Method for diagnosing advanced liver fibrosis or cirrhosis

Abstract

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

Description

Technical Field

[0001] This invention relates to a method for diagnosing advanced liver fibrosis or cirrhosis, for prognostic purposes or for monitoring the progression of liver fibrosis in subjects, or for evaluating the efficacy of antifibrotic agents. The invention also relates to compounds for treating liver fibrosis, wherein the subject to be treated is identified according to the method of the invention. Background Technology

[0002] Liver fibrosis is a common cause of liver injury and disease, and can have many chronic or non-chronic causes, including viral hepatitis B and hepatitis 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-on-chronic liver failure (ACLF), Wilson's disease, metabolic dysfunction-associated fatty liver disease (MAFLD), metabolic dysfunction-associated fatty liver disease (MASH), alcoholic 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, genetic abnormalities, cancer, alcohol consumption, and obesity. Over time, conditions that damage the liver can lead to scarring (cirrhosis), which can result in liver failure, a life-threatening condition requiring urgent medical care. Liver failure occurs when much of the liver becomes damaged beyond repair.

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

[0005] Predicting liver fibrosis is a critical step in assessing and managing patients with liver injury and / or liver disease. Therefore, quantitative measurements are essential for assessing disease progression, as early and accurate assessment of the severity and status of liver fibrosis is crucial for diagnosis, monitoring, and prognosis.

[0006] Most forms of liver disease are asymptomatic until they progress to later stages. Therefore, early detection is challenging. The risk of liver-related death increases exponentially with the stage of fibrosis, and once cirrhosis develops, mortality and morbidity increase exponentially. Cirrhosis is the point at which the liver is completely scarred and beyond its ability to heal itself.

[0007] Cirrhosis is a leading cause of death and morbidity worldwide. It is the 11th leading cause of death and the 15th leading cause of disease, accounting for 2.2% of global deaths and 1.5% of disability-adjusted life years (DAWs) in 2016. In 2017, chronic liver disease caused 1.32 million deaths globally (Sepanlou et al., The global, regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017). Lancet Gastroenterol Hepatol 2020;5:245-266.). Cirrhosis is called compensated when the patient has no visible symptoms of the disease, and decompensated when it progresses to the point where liver function is impaired and symptoms of the disease appear. Although the clinical features of decompensated cirrhosis are well described (i.e., 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 developed scoring systems for staging liver fibrosis, such as the BRUNT / KLEINER system, in which: F0 refers to subjects who do not have liver fibrosis; F1 refers to subjects with portal vein or perisinus fibrosis; F2 refers to subjects with portal vein / periportal and perisinusoidal fibrosis; F3 refers to subjects with septal or bridging liver fibrosis; F4 refers to subjects with cirrhosis.

[0009] Since severe liver disease (fibrosis stage F>2) can develop into hepatocellular carcinoma, accurate staging of liver fibrosis in these liver diseases, especially early diagnosis of advanced 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 must be performed multiple times. Therefore, the test must be repeatable, risk-free, reliable, and accurate. Thus, non-invasive assays are needed to diagnose advanced liver fibrosis (F3 or F4) and cirrhosis (F4). It is also important to specifically differentiate patients with cirrhosis (F=4) among those with severe liver disease, as these F4 patients require urgent treatment.

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

[0012] However, liver biopsy has several recognized limitations, including sampling error, inter-observer variability, and hospitalization. A major drawback is the significant risk of complications, including bleeding, pain, and even death. Furthermore, biopsy cannot reflect changes throughout the entire liver or differentiate between early and advanced cirrhosis, and therefore cannot constitute a reliable prognostic predictor (Sumida Y et al., Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease / nonalcoholicsteatohepatitis, World J Gastroenterol 2014;20:475-485).

[0013] To avoid the life-threatening risks and diagnostic weaknesses detailed above, in vitro noninvasive diagnostic methods using biomarkers, scoring, and physical approaches have been developed. These methods capture the dynamic process of fibrosis as it progresses and regresses, unlike biopsies which cannot be repeated without inconvenience, because the measurements are reproducible. These methods are based on readily available biochemical data and clinical features, such as the FIB4 test (Sterling RK et al., Development of a simple noninvasive index to predict significant fibrosis in patients with HIV / HCV coinfection. Hepatology 2006; 43:1317-1325), or assays that directly measure markers of fibrosis and fibrinolysis, such as enhanced liver fibrosis (ELF) tests (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).

[0014] However, the use of these methods to replace liver biopsy remains controversial and is not universally accepted due to insufficient diagnostic performance. The main problem with these two biochemical assays is their inability to reliably distinguish advanced stages of liver fibrosis in individual patients. Furthermore, the ELF test is expensive, which is a drawback in cases of repeated testing. Additionally, FIB4 performs poorly in patients under 35 years of age and has low specificity in patients over 65 years of age.

[0015] Physical methods include high-frequency sound wave-based imaging (ultrasound and echocardiography), computed tomography (CT), magnetic resonance imaging (MRI), transient elastography (TE, FibroScan), and scintillation scanning. The main drawbacks of physical measurements are high cost, low equipment availability, and methodological complexity, which limit their use in routine clinical practice.

[0016] Furthermore, obesity, ascites, acute inflammation, hepatic congestion, and elevated portal vein pressure may, for example, reduce the accuracy of ultrasound TE (Fibroscan) by affecting the velocity of the shear wave. Additionally, a false increase in liver stiffness due to postprandial portal vein pressure has been observed using this method.

[0017] Therefore, there remains an unmet medical need to develop new non-invasive diagnostic methods that offer the best diagnostic accuracy compared to biopsies, and can also be used to monitor the temporal course of liver fibrosis and / or show whether there is a response to a given drug treatment. Furthermore, it is important to provide a new method that delivers optimal predictivity at the lowest cost and with the greatest ease of feasibility. Summary of the Invention

[0018] The inventors have conducted several very fine and comprehensive analyses on a cohort of 3,875 patients with different stages of fibrosis to provide a novel and highly sensitive non-invasive method for the diagnosis and monitoring of advanced liver fibrosis (F3 or F4) and cirrhosis (F4). WO 2024 / 013228 describes a non-invasive diagnostic method for these diseases by measuring the circulating levels of three biomarkers: soluble vascular cell adhesion molecule-1 (sVCAM), platelet-reactive protein 2 (TSP-2), and α2-macroglobulin (A2M). This method has demonstrated accurate diagnosis of advanced liver fibrosis and cirrhosis compared to liver biopsy and existing solutions. However, the inventors recently observed that the diagnostic performance of this method may be heterogeneous across different age subgroups. To address this issue, the inventors have successfully developed a novel mathematical function that allows for the correction of age for the influence of diagnostic performance by combining the circulating levels of these three biomarkers measured from a biological fluid sample separated from the subject within the mathematical function with the subject's age. Compared to existing solutions, this mathematical function provides improved diagnosis of advanced liver fibrosis (F3 or F4) and cirrhosis (F4). Most importantly, compared to methods that only combine sVCAM, TSP-2, and A2M levels, the method of this invention provides consistently high clinical performance regardless of patient age, allowing for reliable follow-up of liver fibrosis.

[0019] Therefore, a first aspect of the present invention relates to an in vitro method for diagnosing advanced liver fibrosis (F3 or F4) or cirrhosis (F4) in a subject, the method comprising: a) Provide (i) circulating levels of soluble vascular cell adhesion molecule-1 (sVCAM), platelet-reactive protein 2 (TSP-2), and α2-macroglobulin (A2M) in biofluid samples isolated from the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2 and A2M levels with age in a mathematical function to assign a score. c) Compare the scores to cutoff values ​​to diagnose or predict advanced liver fibrosis or cirrhosis in the subject.

[0020] In one specific implementation, the score SA is obtained through the following mathematical function:

[0021] and

[0022] y = β0 + β1 log10(TSP2 (ng / mL)) + β2 log10(sVCAM (ng / mL)) + β3 (A2M(g / L)) + β4 (Age (years)) 3 )

[0023] in: β0 is contained between -37 and -12, especially between -31 and -17; β1 is contained between 1.5 and 7.9, especially between 2 and 7; β2 is contained between 1 and 11, especially between 1.7 and 8.5; β3 is contained between 0.01 and 2.5, particularly between 0.1 and 1.5; and β4 is contained in -8 e -06 and -0.5 e -7 Between, especially at -6.5 e -6 and -1.0 e -6 between.

[0024] In a more specific implementation, a score SA above the cutoff value of co1 indicates advanced liver fibrosis, particularly a co1 score between 0.2 and 0.7, and more particularly a co1 score between 0.25 and 0.63.

[0025] In another particular implementation, a score SA above a cutoff CO2 indicates cirrhosis, specifically CO2 levels between 0.4 and 1.1, and more specifically CO2 levels between 0.5 and 1.0.

