Methods of diagnosing advanced hepatic fibrosis or cirrhosis

Abstract

Provided herein is a method of diagnosing or prognosing advanced hepatic fibrosis or cirrhosis in a patient. The method is achieved by determining a score on the basis of the circulation levels of the two markers in serum.

Description

[0001] The present invention relates to a method for diagnosing advanced liver fibrosis or cirrhosis, prognosing or monitoring the progression of liver fibrosis or evaluating the efficacy of anti-fibrotic agents in a subject. The present invention also relates to a kit for implementing the method of the present invention and a compound for treating liver fibrosis, wherein the subject to be treated is identified according to the method of the present invention. Background Art

[0002] Liver fibrosis is common in liver injury and liver disease and may have many chronic or non-chronic causes, 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's disease, nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH), alcohol-related liver disease (ARLD), alcoholic liver disease, and hemochromatosis.

[0003] Liver problems can be caused by a variety of factors that damage the liver, such as viruses, immune system abnormalities, 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 requires emergency medical care. Liver failure occurs when large parts of the liver are damaged beyond repair.

[0004] Liver fibrosis is an abnormal wound repair process characterized by excessive accumulation of extracellular matrix proteins. It is stimulated by chronic inflammation and occurs as a result of the liver's healing process when the liver scars.

[0005] Prediction of liver fibrosis is a key step in the evaluation and management of patients with liver damage and / or liver disease. Therefore, since early and accurate assessment of the severity and status of liver fibrosis is essential for diagnosis, monitoring, and prognosis, quantitative measurements are essential for assessing disease progression.

[0006] Most forms of liver disease are asymptomatic until the disease has progressed to an advanced stage. Therefore, early detection of the disease is challenging. The risk of liver-related death increases exponentially with increasing stage of fibrosis, and once cirrhosis develops, mortality and morbidity increase exponentially. Cirrhosis is the stage where the liver is completely scarred and beyond its ability to heal itself.

[0007] Cirrhosis is a leading cause of mortality and morbidity worldwide. In 2016, it was the 11th leading cause of death and the 15th leading cause of morbidity, accounting for 2.2% of deaths and 1.5% of disability-adjusted life years worldwide. In 2017, chronic liver disease caused 1.32 million deaths worldwide (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). When patients do not have any visible symptoms of the disease, cirrhosis is called compensated, whereas when cirrhosis has progressed to the point where the liver is not functioning properly and symptoms of the disease appear, cirrhosis is decompensated. Although the clinical features of decompensated cirrhosis are well described (ie, ascites, spider angiomas, jaundice, signs of hepatic encephalopathy), patients with compensated cirrhosis often have no clinical signs and may be completely asymptomatic.

[0008] Hepatologists and healthcare providers have proposed scoring systems for staging liver fibrosis such as the BRUNT / KLEINER system, in which:

[0009] F0 refers to subjects without liver fibrosis;

[0010] F1 refers to subjects with portal or perisinusoidal fibrosis;

[0011] F2 refers to subjects with portal / periportal and perisinusoidal fibrosis;

[0012] F3 refers to subjects with interstitial or bridging liver fibrosis;

[0013] F4 refers to subjects with liver cirrhosis.

[0014] Since severe (fibrosis stage F>2) liver diseases can progress to hepatocellular carcinoma, accurate staging of liver fibrosis, especially early diagnosis of advanced fibrosis (fibrosis stage F3 or F4) and cirrhosis (fibrosis stage F4) is crucial in these liver diseases.

[0015] Furthermore, the rate at which fibrosis progresses varies over time and the diagnostic assay must be performed several times. Therefore, such a test must be reproducible, pose no risk to the patient, reliable, and accurate. Therefore, non-invasive assays are needed to diagnose advanced fibrosis (F3 or F4) and cirrhosis (F4). It is also important to specifically distinguish patients with cirrhosis (F=4) from those with severe liver disease, as these F4 patients require urgent treatment.

[0016] To date, invasive liver biopsy remains the gold standard for evaluating liver fibrosis.

[0017] However, liver biopsy has several recognized limitations, including sampling error, interobserver variability, and the need for hospitalization. The main disadvantage is the significant risk of complications, including bleeding, pain, and even death. In addition, biopsy does not reflect changes in the entire liver and cannot distinguish early cirrhosis from advanced cirrhosis, and therefore does not constitute a reliable predictor of prognosis (Sumida Y et al., Limitations of liver biopsy and non-invasive diagnostic testsfor the diagnosis of nonalcoholic fatty liver disease / nonalcoholicsteatohepatitis, World J Gastroenterol 2014; 20: 475-485).

[0018] To avoid the life-threatening risks and diagnostic weaknesses detailed above, in vitro non-invasive diagnostic methods using biomarkers, scores, and physical methods have been developed. Compared to biopsies, which cannot be easily repeated, these methods can capture the dynamic process of fibrosis caused by progression and regression because the measurements are reproducible. These approaches 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 fibrinolysis, such as the enhanced liver fibrosis (ELF) test (Guha, IN 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).

[0019] However, replacing liver biopsy with these methods remains controversial and has not been universally accepted due to insufficient diagnostic performance. The main problem with these two biochemical assays is that they cannot reliably distinguish the advanced stage of liver fibrosis in individual patients. In addition, ELF testing is expensive, which constitutes a disadvantage in the case of repeated testing. In addition, FIB4 performs poorly in patients younger than 35 years of age and has low specificity in patients older than 65 years of age.

[0020] Physical methods include imaging based on high-frequency sound waves (ultrasound and echocardiography), computed tomography (CT), magnetic resonance imaging (MRI), transient elastography (TE, FibroScan), and scintigraphy. The main disadvantages of physical measurements are high cost, low equipment availability, and methodological complexity that limits daily clinical practice.

[0021] Furthermore, conditions such as obesity, ascites, acute inflammation, hepatic congestion, and increased portal pressure may reduce the accuracy of ultrasound TE (Fibroscan) by affecting the velocity of the shear wave. Moreover, a false increase in liver stiffness due to an increase in postprandial portal pressure has been observed with this approach.

[0022] Therefore, there is still an unmet medical need to develop new non-invasive diagnostic methods that have the best diagnostic accuracy compared to biopsy and can be used to monitor the time course of liver fibrosis and / or show whether there is a response to a given drug. In addition, it is important to provide a new method that gives the best predictive ability at the lowest cost and simplest feasibility. Summary of the invention

[0023] The inventors have performed several very sophisticated and complete analyses of a cohort of 1063 patients with NASH and fibrosis, providing a new highly sensitive non-invasive diagnostic and monitoring method for advanced liver fibrosis and cirrhosis. The inventors have identified a combination of two biomarkers, soluble vascular cell adhesion molecule-1 (sVCAM) and thrombospondin 2 (TSP-2). This new biomarker combination provides an accurate diagnosis or prognosis of advanced liver fibrosis (F3 or F4) and cirrhosis (F4) compared to existing solutions and represents a valuable alternative to liver biopsy. Measuring these markers in biofluid samples allows for safe and regular tracking of liver fibrosis. In addition, the diagnostic method of the present invention is more accurate and cheaper than the methods currently used in diagnosing advanced liver fibrosis and cirrhosis, such as ELF and FIB4.

[0024] Therefore, a first aspect of the present invention relates to an in vitro method for diagnosing advanced liver fibrosis (F3 or F4) or liver cirrhosis (F4) in a subject, the method comprising:

[0025] a) measuring circulating levels of soluble vascular cell adhesion molecule (sVCAM) and thrombospondin 2 (TSP-2) in a biological fluid sample isolated from the subject;

[0026] b) comparing the levels of sVCAM and TSP-2 to reference levels of sVCAM and TSP-2, wherein the comparison between the measured levels and the reference levels indicates the presence or absence of advanced liver fibrosis or cirrhosis.

[0027] In a specific embodiment, the present invention relates to an in vitro method for prognosing advanced liver fibrosis (F3 or F4) or liver cirrhosis (F4) in a subject, the method comprising:

[0028] a) measuring circulating levels of soluble vascular cell adhesion molecule (sVCAM) and thrombospondin 2 (TSP-2) in a biological fluid sample isolated from the subject;

[0029] b) comparing the levels of sVCAM and TSP-2 to reference levels of sVCAM and TSP-2, wherein the comparison between the measured levels and the reference levels indicates the presence or absence of advanced liver fibrosis or cirrhosis.

[0030] In a specific embodiment, step (b) of the method comprises comparing the score A (SA) with a cutoff value, wherein the SA is obtained from the levels of sVCAM and TSP-2 measured in step (a), and the cutoff value is obtained from a reference level of sVCAM and TSP-2. Specifically, the SA and the cutoff value are calculated using an algorithm discovered using a logistic regression function. More specifically, the cutoff value is obtained from a Youden statistical analysis of a training population.

