Method for diagnosing progressive hepatic fibrosis or cirrhosis

Abstract

This specification provides a method for diagnosing or prognosticating progressive hepatic fibrosis or cirrhosis in a patient. This method is achieved by determining a score based on the circulating levels in serum of two markers.

Description

Technical Field

[0001] The present invention relates to a method for diagnosing progressive liver fibrosis or cirrhosis for prognostic diagnosis or monitoring the progression of liver fibrosis in a subject, or for evaluating the effectiveness of antifibrinolytics. The present invention also relates to a kit for implementing the method of the present invention and a compound for use in the treatment of liver fibrosis, wherein the subject to be treated is identified according to the method of the present invention.

Background Art

[0002] Liver fibrosis, which is common to liver injury and liver diseases, can have a number of chronic or otherwise etiologies, including viral hepatitis B and C (HBV and HCV), co-infection with human immunodeficiency virus (HIV) and hepatitis C virus (HCV), 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 or chronic (Acute on Chronic) liver failure (ACLF), Wilson's disease, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), alcohol-related liver diseases (ARLD), alcoholic liver diseases, and hemochromatosis.

[0003] Liver problems can be caused by various factors that damage the liver, such as viruses, immune system abnormalities, hereditary abnormal genes, cancer, alcohol intake, and obesity. Over time, a state of liver damage can lead to scarring (cirrhosis), which can result in liver failure, a life-threatening condition requiring urgent medical treatment. Liver failure occurs when most of the liver is damaged beyond repair.

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

[0005] The prediction of liver fibrosis is a major step in the evaluation and management of patients with liver injury and / or liver disease. Therefore, since an early and accurate assessment of the severity and status of liver fibrosis is essential for diagnosis, monitoring, and prognosis, quantitative measurements are critical for evaluating disease progression.

[0006] Most forms of liver disease are asymptomatic until the disease progresses to a more advanced stage. Therefore, early detection of the disease is difficult. The risk of liver-related mortality increases exponentially with increasing fibrosis stage, and once cirrhosis occurs, mortality and morbidity increase exponentially. Cirrhosis is the point at which the liver has become completely scarred and has exceeded its ability to heal itself.

[0007] Cirrhosis is a major cause of death and morbidity worldwide. It is the 11th leading cause of death and the 15th leading cause of morbidity, accounting for 0.2% of deaths and 1.5% of disability-adjusted life years globally in 2016. Chronic liver disease caused 1.32 million deaths globally in 2017 (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 have no visible disease symptoms, cirrhosis is said to be compensated, and when cirrhosis progresses to the point where the liver functions poorly and disease symptoms occur, cirrhosis is decompensated. The clinical features of decompensated cirrhosis are well described (i.e., ascites, spider nevi, jaundice, signs of hepatic encephalopathy), but patients with compensated cirrhosis often have no clinical signs and can be completely asymptomatic.

[0008] Hepatologists and healthcare providers have proposed scoring systems for staging liver fibrosis, such as the BRUNT / KLEINER system, F0 refers to subjects without liver fibrosis, F1 refers to subjects with fibrosis around the portal vein or the periportal area, F2 refers to subjects with fibrosis around the portal vein / peri-portal area and the periportal area, F3 refers to subjects with septal or bridging liver fibrosis, F4 refers to subjects with cirrhosis.

[0009] Severe (fibrosis stage F>2) liver diseases may progress to hepatocellular carcinoma. Therefore, accurate staging of liver fibrosis in these liver diseases, especially early diagnosis of progressive liver fibrosis (fibrosis stage F3 or F4) and cirrhosis (fibrosis stage F4), is crucial.

[0010] Furthermore, the rate of fibrosis progression evolves over time, and diagnostic assays need to be performed several times. As a result, this test must be repeatable, safe for patients, reliable, and accurate. Therefore, non-invasive assays are needed for the diagnosis of progressive liver fibrosis (F3 or F4) and cirrhosis (F4). Also, since patients with cirrhosis (F = 4) require urgent treatment, it is also important to specifically identify these F4 patients among those with severe liver diseases.

[0011] To date, invasive liver biopsy remains the gold standard for the evaluation of liver fibrosis.

[0012] However, liver biopsy has several recognized limitations, including sampling error, inter-observer variability, and hospitalization. The main disadvantage is the significant risk of complications, including bleeding, pain, and even death. Furthermore, biopsy does not reflect changes throughout the liver, does not distinguish early fibrosis from advanced fibrosis, and thus does not constitute a reliable prognostic indicator (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).

[0013] 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 biopsy, which cannot be repeated without inconvenience, these methods can capture the dynamic process of fibrosis resulting from progression and regression because the measurements are repeatable. These methods are based on readily available biochemical data and clinical characteristics, 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, I.N. 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).

[0014] However, replacing liver biopsy with these methods still has room for discussion and is not generally accepted because their diagnostic performance is insufficient. The main problem with these two biochemical assays is that, in individual patients, they cannot reliably distinguish the stage of liver fibrosis progression. Furthermore, the ELF test is expensive, which is a drawback when repeating the test. Additionally, FIB4 has poor performance in patients under 35 years old and low specificity in patients over 65 years old.

[0015] Physical methods include high-frequency sound waves (ultrasound and echocardiography), computed tomography (CT), magnetic resonance imaging (MRI), transient elastography (TE, FibroScan), and scintigraphy-based imaging. The main drawbacks of physical measurements are high cost, low availability of equipment, and the complexity of the methods that limit daily clinical practice.

[0016] Furthermore, obesity, ascites, acute inflammation, hepatic congestion, and increased portal pressure can reduce the accuracy of ultrasonic TE (Fibroscan), for example, by affecting the shear wave velocity. Additionally, a falsely increased liver stiffness caused by a postprandial increase in portal pressure has been observed with this method.

Prior Art Documents

Non-Patent Documents

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Non-Patent Document 1

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Non-Patent Document 3

Non-Patent Document 4

Non-Patent Document 5

Summary of the Invention

Problems to be Solved by the Invention

[0018] Therefore, there still exist unmet medical needs to develop a new non-invasive diagnostic method that has optimal diagnostic accuracy compared to biopsy, is also useful in monitoring the progression of liver fibrosis over time and / or indicates whether there is a response to a given medicine. Furthermore, it is important to provide a new method that gives the best predictability at the lowest cost and with the greatest ease of implementation.

Means for Solving the Problem

[0019] The inventors conducted several very detailed and complete analyses of a cohort of 1063 patients suffering from NASH and fibrosis, and provided a novel and highly sensitive non-invasive diagnostic and monitoring method for progressive hepatic fibrosis and cirrhosis. The inventors identified a combination of two biological markers, namely soluble vascular cell adhesion molecule-1 (sVCAM) and thrombospondin 2 (TSP-2). This new combination of biomarkers provides an accurate diagnosis or prognosis of progressive hepatic fibrosis (F3 or F4) and cirrhosis (F4) compared to existing solutions, representing a valuable alternative to liver biopsy. Measurement of these markers in a biological fluid sample enables safe and regular follow-up of hepatic fibrosis. Furthermore, the diagnostic method of the present invention is more accurate and less expensive than currently used methods for the diagnosis of progressive hepatic fibrosis and cirrhosis such as ELF and FIB4.

[0020] Accordingly, a first aspect of the present invention is an in vitro method for diagnosing progressive hepatic fibrosis (F3 or F4) or cirrhosis (F4) in a subject, comprising: a) measuring the circulating levels of soluble vascular cell adhesion molecule (sVCAM) and thrombospondin 2 (TSP-2) in a biological fluid sample isolated from the subject; b) comparing the levels of sVCAM and TSP-2 with reference levels of sVCAM and TSP-2, wherein the comparison of the measured levels with the reference levels indicates the presence or absence of progressive hepatic fibrosis or cirrhosis; and a method comprising the steps.