[0026] Another aspect of the present invention relates to an in vitro method for monitoring the progression of liver fibrosis in a subject, comprising the following steps: a) Provide (i) circulating levels of sVCAM, TSP-2, and A2M in biofluid samples isolated from the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the SC score; and c) Compare the score SC with the score SB, which is obtained by combining the levels of sVCAM, TSP-2, A2M, and age previously measured in the same subject with the same mathematical function.

[0027] In one specific implementation, an increase in SC compared to SB indicates progression of liver fibrosis. In another specific implementation, a decrease in SC compared to SB indicates regression of liver fibrosis. In one specific implementation, no significant change between SC and SB measured over a period of time in the same subject means that liver fibrosis is stable.

[0028] In a particular embodiment of the method, step (a) of the method for monitoring the progression of liver fibrosis is performed at least 3 months after a previous measurement of sVCAM, TSP-2 and A2M levels, particularly within a period of 3 months to 10 years after a previous measurement of sVCAM, TSP-2 and A2M levels, and more particularly within a period of 3 months to 2 years.

[0029] In one specific embodiment of the method, an event related to the evolution of the pathological state occurs during monitoring between the SB and SC. In one specific embodiment, the event is selected from liver transplantation, acute-on-chronic liver fibrosis, compensated cirrhosis, decompensated cirrhosis, ascites episode, and esophageal varices present during endoscopy.

[0030] In a preferred embodiment, SB and SC are calculated using the mathematical functions defined above.

[0031] A third aspect of the present invention relates to a method for evaluating the efficacy of an antifibrotic agent in the treatment of advanced liver fibrosis or cirrhosis, comprising: a) Provide (i) circulating levels of sVCAM, TSP-2, and A2M in a biofluid sample isolated from a subject with advanced liver fibrosis, wherein the subject had been administered an antifibrotic agent prior to the isolation of the biofluid sample, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the score SE; and c) The score SE is compared with the score SD, which is obtained by combining the levels of sVCAM, TSP-2, A2M and age measured in advance before administering the antifibrotic agent to the same subject and using the same mathematical function.

[0032] Specifically, a decrease in SE compared to SD indicates the efficacy of the antifibrotic agent; an increase in SE compared to SD indicates the inefficiency of the antifibrotic agent and / or the patient's non-responsiveness.

[0033] In a preferred embodiment, the SE and SD are calculated using the mathematical functions defined above.

[0034] In a preferred embodiment, the biofluid sample of the subject used in the method of the present invention is an interstitial fluid, saliva, urine, or whole blood sample. In a particular embodiment, the biofluid sample of the subject used in the method of the present invention is a blood sample, plasma sample, or serum sample. Preferably, the biofluid sample is cell-free. More preferably, the blood sample is a serum sample from the subject.

[0035] Another aspect of the present invention relates to a method for predicting the progression of liver fibrosis in subjects with biopsy-confirmed stage 1, 2, or 3 liver fibrosis, the method comprising: a) Provide (i) the circulating levels of sVCAM, TSP-2, and A2M in the biofluid samples of the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the score SF; and c) The scores are compared to cutoff values ​​to predict the progression of liver fibrosis in the subjects.

[0036] In one specific implementation, an SF score above the cutoff value indicates that the subject's liver fibrosis will progress (i.e., the subject will be a worsening liver fibrosis patient), an SF score below the cutoff value indicates that the subject's liver fibrosis will regress (i.e., the subject will be an improving liver fibrosis patient), and an SF score neither above nor below the cutoff value indicates that the subject's liver fibrosis will be stable.

[0037] Another aspect of the invention also provides an antifibrotic agent for treating subjects with advanced liver fibrosis or cirrhosis, wherein the subjects are diagnosed with advanced liver fibrosis or cirrhosis according to the method of the invention, wherein the agent is selected from pebefamine, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanilanol, liraglutide, obeticholic acid, pioglitazone, retemetilol, salroglucizamagnesin, sradap, semaglutide, sitagliptin, TERN-101, TERN-201, topiramate, ambesentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642, and orlistat.

[0038] In one specific implementation, the agent is retinoic acid. Attached Figure Description

[0039] Figure 1A The mean score of the modeled STA is represented by the average score of patients under 50 years of age (0) and over 60 years of age (1) in the homogenized subgroup (n=339 patients per category). p < 0.0001.

[0040] Figure 1B The mean scores of the modeled STAII represent the scores of patients younger than 50 years (0) and older than 60 years (1) in the homogenized subgroups (n=339 patients per category). “ns”: no significant difference in scores obtained from the two groups of patients.

[0041] Figure 2A This indicates the clinical performance of the modeled STA used to detect F3. "Spe": Specificity; "Sen": Sensitivity.

[0042] Figure 2B This indicates the clinical performance of the modeled STAII used to detect F3. "Spe": Specificity; "Sen": Sensitivity.

[0043] Figure 3 The Roc curves represent the results of testing advanced liver fibrosis using modeled STAII and other commonly used non-invasive tests (FIB-4, ELF, NFS, and APRI) in a validation cohort of 1,850 subjects.

[0044] Figure 4 The Roc curves represent the results of testing modeled STAII and other commonly used non-invasive tests (FIB-4, ELF, NFS, and APRI) in a validation cohort of 1,850 subjects for liver cirrhosis.

[0045] Figure 5 This box plot represents the SF score calculated at the screening visit (V0), categorizing patients with biopsy-confirmed stage 1, 2, or 3 liver fibrosis at V0 as "improved," "stable," or "worsening." Improved: Patients with a lower fibrosis stage at the 7th visit (after 81 weeks) compared to V0; Stable: Patients with the same fibrosis stage at the 7th visit compared to V0; Worsening: Patients with a higher fibrosis stage at the 7th visit compared to V0. The line above the box plot represents the p-value (Student test) through paired box plot values. Detailed Implementation

[0046] definition

[0047] Fibrosis scoring / staging

[0048] According to the present invention, the term "fibrosis" or "liver fibrosis" refers to a pathological condition characterized by excessive deposition of fibrous connective tissue in the liver. More specifically, fibrosis is a pathological process that includes persistent fibrotic scar formation as a response to tissue damage and excessive production of extracellular matrix by connective tissue. Physiologically, the deposition of connective tissue can impair the structure and function of the liver.

[0049] Different stages of liver fibrosis are defined by the Kleiner scoring system (Kleiner et al., Hepatology 2005, Vol. 41, No. 6, 1313-1321), among which: F0 refers to subjects who do not have liver fibrosis; F1 refers to subjects with portal vein or perisinus fibrosis; F2 refers to subjects with portal vein / periportal and perisinusoidal fibrosis; F3 refers to subjects with septal or bridging liver fibrosis; F4 refers to subjects with cirrhosis.

[0050] F0-2 was assigned to subjects with early liver fibrosis, F3 or F4 to subjects with advanced liver fibrosis, and F4 to subjects with cirrhosis.

[0051] Using this fibrosis staging system, patients with little or no 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 of 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.

[0052] In the context of this invention, the terms "late-stage fibrosis" or "late-stage liver fibrosis" refer to fibrosis stages with F≥3, i.e., fibrosis stage F3 or fibrosis stage F4.

[0053] In the context of this invention, the term "cirrhosis" or "liver cirrhosis" refers to the fibrotic stage of F4.

[0054] The terms “subject” and “patient” are used interchangeably in this document and refer to human subjects.

[0055] In the context of this invention, the terms "biomarker," "marker," and "biological marker" are used interchangeably.

[0056] Within the scope of this invention, any scope must be considered to include both the upper and lower limits.

[0057] Parallel non-invasive testing

[0058] The Fibrosis-4 (FIB-4) index is calculated as age (years) x AST (U / L) / platelets (x 10⁻¹²). 9 / l) / √ALT(U / L), where AST is aspartate aminotransferase and ALT is alanine aminotransferase.

[0059] The Enhanced Liver Fibrosis Fibroid Group (ELF, Siemens Healthcare GmbH, Eriangen, Germany) is a test that predicts fibrosis based on three fibrosis biomarkers: hyaluronic acid (HA), tissue inhibitor of matrix metalloproteinase-1 (TIMP-1), and N-terminal propeptide of type III procollagen (PIIINP). The test calculates the ELF score using the following equation: 2.278 + 0.851 In( CHA )+0.751 In( CPIIINP )+0.394 In( CTIMP-1 )), where C is the concentration of the biomarker.

[0060] The NFS score is calculated as follows: NFS = -1.675 + 0.037 x age (years) + 0.094 x BMI (kg / m²) 2 +1.13 IGF / Diabetes (Yes=1, No=0) + 0.99 x AST / ALT ratio – 0.013 x platelets (x 10) 9 / l)–0.66 x albumin (g / dl)

[0061] APRI scores are calculated as follows: APRI = [(AST level (IU / L) / upper limit of normal AST range (IU / L)) x 100] / platelet count (x 10) 9 / L).