[0031] In a more specific embodiment, the SA is calculated by the following algorithm equation:

[0032] Rating = e y / (1 + e y ),in

[0033] y = k + a × A + b × B

[0034] A is the level of sVCAM, expressed in log10 ng / mL;

[0035] B is the level of TSP-2, in log10 ng / mL;

[0036] k is a constant of the algorithm equation;

[0037] a is the coefficient associated with the level of sVCAM;

[0038] b is the coefficient associated with the level of TSP-2;

[0039] And among them:

[0040] k is a number between -37.23 and -27.54, specifically -30.000;

[0041] a is a number between 5.432 and 8.646, specifically 7.250;

[0042] b is a number between 5.746 and 7.727, in particular 6.921; and

[0043] In this particular aspect of the invention, the score corresponds to SA in the above algorithm equation.

[0044] In a specific embodiment, SA above a cutoff value co1 indicates advanced liver fibrosis, particularly co1 is between 0.200 and 0.500, more particularly co1 may be 0.204, 0.3383, 0.4852.

[0045] More specifically, co1 is equal to 0.3383.

[0046] In another specific embodiment, SA above a cutoff value of co2 indicates liver cirrhosis, particularly co2 between 0.510 and 0.790, more particularly co2 may be 0.5110, 0.6073, 0.7791.

[0047] More specifically, co2 is equal to 0.6073.

[0048] Another aspect of the present invention relates to an in vitro method for monitoring the progression of liver fibrosis in a subject, the method comprising the steps of:

[0049] a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said subject, and

[0050] b) The levels were compared with the levels of sVCAM and TSP-2 previously measured in the same subjects.

[0051] In a specific embodiment, step (b) of the method comprises comparing score C (SC) with score B (SB), wherein SC is a score obtained from the levels of sVCAM and TSP-2 measured in step (a), and SB is a score obtained from the levels of sVCAM and TSP-2 previously measured, and the score is calculated using an algorithm equation discovered using logistic regression. In a specific embodiment, an increase in SC compared to SB indicates progression of liver fibrosis. In another specific embodiment, a decrease in SC compared to SB indicates regression of liver fibrosis. In a specific embodiment, in the same subject, the absence of a significant change between SC and SB measured over a certain period of time means that liver fibrosis is stable.

[0052] In a specific embodiment of the method, step (a) of the method for monitoring the progression of liver fibrosis is carried out at least 3 months after the previous measurement of sVCAM and TSP-2 levels, in particular in a period between 3 months and 10 years after the previous measurement of sVCAM and TSP-2 levels, more particularly in a period between 3 months and 2 years.

[0053] In a specific embodiment of the method, an event associated with the evolution of the pathological state occurs during the monitoring between SB and SC. In a specific embodiment, the event is selected from liver transplantation, acute-on-chronic liver fibrosis, compensated cirrhosis, decompensated cirrhosis, onset of ascites, and the presence of esophageal varices during endoscopy.

[0054] In a preferred embodiment, the SB and SC are calculated by the algorithm equations defined above.

[0055] A third aspect of the present invention relates to a method for evaluating the efficacy of an anti-fibrotic agent in treating advanced liver fibrosis or cirrhosis, the method comprising:

[0056] a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from a subject with advanced liver fibrosis, wherein said subject has been administered an anti-fibrotic agent prior to said measuring, and

[0057] b) comparing said levels of sVCAM and TSP-2 with the levels of sVCAM and TSP-2 previously measured before administration of said anti-fibrotic agent to the same subject, to assess the efficacy of said anti-fibrotic agent.

[0058] In a specific embodiment of this method, step (b) comprises comparing score E (SE) with score D (SD), wherein SD is a score obtained from the levels of sVCAM and TSP-2 measured before the anti-fibrotic agent is administered to the subject, and SE is a score obtained from the levels of sVCAM and TSP-2 measured in step (a), i.e., after the anti-fibrotic agent is administered to the subject, and SE and SD are calculated by an algorithm. Specifically, a decrease in SE compared to SD indicates the efficacy of the anti-fibrotic agent, and an increase in SE compared to SD indicates the inefficacy of the anti-fibrotic agent and / or the non-responsiveness of the patient.

[0059] In a preferred embodiment, the SE and SD are calculated by the algorithm equations defined above.

[0060] In a preferred embodiment, the biological fluid sample of the subject used in the method of the present invention is interstitial fluid, saliva, urine or whole blood sample. In a specific embodiment, the biological fluid sample of the subject used in the method of the present invention is a blood sample. Preferably, the biological fluid sample is acellular. More preferably, the blood sample is a plasma or serum sample from the subject.

[0061] Another aspect of the present invention also provides an anti-fibrotic agent for treating advanced liver fibrosis or cirrhosis in a subject, wherein the subject is diagnosed as having advanced liver fibrosis or cirrhosis according to the method of the present invention, wherein the agent is selected from pembefumin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanilanol, liraglutide, obeticholic acid, pioglitazone, resmetirox, saroglitazar magnesium, seladepa, semaglutide, sitagliptin, TERN-101, TERN-201, tropifexor, ambrisentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642 and orlistat.

[0062] The present invention also provides a kit for diagnosing advanced liver fibrosis or cirrhosis in a subject, the kit comprising means for determining the levels of sVCAM and TSP-2. In a specific embodiment, the means are antibodies or aptamers or peptides against sVCAM and antibodies or aptamers or peptides against TSP-2. DETAILED DESCRIPTION

[0063] definition

[0064] Fibrosis score / stage

[0065] According to the present invention, the term "fibrosis" or "liver fibrosis" refers to a pathological condition in which fibrous connective tissue is excessively deposited in the liver. More specifically, fibrosis is a pathological process that includes persistent fibrotic scarring and overproduction of extracellular matrix by connective tissue as a result of a response to tissue damage. Physiologically, the deposits of connective tissue destroy the architecture and function of the liver.

[0066] The different stages of liver fibrosis are defined by the Kleiner scoring system (Kleiner et al., Hepatology 2005, Vol 41, Issue 6, 1313-1321), where:

[0067] F0 refers to subjects without liver fibrosis;

[0068] F1 refers to subjects with portal or perisinusoidal fibrosis;

[0069] F2 refers to subjects with portal / periportal and perisinusoidal fibrosis;

[0070] F3 refers to subjects with interstitial or bridging liver fibrosis;

[0071] F4 refers to subjects with liver cirrhosis.

[0072] Stages F0-2 were assigned to subjects with early liver fibrosis, F3 or F4 were assigned to subjects with advanced liver fibrosis, and stage F4 was assigned to subjects with cirrhosis.

[0073] Using this fibrosis staging system, patients with no or minimal fibrosis (F = 0 or 1) are generally not considered to be at risk for cirrhosis, liver failure, HCC (hepatocellular carcinoma), or liver-related death. Patients with severe (F>2) liver fibrosis are at risk for cirrhosis, liver failure, HCC, and liver-related death. Patients with compensated cirrhosis (F = 4) are at high risk for liver failure (decompensated cirrhosis), HCC, and liver-related death.

[0074] In the context of the present invention, the term "advanced fibrosis" or "advanced liver fibrosis" refers to a fibrosis stage of F ≥ 3, ie, fibrosis stage F3 or fibrosis stage F4.

[0075] In the context of the present invention, the term "cirrhosis" or "liver cirrhosis" refers to a fibrosis stage of F4.

[0076] The terms "subject" and "patient" are used interchangeably herein and refer to a human subject.

[0077] In the context of the present invention, the terms "biomarker", "marker", "biological marker" are interchangeable.

[0078] Within the scope of the present invention, any range must be considered to include both the upper and lower limits.

[0079] Concurrent non-invasive testing

[0080] Fibrosis-4 (FIB-4) index was calculated as age (years) x AST (U / L) / platelets (x 10 9 / L) / √ALT (U / L), where AST is aspartate aminotransferase and ALT is alanine aminotransferase.

[0081] The Enhanced Liver Fibrosis Panel (ELF, Siemens Healthcare GmbH, Eriangen, Germany) is a test that predicts fibrosis based on three fibrosis biomarkers: hyaluronic acid (HA), tissue inhibitor of matrix metalloproteinases-1 (TIMP-1), and amino-terminal propeptide of type III procollagen (PIIINP). This test calculates the ELF score using the following equation (2.278 + 0.851 ln( CHA ) + 0.751 ln( CPIIINP ) + 0.394 ln( CTIMP-1)), where C is the concentration of the biomarker.

[0082] Diagnostic performance analysis

[0083] Sensitivity is the ability of a test to correctly identify people with the disease in question. Sensitivity measures the proportion of positive ("disease") cases in a population of diseased patients evaluated using a reference method ("gold standard") or biopsy analysis that is considered as reliable as possible. Sensitivity (Se) is the proportion of positive results (true positives = TP) divided by the total number of diseased patients (TP + false negatives = FN): Se = TP / (TP + FN). Sensitivity is usually expressed as a percentage (%), ranging from 0 to 100%.

[0084] A true positive (TP) subject is a subject with the disease whose value for the parameter of interest is above the cutoff value.

[0085] A false negative (FN) subject is a subject with the disease whose value for the parameter of interest is below the cutoff value.