[0021] In certain embodiments, the present invention is an in vitro method for prognosticating progressive hepatic fibrosis (F3 or F4) or cirrhosis (F4) in a subject, comprising: a) measuring the circulating levels of soluble vascular cell adhesion molecule (sVCAM) and thrombospondin 2 (TSP-2) in a biological fluid sample isolated from the subject; b) A step of comparing the levels of sVCAM and TSP-2 with their respective reference levels, wherein the comparison between the measured level and the reference level indicates the presence or absence of progressive hepatic fibrosis or cirrhosis relates to a method comprising the same.

[0022] In certain embodiments, step (b) of the method comprises comparing a score A (SA) with a cut-off value, wherein the SA is obtained from the levels of sVCAM and TSP-2 measured in step (a), and the cut-off value is obtained from the reference levels of sVCAM and TSP-2. Specifically, the SA and the cut-off value are calculated using an algorithm found using a logistic regression function. More specifically, the cut-off value is obtained from Youden statistical analysis for a training population.

[0023] In a more detailed embodiment, the SA is the following algorithmic equation: Score = e y / (1 + e y ) [wherein, y = k + a×A + b×B, A is the level of sVCAM expressed in log10 ng / mL, B is the level of TSP-2 expressed in log10 ng / mL, k is a constant of the algorithmic equation, a is a coefficient related to the level of sVCAM, b is a coefficient related to the level of TSP-2, and further wherein, k is a number included between -37.23 and -27.54, particularly -30.000, a is a number included between 5.432 and 8.646, particularly 7.250, b is a number included between 5.746 and 7.727, particularly 6.921) is calculated through.

[0024] In this particular aspect of the invention, the score corresponds to SA in the above algorithmic equation.

[0025] In certain embodiments, SA being higher than the cut-off value co1 indicates progressive hepatic fibrosis. Specifically, co1 is included between 0.200 and 0.500, and more specifically, co1 can be 0.204, 0.3383, 0.4852.

[0026] More specifically, co1 is equal to 0.3383.

[0027] In another specific embodiment, SA being higher than the cut-off value co2 indicates cirrhosis. Specifically, co2 is included between 0.510 and 0.790, and more specifically, co2 can be 0.5110, 0.6073, 0.7791.

[0028] More specifically, co2 is equal to 0.6073.

[0029] Another aspect of the present invention is an in vitro method for monitoring the progression of hepatic fibrosis in a subject, comprising: a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from the subject; b) comparing the levels with the levels of sVCAM and TSP-2 previously measured in the same subject. The method relates to a method comprising the steps of:

[0030] In certain embodiments, step (b) of the method comprises comparing a score C (SC) with a score B (SB), where 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. The scores are calculated by using an algorithm equation found using logistic regression. In certain embodiments, an increase in SC compared to SB indicates progression of hepatic fibrosis. In another specific embodiment, a decrease in SC compared to SB indicates regression of hepatic fibrosis. In a specific embodiment, no significant change between SC and SB measured over a period of time in the same subject means that hepatic fibrosis is stable.

[0031] In certain embodiments of the method, step (a) of the method for monitoring the progression of liver fibrosis is performed at least 3 months after the previous measurement of the levels of sVCAM and TSP-2, specifically, after a period of 3 months to 10 years, more specifically a period of 3 months to 2 years, from the previous measurement of the levels of sVCAM and TSP-2.

[0032] In a specific embodiment of the method, one phenomenon related to the evolution of the pathological state occurs between SB and SC during monitoring. In certain embodiments, the phenomenon is selected from liver transplantation, acute or acute on chronic liver fibrosis, compensated cirrhosis, decompensated cirrhosis, the appearance of ascites, and the presence of esophageal varices on endoscopy.

[0033] In a preferred embodiment, the SB and SC are calculated through the algorithmic equations defined above.

[0034] A third aspect of the present invention is a method for evaluating the effectiveness of an antifibrinolytic agent in the treatment of progressive liver fibrosis or cirrhosis, comprising: a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from a subject suffering from progressive liver fibrosis, wherein the subject has been administered an antifibrinolytic agent prior to the measurement; b) comparing the levels of sVCAM and TSP-2 with the levels of sVCAM and TSP-2 measured prior to the administration of the antifibrinolytic agent to the same subject to evaluate the effectiveness of the antifibrinolytic agent. The method is related to the method including the above steps.

[0035] In certain embodiments of the present method, it includes the step of comparing the score E (SE) of step (b) with the score D (SD), where SD is a score obtained from the levels of sVCAM and TSP-2 measured before the administration of the antifibrinolytic agent to the subject, and SE is a score obtained from the levels of sVCAM and TSP-2 measured in step (a), that is, after the administration of the antifibrinolytic agent to the subject. SE and SD are calculated through an algorithm. Specifically, a decrease in SE compared to SD indicates the effectiveness of the antifibrinolytic agent, and an increase in SE compared to SD indicates the ineffectiveness of the antifibrinolytic agent and / or the non-responsiveness of the patient.

[0036] In a preferred embodiment, the SE and SD are calculated through the algorithm equation defined above.

[0037] In a preferred embodiment, the biological fluid sample of the subject used in the method of the present invention is an interstitial fluid, saliva, urine, or whole blood sample. In certain embodiments, 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 cell-free. More preferably, the blood sample is a plasma or serum sample from the subject.

[0038] Another aspect of the present invention is also an antifibrinolytic agent for use in the treatment of progressive hepatic fibrosis or cirrhosis in a subject, wherein the subject is diagnosed with progressive hepatic fibrosis or cirrhosis according to the method of the present invention, and the 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.

[0039] The present invention also provides a kit for diagnosing progressive hepatic fibrosis or cirrhosis in a subject, the kit comprising means for determining the levels of sVCAM and TSP-2. In certain embodiments, the means are an antibody or aptamer or peptide against sVCAM, and an antibody or aptamer or peptide against TSP-2.

Mode for Carrying Out the Invention

[0040] Definitions Scoring / Staging of Fibrosis According to the present invention, the term "fibrosis" or "hepatic fibrosis" refers to a pathological condition of excessive deposition of fibrous connective tissue in the liver. More specifically, fibrosis is a pathological process that includes persistent fibrous scar formation as a response to tissue damage and overproduction of extracellular matrix by connective tissue. Physiologically, the deposition of connective tissue may obliterate the structure and function of the liver.

[0041] The various stages of hepatic fibrosis are defined by the Kleiner scoring system (Kleiner et al., Hepatology 2005, Vol. 41, No. 6, 1313-1321), where F0 refers to a subject without hepatic fibrosis, F1 refers to a subject with portal or perisinusoidal fibrosis, F2 refers to a subject with portal / portal-periportal and perisinusoidal fibrosis, F3 refers to a subject with septal or bridging hepatic fibrosis, F4 refers to a subject with cirrhosis.

[0042] Stages F0-2 are assigned to subjects with early hepatic fibrosis, F3 or F4 are assigned to subjects with progressive hepatic fibrosis, and stage F4 is assigned to subjects with cirrhosis.

[0043] Using this fibrosis staging system, patients without or with minimal fibrosis (F = 0 or 1) are generally not considered at risk for cirrhosis, liver failure, HCC (hepatocellular carcinoma), or liver-related death. Patients with severe (F>2) liver fibrosis are at risk for developing 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.

[0044] In the context of the present invention, the terms "progressive fibrosis" or "progressive liver fibrosis" refer to fibrosis stages where F≧3, i.e., fibrosis stage F3 or fibrosis stage F4.

[0045] In the context of the present invention, the terms "cirrhosis" or "liver cirrhosis" refer to the fibrosis stage of F4.

[0046] The terms "subject" and "patient" may be used interchangeably herein and refer to a human subject.

[0047] Within the context of the present invention, the terms "biomarker", "marker", "biological marker" are interchangeable.

[0048] Within the scope of the present invention, any range must be considered to include its upper and lower limits.