[0062] Diagnostic performance analysis

[0063] Sensitivity is a test of the ability to correctly identify individuals with a disease. Sensitivity is measured as the proportion of positive (“disease”) cases in a population of patients assessed using a reference method (“gold standard”) or considering biopsy analysis to assess the disease as reliably as possible. Sensitivity (Se) is the proportion of positive results (true positive = TP) divided by the total number of patients with the disease (TP + false negative = FN): Se = TP / (TP + FN). Sensitivity is typically expressed as a percentage (%), from 0 to 100%.

[0064] True positive (TP) subjects are those with a disease whose relevant (of interest) parameter values ​​are higher than the cutoff value.

[0065] False negative (FN) subjects are those with a disease whose relevant parameter values ​​are below the cutoff value.

[0066] Specificity is the ability to correctly identify individuals who do not have the disease. Specificity measures the proportion of negative (“healthy”) cases in a healthy patient population assessed using a reference method (“gold standard”). Specificity (Sp) is the proportion of negative results (true negative = TN) divided by the total number of healthy patients (TN + false positive = FP): Sp = TN / (TN + FP).

[0067] True negative (TN) subjects are those without disease whose relevant parameters are below the cutoff value.

[0068] False positive (FP) subjects are subjects without disease whose relevant parameters are higher than the cutoff value.

[0069] The prevalence of a given population is the number of cases of disease within that population.

[0070] Positive predictive value (PPV) is the probability of having a disease when the test is positive. PPV = True positives / (True positives + False positives). PPV = TP / (TP + FP).

[0071] The negative predictive value (NPV) is the probability that a test result of negative indicates that the person does not have the disease. NPV = number of true negatives / (number of true negatives + number of false negatives). NPV = TN / (TN + FN).

[0072] Disease prevalence has different effects on PPV and NPV. As disease prevalence increases in the population, PPV increases while NPV decreases. Although changes in PPV are more significant, NPV is less affected by disease prevalence. For low prevalence (5-10%), PPV is low, while NPV is high. For high prevalence (80-90%), PPV is high, and vice versa for NPV.

[0073] The likelihood ratio is defined as the ratio of the expected test result of a subject with a certain condition / disease to that of a subject without that condition.

[0074] The likelihood ratio (LR+) of a positive test result represents the probability of a positive test result occurring in subjects with the disease compared to subjects without the disease. LR+ = Sensitivity / (1 - Specificity). LR+ is the optimal indicator for diagnosis. The higher the LR+, the more indicative the test is of the disease. LR+ > 5 indicates moderate to significant evidence of the disease. Good diagnostic tests have LR+ > 10, and their positive results contribute significantly to the diagnosis.

[0075] The likelihood ratio (LR-) of a negative test result represents the ratio of the probability of a negative result in subjects with the disease to the probability of the same result in subjects without the disease. LR- = (1 - sensitivity) / specificity. LR- is a good indicator for excluding a diagnosis. An LR- between 0.1 and 0.2 indicates a moderate probability of a good diagnosis in patients without the study condition. A good diagnostic test for patients without the study condition has an LR- < 0.1.

[0076] For each individual cutoff value, there exists a pair of diagnostic sensitivity and specificity values. To construct a receiver operating characteristic (ROC) plot, these value pairs 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 ROC curve (AUROC) indicate how discriminative the test is. The closer the curve is to the top left corner and the larger the area under the curve, the better the test distinguishes between diseased and undiseased subjects. The area under the curve can have any value between 0.5 and 1, and it is a good indicator of test goodness. A perfect diagnostic test has an AUROC of 1.0. A non-discriminatory test has an AUROC of 0.5. AUROC is an overall measure of diagnostic accuracy.

[0077] The “average score” is the average of the test results calculated across a set.

[0078] The method of the present invention

[0079] As stated above, advanced liver fibrosis and cirrhosis are associated with liver-related death, therefore, easy detection of subjects with advanced fibrosis and cirrhosis is of paramount importance. This invention provides a solution to these unmet needs.

[0080] In the method of this invention, the levels of three circulating biomarkers measured from a biological fluid sample separated from the subject are combined with the subject's age in a mathematical function to assign a score. The three circulating biomarkers are: soluble vascular cell adhesion molecule-1 (sVCAM), platelet-reactive protein 2 (TSP-2), and α2-macroglobulin (A2M). sVCAM is also known as VCAM1, INCAM-100, and CD106, and is registered in the UniProt database with number P19320. TSP-2 is also known as THBS2 and is registered in the UniProt database with number P35442. A2M is also known as protein 5 containing the C3 and PZP-like alpha-2-macroglobulin domain (C3, PZP-like alpha-2-macroglobulin domain-containing protein 5) and is registered in the UniProt database with number P01023.

[0081] According to the present invention, circulating levels of sVCAM, TSP-2, and A2M are measured in a subject's biofluid sample. In all the methods and embodiments presented herein, the biofluid sample can be a sample of blood or a blood-derived fluid (e.g., serum and plasma), saliva, interstitial fluid, or urine. In one specific embodiment, the subject's biofluid sample is a blood, serum, or plasma sample. In a more specific embodiment, the sample is a serum sample. In another specific embodiment, the biofluid sample is a cell-free sample.

[0082] According to the present invention, biological fluid samples can be analyzed immediately after collection or stored in a cold environment, such as a freezer, for further analysis.

[0083] Circulating levels of sVCAM, TSP-2, and A2M can be measured using any conventional method well known in the art, such as immunoassays (e.g., ELISA, immunoturbidimetry, immunoturbidimetric assay, immune cell counting, protein arrays). For example, levels of sVCAM, TSP-2, and A2M can be determined using antibodies, aptamers, or peptides targeting the markers, respectively.

[0084] In some methods of this invention, the circulating levels of sVCAM, TSP-2, and A2M are measured from samples of multiple blood sources isolated from the subject at the same or different time points over a period of months to years. In this case, the same type of sample is used each time a measurement must be performed. For clarity, this means that if a previous measurement was performed from a serum sample, subsequent measurements are performed from serum samples from the same subject. Similarly, if a previous measurement was performed from a blood or plasma sample, subsequent measurements are performed from blood or plasma samples from the same subject, respectively.

[0085] According to the method of the present invention, the levels of three biomarkers measured from a biological fluid sample isolated from the subject and the subject's age are combined in a mathematical function to assign a score. For clarity, the subject's age used in the mathematical function is the age at which the biological fluid sample was isolated from the subject to measure the levels of the three biomarkers.

[0086] The score is correlated with the subject's fibrosis status. It can be used for the diagnosis or prognosis of advanced liver fibrosis or cirrhosis in the subject. Furthermore, the score is also correlated with the evolution of liver fibrosis, which not only allows for diagnosis but also allows for monitoring the progression of liver fibrosis or assessing the efficacy of antifibrotic drugs in the treatment of advanced liver fibrosis or cirrhosis.

[0087] In the context of this invention, those skilled in the art will recognize many suitable methods for developing mathematical functions, and all of these are within the scope of this invention. In a particular embodiment, the mathematical function includes a logistic regression equation.

[0088] In a further implementation, the score is assigned by the following mathematical function:

[0089] and

[0090] y = β0 + β1 log 10 (TSP2 (ng / mL)) + β2 log 10 (sVCAM (ng / mL)) + β3 (A2M(g / L)) + β4 (Age) 3 )

[0091] in: β0 is contained between -37 and -12, especially between -31 and -17; β1 is contained between 1.5 and 7.9, especially between 2 and 7; β2 is contained between 1 and 11, especially between 1.7 and 8.5; β3 is contained between 0.01 and 2.5, particularly between 0.1 and 1.5; and β4 is contained in -8 e -06 and -0.5 e -7 Between, especially at -6.5 e -6 and -1.0 e -6 between.

[0092] In a particular embodiment, the present invention relates to an in vitro method for diagnosing or predicting advanced liver fibrosis or cirrhosis in a subject, the method comprising: a) Provide (i) circulating levels of sVCAM, TSP-2, and A2M in biofluid samples isolated from the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign scores; and c) Compare the scores to cutoff values ​​to diagnose or predict advanced liver fibrosis or cirrhosis in the subject.

[0093] The scores obtained by comparing with specific cutoff values ​​can be used to distinguish subjects with advanced liver fibrosis or cirrhosis from those without, or to predict the development of liver fibrosis in subjects.

[0094] According to a particular embodiment, the present invention relates to an in vitro method for diagnosing advanced liver fibrosis or cirrhosis in a subject, wherein the score is a score SA obtained by a mathematical function defined above.

[0095] According to one embodiment of the invention, the SA value is compared with a cutoff value (C0). The value can be calculated by performing a Youden statistical analysis on the SA scores obtained from the training population. The "training population" refers to a group of a given number of subjects, where the fibrosis stage of each subject has been determined by existing methods (such as liver biopsy).

[0096] Specifically, mathematical functions were used to calculate the SA and cutoff values ​​to distinguish patients with advanced liver fibrosis from those without.

[0097] Alternatively, mathematical functions can be used to calculate the SA and cutoff values ​​to distinguish between patients with cirrhosis and those without.