[0086] Specificity is the ability of a test to correctly identify people who do not have the disease in question. Specificity measures the proportion of negative ("healthy") cases in a population of healthy patients assessed using a reference method ("gold standard"). Specificity (Sp) is the proportion of negative results (true negatives = TN) divided by the total number of healthy patients (TN + false positives = FP): Sp = TN / (TN + FP).

[0087] True negative (TN) subjects are subjects without the disease whose values ​​for the parameter of interest are below the cutoff value.

[0088] False positive (FP) subjects are subjects without the disease whose values ​​for the parameter of interest are above the cutoff value.

[0089] The prevalence of a given population is the number of cases of the disease in that population.

[0090] Positive predictive value (PPV) is the probability of having the disease when the test is positive. PPV = number of true positives / (number of true positives + number of false positives). PPV = TP / (TP + FP).

[0091] Negative predictive value (NPV) is the probability of not having the disease when the test is negative. NPV = number of true negatives / (number of true negatives + number of false negatives). NPV = TN / (TN + FN).

[0092] Prevalence affects PPV and NPV differently. As the prevalence of a disease in a population increases, PPV increases, while NPV decreases. Although PPV changes more dramatically, NPV is slightly less affected by disease prevalence. For low prevalence (5-10%), PPV values ​​are low. At the same time, NPV is high. For high prevalence (80-90%), PPV values ​​are high, while NPV is the opposite.

[0093] The likelihood ratio is defined as the ratio of expected test results in subjects with a certain condition / disease to subjects without the disease.

[0094] The likelihood ratio (LR+) of a positive test result represents the ratio of the probability that a positive test result will occur in a subject with the disease to a subject without the disease. LR+ = Sensitivity / (1-Specificity). LR+ is the best indicator for inclusion in a diagnosis. The higher the LR+, the more indicative of the disease the test is. LR+>5 indicates moderate to severe increased evidence of the presence of the disease. A good diagnostic test has an LR+>10, and its positive results contribute significantly to the diagnosis.

[0095] The likelihood ratio (LR-) of a negative test result represents the ratio of the probability that a negative result will occur in a subject with the disease to the probability that the same result will occur in a subject without the disease. LR- = (1-sensitivity) / specificity. LR- is a good indicator of a diagnosis of exclusion. An LR- between 0.1 and 0.2 indicates a moderate probability of a good diagnosis of a patient without the condition under investigation. A good diagnostic test for a patient without the condition under investigation has an LR- < 0.1.

[0096] For each individual cutoff value, there is a pair of diagnostic sensitivity and specificity values. To construct a receiver operating characteristic (ROC) plot, these pairs of values ​​are plotted on a graph with 1-specificity on the x-axis and sensitivity on the y-axis. The shape of the ROC curve and the area under the receiver operating characteristic curve (AUROC) show how discriminatory the test is. The closer the curve is to the upper left corner, the greater the area under the curve, and the better the test is at distinguishing 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 how good the test is. A perfect diagnostic test would have an AUROC of 1.0. A non-discriminatory test would have an AUROC of 0.5. The AUROC is an overall measure of diagnostic accuracy.

[0097] Method of the present invention

[0098] As mentioned above, advanced liver fibrosis and cirrhosis are associated with liver-related mortality, and therefore, it is extremely important to easily detect subjects with advanced fibrosis and subjects with cirrhosis. The present invention provides a solution to these unmet needs.

[0099] In the method of the present invention, the levels of two circulating markers in a blood, serum or plasma sample from a subject are measured. The two circulating markers are: soluble vascular cell adhesion molecule-1 (sVCAM) and thrombospondin 2 (TSP-2). sVCAM is also known as VCAM 1, INCAM-100, CD106, and is registered in the UniProt database with the number P19320. TSP-2 is also known as THBS2 and is registered in the UniProt database with the number P35442.

[0100] In a specific embodiment, the present invention relates to an in vitro method for diagnosing or prognosing advanced liver fibrosis or cirrhosis in a subject, the method comprising:

[0101] a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said subject;

[0102] b) comparing the levels of sVCAM and TSP-2 to reference levels of sVCAM and TSP-2, wherein the comparison between the measured levels and the reference levels indicates the presence or absence of advanced liver fibrosis or cirrhosis.

[0103] The measurement of the circulating levels of sVCAM and TSP-2 is performed in a biological fluid sample of a subject. In all methods and embodiments presented herein, the biological fluid sample can be a sample of blood or blood-derived fluid (e.g., serum and plasma), saliva, interstitial fluid, or urine. In a specific embodiment, the biological fluid sample of the subject is a blood, serum, or plasma sample. In a more specific embodiment, the sample is a serum sample. In another specific embodiment, the biological fluid sample is a cell-free sample.

[0104] The circulating levels of sVCAM and TSP-2 can be measured by any conventional method known in the art, such as immunoassay (e.g., ELISA (enzyme-linked immunosorbent assay), immunoturbidimetry, immunoturbidimetry, immunocytometry, protein array). For example, the levels of sVCAM and TSP-2 can be determined by antibodies, aptamers or peptides directed against the markers, respectively.

[0105] The "reference levels" of sVCAM and TSP-2 can be measured from a group of healthy subjects and / or a group of subjects diagnosed with early liver fibrosis and / or a group of subjects diagnosed with advanced liver fibrosis but without cirrhosis and / or a group of subjects diagnosed with cirrhosis. In a specific embodiment, the reference levels of sVCAM and TSP-2 are measured from a "training population".

[0106] A "training population" refers to a population consisting of a given number of subjects, wherein the fibrosis stage of each subject has been determined by state of the art methods (eg, liver biopsy).

[0107] In certain embodiments, the circulating levels of sVCAM and TSP-2 are compared to specific positive controls used to validate the measurements. The positive controls contain different concentrations of sVCAM and TSP-2, respectively, corresponding to the ranges of these markers measured in the training population. For example, a positive control for TSP-2 may contain 11 to 300 ng / mL of TSP-2, and a positive control for sVCAM may contain 390 to 6190 ng / mL of sVCAM.

[0108] In certain embodiments, the values ​​of the levels of circulating markers sVCAM and TSP-2 measured in a biological fluid sample of the subject can be introduced into a mathematical function (i.e., a statistical algorithm) to obtain a score A(SA) that can accurately predict advanced liver fibrosis or cirrhosis, respectively, according to a specific cutoff value.

[0109] Thus, by comparison with a specific cutoff value, the SA can be used to distinguish subjects with advanced liver fibrosis or cirrhosis from patients without advanced liver fibrosis or cirrhosis. A large number of suitable methods for developing mathematical functions are known to those skilled in the art, and all such methods are within the scope of the present invention. In a specific embodiment, the mathematical function comprises a logistic regression equation.

[0110] In a specific embodiment, in step (b) of the above method, the score SA is obtained from the levels of sVCAM and TSP-2 measured in step (a) and the cutoff value is compared with a cutoff value obtained from a reference level of sVCAM and TSP-2. The SA and the cutoff value are calculated using an algorithm equation.

[0111] According to one embodiment of the present invention, the SA value is compared with a cutoff value (co). The value can be calculated from a Youden statistical analysis of a training population. Specifically, the SA and the cutoff value are calculated using an algorithm equation to distinguish between patients with advanced liver fibrosis and patients without advanced liver fibrosis.

[0112] Alternatively, the SA and cutoff value are calculated using an algorithm equation to distinguish patients with cirrhosis from patients without cirrhosis.

[0113] A cut-off value co1 may be determined to indicate the presence or absence of advanced liver fibrosis. A cut-off value co2 may be determined to indicate the presence or absence of liver cirrhosis.

[0114] In a more specific embodiment, the present invention relates to an in vitro method for diagnosing advanced liver fibrosis in a human subject, the method comprising:

[0115] a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said human subject,

[0116] b) calculating the score SA on the basis of the measurements in step (a) by using the algorithm equation,

[0117] c) compare this SA with the cutoff value co1,

[0118] d) wherein SA greater than the cutoff value co1 indicates that the subject has advanced liver fibrosis.

[0119] In contrast, calculated SA values ​​below the cut-off value co1 indicate that the subject does not have advanced liver fibrosis.

[0120] In another more specific embodiment, the present invention relates to an in vitro method for diagnosing liver cirrhosis in a human subject, the method comprising:

[0121] a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said human subject,

[0122] b) calculating the score SA on the basis of the measurements in step (a) by using the algorithm equation,

[0123] c) compare this SA with the cutoff value co2,

[0124] d) wherein a SA greater than the cut-off value co2 indicates that the subject has cirrhosis.

[0125] In contrast, calculated SA values ​​below the cut-off value of CO2 indicate that the subject does not have cirrhosis.

[0126] To find the algorithm equation that separated patients with advanced fibrosis from those without it and patients with cirrhosis from those without cirrhosis, respectively, a correlation analysis was performed to cluster highly correlated covariates (Pearson coefficient ≥ 0.5). The most discriminatory variables in each group, as determined by calculating p-values ​​from Student's t-test, were retained. Thus, a set of variables was selected to avoid problems with high multicollinearity. This subset of variables was analyzed by running logistic regression analysis, retaining the variables that were significantly associated with advanced fibrosis and / or cirrhosis (sVCAM and TSP-2), and incorporating their level values ​​in the algorithm, resulting in an equation with coefficients (a, b, c) and a constant k.