[0049] Concurrent non-invasive tests The Fibrosis-4 (FIB-4) index is calculated as age (in years) × AST (U / L) / platelet (×10 9 / L) / √ALT (U / L), where AST is aspartate aminotransferase and ALT is alanine aminotransferase.

[0050] The Enhanced Liver Fibrosis Panel (ELF, Siemens Healthcare GmbH, Erlangen, Germany) is a test that predicts fibrosis based on three fibrosis biomarkers, namely hyaluronic acid (HA), tissue inhibitor of metalloproteinase-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].

[0051] Analysis of Diagnostic Performance Sensitivity is the ability of a test to correctly identify those who have the disease. Sensitivity measures the proportion of positive ("diseased") cases among a population of patients with the disease, evaluated as reliably as possible using a reference method ("gold standard") or taking biopsy profiling into account. Sensitivity (Se) is the ratio of positive results (true positives = TP) divided by the total number of patients with the disease (TP + false negatives = FN): Se = TP / (TP + FN). Sensitivity is usually expressed as a percentage (%) between 0 and 100%.

[0052] True positive (TP) subjects are those with the disease whose value of the parameter of interest exceeds the cut-off.

[0053] False negative (FN) subjects are those with the disease whose value of the parameter of interest is below the cut-off.

[0054] Specificity is the ability of a test to correctly identify those who do not have the disease. Specificity measures the proportion of negative ("healthy") cases among a population of healthy patients, evaluated using a reference method ("gold standard"). Specificity (Sp) is the ratio of negative results (true negatives = TN) divided by the total number of healthy patients (TN + false positives = FP): Sp = TN / (TN + FP).

[0055] True negative (TN) subjects are disease-free subjects with a value of the parameter of interest below the cut-off.

[0056] False positive (FP) subjects are disease-free subjects with a value of the parameter of interest above the cut-off.

[0057] The prevalence of a given population is the number of disease cases within the population.

[0058] 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). 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).

[0059] Prevalence has a different impact on PPV and NPV. As the prevalence of the disease in the population increases, PPV increases while NPV decreases. The change in PPV is greater, while NPV is less affected by the prevalence of the disease. At low prevalence (5 - 10%), the PPV value is low. In parallel, NPV is high. At high prevalence (80 - 90%), the PPV value is high, and the same is true for NPV in reverse.

[0060] Likelihood ratio is defined as the ratio of the expected test results of subjects with a particular condition / disease to those of subjects without the disease.

[0061] The likelihood ratio of a positive test result (LR+) represents the ratio of the probability of a positive test result occurring in subjects with the disease to that in those without the disease. LR+ = sensitivity / (1 - specificity). LR+ is the best indicator for making a diagnostic decision. The higher the LR+, the better the test is as an indicator of the disease. LR+ > 5 indicates a moderate to large increase in the evidence of the presence of the disease. A good diagnostic test has LR+ > 10, and its positive result makes a significant contribution to the diagnosis.

[0062] The likelihood ratio for a negative test result (LR-) represents the ratio of the probability of a negative result occurring in subjects with the disease to the probability of the same result occurring in subjects without the disease. LR- = (1 - sensitivity) / specificity. LR- is a good indicator for ruling out a diagnosis. An LR- of 0.1 - 0.2 indicates a moderate probability for a good diagnosis of patients without the studied condition. A good diagnostic test for patients without the studied condition has an LR- < 0.1.

[0063] For every individual cut-off, there is a pair of diagnostic sensitivity and specificity values. To construct a receiver operating characteristic (ROC) graph, these pairs of values are plotted on the 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) indicate how high the discriminative power of the test is. The closer the curve is to the upper left corner and the larger the area under the curve, the better the test discriminates between diseased and non-diseased subjects. The area under the curve can have any value from 0.5 to 1 and is a good indicator of the goodness of the test. A perfect diagnostic test has an AUROC of 1.0, while a non-discriminative test has an AUROC of 0.5. AUROC is a comprehensive measure of diagnostic accuracy.

[0064] The method of the present invention As mentioned above, progressive liver fibrosis and cirrhosis are associated with liver-related death, and thus, the easy detection of progressive fibrosis subjects and cirrhosis subjects is of utmost importance. The present invention provides a solution to these unmet needs.

[0065] In the method of the present invention, the levels of two circulating markers are measured from a sample of blood, serum, or plasma from a subject. The two circulating markers are soluble vascular cell adhesion molecule-1 (sVCAM) and thrombospondin 2 (TSP-2). sVCAM is also known as VCAM1, INCAM-100, CD106 and is registered in the UniProt database under the number P19320. TSP-2 is also known as THBS2 and is registered in the UniProt database under the number P35442.

[0066] In certain embodiments, the present invention is an in vitro method for diagnosing or prognosticating progressive hepatic fibrosis or cirrhosis in a subject, comprising: a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said subject; b) comparing the levels of sVCAM and TSP-2 with reference levels of sVCAM and TSP-2, wherein the comparison of the measured levels with the reference levels indicates the presence or absence of progressive hepatic fibrosis or cirrhosis; and relates to a method comprising the same.

[0067] The measurement of the circulating levels of sVCAM and TSP-2 is performed on a biological fluid sample of the subject. In all methods and embodiments presented herein, the biological fluid sample can be a sample of blood or a blood-derived fluid such as serum and plasma, a sample of saliva, a sample of interstitial fluid, or a sample of urine. In certain embodiments, the biological fluid sample of the subject is a sample of blood, serum, or plasma. In a more specific embodiment, the sample is a serum sample. In another specific embodiment, the biological fluid sample is a cell-free sample.

[0068] The circulating levels of sVCAM and TSP-2 can be measured by any conventional method well known in the art, such as immunoassays (e.g., ELISA (enzyme-linked immunosorbent assay), immunonephelometry, immunoturbidimetry, flow cytometry, protein arrays). For example, the levels of sVCAM and TSP-2 can be determined by an antibody, aptamer, or peptide specific for said marker, respectively.

[0069] The "reference levels" of sVCAM and TSP-2 can be measured from a group of healthy subjects and / or a group of subjects diagnosed as having early hepatic fibrosis and / or a group of subjects diagnosed as having progressive hepatic fibrosis but no cirrhosis and / or a group of subjects diagnosed as having cirrhosis. In certain embodiments, the reference levels of sVCAM and TSP-2 are measured from a "training population".

[0070] The "training population" refers to a population consisting of a predetermined number of subjects, in which the fibrosis stage of each subject has already been determined by a conventional method such as liver biopsy.

[0071] 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, the positive control for TSP-2 may contain 11 - 300 ng / mL of TSP-2, and the positive control for sVCAM may contain 390 - 6190 ng / mL of sVCAM.

[0072] In certain embodiments, values of the levels of the circulating markers sVCAM and TSP-2 measured in a subject's biological fluid sample can be introduced into a mathematical function (i.e., a statistical algorithm) to obtain a score A (SA). The score can accurately predict progressive liver fibrosis or cirrhosis according to specific cut-off values, respectively.

[0073] Accordingly, SA can be used to distinguish subjects having progressive liver fibrosis or cirrhosis from subjects not having progressive liver fibrosis or cirrhosis by comparing it to a specific cut-off value. Those skilled in the art are aware of numerous suitable methods for developing mathematical functions, and all of these are within the scope of the present invention. In certain embodiments, the mathematical function includes a logistic regression equation.

[0074] In certain embodiments, in step (b) of the above method, the score SA is compared to a cut-off value, wherein the SA is obtained from the levels of sVCAM and TSP-2 measured in step (a), the cut-off value is obtained from the reference levels of sVCAM and TSP-2, and the SA and the cut-off value are calculated using an algorithm equation.

[0075] According to one embodiment of the present invention, the SA value is compared with a cut-off value (co). Said value can be calculated from the Youden statistical analysis for the training population. Specifically, said SA and cut-off value are calculated using an algorithmic equation to distinguish patients with progressive hepatic fibrosis from patients without progressive hepatic fibrosis.