[0098] A cutoff value of CO1 can be determined to indicate the presence or absence of advanced liver fibrosis. A cutoff value of CO2 can be determined to indicate the presence or absence of cirrhosis.

[0099] In a more specific embodiment, the present invention relates to an in vitro method for diagnosing advanced liver fibrosis in human subjects, the method comprising: a) Provide (i) the circulating levels of sVCAM, TSP-2, and A2M in a biofluid sample isolated from the subject, and (ii) the age of the subject. b) Calculate the score SA based on step (a) using mathematical functions. c) Compare the SA with the cutoff value co1. d) SA values ​​greater than the cutoff value co1 indicate that the subject has advanced liver fibrosis.

[0100] Conversely, SA values ​​calculated below the cutoff value of co1 indicate that the subject did not have advanced liver fibrosis.

[0101] In another, more specific embodiment, the present invention relates to an in vitro method for diagnosing liver cirrhosis in a human subject, comprising: a) Provide (i) the circulating levels of sVCAM, TSP-2, and A2M in a biofluid sample isolated from the subject, and (ii) the age of the subject. b) Calculate the score SA based on step (a) using mathematical functions. c) Compare the SA with the cutoff value co2. d) SA values ​​greater than the cutoff value of CO2 indicate that the subject has cirrhosis.

[0102] Conversely, SA values ​​calculated below the cutoff CO2 value indicate that the subject does not have cirrhosis.

[0103] In one specific implementation, a score A(SA), cutoff value co1, and cutoff value co2 for diagnosing advanced liver fibrosis or cirrhosis are calculated for the training population according to the mathematical functions defined above.

[0104] In a particular implementation, SA is compared to a cutoff value co1, which indicates advanced liver fibrosis. Specifically, co1 is contained between 0.2 and 0.7, more particularly between 0.25 and 0.63, and specifically equal to 0.5801.

[0105] In another specific implementation, SA is compared to a cutoff value for CO2, which indicates cirrhosis. Specifically, CO2 is contained between 0.4 and 1.1, more specifically between 0.5 and 1.0, and particularly equal to 0.6103.

[0106] According to another aspect, the present invention relates to a computer program comprising instructions that, when executed by a processor / processing tool, cause the processor / processing tool to: - Receive the levels of sVCAM, TSP-2, and A2M measured in biofluid samples separated from the subject, as well as the subject's age; - SA is calculated from these measurement levels and age based on the mathematical functions described in this article; and - Subjects were assigned to a group with advanced liver fibrosis or cirrhosis based on their calculated scores compared to a predetermined cutoff value.

[0107] The present invention also 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.

[0108] The present invention also relates to an in vitro method for predicting the evolution of liver fibrosis in subjects with biopsy-confirmed stage 1, 2, or 3 liver fibrosis, the method comprising: a) Provide (i) circulating levels of sVCAM, TSP-2, and A2M in biofluid samples isolated from the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the score SF; and c) The scores are compared to cutoff values ​​to predict the evolution of liver fibrosis in the subjects.

[0109] According to this implementation plan, a subject's SF score above the cutoff value indicates that the subject's liver fibrosis will progress; a subject's SF score below the cutoff value indicates that the subject's liver fibrosis will regress; and a subject's SF score that is neither above nor below the cutoff value indicates that the subject's liver fibrosis will be stable.

[0110] The cutoff value used in this method depends on the subject's stage of liver fibrosis. According to this implementation scheme, the SF score of subjects with biopsy-confirmed stage 1 liver fibrosis is compared with the cutoff value CO-F1; the SF score of subjects with biopsy-confirmed stage 2 liver fibrosis is compared with the cutoff value CO-F2; and the SF score of subjects with biopsy-confirmed stage 3 liver fibrosis is compared with the cutoff value CO-F3.

[0111] Cutoff values ​​CO-F1, CO-F2, and CO-F3 can be determined in training populations with stable biopsy-confirmed stage 1, 2, or 3 liver fibrosis, respectively. For example, the cutoff value CO-F1 is determined in a training population with stable biopsy-confirmed stage 1 liver fibrosis. A patient is considered to have stable liver fibrosis if their liver fibrosis stage remains unchanged at the screening visit and 18 months after the screening visit. For example, if a patient's liver fibrosis stage remains stage 1 18 months after the screening visit, a patient with biopsy-confirmed stage 1 liver fibrosis is considered to have stable liver fibrosis.

[0112] According to the implementation scheme, the cutoff value for the SF score can be a range of values. Specifically, the cutoff value CO-F1 is within the range of 0.06 to 0.20; the cutoff value CO-F2 is within the range of 0.25 to 0.44; and the cutoff value CO-F3 is within the range of 0.47 to 0.86. In one specific implementation scheme, when a subject with biopsy-confirmed stage 1 liver fibrosis has an SF score higher than the range of 0.06 to 0.20, it indicates that the subject's liver fibrosis will progress; when the subject's SF score is lower than the range, it indicates that the subject's liver fibrosis will regress; and when the subject's SF score is within the range, it indicates that the subject's liver fibrosis will be stable.

[0113] In another specific implementation, when a subject with biopsy-confirmed stage 2 liver fibrosis has an SF score higher than the range of 0.25 to 0.44, it indicates that the subject's liver fibrosis will progress; when the subject's SF score is lower than the range, it indicates that the subject's liver fibrosis will regress; and when the subject's SF score is within the range, it indicates that the subject's liver fibrosis will be stable.

[0114] In another specific implementation, when a subject with biopsy-confirmed stage 3 liver fibrosis has an SF score higher than the range of 0.47 to 0.86, it indicates that the subject's liver fibrosis will progress; when the subject's SF score is lower than the range, it indicates that the subject's liver fibrosis will regress; and when the subject's SF score is within the range, it indicates that the subject's liver fibrosis will be stable.

[0115] According to another specific implementation, the cutoff value for the SF score can be a specific value. According to a more specific implementation, the cutoff value CO-F1 is a value selected within the range of 0.06 to 0.20; the cutoff value C0-F2 is a value selected within the range of 0.25 to 0.44; and the cutoff value C0-F3 is a value selected within the range of 0.47 to 0.86. More specifically, the cutoff value C0-F1 can be 0.15; the cutoff value CO-F2 can be 0.32; and the cutoff value C0-F3 can be 0.64.

[0116] In one specific implementation, when a subject with biopsy-confirmed stage 1 liver fibrosis has an SF score higher than a selected CO-F1 value in the range of 0.06 to 0.20, it indicates that the subject's liver fibrosis will progress; when the subject's SF score is lower than the selected CO-F1 value, it indicates that the subject's liver fibrosis will regress; and when the subject's SF score is the same as the selected CO-F1 value, it indicates that the subject's liver fibrosis will be stable.

[0117] In another specific implementation, when a subject with biopsy-confirmed stage 2 liver fibrosis has an SF score higher than a selected CO-F2 value in the range of 0.25 to 0.44, it indicates that the subject's liver fibrosis will progress; when the subject's SF score is lower than the selected CO-F2 value, it indicates that the subject's liver fibrosis will regress; and when the subject's SF score is the same as the selected CO-F2 value, it indicates that the subject's liver fibrosis will be stable.

[0118] In another specific implementation, when a subject with biopsy-confirmed stage 3 liver fibrosis has an SF score higher than a selected CO-F3 value within the range of 0.47 to 0.86, it indicates that the subject's liver fibrosis will progress; when the subject's SF score is lower than the selected CO-F3 value, it indicates that the subject's liver fibrosis will regress; and when the subject's SF score is the same as the selected CO-F3 value, it indicates that the subject's liver fibrosis will be stable.

[0119] The present invention also relates to a computer program comprising instructions that, when executed by a processor / processing tool, cause the processor / processing tool to: - Receive (i) levels of sVCAM, TSP-2, and A2M measured in biofluid samples from subjects with biopsy-confirmed stage 1, 2, or 3 liver fibrosis; and (ii) the age of the subjects. - SF scores are calculated from the levels of these measurements using mathematical functions; - Compare the SF rating with the predetermined cutoff value for the SF rating, and -Specify the subject as: When the score SF is higher than the cutoff value, the liver fibrosis will progress in the subject; When the score SF is below the cutoff value, the liver fibrosis of the subject will regress; When the SF score is neither higher nor lower than the cutoff value, the liver fibrosis of the subject will be stable.

[0120] The present invention also provides an in vitro method for monitoring the progression of liver fibrosis in subjects by measuring the levels of three circulating biomarkers (i.e., sVCAM, TSP-2, and A2M).

[0121] Specifically, the method includes the following steps: a) Provide (i) circulating levels of sVCAM, TSP-2, and A2M in biofluid samples isolated from the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the SC score; and c) Compare the score SC with the score SB, which is obtained by combining the levels of sVCAM, TSP-2, A2M, and age previously measured in the same subject with the same mathematical function.