[0127] The algorithm produces a score ranging from 0 to 1 (continuous).

[0128] For each equation, the log-likelihood function (LL) was calculated using Excel Solver, which is the conditional probability that the predicted dependent variable (here the coefficients a, b, c and the constant k) corresponds to the actual observed value (in this case, the disease; liver fibrosis status) given the input values ​​of the independent variables (in this case, the values ​​of the measured markers).

[0129] Conditional probability Pr(Yi=yi|X1i,X2i,…Xki)

[0130] Where Pr = probability,

[0131] yi=coefficient,

[0132] X1i, X2i, ..., Xki are measured variables,

[0133] Y is the dependent variable or score S. If the score = 0, it means that the patient is diagnosed as not having advanced liver fibrosis or cirrhosis, and if the score = 1, it means that the patient is diagnosed as having advanced liver fibrosis or cirrhosis.

[0134] i is any patient.

[0135] For convenience, the conditional probability is abbreviated as Pr(Y=y|X) and is calculated by the following formula:

[0136]

[0137] Taking the natural logarithm of both sides, we get the following formula:

[0138]

[0139] Therefore, the log-likelihood function LL is the sum of the terms ln [ Pr(Y=y|X)] for all data records according to the following formula:

[0140]

[0141] The goal of logistic regression is to determine the algorithm equation parameters (e.g., a, b, c, k) using Excel Solver that maximize LL (lowest value of LL), meaning that the score calculated by the algorithm corresponds to the highest probability of the disease state actually observed.

[0142] A subset of 2 variables (i.e., sVCAM and TSP-2) was analyzed by logistic regression (using Excel Solver) to create the following algorithm:

[0143] Rating = e y / (1 + e y )

[0144] where y = k + a × (Log 10 sVCAM [ng / L]) + b × (Log 10 TSP-2 [ng / mL]).

[0145] Specifically, the score is calculated by the following algorithm equation:

[0146] Rating = e y / (1 + e y ),in

[0147] y = k + a × A + b × B

[0148] A is the level of sVCAM, expressed in log10 ng / mL;

[0149] B is the level of TSP-2, in log10 ng / mL;

[0150] k is a constant of the algorithm equation;

[0151] a is the coefficient associated with the level of sVCAM;

[0152] b is the coefficient associated with the level of TSP-2;

[0153] And among them:

[0154] k is a number between -37.23 and -27.54, specifically -30.000;

[0155] a is a number between 5.432 and 8.646, specifically 7.250;

[0156] b is a number between 5.746 and 7.727, specifically 6.921.

[0157] In a specific embodiment, the score A(SA), the cutoff value co1 and the cutoff value co2 for diagnosing advanced liver fibrosis or cirrhosis are calculated on the training population according to the algorithm equation defined above.

[0158] For example, the following equation can be used to diagnose advanced liver fibrosis or cirrhosis:

[0159] y= -30.000 + 7.250 × log10 (sVCAM (ng / L)) + 6.921 x log10 (TSP-2(ng / mL))

[0160] y= -34 + 8.6 × log10 (sVCAM (ng / L)) + 6.7 x log10 (TSP-2 (ng / mL))

[0161] or

[0162] y= -30.001 + 8.64 × log10 (sVCAM (ng / L)) + 5.750 x log10 (TSP-2 (ng / mL))

[0163] or

[0164] y= -27.2 + 5.9 × log10 (sVCAM (ng / L)) + 7.7 x log10 (TSP-2 (ng / mL))

[0165] or

[0166] y=-31.2 + 5.91 × log10 (sVCAM (ng / L)) + 7.71 x log10 (TSP-2 (ng / mL))

[0167] or

[0168] y= -30.012 + 6.6 × log10 (sVCAM (ng / L)) + 7.69 x log10 (TSP-2 (ng / mL))

[0169] y= -30.000 + 7.250 × log10 (sVCAM (ng / L)) + 6.921 x log10 (TSP-2(ng / mL))

[0170] or

[0171] y= -30.001 + 8.64 × log10 (sVCAM (g / L)) + 5.750 x log10 (TSP-2 (ng / mL))

[0172] is preferred.

[0173] In a particular embodiment, SA is compared to a cut-off value co1 indicative of advanced liver fibrosis. Specifically, co1 is between 0.200 and 0.500, more particularly between 0.2040 and 0.4852, and in particular equal to 0.3383.

[0174] In another specific embodiment, SA is compared to a cut-off value of co2 indicative of liver cirrhosis. Specifically, co2 is between 0.510 and 0.790, more specifically between 0.5110 and 0.7791, and in particular equal to 0.6073.

[0175] According to another aspect, the invention relates to a computer program comprising instructions which, when executed by a processor / processing means, cause the processor / processing means to:

[0176] - Receive measured levels of sVCAM and TSP-2;

[0177] - calculate the SA from these measured levels according to the mathematical function described herein; and

[0178] - assigning the subject to the group of subjects with advanced liver fibrosis or cirrhosis based on a comparison of the calculated score with a predetermined cut-off value.

[0179] The invention further provides a computer readable medium comprising the computer program described herein. According to a particular embodiment, the computer readable medium is a non-transitory medium or a storage medium.

[0180] The present invention also provides an in vitro method for monitoring the progression of liver fibrosis in a subject by measuring the levels of two circulating markers, namely sVCAM and TSP-2.

[0181] Specifically, the method comprises the following steps:

[0182] a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said subject, and

[0183] b) The levels were compared with the levels of sVCAM and TSP-2 previously measured in the same subjects.

[0184] The circulating levels of sVCAM and TSP-2 measured in step (a) can be introduced into the algorithm equation as defined above to calculate the score. Since the score is calculated by using a non-invasive method and correlated with the evolution of liver fibrosis, it allows not only diagnosis but also monitoring of the progression of liver fibrosis by repeated measurements.

[0185] In a specific embodiment, the circulating levels of sVCAM and TSP-2 are measured from one or more blood-derived samples of the subject. In this case, the same type of sample is used each time a measurement must be made. For clarity, this means that if the previous measurement was made from a serum sample, the subsequent measurement is made from a serum sample of the same subject. Similarly, if the previous measurement was made from a blood or plasma sample, the subsequent measurement is made from a blood or plasma sample of the same subject, respectively. In a specific embodiment, the circulating levels of the markers are measured from one or more serum samples of the subject.

[0186] Collecting several samples from the same subject over time allows for the assessment of longitudinal changes in the score. For example, a first score, named SB, can be calculated from the levels of sVCAM and TSP-2 previously measured in a biofluid sample of a subject, and a second score, named SC, can be calculated from the levels of sVCAM and TSP-2 subsequently measured in a biofluid sample of the same subject. SB and SC can be obtained by the algorithm equation defined above. If the score increases over time in the same subject, i.e., SC is higher than SB, it means that liver fibrosis worsens, and if SC decreases over time in the same subject, i.e., SC is lower than SB, it means that liver fibrosis decreases. There is no significant change between SC and SB measured in the same subject over a certain period of time, meaning that liver fibrosis is stable.

[0187] Therefore, the change in the value between SC and SB during the follow-up period is an indicator of liver fibrosis progression or liver fibrosis regression.

[0188] In a specific embodiment, the method comprises the steps of:

[0189] a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said subject to obtain a score C, and

[0190] b) Compare said score C with score B obtained using the levels of sVCAM and TSP-2 previously measured in the same subject.

[0191] Since the score calculated according to the algorithm equation defined above is a linear value, the method of the present invention also allows determining the likelihood of progression to cirrhosis or worsening of cirrhosis in a subject with advanced liver fibrosis by comparing score C (SC) with score B (SB).

[0192] In a specific embodiment, SC is measured at the occurrence of an event associated with the evolution of a pathological state. Such events include liver transplantation, acute-on-chronic liver fibrosis (ACLF), compensated and decompensated cirrhosis, onset of ascites, and the presence of esophageal varices at endoscopy. Preferably, the event is acute-on-chronic liver fibrosis, compensated and decompensated cirrhosis.

[0193] In a specific embodiment, the SC is measured at least 3 months after the SB measurement, in particular in a time period between 3 months and 10 years after the SB measurement, preferably in a time period between 3 months and 2 years, more preferably between 1 year and 2 years.

[0194] In a more specific embodiment, if the subject is diagnosed with cirrhosis, for example by the method of the invention described above, SC is measured in a period between 3 months and 2 years after SB measurement, preferably 3 months. The appropriate timing of measuring SC may depend on comorbidities.

[0195] Comorbidities included malignancy, type 2 diabetes, overweight and obesity, heart disease, and kidney disease.

[0196] In another specific embodiment, if the subject is diagnosed with advanced liver fibrosis, for example by the method of the invention described above, the SC is measured in a period between 1 and 10 years after the SB measurement. The appropriate timing for measuring the SC depends on the comorbidity.

