAM, TSP-2 and A2M are measured from at least two blood-derived samples from the subject. The collection over time of at least two samples from the same subject allows to assess longitudinal changes in the score. For instance, a first score, named SB, may be calculated from the levels of sVCAM, TSP-2, A2M previously measured in a biological fluid sample of a subject and the corresponding age, and a second score, named SC, may be calculated from the levels sVCAM, TSP-2, A2M subsequently measured in a biological fluid sample of the same subject and the corresponding age. SB and SC may be obtained through the mathematical function as defined above. If the score increases with time in the same subject, i.e. SC is higher than SB, it means that liver fibrosis worsens, whereas if the SC decreases with time in the same subject, i.e. SC is lower than SB, it means that liver fibrosis decreases. No significant change between SC and SB measured in a certain lapse of time in a same subject means that liver fibrosis is stable.
Since the score calculated according to the mathematical function as defined above is a linear value, by comparing the score C (SC) with the score B (SB), the method of the invention also allows to determine the likelihood of a subject with advanced liver fibrosis to progress towards cirrhosis or towards worsening cirrhosis. During a follow-up, the change in the value between SC and SB is therefore an indicator of liver fibrosis progression or liver fibrosis regression.
In a particular embodiment, SC is measured while an event linked to the evolution of pathological state occurs. Said events comprise liver transplantation, acute on chronic liver fibrosis (ACLF), compensated cirrhosis and decompensated cirrhosis, episode of ascites and presence of esophageal varices at endoscopy. Preferably, the events are acute on chronic liver fibrosis, compensated and decompensated cirrhosis.
In a particular embodiment, SC is measured at least 3 months afterthe measurement of SB, particularly in a period between 3 months and 10 years, preferably in a period between 3 months and 2 years, more preferably between 1 and 2 years, after the measurement of SB.
In a more particular embodiment, if the subject is diagnosed as having liver cirrhosis, e.g. by the method of the invention as described above, then SC is measured in a period between 3 months and 2 years, and preferentially 3 months, after the measurement of SB. The suitable moment for the measurement of SC may depend on comorbidities.
Comorbidities comprise malignancy, type 2 diabetes, overweight and obesity, heart disease and kidney diseases. In another particular embodiment, if the subject is diagnosed as having advanced liver fibrosis, e.g. by the method of the invention as described above, then SC is measured in a period between 1 and 10 years after the measurement of SB. The suitable moment for the measurement of SC depends on comorbidities.
Thanks to the methods of the invention, a decision may be taken to give life-style recommendations to a subject (such as a food regimen or providing physical activity recommendations), to medically take care of a subject (e.g. by setting regular visits to a physician or regular examinations, for example for regularly monitoring markers of liver damage), or to administer at least one liver fibrosis therapy to the patient, to treat advanced liver fibrosis or liver cirrhosis. Particularly, a decision may be taken to give life-style recommendations to a subject or to administer at least one liver fibrosis therapy. The invention thus further relates to an anti-fibrotic compound for use in a method for treating advanced liver fibrosis or liver cirrhosis in a subject in need thereof, wherein the subject has been identified thanks to a method according to the invention.
The invention also relates to a computer program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
- receive levels of sVCAM, TSP-2 and A2M measured in a biological fluid sample isolated from a subject and the age of the subject;
- calculate a score SC from these measured levels and the age, from a mathematical function as described above;
- compare the score SC with a score SB, which is obtained by combining levels of sVCAM, TSP-2, A2M and the age previously measured in the same subject with the same mathematical function and
- assign the subject as : having the progression of liver fibrosis when the score SC is higher than SB; having the regression of liver fibrosis when the score SC is lower than SB; or having stable liver fibrosis when there is no significant change between SC and SB.
The invention thus further relates to an anti-fibrotic compound for use in a method for treating advanced liver fibrosis or liver cirrhosis in a subject in need thereof, wherein the subject has been identified thanks to a method according to the invention. The term "treatment", as used herein, relates to both therapeutic measures and prophylactic or preventive measures, wherein the goal is to prevent or slow down (lessen) an undesired physiological change or disorder. Beneficial or desired clinical results include, but are not limited to, alleviation of symptoms, stabilizing pathological state (specifically not worsening), slowing down or stopping the progression of the disease, improving or mitigating the pathological condition. Particularly, for the purpose of the present invention, treatment is directed to slow the progression of fibrosis and reduce the risk of further complications. It can also involve prolonging survival in comparison with the expected survival if the treatment is not received.