[0122] As used in this article, the term "surveillance" refers to tracking the evolution of a disease or condition. Surveillance is the continuous and systematic collection and analysis of data as a protocol or condition progresses, such as during clinical research or treatment programs.

[0123] Because the SC score is calculated using a non-invasive method and is associated with the evolution of liver fibrosis, it can be easily used to diagnose and monitor the progression of liver fibrosis through repeated measurements.

[0124] In one specific implementation, circulating levels of sVCAM, TSP-2, and A2M are measured from samples from at least two blood sources from the subject.

[0125] Collecting at least two samples from the same subject over time allows for the assessment of longitudinal changes in the score. For example, a first score, called SB, can be calculated based on the levels of sVCAM, TSP-2, and A2M previously measured in the subject's biofluid sample and the corresponding age, and a second score, called SC, can be calculated based on the levels of sVCAM, TSP-2, and A2M subsequently measured in the same subject's biofluid sample and the corresponding age. SB and SC can be obtained using mathematical functions as defined above. If the score increases over time in the same subject, i.e., SC is higher than SB, it indicates worsening liver fibrosis, while if the SC decreases over time in the same subject, i.e., SC is lower than SB, it indicates a decrease in liver fibrosis. No significant change between SC and SB measured over a period of time in the same subject indicates that liver fibrosis is stable.

[0126] Since the score calculated according to the mathematical function defined above is a linear value, the method of the present invention also allows for determining the likelihood of a subject with advanced liver fibrosis progressing to cirrhosis or worsening cirrhosis by comparing the score C (SC) with the score B (SB). Therefore, the change in the values ​​between SC and SB during follow-up is an indicator of liver fibrosis progression or regression.

[0127] In one specific implementation, SC is measured when an event associated with the evolution of the pathological state occurs. Such events include liver transplantation, acute-on-chronic liver fibrosis (ACLF), compensated and decompensated cirrhosis, ascites episodes, and the presence of esophageal varices on endoscopy. Preferably, the event is acute in chronic liver fibrosis, compensated, and decompensated cirrhosis.

[0128] In one specific implementation, the SC is measured at least 3 months after the SB is measured, particularly within a period of 3 months to 10 years after the SB is measured, preferably within a period of 3 months to 2 years, and more preferably within a period of 1 to 2 years.

[0129] In a more specific implementation, if the subject is diagnosed with cirrhosis, for example by the method of the present invention as described above, the SC is measured within a time period of 3 months to 2 years after the SB measurement, and preferably 3 months later. The appropriate timing for measuring the SC may depend on comorbidities.

[0130] Comorbidities include malignant tumors, type 2 diabetes, overweight and obesity, heart disease, and kidney disease.

[0131] In another specific embodiment, if the subject is diagnosed with advanced liver fibrosis, for example by the method of the present invention as described above, the SC is measured over a period of 1 to 10 years after the SB measurement. The appropriate timing for measuring the SC depends on the comorbidities.

[0132] Because of the method of the present invention, it is possible to decide to provide lifestyle advice (e.g., a dietary plan or advice on physical activity) to a subject, to provide medical care to the subject (e.g., through regular doctor visits or regular examinations, such as regular monitoring of markers of liver damage), or to administer at least one liver fibrosis therapy to a patient to treat advanced liver fibrosis or cirrhosis. In particular, it is possible to decide to provide lifestyle advice or administer at least one liver fibrosis therapy to a subject. Therefore, the present invention also relates to an antifibrotic compound for treating advanced liver fibrosis or cirrhosis in a subject in need, wherein the subject has been identified by the method of the present invention.

[0133] The present invention also relates to a computer program comprising instructions that, when executed by a processor / processing tool, cause the processor / processing tool to: - Receive the levels of sVCAM, TSP-2, and A2M measured in a biofluid sample separated from the subject, as well as the subject's age; - The score SC is calculated from these measurement levels and age based on the mathematical function described above; - Compare the SC score with the SB score, which is obtained by combining previously measured levels of sVCAM, TSP-2, A2M, and age in the same subject with the same mathematical function. -Specify the subject as: When the score SC is higher than SB, there is progression of liver fibrosis; When the score SC is lower than SB, liver fibrosis has regressed; or When there is no significant change between SC and SB, there is stable liver fibrosis.

[0134] Therefore, the present invention also relates to an antifibrotic compound and a method for treating advanced liver fibrosis or cirrhosis in a subject of need, wherein the subject has been identified by the method of the present invention.

[0135] As used herein, the term "treatment" encompasses both therapeutic and preventative or safeguarding measures, where the goal is to prevent or mitigate (alleviate) undesirable physiological changes or symptoms. Beneficial or desired clinical outcomes include, but are not limited to, symptom relief, stabilization of the pathological state (particularly, prevention of deterioration), slowing or halting disease progression, and improving or alleviating the pathological condition. In particular, for the purposes of this invention, treatment involves slowing the progression of fibrosis and reducing the risk of further complications. It may also involve prolonging survival compared to expected survival without treatment.

[0136] Antifibrotic agents are administered at therapeutically effective amounts. As used herein, the term "therapeutically effective amount" refers to the amount of medicine that effectively achieves the desired therapeutic outcome. The therapeutically effective amount of a medicine can vary depending on factors such as an individual's disease state, age, sex, weight, and the ability of the medicine to elicit the desired response in the individual. Therapeutically effective amount is also the amount in which the beneficial therapeutic effect of the medicine outweighs any toxic or harmful effects. The effective dose and dosage regimen of the medicine depend on the disease or condition to be treated and can be determined by those skilled in the art. A physician with ordinary skill in the art can readily determine and prescribe the effective amount of the desired pharmaceutical composition. For example, a physician may start with a dose of the medicine used in the pharmaceutical composition at a level below that required to achieve the desired therapeutic effect and gradually increase the dose until the desired effect is achieved. Generally, the appropriate dose of the composition of the present invention will be the amount of the lowest dose of compound that effectively produces a therapeutic effect according to a specific dosage regimen. Such an effective dose will generally depend on the factors described above.

[0137] The present invention also relates to an antifibrotic compound for a method of treating liver fibrosis in patients with F3 or F4 liver disease, 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 a method of treating liver fibrosis, wherein, due to the method of the present invention, a subject diagnosed or classified as having advanced liver fibrosis or cirrhosis is treated with an antifibrotic compound as defined below.

[0138] Anti-fibrotic compounds include: -A compound of formula (I) or a pharmaceutically acceptable salt thereof: (I) in: X1 represents a halogen atom, an R1 group, or a G1-R1 group; A represents the CH=CH or CH2-CH2 group; X2 represents the G2-R2 group; G1 represents an oxygen atom; G2 represents an oxygen or sulfur atom; R1 represents a hydrogen atom, an unsubstituted alkyl group, an aryl group, or an alkyl group substituted by one or more substituents selected from halogen atoms, alkoxy groups, alkylthio groups, cycloalkyl groups, cycloalkylthio groups, and heterocyclic groups; R2 represents an alkyl group substituted with a -COOR3 group, where R3 represents a hydrogen atom or an alkyl group substituted or unsubstituted by one or more substituents selected from halogen atoms, cycloalkyl and heterocyclic groups; R4 and R5 may be the same or different, indicating an alkyl group that is substituted or unsubstituted by one or more substituents selected from halogen atoms, cycloalkyl groups, and heterocyclic groups; -AMP-activated protein kinase stimulants, such as PXL-770, MB-11055, Debio-0930B, metformin, CNX-012, O-304, mangiferin calcium salt, eltrombopag, carotuximab, and imeglimin. - Bile acids, such as obeticholic acid (OCA), ursodeoxycholic acid (UDCA), norursodeoxycholic acid and ursodiol. -CCR antagonists, such as cenicriviroc (CCR2 / 5 antagonist), PG-092, RAP-310, INCB-10820, RAP-103, PF-04634817 and CCX-872; - Dipeptidyl peptidase IV (DPP4) inhibitors, such as evogliptin, vildagliptin, fogliptin, alogliptin, saxagliptin, tilogliptin, alagliptin, sitagliptin, repagliptin, megliptin, gogliptin, tregliptin, terlilitin, dugliptin, linagliptin, giglitin, eugliptin, betagliptin, imigliptin, ocagliptin, vildagliptin, and degliptin; - Farnesol 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 functional engineered variant of FGF-19; - Fibroblast growth factor 21 (FGF-21) agonists, such as PEG-FGF21 (pegbelfermin, formerly known as 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) analogues, such as semaglutide, liraglutide, exenatide, abiglutide, dulaglutide, lixilatide, loxenatide, efpeglenatide, tasglutide, MKC-253, DLP-205 and ORMD-0901; - Niacin, such as nicotinic acid and vitamin B3; - Nitrozonide (NTZ), its active metabolite tezolinide (TZ), or other prodrugs of TZ such as RM-5061; -PPARα agonists, such as fenofibrate, ciprofibrate, pemafibrate, gemfibrozil, clofibrate, binifibrate, crifibrate, clofibrate, nicofibrate, pirifibrate, prasabide, clonicofibrate, theofibrate, tocofibrate, and SR10171; -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; -PPAR δ agonists, such as GW501516 (Endurabol or ({4-[({4-methyl-2-[4-(trifluoromethyl)phenyl]-1,3-thiazolyl}methyl)thio]-2-methylphenoxy}acetic acid)), MBX8025 (Seladelpar or {2-methyl-4-[5-methyl-2-(4-trifluoromethyl-phenyl)-2H-[1,2,3]triazol-4-ylmethylthio]-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 α / γ dual agonists (also known as glitazars), such as salogrieza, aglitazar, moglitazar, ticagrieza, and DSP-8658; -PPAR γ / δ dual agonists, such as conjugated linoleic acid (CLA) and T3D-959; -PPAR α / γ / δ pan-agonists or PPAR pan-agonists, such as IVA337, TTA (tetradecyl thioacetic acid), bavachinin, GW4148, GW9135, bezafibrate, lanifibranor, lobeglitazone, and CS038; Sodium-glucose transporter (SGLT) 2 inhibitors, such as licoglifozin, repaglizine, dapaglizine, empaglizine, eleglizine, soglizine, ioglizine, tianagliflozin, canagliflozin, togliflozin, janagliflozin, bexagliflozin, luggliflozin, seraglizine, HEC-44616, AST-1935, and PLD-101.