[0197] Due to the method of the invention, it may be decided to provide a subject with lifestyle advice (such as a diet regimen or providing physical activity advice), to take care of the subject medically (e.g. by regular visits to a doctor or regular checkups, such as regular monitoring of markers of liver damage), or to administer to the patient at least one liver fibrosis therapy to treat advanced liver fibrosis or cirrhosis. In particular, it may be decided to provide a subject with lifestyle advice or to administer at least one liver fibrosis therapy. Therefore, the present invention also relates to an anti-fibrotic compound for use in a method for treating advanced liver fibrosis or cirrhosis in a subject in need thereof, wherein the subject has been identified by the method according to the invention.

[0198] Therefore, the present invention also relates to an anti-fibrotic compound for use in a method of treating advanced liver fibrosis or cirrhosis in a subject in need thereof, wherein said subject has been identified by a method according to the present invention.

[0199] The term "treatment" as used herein refers to both therapeutic measures and prophylactic or preventive measures, wherein the goal is to prevent or slow down (mitigate) an undesirable physiological change or disorder. Beneficial or desired clinical results include, but are not limited to, relieving symptoms, stabilizing the pathological state (particularly without deterioration), slowing down or preventing disease progression, improving or alleviating pathological conditions. Specifically, for the purposes of the present invention, treatment is intended to slow the progression of fibrosis and reduce the risk of further complications. It can also involve prolonging survival compared to the expected survival without treatment.

[0200] The anti-fibrotic agent is administered in a therapeutically effective amount. The expression "therapeutically effective amount" as used herein refers to the amount of the drug that effectively achieves the desired therapeutic result. The therapeutically effective amount of the drug can vary according to factors such as the disease state, age, sex and weight of the individual and the ability of the drug to induce the desired response in the individual. The therapeutically effective amount also refers to the amount in which the therapeutic beneficial effect of the agent exceeds any toxic or harmful effect. The effective dose and dosage regimen of the drug depend on the disease or condition to be treated and can be determined by those skilled in the art. A doctor with ordinary skills in the art can easily determine and prescribe the effective amount of the desired pharmaceutical composition. For example, the doctor can start the dosage of the drug used in the pharmaceutical composition at a level lower than that required to achieve the desired therapeutic effect, and gradually increase the dosage until the desired effect is achieved. In general, the suitable dosage of the composition of the present invention will be the amount of the compound at the minimum dose that effectively produces a therapeutic effect according to a specific dosage regimen. This effective dose generally depends on the above factors.

[0201] The present invention also relates to an antifibrotic 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 cirrhosis according to the method of the present invention. The present invention also relates to an antifibrotic compound for use in a method for treating liver fibrosis, wherein the subject diagnosed or classified as having advanced liver fibrosis or cirrhosis as a result of the method of the present invention is treated with an antifibrotic compound as defined below.

[0202] Antifibrotic compounds include:

[0203] - A compound of formula (I) or a pharmaceutically acceptable salt thereof:

[0204] (I)

[0205] in:

[0206] X1 represents a halogen atom, an R1 group or a G1-R1 group;

[0207] A represents a CH=CH or CH2-CH2 group;

[0208] X2 represents a G2-R2 group;

[0209] G1 represents an oxygen atom;

[0210] G2 represents an oxygen or sulfur atom;

[0211] R1 represents a hydrogen atom, an unsubstituted alkyl group, an aryl group or an alkyl group substituted by one or more substituents selected from the group consisting of a halogen atom, an alkoxy group, an alkylthio group, a cycloalkyl group, a cycloalkylthio group and a heterocyclic group;

[0212] R2 represents an alkyl group substituted by a -COOR3 group, wherein R3 represents a hydrogen atom or an alkyl group substituted or unsubstituted by one or more substituents selected from a halogen atom, a cycloalkyl group and a heterocyclic group;

[0213] R4 and R5 are the same or different and represent an alkyl group which is substituted or unsubstituted by one or more substituents selected from halogen atoms, cycloalkyl groups and heterocyclic groups;

[0214] - AMP-activated protein kinase stimulators, such as PXL-770, MB-11055, Debio-0930B, metformin, CNX-012, O-304, mangiferin calcium salt, eltrombopag, catuximab, and imeglimin;

[0215] - Bile acids, such as obeticholic acid (OCA), ursodeoxycholic acid (UDCA), norursodeoxycholic acid, and ursodiol;

[0216] - CCR antagonists, such as cenicriviroc (CCR2 / 5 antagonist), PG-092, RAP-310, INCB-10820, RAP-103, PF-04634817, and CCX-872;

[0217] - dipeptidyl peptidase IV (DPP4) inhibitors, such as imipenem, vildagliptin, fumonisin, alogliptin, saxagliptin, tilogliptin, anagliptin, sitagliptin, repagliptin, merolidin, gonagliptin, trelagliptin, tenegliptin, dulagliptin, linagliptin, gemigliptin, yogliptin, betagliptin, imigliptin, omagliptin, vildagliptin, and denagliptin;

[0218] - 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-dichlorophenyl)-isothiazolyl]-pyridoxine)

[00136] -1-oxazol-4-ylmethoxy]-piperidin-1-yl}-1-methyl-1H-indole-3-carboxylic acid), TERN-201, TERN-501 and TERN-301;

[0219] - Fibroblast growth factor 19 (FGF-19) receptor ligands or functional engineered variants of FGF-19;

[0220] - Fibroblast growth factor 21 (FGF-21) agonists, such as PEG-FGF21 (pebevbramine, formerly BMS-986036), YH-25348, BMS-986171, YH-25723, LY-3025876, and NNC-0194-0499;

[0221] - Engineered fibroblast growth factor 19 (FGF-19) analogs, such as NGM-282 (aldafermin);

[0222] - Glucagon-like peptide-1 (GLP-1) analogs, such as semaglutide, liraglutide, exenatide, albiglutide, dulaglutide, lixisenatide, loxenatide, efpeglenatide, tasiroglutide, MKC-253, DLP-205, and ORMD-0901;

[0223] - Niacin, such as niacin and vitamin B3;

[0224] - Nitazoxanide (NTZ), its active metabolite tizoxanide (TZ), or other prodrugs of TZ such as RM-5061;

[0225] - PPARα agonists such as fenofibrate, ciprofibrate, pemafibrate, gemfibrozil, clofibrate, binifibrate, clinofibrate, clofibric acid, nicofibrate, pirfibrate, prafibrate, cloniafibrate, clofibrate, theophylline, tocofibrate, and SR10171;

[0226] - PPARγ 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;

[0227] - PPARδ agonists, such as GW501516 (Endurabol or ({4-[({4-methyl-2-[4-(trifluoromethyl)phenyl]-1,3-thiazol-5-yl}methyl)sulfanyl]-2-methylphenoxy}acetic acid)), MBX8025 (Sladepam or {2-methyl-4-[5-methyl-2-(4-trifluoromethyl-phenyl)-2H-[1,2,3]triazol-4-ylmethylsulfanyl]-phenoxy]-acetic acid), GW0742 ([4-[[[2-[3-fluoro-4-(trifluoromethyl)phenyl]-4-methyl-5-thiazolyl]methyl]sulfonyl]-2-methylphenoxy]acetic acid), L165041, HPP-593 and NCP-1046;

[0228] - PPARα / γ dual agonists (also called glitazars), such as saroglitazar, aleglitazar, moraglitazar, tesaglitazar, and DSP-8658;

[0229] - PPARγ / δ dual agonists, such as conjugated linoleic acid (CLA) and T3D-959;

[0230] - PPARα / γ / δ pan-agonists or PPARpan agonists, such as IVA337, TTA (tetradecylthioacetic acid), psoralen, GW4148, GW9135, bezafibrate, lanilanol, lobeglitazone, and CS038;

[0231] - Sodium-glucose transporter (SGLT) 2 inhibitors, such as ligliflozin, repagliflozin, dapagliflozin, empagliflozin, erpagliflozin, sogliflozin, iplagliflozin, tiagliflozin, canagliflozin, togliflozin, ganagliflozin, bexagliflozin, rupagliflozin, sergliflozin, HEC-44616, AST-1935, and PLD-101.

[0232] - Stearoyl-CoA desaturase-1 inhibitors / fatty acid bile acid conjugates, such as aramchol, GRC-9332, steamchol, TSN-2998, GSK-1940029, and XEN-801;

[0233] - Thyroxine receptor β (THRβ) agonists, such as VK-2809, resmetirome (MGL-3196), MGL-3745, SKL-14763, sobetirome, BCT-304, ZYT-1, MB-07811, and ilotirome;

[0234] - Vitamin E and its isomers; vitamin E in combination with vitamin C and atorvastatin.

[0235] In the present invention, the anti-fibrotic compound is preferably selected from pembefumin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanilanol, liraglutide, obeticholic acid, pioglitazone, resmetirox, saroglitazar magnesium, sladepa, semaglutide, sitagliptin, TERN-101, TERN-201, tropifexor, ambrisentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642 and orlistat (Xenical).

[0236] More preferably, the anti-fibrotic agent is selected from the group consisting of pembefumin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, ranilano, liraglutide, obeticholic acid, pioglitazone, resmetirox, saroglitazone magnesium, seladepa, semaglutide, sitagliptin, TERN-101, TERN-201 and tropifexor.