The anti-fibrotic agent is administered in a therapeutically effective amount. As used herein, the expression "therapeutically effective amount" refers to an amount of the drug effective to achieve a desired therapeutic result. A therapeutically effective amount of a drug may vary according to factors such as the disease state, age, sex, and weight of the individual, and the ability of drug to elicit a desired response in the individual. A therapeutically effective amount is also one in which any toxic or detrimental effects of agent are outweighed by the therapeutically beneficial effects. The effective dosages and dosage regimens for drug depend on the disease or condition to be treated and may be determined by the persons skilled in the art. A physician having ordinary skill in the art may readily determine and prescribe the effective amount of the pharmaceutical composition required. For example, the physician could start doses of drug employed in the pharmaceutical composition at levels lower than that required to achieve the desired therapeutic effect and gradually increase the dosage until the desired effect is achieved. In general, a suitable dose of a composition of the present invention will be that amount of the compound which is the lowest dose effective to produce a therapeutic effect according to a particular dosage regimen. Such an effective dose will generally depend upon the factors described above.
The invention further relates to an anti-fibrotic compound for use in a method for treating liver fibrosis in a F3 or F4 patient, wherein the patient is classified as having advanced liver fibrosis or liver cirrhosis according to the method of the invention. The invention also relates to an anti-fibrotic compound for use in a method for treating liver fibrosis, wherein the subject, diagnosed or classified as having advanced liver fibrosis or liver cirrhosis, is treated with an anti-fibrotic compound as defined here below, thanks to a method according to the invention.
Anti-fibrotic compounds comprise: - a compound of formula (I) or a pharmaceutically acceptable salt thereof: wherein:
XI represents a halogen atom, a R1 group or Gl-Rl group;
A represents a CH=CH or CH2-CH2 group;
X2 represents a G2-R2 group;
G1 represents an atom of oxygen;
G2 represents an atom of oxygen or sulfur;
R1 represents a hydrogen atom, an unsubstituted alkyl group, an aryl group or an alkyl group that is substituted by one or more substituents selected from halogen atoms, alkoxy groups, alkylthio groups, cycloalkyl groups, cycloalkylthio groups and heterocyclic groups;
R2 represents an alkyl group substituted by a -COOR3 group, wherein R3 represents a hydrogen atom or an alkyl group that is substituted or not by one or more substituents selected from halogen atoms, cycloalkyl groups and heterocyclic groups.
R4 and R5, identical or different, represent an alkyl group that is substituted or not by one or more substituent selected from halogen atoms, cycloalkyl groups and heterocyclic groups;
- AMP activated protein kinase stimulators such as PXL-770, MB-11055, Debio-0930B, metformin, CNX- 012, 0-304, mangiferin calcium salt, eltrombopag, carotuximab, and imeglimin;
- Bile acids such as obeticholic acid (OCA), ursodeoxycholic acid (UDCA), norursodeoxycholic acid, and ursodiol;
- CCR antagonists such as cenicriviroc (CCR2/5 antagonist), PG-092, RAP-310, INCB-10820, RAP-103, PF-04634817, and CCX-872;
- Dipeptidyl peptidase IV (DPP4) inhibitors such as evogliptin, vidagliptin, fotagliptin, alogliptin, saxagliptin, tilogliptin, anagliptin, sitagliptin, retagliptin, melogliptin, gosogliptin, trelagliptin, teneligliptin, dutogliptin, linagliptin, gemigliptin, yogliptin, betagliptin, imigliptin, omarigliptin, vidagliptin, and denagliptin;
- Farnesoid X receptor (FXR) agonists such as obeticholic acid (OCA), tropifexor (LJN452), cilofexor (GS9674), Nidufexor (LMB763), EDP-305, AKN-083, INT-767, GNF-5120, LY2562175, INV-33, NTX-023- 1, EP-024297, Px-103, SR-45023, TERN-101 (6-{4-[5-Cyclopropyl-3-(2,6-dichloro-phenyl)-isoxazol-4- ylmethoxy]-piperidin-l-yl}-l-methyl-lH-indole-3 carboxylic acid), TERN-201, TERN-501 and TERN-301; - Fibroblast Growth Factor 19 (FGF-19) receptor ligand or functional engineered variant of FGF-19;
- Fibroblast Growth Factor 21 (FGF-21) agonists such as PEG-FGF21 (pegbelfermin, formely BMS- 986036), YH-25348, BMS-986171, YH-25723, LY-3025876, and NNC-0194-0499;
- engineered Fibroblast Growth Factor 19 (FGF-19) analogues such as NGM-282 (aldafermin);
- Glucagon-like peptide-1 (GLP-1) analogs such as semaglutide, liraglutide, exenatide, albiglutide, dulaglutide, lixisenatide, loxenatide, efpeglenatide, taspoglutide, MKC-253, DLP-205, and ORMD-0901;
- Nicotinic acid such as Niacin and Vitamin B3;
- nitazoxanide (NTZ), its active metabolite tizoxanide (TZ) or other prodrugs of TZ such as RM-5061;
- PPAR alpha agonists such as fenofibrate, ciprofibrate, pemafibrate, gemfibrozil, clofibrate, binifibrate, clinofibrate, clofibric acid, nicofibrate, pirifibrate, plafibride, ronifibrate, theofibrate, tocofibrate, and SR10171;