[0139] - Stearoyl-CoA desaturase-1 inhibitors / fatty acid bile acid conjugates, such as aramchol, GRC-9332, steamchol, TSN-2998, GSK-1940029 and XEN-801; -Thyroid receptor β (THRβ) agonists, such as VK-2809, remesetirome (MGL-3196), MGL-3745, SKL-14763, sobetirome, BCT-304, ZYT-1, MB-07811 and eprotirome; - Vitamin E and its isoform; Vitamin E combined with Vitamin C and atorvastatin.

[0140] In this invention, the antifibrotic compound is preferably selected from pebevamine, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanilano, liraglutide, obeticholic acid, pioglitazone, retemetilol, salroglucizamagnesin, sradap, semaglutide, sitagliptin, TERN-101, TERN-201, topiramate, ambesentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642, and orlistat (Xenical).

[0141] More preferably, the antifibrotic agent is selected from pebevamine, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanilano, liraglutide, obeticholic acid, pioglitazone, retemetilol, salroglucizamagnesin, sradpar, semaglutide, sitagliptin, TERN-101, TERN-201 and topiramate.

[0142] In a more specific embodiment, the antifibrotic agent is retinoic acid.

[0143] The present invention also relates to a method for evaluating the efficacy of antifibrotic agents in subjects with advanced liver fibrosis or cirrhosis, the method comprising: a) Provide (i) circulating levels of sVCAM, TSP-2, and A2M in a biofluid sample isolated from a subject with advanced liver fibrosis, wherein the subject had been administered an antifibrotic agent prior to the isolation of the biofluid sample, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the score SE; and c) The score SE is compared with the score SD, which is obtained by combining the levels of sVCAM, TSP-2, A2M and age measured in advance before administering the antifibrotic agent to the same subject and using the same mathematical function.

[0144] According to the method, the levels of sVCAM, TSP-2, and A2M are measured in samples separated before and after the application of the antifibrotic agent to obtain a first score called SD (before application) and a second score called SE (after application).

[0145] The SD and SE can be obtained through the same mathematical function, specifically the mathematical function defined above.

[0146] If the SE is higher than the SD, liver fibrosis has worsened, which means that the subject has not responded to or has no effect on the treatment of anti-fibrotic agents.

[0147] If the SE is lower than the SD, liver fibrosis has regressed, which means that the subject has responded to the anti-fibrotic agent and the treatment is effective.

[0148] If SD equals SE, then liver fibrosis is stable.

[0149] The present invention also relates to a computer program comprising instructions that, when executed by a processor / processing tool, cause the processor / processing tool to: -Receive (i) the levels of sVCAM, TSP-2 and A2M measured in a biofluid sample isolated from a subject with advanced liver fibrosis, wherein the subject had been administered an antifibrotic agent prior to the isolation of the biofluid sample, and (ii) the age of the subject; -Calculate SE from the levels of these measurements and age using the mathematical functions described above; - Compare the score SC with the score SD, which is obtained by combining the levels of sVCAM, TSP-2, A2M, and age previously measured before administration of the antifibrotic agent to the same subject and using the same mathematical function; and -Specify the subject as: Subjects who did not respond to antifibrotic agent treatment when the SE score was higher than SD; When the SE score is lower than SD, it indicates a response to treatment with antifibrotic agents; or When there is no significant change between SE and SD, there is stable liver fibrosis.

[0150] The present invention also relates to a data processing apparatus comprising tools for performing one of the methods of the present invention as described above.

[0151] The present invention also provides a computer program product or computer-readable storage medium containing instructions that, when executed by a computer, cause the computer to perform one of the methods of the present invention as described above.

[0152] The invention is further described with reference to the following non-limiting embodiments.

[0153] Example

[0154] 1. Dataset

[0155] The study cohort consisted of patients screened for potential inclusion in the Resolve-It Phase 3 clinical trial. All patients with biopsy results, and blood samples for biomarker measurements, were selected less than 90 days between the biopsy and blood collection dates, as potential utility for this study.

[0156] Training dataset

[0157] Patients were selected who had complete data on numerous biomarkers, as well as on common demographic and clinical parameters (age, sex, type 2 diabetes, dyslipidemia, hypertension [HT], BMI) to maximize the number of patients and biomarkers. This selection resulted in a total of N=1640 patients with complete data for 40 biomarkers. To control for potential confounding factors in the modeling and selection of relevant biomarkers, two propensity score matching (PSM) algorithms were applied to normalize the following parameters first in patients with F<3 (F0F2) and F=4 (F4), and secondly in previously matched patients with F=4 and F=3 (F3).

[0158] To balance the distribution of fibrosis stages in the training dataset, in the first PSM, two patients from the F0F2 group were matched with each patient from the F4 group. This process resulted in the selection of n=272 patients, 136 from the F0F2 group, 68 from the F3 group, and 68 from the F4 group, which is well balanced for the parameters shown in the list above (Table 1). In this training group, the mean age of the patients was 58 years, 60% were male, and 53% had T2D (type 2 diabetes).

[0159] Validation dataset

[0160] The training process resulted in the selection of a biomarker group containing three biomarkers: TSP2, sVCAM, and A2M.

[0161] Based on these results, we selected all patients with available measurements of these three biomarkers, meeting the criteria for biopsy result availability, having a blood sample and biopsy date ≤90 days apart, and not being part of the training dataset. This resulted in an independent validation dataset containing N=1850 patients. Key features are reported in Table 1 below.

[0162] Statistical analysis

[0163] Descriptive statistics of baseline characteristics were generated and reported as mean ± sd or %(n), and patients without and with advanced fibrosis or cirrhosis were compared using appropriate 2-sample tests (Student's t-test for comparing means and χ² test for comparing percentages).

[0164] The modeling process uses a stepwise algorithm, comparing sequential binomial logistic regression models for detecting advanced fibrosis or cirrhosis based on their associated BIC (Bayesian Information Criterion). Starting with an empty model, variables that minimize this criterion are sequentially inserted. The process stops when none of the remaining variables contributes to a decrease in the BIC value. The stat R package (step function) is used for this purpose. The regression coefficients of the selected model are extracted, and the associated 95% CI is reported.

[0165] Specific cutoff values ​​were derived using the training dataset. For detections with F≥3, a low cutoff value achieving 85% sensitivity (Sen) and a high cutoff value achieving 90% specificity (Spe) were extracted, and the diagnostic performance (Sen, Spe, PPV, NPV, Acc) associated with these cutoff values ​​was calculated on both the training and validation datasets. For detections with F=4, a low cutoff value achieving 85% Sen and a high cutoff value achieving 95% Spe were extracted, and the diagnostic performance (Sen, Spe, PPV, NPV, Acc) associated with these cutoff values ​​was calculated on both the training and validation datasets. Furthermore, the Youden cutoff value maximizing Sen+Spe for each endpoint was reported.

[0166] The overall diagnostic performance of noninvasive tests and biomarkers was estimated using AUROC values, and the Delong test was used to test for significant differences in these statistics. AUROC values ​​were reported using the 95% CI estimated using a bootstrap sample of 1000.

[0167] The box plots are presented together with the relevant Student's t-test used for comparing means, and the p-values ​​are reported graphically as follows: If p < 0.0001, p<0.001, p<0.01, p < 0.05 and NS if p ≥ 0.05 All propensity score matching algorithms were executed using the matchit function in the MatchIt R package, and all statistical analyses were performed using R version 4.3.0.

[0168] Use R packages and R version 4.3.0 for all statistical analyses.