[0076] Alternatively, said SA and cut-off value are calculated using an algorithmic equation to distinguish patients with cirrhosis from patients without cirrhosis.

[0077] The cut-off value co1 can be determined for use in indicating the presence or absence of progressive hepatic fibrosis. The cut-off value co2 can be determined for use in indicating the presence or absence of cirrhosis.

[0078] In a more detailed embodiment, the present invention is an in vitro method for diagnosing progressive hepatic fibrosis in a human subject, comprising: a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said human subject; b) calculating a score SA based on the measurements of step (a) using an algorithmic equation; c) comparing this SA with a cut-off value co1; and d) that an SA higher than the cut-off value co1 indicates that the subject has progressive hepatic fibrosis. relates to a method.

[0079] In contrast, a calculated SA value lower than the cut-off value co1 indicates that the subject does not have progressive hepatic fibrosis.

[0080] In another more detailed embodiment, the present invention is an in vitro method for diagnosing cirrhosis in a human subject, comprising: a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from said human subject; b) calculating a score SA based on the measurement values in step (a) using an algorithm equation; c) comparing this SA with a cut-off value co2; and d) that SA being higher than the cut-off value co2 indicates that the subject has cirrhosis. relates to a method.

[0081] In contrast, the calculated SA value being lower than the cut-off value co2 indicates that the subject does not have cirrhosis.

[0082] In order to find an algorithm equation that separates patients with progressive hepatic fibrosis from patients without progressive hepatic fibrosis, and patients with cirrhosis from patients without cirrhosis, correlation analysis was performed to cluster co-variables with a high correlation (Pearson coefficient ≥ 0.5). The most discriminatory variable for each group, determined by calculating the P-value from a Student's t-test, was retained. In this way, a set of variables was selected to avoid the problem of high multicollinearity. This subset of variables was analyzed by performing a logistic regression analysis, and those (sVCAM and TSP-2) that were significantly associated with progressive fibrosis and / or cirrhosis were retained, and the values at that level were combined in the algorithm, resulting in an equation using coefficients (a, b, c) and a constant k.

[0083] The algorithm produced scores in the range of 0 to 1 (continuous).

[0084] For each equation, using Excel Solver, the Log-likelihood function (LL), which is the conditional probability (with coefficients a, b, c, and constant k) of predicting the dependent variable corresponding to the actual observed value (disease, hepatic fibrosis state in this case) considering the values of the independent variable inputs (values of the markers measured in this case), was calculated.

[0085] Conditional probability Pr(Yi = yi|X1i,X2i,···Xki) [where Pr = probability, yi is a coefficient, X1i, X2i, ··· Xki are measured variables, Y is the dependent variable or score S. When this score = 0, it means that the patient is diagnosed as not having progressive hepatic fibrosis or cirrhosis. When this score = 1, it means that the patient is diagnosed as having progressive hepatic fibrosis or cirrhosis, and i is any patient]. The conditional probability is abbreviated for convenience as Pr(Y = y|X) and is calculated by the following formula: Pr(Y = y|X)=P(X)Y * [1 - P(X)](1 - Y).

[0086] Taking the natural logarithm on both sides, the following is obtained: ln[Pr(Y = y|X)]=y * ln[P(X)] * (1 - y) * ln[[1 - P(X)]].

[0087] Then, the Log-likelihood function, LL, is the sum of the ln[Pr(Y = y|X)] terms for all data records according to the following formula: LL = ΣYi * P(Xi)+(1 - Yi) * (1 - P(Xi)).

[0088] The purpose of logistic regression is to use Excel Solver to maximize LL (the minimum value of LL), which means that the score calculated by the algorithm corresponds to the highest probability of the actual observed state of the disease, and determine the parameters (a, b, c, k, etc.) of the algorithm equation.

[0089] Two variables, namely, subsets of sVCAM and TSP-2, were analyzed by logistic regression (using Excel Solver) to create the following algorithm: Score = e y / (1 + e y ) [where y = k + a×(Log 10 sVCAM [ng / L]) + b×(Log 10 TSP-2 [ng / mL])].

[0090] Specifically, the score is calculated according to the following algorithm equation: Score = e y / (1 + e y ) [where y = k + a×A + b×B, A is the level of sVCAM expressed in log10 ng / mL, B is the level of TSP-2 expressed in log10 ng / mL, k is a constant of the algorithm equation, a is a coefficient related to the level of sVCAM, b is a coefficient related to the level of TSP-2, Furthermore, in the equation, k is a number included between -37.23 and -27.54, particularly -30.000, a is a number included between 5.432 and 8.646, particularly 7.250, b is a number included between 5.746 and 7.727, particularly 6.921)] is calculated through.

[0091] In a specific embodiment, the score A (SA), cut-off value co1, and cut-off value co2 for use in the diagnosis of progressive hepatic fibrosis or cirrhosis are calculated for the training population according to the algorithm equation defined above.

[0092] As an example, the following equations can be used for the diagnosis of progressive hepatic fibrosis or cirrhosis: y = -30.000 + 7.250×log10(sVCAM (ng / L)) + 6.921×log10(TSP-2 (ng / mL)) y = -34 + 8.6×log10(sVCAM (ng / L)) + 6.7×log10(TSP-2 (ng / mL)) Or y = -30.001 + 8.64×log10(sVCAM(ng / L)) + 5.750×log10(TSP-2(ng / mL)) or y = -27.2 + 5.9×log10(sVCAM(ng / L)) + 7.7×log10(TSP-2(ng / mL)) or y = -31.2 + 5.91×log10(sVCAM(ng / L)) + 7.71×log10(TSP-2(ng / mL)) or y = -30.012 + 6.6×log10(sVCAM(ng / L)) + 7.69×log10(TSP-2(ng / mL)) y = -30.000 + 7.250×log10(sVCAM(ng / L)) + 6.921×log10(TSP-2(ng / mL)) or y = -30.001 + 8.64×log10(sVCAM(g / L)) + 5.750×log10(TSP-2(ng / mL)) These are preferred.

[0093] In certain embodiments, SA is compared to a cut-off value co1 indicative of progressive hepatic fibrosis. Specifically, co1 is included between 0.200 and 0.500, more specifically between 0.2040 and 0.4852, and in particular equal to 0.3383.

[0094] In another specific embodiment, SA is compared to a cut-off value co2 indicative of cirrhosis. Specifically, co2 is included between 0.510 and 0.790, more specifically between 0.5110 and 0.7791, and in particular equal to 0.6073.

[0095] According to another aspect, the present invention, when executed by a processor / processing means, causes the processor / processing means to - receive measurement levels of sVCAM and TSP-2, - calculate SA from these measurement levels using the mathematical functions described herein, Assign the subject to a group of subjects having progressive hepatic fibrosis or cirrhosis based on a calculated score compared to a pre-determined cut-off value A computer program comprising instructions to do so.

[0096] The present invention further provides a computer-readable medium comprising the computer program described herein. According to certain embodiments, the computer-readable medium is a non-transitory medium or a storage medium.

[0097] The present invention also provides an in vitro method for monitoring the progression of hepatic fibrosis in a subject by measuring the levels of two circulating markers, namely sVCAM and TSP-2.

[0098] Specifically, the method comprises a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from the subject; b) comparing the levels with the levels of sVCAM and TSP-2 previously measured in the same subject; and

[0099] The circulating levels of sVCAM and TSP-2 measured in step (a) can be introduced into the algorithm equation defined above to calculate a score. The score is calculated by using a non-invasive method and is correlated with the evolution of hepatic fibrosis, which makes it possible not only to diagnose but also to monitor the progression of hepatic fibrosis by repeated measurements.

[0100] In certain embodiments, the circulating levels of sVCAM and TSP-2 are measured from one or more blood-derived samples from a subject. In that case, when measurements have to be made, the same type of sample is used each time. For clarity purposes, this means that if the previous measurement was made from a serum sample, subsequent measurements are made from serum samples of the same subject. Similarly, if the previous measurement was made from a blood or plasma sample, subsequent measurements are made from blood or plasma samples, respectively, of the same subject. In certain embodiments, the circulating levels of the markers are measured from one or more serum samples from a subject.