- PPAR gamma agonists such as pioglitazone, deuterated pioglitazone, rosiglitazone, efatutazone, ATx08-001, OMS-405, CHS-131, THR-0921, SER-150-DN, KDT-501, GED-0507-34-Levo, CLC-3001, and ALL-4;
- PPAR delta agonists such as GW501516 (Endurabol or ({4-[({4-methyl-2-[4-(trifluoromethyl)phenyl]- l,3-thiazol-5-yl}methyl)sulfanyl]-2-methylphenoxy}acetic acid)), MBX8025 (Seladelpar or {2-methyl-4- [5-methyl-2-(4-trifluoromethyl- phenyl)-2H-[l,2,3]triazol-4-ylmethylsylfanyl]-phenoxy}-acetic acid), GW0742 ([4-[[[2-[3-fluoro-4-(trifluoromethyl)phenyl]-4-methyl-5-thiazolyl]methyl]thio]-2-methyl phenoxy]acetic acid), L165041, HPP-593, and NCP-1046;
- PPAR alpha/gamma dual agonists (also named glitazars) such as saroglitazar, aleglitazar, muraglitazar, tesaglitazar, and DSP-8658;
- PPAR gamma/delta dual agonists such as conjugated linoleic acid (CLA), and T3D-959;
- PPAR alpha/gamma/delta pan agonists or PPARpan agonists such as IVA337, TTA (tetradecylthioacetic acid), bavachinin, GW4148, GW9135, bezafibrate, lanifibranor, lobeglitazone, and CS038;
- Sodium-glucose transport (SGLT) 2 inhibitors such as licoglifozin, remogliflozin, dapagliflozin, empagliflozin, ertugliflozin, sotagliflozin, ipragliflozin, tianagliflozin, canagliflozin, tofogliflozin, janagliflozin, bexagliflozin, luseogliflozin, sergliflozin, HEC-44616, AST-1935, and PLD-101.
- stearoyl CoA desaturase-1 inhibitors/fatty acid bile acid conjugates such as aramchol, GRC-9332, steamchol, TSN-2998, GSK-1940029, and XEN-801;
- thyroid receptor P (THR ) agonists such as VK-2809, resmetirom (MGL-3196), MGL-3745, SKL-14763, sobetirome, BCT-304, ZYT-1, MB-07811 and eprotirome;
- Vitamin E and isoforms; vitamin E combined with vitamin C and atorvastatin. In the present invention, anti-fibrotic compounds are preferably selected in the group consisting of pegbelfermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, seladelpar, semaglutide, sitagliptin, TERN-101, TERN-201, tropifexor, ambrisentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642, and orlistat (Xenical).
More preferably, the anti-fibrotic agent is selected from pegbelfermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, seladelpar, semaglutide, sitagliptin, TERN-101, TERN-201 and tropifexor.
In a more particular embodiment, the anti-fibrotic agent is resmetirom.
The invention further relates to a method for assessing the efficacy of an anti-fibrotic agent in a subject suffering from advanced liver fibrosis or liver cirrhosis, comprising: a) providing (i) the circulating levels of sVCAM, TSP-2 and A2M in a biological fluid sample isolated from a subject suffering from advanced liver fibrosis, wherein said subject has been administered an anti- fibrotic agent before the isolation of the biological fluid sample, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SE; and c) comparing the score SE with a score SD, which is obtained by combining the levels of sVCAM, TSP- 2, A2M and the age, previously measured before administration of the anti-fibrotic agent to the same subject and with the same mathematical function.
According to said method, the levels of sVCAM, TSP-2 and A2M are respectively measured in a sample isolated before administration of an anti-fibrotic agent and a sample isolated after said administration to obtain a first score named SD (before the administration) and a second score named SE (after the administration).
Said SD and SE may be obtained through a same mathematical function, especially the mathematical function as defined above.
If SE is higher than SD, then liver fibrosis worsens, meaning either that the subject does not respond to the treatment with the anti-fibrotic agent or the treatment is not effective. If SE is lower than SD, then liver fibrosis regresses, meaning that the subject responds to the treatment with the anti-fibrotic agent and the treatment is effective.
If SD is equal to SE, then liver fibrosis is stable.
The invention also relates to a computer program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
- receive (i) levels of sVCAM, TSP-2 and A2M measured in a biological fluid sample isolated from a subject suffering from advanced liver fibrosis, wherein said subject has been administered an anti- fibrotic agent before the isolation of the biological fluid sample and (ii) the age of the subject;
- calculate SE from these measured levels and the age, from a mathematical function as described above;
- compare the score SC with a score SD, which is obtained by combining the levels of sVCAM, TSP-2, A2M and the age, previously measured before administration of the anti-fibrotic agent to the same subject and with the same mathematical function; and
- assign the subject as: a subject who does not respond to the treatment with the anti-fibrotic agent, when the score SE is higher than SD; a subject who responds to the treatment with the anti-fibrotic agent, when the score SE is lower than SD ;.or having stable liver fibrosis when there is no significant change between SE and SD.
The present invention also relates to a data-processing device comprising means for carrying one of the methods of the present invention as described above.