[0169] Table 1: Descriptive Characteristics of Groups

[0170] 2. Results

[0171] 2.1 Model-based development

[0172] When the stepwise algorithm was executed, the combination of TSP2, SVCAM, and A2M was found to be the optimal combination of biomarkers because it returned the lowest BIC, and adding further biomarkers did not result in a reduction that was considered significant enough.

[0173] Furthermore, it is important to ensure that any diagnostic test can be interpreted regardless of the patient's characteristics. Therefore, the effects of various potential confounding factors (i.e., age, sex, prevalence of type 2 diabetes (T2D), dyslipidemia, hypertension (HT), body mass index (BMI), fibrosis, steatosis, and categorized ballooning and lobular inflammation) were analyzed using a modeled STA.

[0174] and

[0175] y = -26.85 + 3.125 × log 10 (A2M(g / L))+5.606 × log 10 (sVCAM (ng / mL)) + 6.325 × log 10 (TSP-2(ng / mL))

[0176] This process resulted in the creation of two subgroups for each relevant parameter (age, sex, gender, hypertension, dyslipidemia, and T2D), each of which was well-balanced with respect to the other parameters listed above. It is important to note that for age, we created two subgroups: one including patients aged 50 years and the second including patients aged 60 years, to ensure clear age separation between the two subgroups, and a second PSM for extreme case studies, where patients were ≤45 years and ≥65 years old. For BMI, two groups were created with patients with a BMI ≤29, and a second group with patients with a BMI ≥31. For sex, age, T2D, HT, BMI, and dyslipidemia, we extracted a total of 1124 patients (562 per group), 678 patients (339 per group), 1048 patients (524 per group), 1084 patients (542 per group), 750 patients (375 per group), and 1052 patients (526 per group), respectively.

[0177] Of the six confounding factors, age was the only one that significantly affected the mean score of the modeled STA, with patients older than 60 years having significantly higher scores (p < 0.0001, see [link]). Figure 1A ).

[0178] A novel mathematical model was developed to obtain scores that are independent of the age of the subjects.

[0179] The following equation (named STAII) is obtained using a binomial logistic regression model:

[0180] and

[0181] y = β0 + β1 log 10 (TSP2 (ng / mL)) + β2 log 10 (sVCAM (ng / mL)) + β3 (A2M(g / L)) + β4 (Age (years)) 3 )

[0182] The coefficients of the model are reported in Table 2, with a 95% CI of correlation. They all reached high significance, with a p-value of <0.0001.

[0183] Table 2: Summary of Coefficients

[0184] Note: Coefficients are used and The report is related to p-values ​​<0.05 and p-values ​​<0.0001, respectively.

[0185] The effect of age on the scores and mean of the biomarkers Gpe 0 (less than 50 years) and Gpe 1 (greater than 60 years) is shown in the figure. Figure 1B middle.

[0186] These results indicate that modeled STAII demonstrates consistent clinical performance, independent of patient age classification, compared to the mean score of modeled STA. To illustrate this, the inventors extracted the Youden cutoff value (0.3457) for detecting F3 in the validation dataset and calculated the clinical performance of modeled STA and STA II. We can observe that the specificity of modeled STA decreases with increasing patient age, while the sensitivity increases with increasing age. Figure 2A This result indicates that the clinical performance of the modeled STA is not homogeneous across all age groups. Conversely, the specificity and sensitivity of the modeled STA II are homogeneous and constant across all age groups. Figure 2B ).

[0187] Based on the modeled STA II and the training dataset, the inventors extracted the following cutoff values ​​to achieve 85% sensitivity and 90% specificity for F≥3 detection in Table 3, and 85% sensitivity and 95% specificity for F=4 detection in Table 4.

[0188]

[0189] Table 4: Low (Lc, 85% Sen), Youden, and High (Hc, 95% Spe) Cutoff Values ​​for Detecting F4

[0190] 2.2 STAII Diagnostic Performance Validation

[0191] The inventors then calculated the diagnostic performance of the modeled STAII on the training and validation datasets at both endpoints. Low and high cutoff values ​​were used to derive these statistics; Table 5 reports the detection results for F≥3, and Table 6 reports the detection results for F=4.

[0192] Table 5: Performance Validation of STAII for Detecting Late Fibrosis

[0193] Table 6: Performance validation of STAII for detecting liver cirrhosis

[0194] Detection of F≥3 In the validation cohort, STAII achieved a sensitivity of 0.8 and a specificity of 0.72 at low cutoff values, with an NPV of 0.87. Similarly, at high cutoff values, STAII achieved a specificity of 0.9 combined with a sensitivity of 0.53 and a PPV of 0.74. Interestingly, only 21% of patients relied on the indeterminate risk zone (IRZ), meaning that nearly four-fifths of patients had a clinically feasible test result for STAII. In conclusion, STAII and its associated performance were validated.

[0195] F=4 detection

[0196] In the validation cohort, at low cutoff values, STAII achieved a sensitivity of 0.85 and a specificity of 0.77, with an NPV of 0.99. Similarly, at high cutoff values, STAII achieved a specificity of 0.96 combined with a sensitivity of 0.42 and a PPV of 0.3. Interestingly, only 20% of patients relied on the indeterminate risk zone (IRZ), meaning that nearly four-fifths of patients had a clinically feasible test result for STAII. In conclusion, STAII and its associated performance were validated.

[0197] 2.3 Performance comparison with other NITs' STAII

[0198] The overall performance of STAII for detecting F≥3 will also be compared with other commonly used non-invasive tests: FIB-4, ELF, NFS and APRI.

[0199] We calculated AUROC values ​​and used the relevant Delong test to examine the AUROC differences between STAII and other NITs. Results for F≥3 are summarized in Table 7, and results for F=4 are summarized in Table 8.

[0200] Table 7: AUROC values ​​for late-stage fibrosis detection

[0201] Table 8: AUROC values ​​for liver cirrhosis detection

[0202] ROC curves used to detect F≥3 Figure 3 The report is presented graphically and used to detect the ROC curve at F=4. Figure 4 The report is presented in a graphical format.

[0203] For detections with F ≥ 3, STAII achieved an AUROC of 0.84, significantly higher than the AUROC achieved by other NITs, with all p-values ​​< 0.0001. For detections with F = 4, STAII achieved an AUROC of 0.88, also significantly higher than the AUROC achieved by other NITs. These results confirm the high performance of the method of the present invention, particularly the gain achieved using this novel diagnostic test compared to conventional diagnostic tests.

[0204] 2.4 Prognostic performance of SF score

[0205] To assess the prognostic performance of this method, 612 patients included in the clinical trial were used. These patients had available information on sVCAM, TSP2, and A2M, as well as biopsy results, at the screening visit (V0) and visit 7 (V7, after 81 weeks) of the clinical trial, and had biopsy-confirmed stage 1, 2, or 3 liver fibrosis at V0. SF scores were calculated for these patients at V0. For each group of patients with initial stage 1, 2, or 3 fibrosis, three categories of fibrosis progression were defined: Improved: Patients with a lower fibrosis stage at V7 compared to V0. Stable: Patients with the same fibrosis stage at V7 compared to V0. Worsening patients: Patients with a higher fibrosis stage at V7 compared to V0. There are a total of 9 subgroups of patients. The number of patients in each subgroup is shown in Table 9 below.

[0206] Table 9:

[0207] Comparison of SF scores within each subgroup showed that a low SF score at V0 indicated regression of liver fibrosis in the subjects, while a high SF score at V0 indicated progression of liver fibrosis in the subjects. Figure 5 ).

[0208] 2.5 Validation of the prognostic performance of the SF score

[0209] To validate the prognostic performance of the SF score, 27 patients with biopsy-confirmed stage 1, 2, or 3 liver fibrosis were classified as V0 according to biopsy-based fibrosis staging. Blood samples were collected from these patients. The SF score of each patient was calculated using the method of the invention and compared to the cutoff ranges corresponding to the initial fibrosis divergence at V0 (i.e., CO-F1: range 0.06 to 0.20; CO-F2: range 0.25 to 0.44; CO-F3: range 0.47 to 0.86). Based on the comparison, patients were predicted to improve, remain stable, or worsen. Disease progression assessed by the score was validated by biopsy at V7. The SF score and disease progression for each patient assessed at V7 are summarized in Table 10. The results confirm the performance of the SF score in predicting the evolution of liver fibrosis in subjects with biopsy-confirmed stage 1, 2, or 3 liver fibrosis.

[0210] Table 10

Claims

1. An in vitro method for diagnosing or predicting advanced liver fibrosis or cirrhosis in a subject, comprising: a) Provide (i) circulating levels of soluble vascular cell adhesion molecule-1 (sVCAM), platelet-reactive protein 2 (TSP-2), and α2-macroglobulin (A2M) in biofluid samples isolated from the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign scores; and c) Compare the score to a cutoff value to diagnose or predict advanced liver fibrosis or cirrhosis in the subject.