[0101] The collection of several samples over time from the same subject allows for the evaluation of long-term 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 biological fluid 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 biological fluid sample of the same subject. SB and SC can be obtained through the algorithmic equations defined above. If the score increases over time in the same subject, i.e., if SC is higher than SB, it means that liver fibrosis is worsening, while if SC decreases over time in the same subject, i.e., if SC is lower than SB, it means that liver fibrosis is decreasing. The absence of a significant change between SC and SB measured over a certain period in the same subject means that liver fibrosis is stable.

[0102] Therefore, during follow-up, the change in the value between SC and SB is an indicator of the progression or regression of liver fibrosis.

[0103] In certain embodiments, the method comprises a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from the subject to obtain a score C; and b) comparing the score C with a score B obtained from the levels of sVCAM and TSP-2 previously measured in the same subject. and

[0104] Since the score calculated according to the algorithm equation defined above is a linear value, by comparing score C (SC) with score B (SB), the method of the present invention also makes it possible to determine the likelihood of progression towards cirrhosis or worsening cirrhosis in a subject with progressive liver fibrosis.

[0105] In certain embodiments, SC is measured while a phenomenon associated with the evolution of the pathological state is occurring. The said phenomena include liver transplantation, acute or chronic (acute on chronic) liver fibrosis (ACLF), compensated and decompensated cirrhosis, the appearance of ascites, and the presence of esophageal varices on endoscopy. Preferably, the phenomena are acute or chronic (acute on chronic) liver fibrosis, compensated and decompensated cirrhosis.

[0106] In certain embodiments, SC is measured at least 3 months after the measurement of SB, specifically, after a period of 3 months to 10 years, preferably 3 months to 2 years, more preferably within a period of 1 to 2 years after the measurement of SB.

[0107] In a more detailed embodiment, if a subject is diagnosed with cirrhosis, for example, by the method of the present invention described above, SC is measured within a period of 3 months to 2 years, preferably 3 months after the measurement of SB. The appropriate timing for measuring SC may depend on the co-existing conditions.

[0108] Co-existing conditions include malignant tumors, type 2 diabetes, overweight and obesity, heart disease, and kidney disease.

[0109] In another specific embodiment, if a subject is diagnosed with progressive liver fibrosis, for example, by the method of the present invention described above, SC is measured after a period of 1 to 10 years after the measurement of SB. The appropriate timing for measuring SC depends on the co-existing conditions.

[0110] By the method of the present invention, it is possible to make a determination to provide a lifestyle recommendation (e.g., providing a food regimen or a physical activity recommendation) to a subject, perform a medical treatment on the subject (e.g., by setting up regular visits to a doctor or regular tests to periodically monitor markers of liver damage), or administer at least one treatment for liver fibrosis to a patient for treating progressive liver fibrosis or cirrhosis. Specifically, it is possible to make a determination to provide a lifestyle recommendation to a subject or to administer at least one treatment for liver fibrosis. Accordingly, the present invention further relates to an antifibrinolytic compound for use in a method for treating progressive liver fibrosis or cirrhosis in a subject in need thereof, as identified by the method according to the present invention.

[0111] Accordingly, the present invention further relates to an antifibrinolytic compound for use in a method for treating progressive liver fibrosis or cirrhosis in a subject in need thereof, as identified by the method according to the present invention.

[0112] As used herein, the term "treatment" relates to both therapeutic and prophylactic or preventive means, the purpose of which is to prevent or slow down (reduce) an undesirable physiological change or disorder. Beneficial or desired clinical outcomes include, but are not limited to, alleviation of symptoms, stabilization of a pathological condition (specifically, not worsening), slowing or stopping the progression of a disease, and improving or alleviating a pathological condition. Specifically, for the purposes of the present invention, treatment is directed at slowing the progression of fibrosis and reducing the risk of further complications. This may also include prolonging survival compared to expected survival in the absence of treatment.

[0113] The antifibrinolytic agent is administered in a therapeutically effective amount. As used herein, the expression "therapeutically effective amount" refers to the amount of a drug effective to achieve a desired therapeutic result. The therapeutically effective amount of a drug can vary depending on factors such as the medical condition, age, sex, and weight of the individual, as well as the ability of the drug to elicit the desired response in the individual. A therapeutically effective amount is an amount where the therapeutically beneficial effect outweighs any toxic or detrimental effect of the agent. The effective dosage and dosing regimen of a drug depend on the disease or condition being treated and can be determined by one of ordinary skill in the art. A physician having ordinary skill in the art can readily determine and prescribe the effective amounts of the pharmaceutical compositions required. For example, a physician can start with a dosage of the drug used in the pharmaceutical composition at a level lower than the level required to achieve the desired therapeutic effect and gradually increase the dosage until the desired effect is achieved. Generally, the appropriate dosage of the compositions of the present invention will be the amount of the compound that is the lowest dosage effective to produce a therapeutic effect according to a particular dosing regimen. Such effective dosages generally depend on the factors described above.

[0114] The present invention further relates to an antifibrinolytic compound for use in a method for treating hepatic fibrosis in F3 or F4 patients classified as having progressive hepatic fibrosis or cirrhosis by the method of the present invention. The present invention also relates to an antifibrinolytic compound for use in a method for treating hepatic fibrosis, the method comprising treating a subject diagnosed or classified as having progressive hepatic fibrosis or cirrhosis by a method according to the present invention with an antifibrinolytic compound as defined hereinafter in the specification.

[0115] The antifibrinolytic compound comprises: - a compound of formula (I) or a pharmaceutically acceptable salt thereof:

[0116]

Chemical formula

[0117] [wherein, X1 represents a halogen atom, an R1 group, or a G1-R1 group, A represents a CH=CH or CH2-CH2 group, X2 represents a G2-R2 group, G1 represents an oxygen atom, G2 represents an oxygen or sulfur atom, R1 represents an aryl group or an alkyl group substituted by one or more substituents selected from a hydrogen atom, an unsubstituted alkyl group, a halogen atom, an alkoxy group, an alkylthio group, a cycloalkyl group, a cycloalkylthio group, and a heterocyclic group, R2 represents an alkyl group substituted by a -COOR3 group, and 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, R4 and R5 are the same or different and represent an alkyl group substituted or unsubstituted by one or more substituents selected from a halogen atom, a cycloalkyl group, and a heterocyclic group], - AMP-activated protein kinase stimulators, such as PXL-770, MB-11055, Debio-0930B, metformin, CNX-012, O-304, mangiferin calcium salt, eltrombopag, carotuximab, and imiglimin, - 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, megligliptin, 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-1-yl}-1-methyl-1H-indole-3-carboxylic acid), TERN-201, TERN-501, and TERN-301, - Fibroblast growth factor 19 (FGF-19) receptor ligand or a functionally engineered variant of FGF-19, - Fibroblast growth factor 21 (FGF-21) agonists, such as PEG-FGF21 (pegbelfermin, formerly BMS-986036), YH-25348, BMS-986171, YH-25723, LY-3025876, and NNC-0194-0499, - Engineered fibroblast growth factor 19 (FGF-19) analogs, such as NGM-282 (aldafermin), - Glucagon-like peptide-1 (GLP-1) analogs, such as semaglutide, liraglutide, exenatide, albiglutide, dulaglutide, lixisenatide, locisenatide, 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, vinifibrate, clinofibrate, clofibric acid, nicofibrate, pirifibrate, plafibride, ronifibrate, teofibrate, 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]-1,3-thiazol-5-yl}methyl)sulfanyl]-2-methylphenoxy}acetic acid)), MBX8025 (seladelpar or {2-methyl-4-[5-methyl-2-(4-trifluoromethyl-phenyl)-2H-[1,2,3]triazol-4-ylmethylsylfanyl(ylmethylsylfanyl)]-phenoxy}-acetic acid), GW0742 ([4-[[[2-[3-fluoro-4-(trifluoromethyl)phenyl]-4-methyl-5-thiazolyl]methyl]thio]-2-methylphenoxy]acetic acid), L165041, HPP-593, and NCP-1046, - PPAR alpha / gamma dual agonists (alias: 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 PPAR pan agonists, such as IVA337, TTA (tetradecylthioacetic acid), barbaticin, GW4148, GW9135, bezafibrate, ranifibranor, 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 hormone receptor beta (THRβ) 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 C, and vitamin E in combination with atorvastatin.