The present invention provides also a computer program product or a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out one of the methods of the present invention as described above.
The invention is further described with reference to the following, non-limiting, examples.
EXAMPLES
1. Datasets The study population consists of patients that were screened for potential inclusion in the Resolve-lt phase3 clinical trial. All patients with biopsy results available, and blood samples used for biomarkers measurements with less than 90days between biopsies and blood collections dates were selected as of potential utility for this study.
• Training dataset
Selected patients are those with full data on numerous biomarkers, as well as on the usual demographical and clinical parameters (age, sex, Type-2 Diab, Dyslipidemia, Arterial Hypertension [HT], BMI) to maximize both the number of patients and of biomarkers. This selection led to a total of N=1640 patients with full data on 40 biomarkers. To control for potential confounding factors when processing the modelization and the selection of relevant biomarkers, two propensity score matching algorithms (PSM) are applied to homogenize the following parameters firstly among patients with F<3 (F0F2) and F=4 (F4) and secondly previously matched F=4 with F=3 (F3).
To balance the fibrosis stages distribution in the training dataset, in the first PSM, two patients from the F0F2 group were matched with each patient from the F4 group. This process led to a selection of n=272 patients, 136 from the F0F2 groups, 68 from the F3 group and 68 from the F4 group, well- balanced for the parameters indicated in the list above (Table 1). In this training cohort, the mean age of patients was 58 years, 60% of them were male and 53% had T2D (type 2 diabetes).
• Validation dataset
The training process led to the selection of a biomarker panel comprising three biomarkers: TSP2, sVCAM and A2M.
Based on this result, we selected all patients with available measures of these three biomarkers, fulfilling the condition regarding biopsies results availability and < 90 days between blood and biopsy dates, and who were not part of the training dataset. This led to an independent validation dataset comprising N=1850 patients. Principal characteristics are reported in Table 1 below.
• Statistical analyses
Descriptive statistics were generated for baseline characteristics and reported as means ± sd or %(n), and patients without and with advanced fibrosis or cirrhosis were compared using appropriate 2- sample tests (Student t test for comparison of means and x2 test for comparison of percentages). The modelization process was performed using a Stepwise algorithm, comparing sequentially binomial logistic regression models for the detection of advanced fibrosis or cirrhosis based on their associated BIC (Bayesian Information Criterion), starting with an empty model and sequentially inserting the variables that minimize this criterion. The process stopped when none of the remaining variables led to a decrease of the BIC value. For this, the stat R package (step function) was used. The regression coefficients of the selected model were extracted and associated 95% Cl were reported.
Specific cutoffs values were derived using the training dataset. For the detection of F>3 a low cutoff to achieve 85% sensitivity (Sen) and a high cutoff to achieve 90% specificity (Spe) were extracted, and diagnostic performances (Sen, Spe, PPV, NPV, Acc) associated to these cutoffs were computed in both the training and validation datasets. For the detection of F=4 a low cutoff to achieve 85% Sen and a high cutoff to achieve 95% Spe were extracted, and diagnostic performances (Sen, Spe, PPV, NPV, Acc) associated to these cutoffs were computed in both the training and validation datasets. Also, a Youden cutoff that maximize Sen + Spe is reported for each endpoint.
The overall diagnostic performance of non-invasive tests and biomarkers are estimated using AUROC values, and these statistics are tested for significant differences using Delong tests. AUROC values are reported with 95%CI estimated using 1000 bootstrap samples.
Boxplots are presented with associated Student t-tests for mean comparisons, and p values are graphically reported as follow :
**** if p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05 and NS if p 3= 0.05
All propensity score matching algorithms were executed using the matchit function from the Matchlt
R package, and all statistical analyses were performed using R version 4.3.0.
Table 1: Cohorts descriptive features
2. Results
2.1 Modelization development
When executing the stepwise algorithm, the combination of TSP2, SVCAM and A2M was found as the best biomarker combination, as it returned the lowest BIC, the addition of further biomarker did not lead to a sufficient decrease to be considered significant.
In addition, it is important to guarantee that any diagnostic test could be interpreted irrespective of the patients' characteristics. Thus, the impact of different potential confounding factors, i.e. age, sex, prevalence of Type-2 Diabetes (T2D), dyslipidemia, arterial hypertension (HT), Body mass index (BMI), fibrosis, steatosis, ballooning and lobular inflammation by class, was analyzed by using a modelization STA:
1 STA = - - - -
1 + exp (— ) with y = -26.85 + 3.125 x loglO (A2M (g/L)) + 5.606 x loglO (sVCAM (ng/mL)) + 6.325x loglO (TSP-2 (ng/mL)) This process led to the creation of 2 subpopulations for each parameter of interest (age, sex, gender, hypertension, dyslipidemia and T2D), each time these subpopulations being well-balanced for the other parameters listed above. To be noted, regarding age, we created 2 subpopulations, one comprising patients aged 50 and the second with patients aged 60 to ensure a clear separation of age between both subpopulations as well as a second PSM for extreme case study with patients ages =£ 45 and patients agesSs 65. For BMI, two groups were created with patients with a BMI =£ 29 and a second with patients with a BMI 5= 31. For gender, age, T2D, HT, BMI and Dyslipidemia, we extracted a total of 1124 patients (562 in each category), 678 (339 in each category), 1048 (524 in each category), 1084 patients (542 in each category), 750 patients (375 in each category) and 1052 patients (526 in each category) respectively.