[0237] The present invention also relates to a method for evaluating the efficacy of an anti-fibrotic agent in a subject with advanced liver fibrosis or cirrhosis, the method comprising:

[0238] a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from a subject with advanced liver fibrosis, wherein said subject has been administered an anti-fibrotic agent prior to said measuring, and

[0239] b) comparing said levels of sVCAM and TSP-2 with the levels of sVCAM and TSP-2 previously measured before administration of said anti-fibrotic agent to the same subject, to assess the efficacy of said anti-fibrotic agent.

[0240] In a particular embodiment, the levels of sVCAM and TSP-2 are measured before and after administration of the antifibrotic agent, respectively, allowing to obtain a first score D (before administration) designated SD and a second score E (after administration) designated SE.

[0241] The SD and SE can be obtained by algorithmic equations, in particular the functions defined above.

[0242] In a more specific embodiment, the method for evaluating the efficacy of an anti-fibrotic agent comprises:

[0243] a) calculating the SD obtained from the measured values ​​of the levels of sVCAM and TSP-2 in a biological fluid sample isolated from a subject with advanced liver fibrosis prior to administration of an anti-fibrotic agent;

[0244] b) calculating the SE obtained from the measurements of the levels of sVCAM and TSP-2 in biological fluid samples isolated from the same subject after treatment with an anti-fibrotic compound;

[0245] c) compare SE and SD;

[0246] If SE is higher than SD, liver fibrosis has worsened and the subject has not responded to treatment with the anti-fibrotic agent or the treatment is ineffective.

[0247] If SE is less than SD, liver fibrosis has regressed, the subject has responded to treatment with the anti-fibrotic agent, and the treatment is effective.

[0248] If SD equals SE, liver fibrosis is stable.

[0249] The invention also relates to a data processing device comprising means for carrying out one of the methods of the invention as described above.

[0250] The present invention also provides a computer program product or a computer-readable storage medium, which contains instructions, which, when executed by a computer, cause the computer to perform one of the methods of the present invention as described above.

[0251] The present invention also relates to a kit for diagnosing advanced liver fibrosis or cirrhosis in a subject. The kit comprises means for determining the levels of sVCAM and TSP-2. In a specific embodiment, the kit comprises specific antibodies, aptamers or peptides for measuring sVCAM and TSP-2.

[0252] More specifically, the kit comprises an antibody, aptamer or peptide against sVCAM and an antibody, aptamer or peptide against TSP-2.

[0253] The antibody against sVCAM may be any monoclonal, polyclonal and / or conjugated antibody against sVCAM known in the art.

[0254] The antibody against TSP-2 may be any monoclonal, polyclonal and / or conjugated antibody against TSP-2 known in the art.

[0255] The kit of the present invention may further comprise immunoassay standards and reagents. In a particular embodiment, the kit comprises at least one specific positive control for TSP-2 and / or at least one specific positive control for sVCAM.

[0256] Specifically, the positive control for TSP-2 contains 11 to 300 ng / mL of TSP-2, and the positive control for sVCAM contains 390 to 6190 ng / mL of sVCAM.

[0257] More specifically, the kit of the present invention further comprises three TSP-2 positive controls and three sVCAM positive controls, wherein the TSP-2 positive controls comprise 30, 50 and 100 ng / mL of TSP-2, respectively, and the sVCAM positive controls comprise 600, 800 and 1100 ng / mL of sVCAM.

[0258] The invention is further described with reference to the following non-limiting examples.

[0259] Example

[0260] Example 1: Selection of biomarkers

[0261] Biomarker selection was performed by logistic regression against a list of biomarkers involved in the mechanism of action of liver fibrosis to select the most significant markers associated with advanced liver fibrosis. Fourteen biomarkers were included, including soluble vascular cell adhesion molecule-1 (sVCAM), thrombospondin 2 (TSP-2), miR34a, glycosylated hemoglobin (HbA1C), age, platelets, aspartate aminotransferase (AST), alanine aminotransferase (ALT), tissue inhibitor of matrix metalloproteinase-1 (TIMP1), N-terminal propeptide of type III collagen (P3nP), and hyaluronic acid.

[0262] In the phase 3 RESOLVE-IT clinical trial ® The individual markers were measured in the serum of 1063 patients in the RESOLVE-IT (NCT) study. ® Elafibranor is a multicenter, randomized, double-blind, placebo-controlled Phase III study (NCT02704403) designed to evaluate the efficacy and safety of Elafibranor in patients with nonalcoholic steatohepatitis (NASH) and fibrosis. Liver biopsy was performed to confirm the diagnosis and stage of liver fibrosis. Clinical data and blood samples were also collected from all patients. Liver fibrosis stage was provided by liver biopsy analysis according to the Kleiner staging system. The prevalence of liver fibrosis stage is reported in Table 1.

[0263]

[0264] Table 1: Distribution of liver fibrosis stages in 1063 patients

[0265] All markers were tested for normality and those showing non-Gaussian curves were log-transformed. Of the 14 markers analyzed, 13 were log-transformed and only TIMP-1 was used in linear data format. After Log10 transformation of the data (except TIMP-1), regression models were designed using ToolPak analysis from Excel software with advanced liver fibrosis and cirrhosis as response variables. Eight biomarkers were significantly associated with advanced liver fibrosis and cirrhosis (p < 0.01) and these markers were analyzed for collinearity. Hyaluronic acid and AST were excluded due to collinearity with TSP-2. A second regression model was fitted using liver fibrosis as the response variable and the six selected markers as explanatory variables for advanced fibrosis and cirrhosis. Three markers were selected that were significantly associated with the fibrosis status of our patients (p value < 0.01) (ALT was excluded because of its p value of 0.153). A second correlation analysis was performed to cluster the covariates associated with fibrosis status from the 2 selected markers (TSP-2 and sVCAM) and confirmed the absence of collinearity between these circulating biomarkers (VIF < 0.2) by calculating the variance inflation factor, a measure of the amount of multicollinearity in a set of multivariate regressors. A subset of these 2 markers as variables was analyzed by logistic regression (using Excel Solver) to create an algorithm from several combinations. These two markers (i.e., sVCAM and TSP-2) were identified as the main variables.

[0266] Then, data from the phase 3 RESOLVE-IT clinical trial were analyzed ® The same procedure was evaluated in 1063 patients from the (NCT) study to develop a logistic regression that distinguished patients with cirrhosis from those without cirrhosis. The individual markers sVCAM and TSP-2 were also identified as the main variables for the diagnosis of cirrhosis.

[0267] Therefore, the combination of two circulating biomarkers, soluble vascular cell adhesion molecule-1 (sVCAM) and thrombospondin 2 (TSP-2), provides an improved diagnosis of advanced liver fibrosis or cirrhosis compared with existing solutions, representing a valuable alternative to liver biopsy.

[0268] Example 2: Diagnosis of advanced liver fibrosis in human subjects using a combination of sVCAM and TSP-2

[0269] Biomarkers were measured in the patients' serum using Quantikine according to the manufacturer's recommendations. ®TSP-2 was quantified by ELISA human thrombospondin-2 immunoassay (Cat. No. DTSP-20, BioTechne, France) and positive controls were included to assess clinically relevant values ​​for fibrosis. sVCAM doses were determined by Quantikine® ELISA human VCAM-1 / CD106 immunoassays (Cat. Nos. DVC00, SVC00, and PDVC00, BioTechne) according to the manufacturer's recommendations and positive controls were included to assess clinically relevant values ​​for fibrosis.

[0270] Three positive controls were developed to quantify TSP-2.

[0271] - Control 1 (C1 = 30 ng / mL TSP-2),

[0272] - Control 2 (C2 = 50 ng / mL TSP-2)

[0273] - Control 3 (C3 = 100 ng / mL TSP-2).

[0274] C1 is made from serum from healthy volunteer blood (Etablissement Français du Sang, France). C1 naturally contains TSP-2. C2 requires dilution of C3 to obtain 50 ng / ml TSP-2 diluted in healthy volunteer serum. C3 is prepared from lyophilized recombinant human TSP-2 diluted in healthy volunteer serum.

[0275] Three positive controls were also developed to quantify sVCAM, namely control 1 (C'1 = 600 ng / mL sVCAM), control 2 (C'2 = 800 ng / mL sVCAM), and control 3 (C'3 = 1100 ng / mL sVCAM).

[0276] C'1 is made from the serum of healthy volunteers (Etablissement Français du Sang, France). C'1 naturally contains sVCAM. C'2 requires dilution of C'3 in the serum of healthy volunteers to obtain 800 ng / ml sVCAM. C'3 is prepared from lyophilized recombinant human sVCAM diluted in the serum of healthy volunteers.

[0277] All controls were divided into aliquots and stored at -80°C until use. Positive controls were processed similarly to sample samples and assayed in duplicate.

[0278] The first score SA score is defined as having k = - 30.00, a = 7.250 and b = 6.921.

[0279] In order to distinguish patients with advanced liver fibrosis from those without advanced liver fibrosis, the cutoff value co1 for the diagnosis of advanced liver fibrosis was set as 0.3471.