[0118] In the present invention, the antifibrinolytic compound is preferably selected from the group consisting of pegbelfermin, selicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, ceradelpar, semaglutide, sitagliptin, TERN-101, TERN-201, tropifexor, ambrisentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642, and orlistat (Xenical).

[0119] More preferably, the antifibrinolytic agent is selected from pegbelfermin, selicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, ceradelpar, semaglutide, sitagliptin, TERN-101, TERN-201, and tropifexor.

[0120] The present invention further relates to a method for evaluating the effectiveness of an antifibrinolytic agent in a subject suffering from progressive hepatic fibrosis or cirrhosis, comprising: a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from a subject suffering from progressive hepatic fibrosis, wherein the subject has been administered an antifibrinolytic agent prior to said measurement; b) comparing the levels of sVCAM and TSP-2 with the levels of sVCAM and TSP-2 measured prior to administration of the antifibrinolytic agent to the same subject to evaluate the effectiveness of the antifibrinolytic agent. The method includes the above steps.

[0121] In certain embodiments, the measurement of the levels of sVCAM and TSP-2 before and after administration of the antifibrinolytic agent respectively allows for obtaining a first score D (before administration), denoted as SD, and a second score E (after administration), denoted as SE.

[0122] The SD and SE can be obtained through algorithmic equations, particularly the functions defined above.

[0123] In a more detailed embodiment, a method for evaluating the effectiveness of an antifibrinolytic agent comprises a) calculating SD obtained from measuring the levels of sVCAM and TSP-2 in a biological fluid sample isolated from a subject suffering from progressive hepatic fibrosis before administration of the antifibrinolytic agent; b) calculating SE obtained from measuring the levels of sVCAM and TSP-2 in a biological fluid sample isolated from the same subject after treatment with the antifibrinolytic compound; c) comparing SE with SD and includes.

[0124] If SE is higher than SD, hepatic fibrosis worsens and the subject does not respond to treatment with the antifibrinolytic agent, or this treatment is not effective.

[0125] If SE is lower than SD, hepatic fibrosis regresses, the subject responds to treatment with the antifibrinolytic agent, and this treatment is effective.

[0126] If SD is equal to SE, hepatic fibrosis is stable.

[0127] The present invention also relates to a data processing apparatus comprising means for implementing one of the methods of the present invention described above.

[0128] The present invention also provides a computer program product or a computer-readable storage medium which, when executed by a computer, comprises instructions for causing the computer to implement one of the methods of the present invention described above.

[0129] The present invention also relates to a kit for diagnosing progressive hepatic fibrosis or cirrhosis in a subject. The kit includes means for determining the levels of sVCAM and TSP-2. In certain embodiments, the kit includes specific antibodies, aptamers, or peptides for measuring sVCAM and TSP-2.

[0130] More specifically, the kit includes an antibody, aptamer or peptide against sVCAM, and an antibody, aptamer or peptide against TSP-2.

[0131] The antibody against sVCAM can be any monoclonal, polyclonal and / or conjugate antibody against sVCAM known in the art.

[0132] The antibody against TSP-2 can be any monoclonal, polyclonal and / or conjugate antibody against TSP-2 known in the art.

[0133] The kit of the present invention may further include immunoassay standards and reagents. In certain embodiments, the kit includes at least one specific positive control for TSP-2 and / or at least one specific positive control for sVCAM.

[0134] Specifically, the positive control for TSP-2 contains TSP-2 at 11 to 300 ng / mL, and the positive control for sVCAM contains sVCAM at 390 to 6190 ng / mL.

[0135] More specifically, the kit of the present invention further includes three positive controls for TSP-2 and three positive controls for sVCAM. The positive controls for TSP-2 contain TSP-2 at 30, 50, and 100 ng / mL respectively, and the positive controls for sVCAM contain sVCAM at 600, 800, and 1100 ng / mL.

[0136] The present invention will be further described with reference to the following non-limiting examples.

Example

[0137] (Example 1) Selection of Biological Markers The selection of biological markers was performed by logistic regression on a list of biological markers involved in the mechanism of action of liver fibrosis, and the most significant markers correlated with progressive liver fibrosis were selected. Fourteen biological markers were included, including soluble vascular cell adhesion molecule-1 (sVCAM), thrombospondin 2 (TSP-2), miR34a, glycated 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.

[0138] Individual markers were measured in the sera of 1063 patients from the phase 3 clinical trial RESOLVE-IT® (NCT) study. RESOLVE-IT® is a multi-center, randomized, double-blind, placebo-controlled, phase III study (NCT02704403) to evaluate the efficacy and safety of elafibranor in patients with non-alcoholic steatohepatitis (NASH) and fibrosis. Liver biopsies were performed to confirm the diagnosis and staging of liver fibrosis. Clinical data and blood samples were also collected for all patients. The liver fibrosis stage was provided by analysis of liver biopsies according to the Kleiner staging system. The prevalence of liver fibrosis stage is reported in Table 1.

[0139]

Table 1

[0140] All markers were tested for their 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), a regression model was designed using ToolPak analysis from Excel software, with progressive hepatic fibrosis and cirrhosis as the response variables. Eight biological markers were significantly correlated with progressive hepatic fibrosis and cirrhosis (p<0.01), and these markers were subjected to collinearity analysis. Hyaluronic acid and AST were excluded due to collinearity with TSP-2. Using hepatic fibrosis as the response variable, the second regression model was fitted using six selected markers as explanatory variables for progressive fibrosis and cirrhosis. Three markers that had a significant correlation (p-value <0.01) with the fibrotic status of the patients of the present invention were selected (ALT was excluded because its p-value was 0.153). A second correlation analysis was performed, and collinearity was not demonstrated among these circulating biomarkers by calculating the variance inflation factor, which is a measure of the amount of multicollinearity in a set of multiple regression variables and was correlated with the fibrotic status (VIF<0.2). Covariance clustering was performed on the selected two markers (TSP-2 and sVCAM). A subset of these two markers was analyzed by logistic regression (using Excel Solver) as variables to create an algorithm from several combinations. These two markers, namely, sVCAM and TSP-2, were identified as the main variables.

[0141] Subsequently, the same procedure was evaluated for 1063 patients from the phase 3 clinical trial RESOLVE-IT® (NCT) study to develop a logistic regression to distinguish cirrhotic patients from those without cirrhosis. The individual markers of sVCAM and TSP-2 were also identified as the main variables for diagnosing cirrhosis.

[0142] Thus, the combination of two circulating biomarkers, namely soluble vascular cell adhesion molecule-1 (sVCAM) and thrombospondin 2 (TSP-2), provides an improved diagnosis of progressive hepatic fibrosis or cirrhosis compared to existing solutions and represents a valuable alternative to liver biopsy.

[0143] (Example 2) Diagnosis of Progressive Hepatic Fibrosis in Human Subjects Using the Combination of sVCAM and TSP-2 The biomarkers were measured in the serum of the patients. TSP-2 was quantified by using the Quantikine® ELISA Human Thrombospondin-2 Immunoassay (Catalog number DTSP-20, BioTechne, France), following the manufacturer's recommendations, by setting a positive control to evaluate clinically relevant values for fibrosis. The sVCAM dosage was performed by using the Quantikine® ELISA Human VCAM-1 / CD106 Immunoassay (Catalog numbers DVC00, SVC00, and PDVC00, BioTechne) following the manufacturer's recommendations, by setting a positive control to evaluate clinically relevant values for fibrosis.