Out of the six confounding factors, age was the only one which had a significant impact on the mean scores of the modelization STA, with a significantly higher score for patient older than 60 (p values <0.0001, see Fig. 1A).
A new mathematical modelization was developed to obtain a score which is not impacted by the age of a subject. The following equation (named STAII) was obtained using a binomial logistic regression model: with y = PO + pi*logio(TSP2 (ng/mL)) + P2*logio(sVCAM (ng/mL)) + 3 * (A2M(g/L)) + 4 *(Age (years)3)
Coefficients of this model are reported in Table 2 with associated 95%CI, all of them reaching high significance with associated p values <0.0001.
Table 2: coefficients summary
Coefficient -25.475(****) 6.1553 (****) 4.8689 (****) 0.4548 (****) -2.668e-06(*)
(-34.456, - (4.6889, (2.0932, (0.0877, (-6.3481e-06,
Cl (95%)
17.5039) 7.7805) 7.9183) 0.8474) -1.031e-07)
Note: Coeff are reported with * and **** , i.e. respectively associated p val<0.05 and p val<0.0001
The impact of age on scores and mean values of biomarkers, Gpe 0 (younger than 50) and Gpe 1 (older than 60) is shown in Fig. IB.
This result shows that compared to mean scores of the modelization STA, the modelization STAII displayed consistent clinical performances independently of the age classes of the patients. To illustrate this, the inventors extracted a youden cutoff (0.3457) for the detection of F3 in the validation dataset and calculated the clinical performances for the modelizations STA and STA II. We can observe that the specificity of the modelization STA decreased with the increase of the age of the patients and the sensitivity increased with increasing age (Fig. 2A). This result shows that the clinical performance of modelization STA is not homogeneous across all groups of age. On the contrary, the specificity and the sensitivity of the modelization STA II are homogeneous and constant across all groups of age (Fig. 2B).
Based on the modelization STA II and the training dataset, the inventors extracted the following cutoff values to achieve a 85% sensitivity and 90% specificity for the detection of F>3 in Table 3 and to achieve a 85% sensitivity and 95% specificity for the detection of F=4 in Table 4. Table 3: Low (Lc, 85%Sen), Youden and high (He, 90%Spe) cut-offs for the detection of F3
Low (Lc) Youden High (He)
STAII 0.3054 0.5801 0.5884
Table 4: Low (Lc, 85%Sen), Youden and high (He, 95%Spe) cut-offs for the detection of F4 >
Low (Lc) Youden High (He)
STAII 0.5874 0.6103 0.9067
2.2 STAII diagnostic performances validation
The inventors then computed the diagnostic performances of the modelization STAII obtained in both the training and validation datasets for both endpoints. The low and high cutoffs were used to derive these statistics, which are reported in Table 5 for the detection of F 3=3 and in Table 6 for the detection of F=4.
Table 5: STAII performances validation for the detection of advanced fibrosis
Training Validation
N Til 1850
Prev 50 35
AUC 0.8535(0.8, 0.9) 0.8395 (0.82, 0.86)
Rule-out
Lc 0.3054 0.3054
AccLc 0.7 0.75
SenLc 0.85 0.8
SpeLc 0.54 0.72
NPV 0.79 0.87
IRZ 0.25 0.21
Rule-in
He 0.5884 0.5884
AccHc 0.81 0.77
SenHc 0.71 0.53
SpeHc 0.9 0.9
PPV 0.87 0.74 Table 6 : STAII performances validation for the detection of cirrhosis
Training Validation
N Til 1850
Prev 25 3.9
AUC 0.8565 (0.81, 0.9) 0.8767 (0.84, 0.91)
Rule-out
Lc 0.5874 0.5874 AccLc 0.76 0.77
SenLc 0.85 0.85
SpeLc 0.73 0.77
NPV 0.94 0.99
IRZ 0.28 0.2
Rule-in
He 0.9067 0.9067
AccHc 0.81 0.94
SenHc 0.38 0.42
SpeHc 0.95 0.96
PPV 0.7 0.3
• Detection of F> 3
In the validation cohort, at low cutoff, STAII achieved a sensitivity of 0.8 and a specificity of 0.72, while the NPV was 0.87. Similarly, at high cutoff, STAII achieved a specificity of 0.9 combined with a sensitivity of 0.53 and a PPV of 0.74. Interestingly, only 21% of patients relied in the indeterminate risk zone (IRZ), meaning that almost 4/5 had clinically actionable test results with STAII. Overall, STAII and its associated performances were validated.