[0280] ELF and FIB-4 were calculated according to the literature. The SA score was then compared with the ELF and FIB4 tests and individual biomarkers by the mean of their area under the receiver operating characteristic curve (AUROC) for the diagnosis of advanced liver fibrosis (Table 2).

[0281]

[0282] Table 2: Diagnostic performance of the SA score (sVCAM and TSP-2) and individual biomarkers and scores such as FIB4 and ELF tests for the diagnosis of advanced liver fibrosis. AUROC: area under the receiver operating characteristic curve, SE: standard error, CI: confidence interval, Sens. (sensitivity), Spec. (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), AF (advanced liver fibrosis), Acc. (accuracy), PPV (positive predictive value), NPV (negative predictive value). 674 patients had fibrosis <3 and 389 patients had fibrosis ≥3.

[0283] Compared to individual biomarkers and scores such as the ELF test and FIB4, the diagnosis using the SA score and co1 yielded the highest performance for diagnosing advanced liver fibrosis, as shown by the AUC. The area under the receiver operating characteristic curve (AUROC) using the SA score (sVCAM and TSP-2) reached 0.897, while the AUC for the ELF test was equal to 0.8333 and the AUC for FIB4 was equal to 0.7911.

[0284] The diagnostic indices (overall accuracy, sensitivity, specificity, positive predictive value / PPV, negative predictive value / NPV, positive likelihood ratio / LR+, and negative likelihood ratio / LR-) with 95% CIs calculated using an asymptotic formula based on a normal approximation (i.e., binomial distribution) are also provided in Table 2. The diagnostic indices allowed comparison of the three assays: the method of the present invention using the S-score (sVCAM and TSP-2), the ELF test, and the FIB4.

[0285] A detailed comparison highlighted that the SA score was a more sensitive diagnostic assay for advanced liver fibrosis compared to the ELF test and FIB4 (sensitivity of the method = 81.75% compared to 75.84% for ELF and 72.24% for FIB4). The superiority of the SA score (sVCAM and TSP-2) was also observed in terms of specificity (specificity of the method = 81.75% compared to 75.82% for ELF and 72.43% for FIB4), the PLR ​​of the SA score was also higher than that of the ELF test and FIB4 (4.48 compared to 3.14 for ELF and 2.62 for FIB4), and the NLR of the SA score was lower than that of the ELF test and FIB4 (0.22 compared to 0.32 for ELF and 0.43 for FIB4). 0.38), the PPV of SA was higher than that of the ELF test and FIB4 (72.11% compared with 64.41% of ELF and 60.3% of FIB4), and the NPV of SA was higher than that of the ELF test and FIB4 (88.59% compared with 84.46% of ELF and 81.82% of FIB4), so the overall performance of the method of the present invention was the highest in terms of the accuracy of SA scoring (81.75% compared with 75.82% of ELF and 72.36% of FIB4).

[0286]

[0287] Table 3: Statistical differences between AUROC fibrosis tests for diagnosing advanced liver fibrosis. Differences in AUROC were assessed using the DeLong test (Reference: DeLong, ER et al., Comparing the Areas under Two or More Correlated ReceiverOperating Characteristic Curves: A Nonparametric Approach, Biometrics 1988, 44: 837-845).

[0288] The statistical difference of the diagnostic performance of the SA score with the other two scores (ELF test and FIB4) was then evaluated. As shown in Table 3, the AUROC of the method of the present invention was significantly different from ELF (p<0.0001) and FIB4 (p<0.0001). Compared with the ELF test and FIB4, the SA score presented the highest AUROC for diagnosing advanced liver fibrosis.

[0289] To distinguish patients with advanced liver fibrosis from those without advanced liver fibrosis, the cutoff value for advanced liver fibrosis diagnosis was set at 0.204 (CO1-1) at 90% sensitivity (Data Table 4). Alternatively, the cutoff value was set at 0.4852 (CO1-2) at 90% specificity (Data Table 5).

[0290]

[0291] Table 4: Diagnostic performance of the SA score (sVCAM and TSP-2) and individual biomarkers and scores such as FIB4 and ELF tests for the diagnosis of advanced liver fibrosis at 90% sensitivity. AUROC: area under the receiver operating characteristic curve, SE: standard error, CI: confidence interval, Sens. (sensitivity), Spec. (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), AF (advanced liver fibrosis), Acc. (accuracy), PPV (positive predictive value), NPV (negative predictive value). 674 patients had fibrosis <3 and 389 patients had fibrosis ≥3.

[0292]

[0293] Table 5: Diagnostic performance of the S score (sVCAM and TSP-2) and individual biomarkers and scores such as FIB4 and ELF tests for the diagnosis of advanced liver fibrosis at 90% specificity. AUROC: area under the receiver operating characteristic curve, SE: standard error, CI: confidence interval, Sens. (sensitivity), Spec. (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), AF (advanced liver fibrosis), Acc. (accuracy), PPV (positive predictive value), NPV (negative predictive value). 674 patients had fibrosis <3 and 389 patients had fibrosis ≥3.

[0294] In terms of diagnosing advanced liver fibrosis, the data indicators of SA score using CO1, CO1-1 and CO1-2 are better than those of ELF test and FIB4.

[0295] The second SA score is defined with k = -34, a = 8.6, and b = 6.7

[0296] The third SA score is defined as having k = -32.227, a = 8.640, and b = 5.750

[0297] The fourth SA score is defined as having k = -27.200, a = 5.900, and b = 7.700.

[0298] The AUROCs generated by these models were not significantly different from the first SA score for diagnosing advanced liver fibrosis (Table 6).

[0299]

[0300] Table 6: AUROC values ​​for diagnosis of advanced liver fibrosis according to different algorithms and coefficient changes within the selected range

[0301] Example 3: Diagnosis of liver cirrhosis in human subjects using a combination of sVCAM and TSP-2

[0302] In order to distinguish patients with cirrhosis from those without cirrhosis (F < 4), the cutoff value of co2 for diagnosis of cirrhosis was set as 0.6073.

[0303] ELF and FIB-4 were calculated according to the literature. The SA score was then compared with the ELF and FIB4 tests and individual biomarkers by the mean of their area under the receiver operating characteristic curve (AUROC) for the diagnosis of cirrhosis (Table 6).

[0304]

[0305] Table 7: Diagnostic performance of the SA score (sVCAM and TSP-2) and individual biomarkers and scores such as FIB4 and ELF tests for the diagnosis of cirrhosis. AUROC: area under the receiver operating characteristic curve, SE: standard error, CI: confidence interval, Sens. (sensitivity), Spec. (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), C prevalence (prevalence of cirrhosis), Acc. (accuracy), PPV (positive predictive value), NPV (negative predictive value). 900 patients had fibrosis <4 and 163 patients had fibrosis ≥4.

[0306] Relative to individual biomarkers and scores such as the ELF test and FIB4, the diagnosis using the SA score and co2 yielded the highest performance for diagnosing cirrhosis, as shown by AUROC. The area under the receiver operating characteristic curve (AUROC) using the SA score (sVCAM and TSP-2) reached 0.9111, while the AUROC for the ELF test was equal to 0.8672 and the AUROC for FIB4 was equal to 0.8103 (Table 7).

[0307] The diagnostic indices (overall accuracy, sensitivity, specificity, positive predictive value / PPV, negative predictive value / NPV, positive likelihood ratio / LR+ and negative likelihood ratio / LR-) with 95% CI calculated using an asymptotic formula based on the normal approximation to the binomial distribution are also provided in Table 7. The diagnostic indices allow comparison of the three assays: the method of the present invention using the SA score (sVCAM and TSP-2), the ELF test and the FIB4.

[0308] A detailed comparison highlighted that the SA score is a more sensitive diagnostic assay for cirrhosis compared to the ELF test and FIB4 (sensitivity of the method = 82.82% compared to 77.91% for ELF and 73.62% for FIB4). The superiority of the SA score (sVCAM and TSP-2) was also observed in terms of specificity (specificity of the method was 82.89% compared to 77.89% for ELF and 73.69% for FIB4). The PLR ​​of the SA score was also higher than that of the ELF test and FIB4 (4.84 compared to 3.92 for the ELF test and 2.8 for FIB4), and the NLR of the SA score was lower than that of the ELF test and FIB4 (0.21 compared to 0.28 for ELF and 0.36 for FIB4). The PPV of SA is higher than that of ELF test and FIB4 (46.71% compared to 38.96% of ELF and 33.71% of FIB4), and the NPV of SA is higher than that of ELF test and FIB4 (96.38% compared to 95.12% of ELF and 93.89% of FIB4). Therefore, in terms of the accuracy of SA scoring, the overall performance of the method of the present invention is higher (82.88% compared to 77.89% of ELF and 73.68% of FIB4).

[0309]

[0310] Table 8: Statistical differences between AUROC fibrosis tests for diagnosing cirrhosis. Differences in AUROC were assessed using the DeLong test.