[0144] Three positive controls were developed to provide TSP-2. - Control 1 (C1 = 30 ng / mL of TSP-2), - Control 2 (C2 = 50 ng / mL of TSP-2) - Control 3 (C3 = 100 ng / mL of TSP-2).

[0145] C1 is made from the serum of healthy volunteers (Etablissement Francais du Sang, France). C1 contains TSP-2 naturally. C2 requires dilution of C3 to obtain 50 ng / ml of TSP-2 diluted in the serum of healthy volunteers. C3 is prepared from lyophilized recombinant human TSP-2 diluted in the serum of healthy volunteers.

[0146] Three positive controls were also developed to quantify sVCAM, namely control 1 (C'1 = 600 ng / mL of sVCAM), control 2 (C'2 = 800 ng / mL of sVCAM), and control 3 (C'3 = 1100 ng / mL of sVCAM).

[0147] C'1 was made from the serum of healthy volunteers (Etablissement Francais du Sang, France). C'1 naturally contains sVCAM. C'2 requires dilution of C'3 to obtain 800 ng / ml of sVCAM in the serum of healthy volunteers. C'3 is prepared from lyophilized recombinant human sVCAM diluted in the serum of healthy volunteers.

[0148] All controls were aliquoted and stored at -8 °C until use. The positive controls were processed in the same way as the sample specimens and assayed in duplicate.

[0149] The first score, the SA score, is defined with k = -30.00, a = 7.250, and b = 6.921.

[0150] To distinguish patients with progressive hepatic fibrosis from those without progressive hepatic fibrosis, the diagnostic cut-off co1 for progressive hepatic fibrosis is set at 0.3471.

[0151] ELF and FIB-4 were calculated according to the literature. Subsequently, the SA score was compared with the ELF and FIB4 tests and individual biomarkers by the mean of the area under the receiver operating characteristic curve (AUROC) for the diagnosis of progressive hepatic fibrosis (Table 2).

[0152]

Table 2

[0153] Diagnosis using the SA score and co1 yields the highest performance for diagnosing progressive liver fibrosis, as shown by the AUC for the individual biomarkers and scores such as the ELF test and FIB4. The area under the receiver operating characteristic curve (AUROC) reaches 0.897 for the SA score (sVCAM and TSP-2), while the AUC for the ELF test equals 0.8333 and the AUC for FIB4 equals 0.7911.

[0154] Diagnostic metrics (overall accuracy, sensitivity, specificity, positive predictive value / PPV, negative predictive value / NPV, positive likelihood ratio / LR+, and negative likelihood ratio / LR-) are also provided in Table 2, and the 95% CIs were calculated using an asymptotic formula based on the normal approximation to the binomial distribution. The diagnostic metrics enable comparison of the three assays: the present invention using the S score (sVCAM and TSP-2), the ELF test, and FIB4.

[0155] A detailed comparison emphasizes that the SA score is a more sensitive diagnostic assay for progressive liver fibrosis compared to the ELF test and FIB4 (sensitivity = 81.75% in the method of the present invention, compared to 75.84% for ELF and 72.24% for FIB4). The superiority of the SA score (sVCAM and TSP-2) is also observed for specificity (81.75% in the method of the present invention, compared to 75.82% for the ELF test and 72.43% for FIB4), the PLR is also higher for the SA score than for the ELF test and FIB4 (4.48 compared to 3.14 for the ELF test and 2.62 for FIB4), the NLR was lower for the SA score than for the ELF test and FIB4 (0.22 compared to 0.32 for ELF and 0.38 for FIB4), the PPV is higher for SA than for the ELF test and FIB4 (72.11% compared to 64.41% for ELF and 60.3 for FIB4), the NPV is higher for SA than for the ELF test and FIB4 (88.59% compared to 84.46% for ELF and 81.82% for FIB4), thus the overall performance of the method of the present invention is the highest with respect to the accuracy of the SA score (81.75% compared to 75.82% for ELF and 72.36% for FIB4).

[0156]

Table 3

[0157] Subsequently, the statistical differences in the diagnostic performance of the SA score were evaluated by comparison with two other scores: the ELF test and FIB4. As shown in Table 3, the AUROC of the method of the present invention is significantly different from that of ELF (p < 0.0001) and FIB4 (p < 0.0001). The SA score represents the highest AUROC for diagnosing progressive liver fibrosis compared to the ELF test and FIB4.

[0158] To distinguish patients with progressive hepatic fibrosis from those without progressive hepatic fibrosis, the diagnostic cut-off for progressive hepatic fibrosis is set at 0.204 (co1-1) with 90% sensitivity (data metrics are in Table 4). Alternatively, the cut-off is set at 0.4852 (co1-2) with 90% sensitivity (data metrics are in Table 5).

[0159]

Table 4

[0160]

Table 5

[0161] The second SA score is defined with k = -34, a = 8.6, and b = 6.7. The third SA score is defined with k = -32.227, a = 8.640, and b = 5.750. The fourth SA score is defined with k = -27.200, a = 5.900, and b = 7.700.

[0162] These models produce an AUROC with no significant difference from the first SA score for diagnosing progressive hepatic fibrosis (Table 6).

[0163]

Table 6

[0164] (Example 3) Diagnosis of cirrhosis in human subjects using a combination of sVCAM and TSP-2. To distinguish patients with cirrhosis from those without cirrhosis (F < 4), the cut-off co2 for the diagnosis of cirrhosis was set at 0.6073.

[0165] ELF and FIB-4 were calculated according to the literature. Subsequently, the SA score was compared with the ELF and FIB4 tests and individual biomarkers by the mean of the area under the receiver operating characteristic curve (AUROC) for the diagnosis of cirrhosis (Table 6).

[0166]

Table 7

[0167] Diagnosis using the SA score and co2 yields the highest performance for diagnosing cirrhosis, as shown by the AUROC for the individual biomarkers and scores such as the ELF test and FIB4. The area under the receiver operating characteristic curve (AUROC) reaches 0.9111 for the SA score (sVCAM and TSP-2), while the AUROC of the ELF test is equal to 0.8672 and the AUROC of FIB4 is equal to 0.8103 (Table 7).

[0168] Diagnostic metrics (overall accuracy, sensitivity, specificity, positive predictive value / PPV, negative predictive value / NPV, positive likelihood ratio / LR+, and negative likelihood ratio / LR-) are also provided in Table 7, and the 95% CIs were calculated using an asymptotic formula based on the normal approximation to the binomial distribution. The diagnostic metrics enable comparison of the three assays: the present invention using the SA score (sVCAM and TSP-2), the ELF test, and FIB4.

[0169] A detailed comparison emphasizes that the SA score is a more sensitive diagnostic assay for cirrhosis compared to the ELF test and FIB4 (sensitivity = 82.82% in the method of the present invention, 77.91% for ELF, and 73.62% for FIB4). The superiority of the SA score (sVCAM and TSP-2) was also observed for specificity (82.89% in the method of the present invention, 77.89% for the ELF test, and 73.69% for FIB4). The PLR was also higher for the SA score than for the ELF test and FIB4 (4.84 compared to 3.92 for the ELF test and 2.8 for FIB4), and the NLR was lower for the SA score than for the ELF test and FIB4 (0.21 compared to 0.28 for ELF and 0.36 for FIB4). The PPV was higher for SA than for the ELF test and FIB4 (46.71% compared to 38.96% for ELF and 33.71% for FIB4), and the NPV was higher for SA than for the ELF test and FIB4 (96.38% compared to 95.12% for ELF and 93.89% for FIB4). Therefore, the overall performance of the method of the present invention is higher with respect to the accuracy of the SA score (82.88% compared to 77.89% for ELF and 73.68% for FIB4).