• Detection of F=4
In the validation cohort, at low cutoff, STAII achieved a sensitivity of 0.85 and a specificity of 0.77, while the NPV was 0.99. Similarly, at high cutoff, STAII achieved a specificity of 0.96 combined with a sensitivity of 0.42 and a PPV of 0.3. Interestingly, only 20% of patients relied in the indeterminate risk zone (IRZ), meaning that almost 4/5 had clinically actionable test results with STAII. Overall, STAII and its associated performances were validated.
2.3 STAII performances comparison vs other NITs
The overall performances of STAII for the detection of F>3 are also compared with other usual non- invasive tests: FIB-4, ELF, NFS and APRL
We calculated the AUROC values and associated Delong tests toe test for AUROC difference between STAII and other NITs. Results for the detection of F>3 are summarized in Table 7 and results for the detection of F=4 are summarized in Table 8.
Table 7: AUROC values for advanced fibrosis detection n Advanced
N fibrosis AUC (95% CI) p value
STAII 1850 648 0.84 (0.82, 0.86) NA
FIB4 1850 648 0.75 (0.72, 0.77) <0.0001
ELF 1850 648 0.77 (0.74, 0.79) <0.0001 NFS 1850 648 0.68 (0.66, 0.71) <0.0001
APRI 1850 648 0.73 (0.7, 0.75) <0.0001
Table 8: AUROC values for cirrhosis detection
N n cirrhosis AUC (95% CI) p value
STAII 1850 73 0.88 (0.84, 0.91) NA
FIB4 1850 73 0.78 (0.72, 0.83) 0.0002
ELF 1850 73 0.83 (0.78, 0.88) 0.0114
NFS 1850 73 0.73 (0.66, 0.78) 0.0000
APRI 1850 73 0.71 (0.65, 0.77) 0.0000
The ROC curves for the detection of F>3 are graphically reported in Fig. 3 and the ROC curves for the detection of F=4 are graphically reported in Fig. 4.
For the detection of F>3, STAII achieved an AUROC of 0.84, significantly higher than those achieved by the other NITs, all p values <0.0001. For the detection of F=4, STAII achieved an AUROC of 0.88, significantly higher than those achieved by the other NITs. These results confirm the high performance of the methods of the invention and notably the gain of using this new diagnostic test over the usual ones.
2.4 Prognostic performance of SF score
In order to evaluate the prognostic performance of the method, 612 patients included in the clinical trial have been used. These patients had available information for sVCAM, TSP2 and A2M and biopsy results both at screening visit (Vo) and visit 7 (V7, 81 weeks later) of the clinical trial and had biopsy- proven stage 1, 2 or 3 liver fibrosis at Vo. The SF scores of these patients were calculated at Vo. For each group of patients with initial stage 1, 2 or 3 fibrosis, three categories of fibrosis evolution were defined:
• Improver: patients who had a lower fibrosis stage at V7 compared to Vo
• Stable: patients who had the same fibrosis stage at V7 compared to Vo
• Worsener: patients who had higher fibrosis stage at V7 compared to Vo
There are 9 subgroups of patients in total. The numbers of patients in each sub-group are shown in
Table 9 below.
Table 9:
The comparison of the SF scores of each sub-group shows that a low SF score at Vo is indicative of that the subject's liver fibrosis will regress and a high score SF at Vo is indicative of that the subject's liver fibrosis will progress (Figure 5). 2.5 Validation of prognostic performance of SF score
In order to verify the prognostic performance of SF score, 27 patients with biopsy-proven stage 1, 2 or 3 liver fibrosis were classified at Vo according to their fibrosis stage based on a biopsy. A blood sample of these patients was taken concomitantly. Patient's SF score was calculated according to the method of the invention and was compared with the cut-off value range corresponding for their original fibrosis stage at VO (ie C0-F1: a range comprised from 0.06 and 0.20; C0-F2: a range comprised between 0.25 and 0.44; C0-F3 : a range comprised between 0.47 and 0.86). After the comparison, the patients were prognosed as either improving, staying stable or worsening. The course of the disease assessed by the score was verified with a biopsy at V7. The SF score of each patient and the disease course assessed at V7 are summarized in Table 10. The results confirm the performance of SF score for prognosing the evolution of liver fibrosis in a subject with biopsy-proven stage 1, 2 or 3 liver fibrosis.
Table 10

Claims

Claims
1. An in vitro method for diagnosing or prognosing advanced liver fibrosis or liver cirrhosis in a subject, comprising a) providing (i) the circulating levels of Soluble Vascular Cell Adhesion Molecule-1 (sVCAM), Thrombospondin 2 (TSP-2) and alpha 2 Macroglobulin (A2M) in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score; and c) comparing said score with cut-off values to diagnose or prognose advanced liver fibrosis or liver cirrhosis in said subject.