[0311] The statistical difference of the diagnostic performance of the SA score relative to the other two scores (ELF test and FIB4) was then evaluated. As shown in Table 8, the AUROC of the method of the present invention was significantly different from ELF (p<0.01) and FIB4 (p<0.0001). Compared with the ELF test and FIB4, the SA score presented the highest AUROC for diagnosing liver cirrhosis.

[0312] To distinguish patients with cirrhosis from those without cirrhosis, the cutoff value for diagnosis of cirrhosis was set at 0.5110 (CO2-1) at 90% sensitivity (Data Table 9). Alternatively, the cutoff value was set at 0.7791 (CO2-2) at 90% specificity (Data Table 10). In both cases, the data using the SA score were better than the ELF test and FIB4.

[0313]

[0314] Table 9: Diagnostic performance of the combined and individual biomarkers of sVCAM and TSP-2 and scores such as FIB4 and ELF tests for the diagnosis of liver cirrhosis at 90% sensitivity. AUROC: area under the receiver operating characteristic curve, SE: standard error, CI: confidence interval, Sens. (sensitivity), Spec. (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), C prevalence (prevalence of liver cirrhosis), Acc. (accuracy), PPV (positive predictive value), NPV (negative predictive value). 900 patients had fibrosis <4 and 163 patients had fibrosis =4.

[0315]

[0316] Table 10: Diagnostic performance of the combined and individual biomarkers of sVCAM and TSP-2 and scores such as FIB4 and ELF tests for the diagnosis of liver cirrhosis at 90% specificity. AUROC: Area under the receiver operating characteristic curve, SE: Standard error, CI: Confidence interval, Sens. (sensitivity), Spec. (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), C prevalence (prevalence of liver cirrhosis), Acc. (accuracy), PPV (positive predictive value), NPV (negative predictive value). 900 patients had fibrosis <4 and 163 patients had fibrosis =4.

[0317] In addition, the data indicators of SA score using co2-1 (Table 9) and co2-2 (Table 10) were superior to ELF test and FIB4 in the diagnosis of cirrhosis.

[0318] The AUROCs produced by the other models were not significantly different from the first score for diagnosing cirrhosis (Table 11).

[0319]

[0320] Table 11: AUROC values ​​according to different algorithms and variation of coefficients within the selected range for the diagnosis of liver cirrhosis.

Claims

1. An in vitro method for diagnosing or prognosing advanced liver fibrosis or cirrhosis in a subject, the method comprising: a) measuring circulating levels of soluble vascular cell adhesion molecule-1 (sVCAM) and thrombospondin 2 (TSP-2) in a biological fluid sample isolated from the subject; b) comparing the levels of sVCAM and TSP-2 to reference levels of sVCAM and TSP-2, wherein the comparison between the measured levels and the reference levels indicates the presence or absence of advanced liver fibrosis or cirrhosis. 2 . The method of claim 1 , wherein the biological fluid sample is a saliva sample, an interstitial fluid sample, a urine sample, a blood sample, a plasma sample or a serum sample.

3. The method according to claim 1 or 2, wherein in step (b) the score A (SA) is compared with a cutoff value, the SA being obtained from the levels of sVCAM and TSP-2 measured in step (a), the cutoff value being obtained from reference levels of sVCAM and TSP-2, and the SA and the cutoff value being calculated using an algorithm equation.

4. The method according to any one of claims 1 to 3, wherein the SA is calculated by the following algorithm equation: SA = e y / (1 + e y ), among them y = k + a × A + b × B A is the level of sVCAM, expressed in log10 ng / mL; B is the level of TSP-2, in log10 ng / mL; k is a constant of the algorithm equation; a is the coefficient associated with the level of sVCAM; b is the coefficient associated with the level of TSP-2; And among them: k is a number between -37.23 and -27.54, specifically -30.000; a is a number between 5.432 and 8.646, specifically 7.250; b is a number between 5.746 and 7.727, specifically 6.

921.

5. The method according to claim 3 or 4, wherein SA above a cut-off value co1 indicates advanced liver fibrosis, particularly co1 between 0.200 and 0.500, more particularly co1 equal to 0.3383.

6. The method according to claim 3 or 4, wherein a SA above a cut-off value of co2 indicates liver cirrhosis, particularly co2 between 0.510 and 0.790, more particularly co2 equal to 0.6073.

7. An in vitro method for monitoring the progression of liver fibrosis in a subject, the method comprising the steps of: a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said subject, and b) The levels were compared with the levels of sVCAM and TSP-2 previously measured in the same subjects.

8. The method of claim 7, wherein in step (b) score C (SC) is compared with score B (SB), SC being a score obtained from the measured levels of sVCAM and TSP-2 of step (a), SB being a score obtained from the levels of sVCAM and TSP-2 previously measured in the same subject, SB and SC being calculated using an algorithm equation; and wherein - Increased SC compared to SB indicates the progression of liver fibrosis; - A decrease in SC compared to SB indicates regression of liver fibrosis; - No difference between SC and SB indicated stable liver fibrosis.

9. The method according to claim 8, wherein the SC is measured at least 3 months after the SB measurement, in particular in a time period between 3 months and 10 years, preferably in a time period between 3 months and 2 years.

10. The method according to any one of claims 7 to 9, wherein SC or SB is calculated by the following algorithm equation: SC or SB = e y / (1 + e y ),in y = k + a × A + b × B A is the level of sVCAM, expressed in log10 ng / mL; B is the level of TSP-2, in log10 ng / mL; k is a constant of the algorithm equation; a is the coefficient associated with the level of sVCAM; b is the coefficient associated with the level of TSP-2; And among them: k is a number between -37.23 and -27.54, specifically -30.000; a is a number between 5.432 and 8.646, specifically 7.250; b is a number between 5.746 and 7.727, specifically 6.

921.

11. An anti-fibrotic agent for treating advanced liver fibrosis or cirrhosis in a subject, wherein the subject is diagnosed with advanced liver fibrosis or cirrhosis according to the method of any one of claims 1 to 6, wherein the agent is selected from pembefumin, Cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanilanol, liraglutide, obeticholic acid, pioglitazone, resmetirox, saroglitazar magnesium, seladepa, semaglutide, sitagliptin, TERN-101, TERN-201, Tropifexor, ambrisentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642 and orlistat.

12. A method for evaluating the efficacy of an anti-fibrotic agent in treating advanced liver fibrosis or cirrhosis, the method comprising: a) measuring circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from a subject with advanced liver fibrosis, wherein said subject has been administered an anti-fibrotic agent prior to said measuring, and b) comparing said levels of sVCAM and TSP-2 with the levels of sVCAM and TSP-2 previously measured before administration of said anti-fibrotic agent to the same subject, to assess the efficacy of said anti-fibrotic agent.

13. The method according to claim 12, wherein in step (b) the score E(SE) is compared with the score D(SD), - SD is a score obtained from the levels of sVCAM and TSP-2 measured before administration of an anti-fibrotic agent to the subject, and - SE is a score obtained from the levels of sVCAM and TSP-2 measured after administration of an anti-fibrotic agent to said subject, - SD and SE are calculated using an algorithmic equation; and Wherein a decrease in SE compared to SD indicates efficacy of the anti-fibrotic agent.

14. The method according to claim 13, wherein SE and SD are calculated by the following algorithm equation: SE or SD = e y / (1 + e y ),in y = k + a × A + b × B A is the level of sVCAM, expressed in log10 ng / mL; B is the level of TSP-2, in log10 ng / mL; k is a constant of the algorithm equation; a is the coefficient associated with the level of sVCAM; b is the coefficient associated with the level of TSP-2; And among them: k is a number between -37.23 and -27.54, specifically -30.000; a is a number between 5.432 and 8.646, specifically 7.250; b is a number between 5.746 and 7.727, specifically 6.

921.

15. A kit for diagnosing advanced liver fibrosis or cirrhosis in a subject, the kit comprising means for determining the levels of sVCAM and TSP-2, wherein the kit comprises at least one specific positive control for TSP-2 and / or at least one specific positive control for sVCAM. 16 . The kit according to claim 15 , comprising an antibody or aptamer or peptide against sVCAM and an antibody or aptamer or peptide against TSP-2.

17. A computer-assisted program comprising instructions which, when executed by a processor / processing means, cause the processor / processing means to: - Receive measured levels of sVCAM and TSP-2; - The SA score is calculated from these measured levels according to the following mathematical function: SA = e y / (1 + e y ), among them y = k + a × A + b × B A is the level of sVCAM, expressed in log10 ng / mL; B is the level of TSP-2, in log10 ng / mL; k is a constant of the algorithm equation; a is the coefficient associated with the level of sVCAM; b is the coefficient associated with the level of TSP-2; And among them: k is a number between -37.23 and -27.54, specifically -30.000; a is a number between 5.432 and 8.646, specifically 7.250; b is a number between 5.746 and 7.727, specifically 6.921; and - assigning the subject to the group of subjects with advanced liver fibrosis or cirrhosis based on a comparison of the calculated score with a predetermined cut-off value.

18. A data processing device comprising means for carrying out the method according to any one of claims 1 to 14.

19. A computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 14.

20. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 14.

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