[0170]

Table 8

[0171] Subsequently, the statistical differences in the diagnostic performance of the SA score were evaluated by comparison with two other scores: the ELF test and FIB4. As shown in Table 8, the AUROC of the method of the present invention is significantly different from that of ELF (p < 0.01) and FIB4 (p < 0.0001). The SA score represents the highest AUROC for diagnosing cirrhosis compared to the ELF test and FIB4.

[0172] To distinguish patients with cirrhosis from those without cirrhosis, the cut-off for cirrhosis diagnosis is set at 0.5110 (co2-1) with 90% sensitivity (data metrics are in Table 9). Alternatively, the cut-off is set at 0.7791 (co2-2) with 90% specificity (data metrics are in Table 10). In either case, the data metrics are better with the SA score than with the ELF test and FIB4.

[0173]

Table 9

[0174]

Table 10

[0175] Furthermore, both data metrics using the SA score with co2-1 (Table 9) and co2-2 (Table 10) are better than the ELF test and FIB4 for the diagnosis of cirrhosis.

[0176] Other models produce an AUROC with no significant difference from the first score for diagnosing cirrhosis (Table 11).

[0177]

Table 11

Claims

1. An in vitro method for diagnosing or prognosticating progressive hepatic fibrosis or cirrhosis in a subject, comprising: a) measuring the circulating levels of soluble vascular cell adhesion molecule-1 (sVCAM) and thrombospondin 2 (TSP-2) in a biological fluid sample isolated from the subject; and b) comparing the levels of sVCAM and TSP-2 with reference levels thereof, wherein the comparison of the measured levels with the reference levels indicates the presence or absence of progressive hepatic fibrosis or cirrhosis. A method comprising the above steps.

2. The method according to 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), a score A (SA) is compared with a cut-off value, the SA is obtained from the levels of sVCAM and TSP-2 measured in step (a), the cut-off value is obtained from the reference levels of sVCAM and TSP-2, and the SA and the cut-off value are calculated using an algorithm equation.

4. SA is calculated by the following algorithm equation: SA = e y / (1 + e y ) [wherein, y = k + a×A + b×B, A is the level of sVCAM expressed in log10 ng / mL, B is the level of TSP-2 expressed in log10 ng / mL, k is a constant of the algorithm equation, a is a coefficient related to the level of sVCAM, b is a coefficient related to the level of TSP-2, and further in the formula, k is a number between -37.23 and -27.54, particularly -30.000, a is a number between 5.432 and 8.646, particularly 7.250, b is a number between 5.746 and 7.727, particularly 6.921] The method according to any one of claims 1 to 3, which is calculated through the above equation.

5. The method according to claim 3 or 4, wherein SA being higher than the cut-off value co1 indicates progressive hepatic fibrosis, specifically, co1 is between 0.200 and 0.500, and more specifically, co1 is equal to 0.3383.

6. The method according to claim 3 or 4, wherein SA being higher than the cut-off value co2 indicates cirrhosis, specifically, co2 is between 0.510 and 0.790, and more specifically, co2 is equal to 0.6073.

7. An in vitro method for monitoring the progression of hepatic fibrosis in a subject, comprising: a) Measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from the subject; b) Comparing said levels with the levels of sVCAM and TSP-2 previously measured in the same subject; A method comprising the above steps. **Claim 8** In step (b), score C (SC) is compared with score B (SB), where SC is the score obtained from the measured levels of sVCAM and TSP-2 in step (a), and SB is the score obtained from the levels of sVCAM and TSP-2 previously measured in the same subject. SB and SC are calculated using an algorithmic equation, - An increase in SC compared to SB indicates progression of liver fibrosis; - A decrease in SC compared to SB indicates regression of liver fibrosis; - No difference between SC and SB indicates stable liver fibrosis. The method according to claim 7. **Claim 9** The method according to claim 8, wherein SC is measured at least 3 months after the measurement of SB, specifically within a period of 3 months to 10 years, preferably within a period of 3 months to 2 years. **Claim 10** SC or SB is calculated using the following algorithmic equation: SC or SB = e y / (1 + e y ) [wherein, y = k + a×A + b×B, A is the level of sVCAM expressed in log10 ng / mL, B is the level of TSP-2 expressed in log10 ng / mL, k is a constant of the algorithmic equation, a is a coefficient related to the level of sVCAM, b is a coefficient related to the level of TSP-2, Furthermore, in the equation, k is a number included between -37.23 and -27.54, particularly -30.000; a is a number included between 5.432 and 8.646, particularly 7.250; b is a number included between 5.746 and 7.727, particularly 6.921.] The method according to any one of claims 7 to 9, which is calculated through the above equation. **Claim 11** An antifibrinolytic agent for use in the treatment of progressive hepatic fibrosis or cirrhosis in a subject, wherein the subject is diagnosed with progressive hepatic fibrosis or cirrhosis according to the method according to any one of claims 1 to 6, and the agent is selected from the group consisting of pegbelfermin, senicriviroc, 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 orezostat, antifibrinolytic agent.

12. A method for evaluating the effectiveness of an antifibrinolytic agent in the treatment of progressive hepatic fibrosis or cirrhosis, comprising: a) measuring the circulating levels of sVCAM and TSP-2 in a biological fluid sample isolated from a subject suffering from progressive hepatic fibrosis, wherein the subject has been administered an antifibrinolytic agent prior to said measurement; b) comparing the levels of sVCAM and TSP-2 with the levels of sVCAM and TSP-2 measured prior to administration of the antifibrinolytic agent to the same subject to evaluate the effectiveness of the antifibrinolytic agent. A method comprising.

13. In step (b), score E (SE) is compared with score D (SD), - SD is the score obtained from the levels of sVCAM and TSP-2 measured prior to administration of the antifibrinolytic agent to the subject, - SE is the score obtained from the levels of sVCAM and TSP-2 measured after administration of the antifibrinolytic agent to the subject, - SD and SE are calculated through an algorithm equation, A decrease in SE compared to SD indicates the effectiveness of the antifibrinolytic agent. The method according to claim 12.

14. SE and SD are calculated using the following algorithm equation: SE or SD = e y / (1 + e y ) [wherein, y = k + a×A + b×B, A is the level of sVCAM expressed in log10 ng / mL, B is the level of TSP-2 expressed in log10 ng / mL, k is a constant of the algorithm equation, a is a coefficient related to the level of sVCAM, b is a coefficient related to the level of TSP-2, Furthermore, in the formula, k is a number included between -37.23 and -27.54, in particular -30.000, a is a number included between 5.432 and 8.646, in particular 7.250 b is a number included between 5.746 and 7.727, in particular 6.921] The method according to claim 13, calculated through.

15. A kit for diagnosing progressive hepatic fibrosis or cirrhosis in a subject, comprising means for determining the levels of sVCAM and TSP-2, and comprising 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. When executed by a processor / processing means, the processor / processing means - receives the measured levels of sVCAM and TSP-2, - calculates the SA score from these measured levels using the following mathematical function: SA = e y / (1 + e y ) [wherein, y = k + a×A + b×B, A is the level of sVCAM expressed in log10 ng / mL, B is the level of TSP-2 expressed in log10 ng / mL, k is a constant of the algorithm equation, a is a coefficient related to the level of sVCAM, b is a coefficient related to the level of TSP-2, and further in the formula, k is a number included between -37.23 and -27.54, in particular -30.000, a is a number included between 5.432 and 8.646, in particular 7.250, b is a number included between 5.746 and 7.727, in particular 6.921] calculates from, - assigns the subject to a group of subjects having progressive hepatic fibrosis or cirrhosis based on the calculated score compared to a pre-determined cut-off value A computer-assisted program comprising instructions to that effect.

18. A data processing apparatus comprising means for implementing the method according to any one of claims 1 to 14.

19. A computer program product comprising instructions such that when the program is executed by a computer, the computer implements the method according to any one of claims 1 to 14.

20. A computer-readable storage medium that, when executed by a computer, comprises instructions for causing the computer to perform the method according to any one of claims 1 to 14.

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