2. The method according to claim 1, wherein the score is a score SA obtained through the following mathematical function:
1
SA = - - — -
1 + exp (— y) with y = P0 + pi*logio(TSP2 (ng/mL)) + 2*logio(sVCAM (ng/mL)) + 3 * (A2M(g/L)) + 4 *(Age (years)3) wherein: 0 is comprised between -37 and -12 in particular between -31 and -17; pi is comprised between 1.5 and 7.9, in particular between 2 and 7;
P2 is comprised between 1 and 11, in particular between 1.7 and 8.5;
P3 is comprised between 0,01 and 2.5, in particular between 0.1 and 1.5; and
P4 is comprised between -8 e 06 and - 0.5 e-7, in particular between -6.5 e-6 and -1.0 e-6.
3. The method according to claim 1 or 2, wherein a score SA higher than a cut-off value col is indicative of advanced liver fibrosis, particularly col being comprised between 0.2 and 0.7, more particularly col being comprised between 0.25 and 0.63.
4. The method according to claim 1 or 2, wherein a score SA higher than a cut-off value co2 is indicative of a liver cirrhosis, particularly co2 being comprised between 0.4 and 1.1, more particularly co2 being comprised between 0.5 and 1.0.
5. An in vitro method for monitoring the progression of liver fibrosis in a subject, comprising the steps of: a) providing (i) the circulating levels of Soluble Vascular Cell Adhesion Molecule-1 (sVCAM), Thrombospondin 2 (TSP-2) and alpha 2 Macroglobulin (A2M) in a biological fluid sample isolated from said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SC; and c) comparing the score SC with a score SB, which is obtained by combining levels of sVCAM, TSP-2, A2M and the age previously measured in the same subject and with the same mathematical function.
6. The method according to claim 5, wherein:
- an increase of SC compared to SB indicates the progression of liver fibrosis;
- a decrease of SC compared to SB indicates the regression of liver fibrosis;
- no difference between SC and SB indicates a stable liver fibrosis.
7. The method according to claim 6, wherein SC is measured at least 3 months after the measurement of SB, particularly in a period between 3 months and 10 years, preferably in a period between 3 months and 2 years.
8. The method according to any one of claims 5 to 7, wherein SC or SB are calculated through the following mathematical function: with y = P0 + pi*logio(TSP2 (ng/mL)) + P2*logio(sVCAM (ng/mL)) + 3 * (A2M(g/L)) + 4 *(Age (years)3) wherein: 0 is comprised between -37 and -12 in particular between -31 and -17; pi is comprised between 1.5 and 7.9, in particular between 2 and 7;
P2 is comprised between 1 and 11, in particular between 1.7 and 8.5;
P3 is comprised between 0,01 and 2.5, in particular between 0.1 and 1.5; and
P4 is comprised between -8 e 06 and - 0.5 e-7, in particular between -6.5 e-6 and -1.0 e-6.
9. A method for assessing the efficacy of an anti-fibrotic agent in treating advanced liver fibrosis or liver cirrhosis, comprising: a) providing (i) the circulating levels of Soluble Vascular Cell Adhesion Molecule-1 (sVCAM), Thrombospondin 2 (TSP-2) and alpha 2 Macroglobulin (A2M) in a biological fluid sample isolated from a subject suffering from advanced liver fibrosis, wherein said subject has been administered an anti- fibrotic agent before the isolation of the biological fluid sample, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SE; and c) comparing the score SE with a score SD, which is obtained by combining the levels of sVCAM, TSP- 2, A2M and the age, previously measured before administration of the anti-fibrotic agent to the same subject and with the same mathematical function.
10. The method according claim 9, wherein a decrease of SE compared to SD indicates the efficacy of the anti-fibrotic agent.
11. The method according claim 9 or 10, wherein SE and SD are calculated through the following mathematical function: with y = P0 + pi*logio(TSP2 (ng/mL)) + P2*logio(sVCAM (ng/mL)) + 3 * (A2M(g/L)) + 4 *(Age (years)3) wherein: 0 is comprised between -37 and -12 in particular between -31 and -17; pi is comprised between 1.5 and 7.9, in particular between 2 and 7;
P2 is comprised between 1 and 11, in particular between 1.7 and 8.5;
P3 is comprised between 0,01 and 2.5, in particular between 0.1 and 1.5; and
P4 is comprised between -8 e 06 and - 0.5 e-7, in particular between -6.5 e-6 and -1.0 e-6.
12. The method according to any one of claims 1 to 11, wherein the biological fluid sample is a saliva sample, an interstitial liquid sample, a urine sample, a blood sample, a plasma sample or a serum sample.
13. The method according to claim 12, wherein the biological fluid sample is a serum sample.
14. An anti-fibrotic agent for use in the treatment of advanced liver fibrosis or liver cirrhosis in a subject, wherein said subject is diagnosed as suffering from advanced liver fibrosis or liver cirrhosis according to the method of any one of claims 1 to 4, wherein said agent is selected from the group consisting of pegbelfermin, Cenicriviroc, Dapagliflozin, Dulaglutide, Empagliflozin, Fenofibrate, Lanifibranor, Liraglutide, obeticholic acid, Pioglitazone, Resmetirom, saroglitazar magnesium, Seladelpar, Semaglutide, Sitagliptin, TERN-101, TERN-201, Tropifexor, Ambrisentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642 and orlistat.
15. An anti-fibrotic agent for use in the treatment of advanced liver fibrosis or liver cirrhosis in a subject, wherein said subject is diagnosed as suffering from advanced liver fibrosis or liver cirrhosis according to the method of any one of claims 1 to 4, wherein said agent is Resmetirom.
16. An in vitro method for prognosing the evolution of liver fibrosis in a subject with biopsy-proven stage 1, 2 or 3 liver fibrosis, comprising a) providing (i) the circulating levels of Soluble Vascular Cell Adhesion Molecule-1 (sVCAM), Thrombospondin 2 (TSP-2) and alpha 2 Macroglobulin (A2M) in a biological fluid sample of said subject, and (ii) the age of the subject; b) combining the provided levels of sVCAM, TSP-2 and A2M and the age in a mathematical function to assign a score SF; and c) comparing said score with a cut-off value to prognose the evolution of liver fibrosis in said subject.
17. The method according to claim 16, wherein the score SF is obtained through the following mathematical function:
1
SF = -
1 + exp (— ) with y = P0 + pi*logio(TSP2 (ng/mL)) + 2*logio(sVCAM (ng/mL)) + 3 * (A2M(g/L)) + 4 *(Age (years)3) wherein: 0 is comprised between -37 and -12 in particular between -31 and -17; pi is comprised between 1.5 and 7.9, in particular between 2 and 7;
P2 is comprised between 1 and 11, in particular between 1.7 and 8.5;
P3 is comprised between 0,01 and 2.5, in particular between 0.1 and 1.5; and
P4 is comprised between -8 e 06 and - 0.5 e-7, in particular between -6.5 e-6 and -1.0 e-6.
18. The method according to claim 16 or 17, wherein the SF score higher than a cut-off value is indicative of that the subject's liver fibrosis will progress; the SF score lower than the cut-off value is indicative of that the subject's liver fibrosis will regress; the SF score neither higher nor lower than the cut-off value is indicative of that the subject's liver fibrosis will be stable.
19. A computer assisted program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
- receive (i) levels of sVCAM, TSP-2, A2M measured in a biological fluid sample isolated from a subject; and (ii) the age of the subject
- calculate a SA score from these measured levels, through a mathematical function; and
- assign the subject into the group of subjects having advanced liver fibrosis or liver cirrhosis upon the calculated score compared to predetermined cutoff values.
20. a computer program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
- receive (i) levels of sVCAM, TSP-2 and A2M measured in a biological fluid sample isolated from a subject and (ii) the age of the subject;
- calculate SC from these measured levels and the age, from a mathematical function as described above;
- compare the score SC with a score SB, which is obtained by combining levels of sVCAM, TSP-2, A2M and the age previously measured in the same subject and with the same mathematical function and
- assign the subject as : having the progression of liver fibrosis when the score SC is higher than SB; having the regression of liver fibrosis when the score SC is lower than SB; or having stable liver fibrosis when there is no significant change between SC and SB.
21. A computer program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
- receive (i) levels of sVCAM, TSP-2 and A2M measured in a biological fluid sample isolated from a subject suffering from advanced liver fibrosis, wherein said subject has been administered an anti- fibrotic agent before the isolation of the biological fluid sample and (ii) the age of the subject;
- calculate a SE score from these measured levels and the age, from a mathematical function as described above; - compare the score SC with a score SD, which is obtained by combining the levels of sVCAM, TSP-2, A2M and the age, previously measured before administration of the anti-fibrotic agent to the same subject and with the same mathematical function; and
- assign the subject as : a subject who does not respond to the treatment with the anti-fibrotic agent, when the score SE is higher than SD; a subject who responds to the treatment with the anti-fibrotic agent, when the score SE is lower than SD ;.or having stable liver fibrosis when there is no significant change between SE and SD.
22. A computer assisted program comprising instructions that, when executed by a processor/processing means, cause the processor/processing means to:
- receive (i) levels of sVCAM, TSP-2, A2M measured in a biological fluid sample of a subject with biopsy- proven stage 1, 2 or 3 liver fibrosis; and (ii) the age of the subject
- calculate a SF score from these measured levels, through a mathematical function;
- compare the score SF with a predetermined cutoff value for score SF, and
- assign the subject as: a subject whose liver fibrosis will progress, when the score SF is higher than the cutoff value; a subject whose liver fibrosis will regress when the score SF is lower than the cutoff value; a subject whose liver fibrosis will be stable when the SF score is neither higher nor lower than the cutoff value.
23. The computer assisted program according to any one of claims 19 to 22, wherein the scores are calculated through the mathematical function as defined in claims 2, 8, 11 and 17.
24. A data-processing device comprising means for carrying the method of any one of claims 1 to 13, 16-18.
25. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of any one of claims 1 to 13, 16-18.
26. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 13, 16-18.
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