2. The method according to claim 1, wherein the score is a score SA obtained by the following mathematical function: in y = β0 + β1 log 10 (TSP2 (ng / mL)) + β2 log 10 (sVCAM (ng / mL)) + β3 (A2M(g / L)) + β4 (Age (years)) 3 ) in: β0 is contained between -37 and -12, especially between -31 and -17; β1 is contained between 1.5 and 7.9, especially between 2 and 7; β2 is contained between 1 and 11, especially between 1.7 and 8.5; β3 is contained between 0.01 and 2.5, particularly between 0.1 and 1.5; and β4 is contained in -8 e -06 and -0.5 e -7 Between, especially at -6.5 e -6 and -1.0 e -6 between.

3. The method of claim 1 or 2, wherein a score SA above the cutoff value co1 indicates advanced liver fibrosis, particularly co1 being between 0.2 and 0.7, and more particularly co1 being between 0.25 and 0.

63.

4. The method of claim 1 or 2, wherein a score SA above the cutoff value of CO2 indicates cirrhosis, particularly CO2 is contained between 0.4 and 1.1, more particularly CO2 is contained between 0.5 and 1.

0.

5. An in vitro method for monitoring the progression of liver fibrosis in subjects, comprising the following steps: a) Provide (i) circulating levels of soluble vascular cell adhesion molecule-1 (sVCAM), platelet-reactive protein 2 (TSP-2), and α2-macroglobulin (A2M) in biofluid samples isolated from the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the score SC; and c) Compare the score SC with the score SB, which is obtained by combining the levels of sVCAM, TSP-2, A2M, and age previously measured in the same subject with the same mathematical function.

6. The method according to claim 5, wherein: - Compared to SB, an increase in SC indicates the progression of liver fibrosis; - Compared to SB, a decrease in SC indicates the regression of liver fibrosis; There is no difference between SC and SB indicating stable liver fibrosis.

7. The method of claim 6, wherein the SC is measured at least 3 months after the SB is measured, particularly within a period of 3 months to 10 years, preferably within a period of 3 months to 2 years.

8. The method according to any one of claims 5 to 7, wherein, Calculate SC or SB using the following mathematical function: in y = β0 + β1 log 10 (TSP2 (ng / mL)) + β2 log 10 (sVCAM (ng / mL)) + β3 (A2M(g / L)) + β4 (Age (years)) 3 ) in: β0 is contained between -37 and -12, especially between -31 and -17; β1 is contained between 1.5 and 7.9, especially between 2 and 7; β2 is contained between 1 and 11, especially between 1.7 and 8.5; β3 is contained between 0.01 and 2.5, particularly between 0.1 and 1.5; and β4 is contained in -8 e -06 and -0.5 e -7 Between, especially at -6.5 e -6 and -1.0 e -6 between.

9. A method for evaluating the efficacy of antifibrotic agents in the treatment of advanced liver fibrosis or cirrhosis, comprising: a) Provide (i) circulating levels of soluble vascular cell adhesion molecule-1 (sVCAM), platelet-reactive protein 2 (TSP-2), and α2-macroglobulin (A2M) in a biofluid sample isolated from a subject with advanced liver fibrosis, wherein the subject had been administered an antifibrotic agent prior to the isolation of the biofluid sample, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the score SE; and c) The score SE is compared with the score SD, which is obtained by combining the levels of sVCAM, TSP-2, A2M and age measured in advance before administering the antifibrotic agent to the same subject and using the same mathematical function.

10. The method of claim 9, wherein the reduction in SE compared to SD indicates the efficacy of the anti-fibrotic agent.

11. The method according to claim 9 or 10, wherein SE and SD are calculated using the following mathematical function: in y = β0 + β1 log 10 (TSP2 (ng / mL)) + β2 log 10 (sVCAM (ng / mL)) + β3 (A2M(g / L)) + β4 (Age (years)) 3 ) in: β0 is contained between -37 and -12, especially between -31 and -17; β1 is contained between 1.5 and 7.9, especially between 2 and 7; β2 is contained between 1 and 11, especially between 1.7 and 8.5; β3 is contained between 0.01 and 2.5, particularly between 0.1 and 1.5; and β4 is contained in -8 e -06 and -0.5 e -7 Between, especially at -6.5 e -6 and -1.0 e -6 between.

12. The method according to any one of claims 1 to 11, wherein the biofluid sample is a saliva sample, interstitial fluid sample, urine sample, blood sample, plasma sample, or serum sample.

13. The method of claim 12, wherein the biofluid sample is a serum sample.

14. An antifibrotic agent for treating a subject with advanced liver fibrosis or cirrhosis, wherein the subject is diagnosed with advanced liver fibrosis or cirrhosis according to any one of claims 1 to 4, wherein the agent is selected from pebefamine, sinivirol, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanilano, liraglutide, obeticholic acid, pioglitazone, remetiro, sarogligazamagnesium, sradap, semaglutide, sitagliptin, TERN-101, TERN-201, topiramate, ambesentan, BMS-963272, BMS-986251, BMS-986263, HepaStem, LYS006, MET409, MET642, and orlistat.

15. An antifibrotic agent for treating a subject with advanced liver fibrosis or cirrhosis, wherein the subject is diagnosed with advanced liver fibrosis or cirrhosis by the method of any one of claims 1 to 4, wherein the agent is remetidine.

16. An in vitro method for predicting the evolution of liver fibrosis in subjects with biopsy-confirmed stage 1, 2, or 3 liver fibrosis, the method comprising: a) Provide (i) the circulating levels of soluble vascular cell adhesion molecule-1 (sVCAM), platelet-reactive protein 2 (TSP-2), and α2-macroglobulin (A2M) in the biofluid sample of the subject, and (ii) the age of the subject; b) Combine the provided sVCAM, TSP-2, and A2M levels with age in a mathematical function to assign the score SF; and c) The scores are compared to cutoff values ​​to predict the evolution of liver fibrosis in the subjects.

17. The method according to claim 16, wherein, The score SF is obtained through the following mathematical function: in y = β0 + β1 log 10 (TSP2 (ng / mL)) + β2 log 10 (sVCAM (ng / mL)) + β3 (A2M(g / L)) + β4 (Age (years)) 3 ) in: β0 is contained between -37 and -12, especially between -31 and -17; β1 is contained between 1.5 and 7.9, especially between 2 and 7; β2 is contained between 1 and 11, especially between 1.7 and 8.5; β3 is contained between 0.01 and 2.5, particularly between 0.1 and 1.5; and β4 is contained in -8 e -06 and -0.5 e -7 Between, especially at -6.5 e -6 and -1.0 e -6 between.

18. The method of claim 16 or 17, wherein an SF score above a cutoff value indicates that the subject's liver fibrosis will progress; an SF score below a cutoff value indicates that the subject's liver fibrosis will regress; and an SF score neither above nor below a cutoff value indicates that the subject's liver fibrosis will be stable.

19. A computer-aided program comprising instructions that, when executed by a processor / processing tool, cause the processor / processing tool to: - Receive (i) the levels of sVCAM, TSP-2, and A2M measured in biofluid samples isolated from the subject; and (ii) the subject's age. - The SA score is calculated from the levels of these measurements using mathematical functions; and - Subjects were assigned to a group with advanced liver fibrosis or cirrhosis based on their calculated scores compared to a predetermined cutoff value.

20. A computer program comprising instructions that, when executed by a processor / processing tool, cause the processor / processing tool to: -Receive (i) the levels of sVCAM, TSP-2, and A2M measured in the biofluid sample separated from the subject and (ii) the age of the subject; -Calculate SC based on the levels of these measurements and the stated age, using the mathematical function described above; - Compare the SC score with the SB score, which is obtained by combining previously measured levels of sVCAM, TSP-2, A2M, and age in the same subject using the same mathematical function. -Specify the subject as: When the score SC is higher than SB, there is progression of liver fibrosis; When the score SC is lower than SB, liver fibrosis has regressed; or When there is no significant change between SC and SB, there is stable liver fibrosis.

21. A computer program comprising instructions that, when executed by a processor / processing tool, cause the processor / processing tool to: -Receive (i) the levels of sVCAM, TSP-2 and A2M measured in a biofluid sample isolated from a subject with advanced liver fibrosis, wherein the subject had been administered an antifibrotic agent prior to the isolation of the biofluid sample, and (ii) the age of the subject; -Calculate the SE score based on the levels of these measurements and the stated age using the mathematical function described above; - Compare the score SC with the score SD, which is obtained by combining the levels of sVCAM, TSP-2, A2M, and age previously measured before administration of the antifibrotic agent to the same subject and using the same mathematical function; and -Specify the subject as: Subjects who did not respond to antifibrotic agent treatment when the SE score was higher than SD; When the SE score is lower than SD, it indicates a response to treatment with antifibrotic agents; or When there is no significant change between SE and SD, there is stable liver fibrosis.

22. A computer-aided program comprising instructions that, when executed by a processor / processing tool, cause the processor / processing tool to: - Receive (i) levels of sVCAM, TSP-2, and A2M measured in biofluid samples from subjects with biopsy-confirmed stage 1, 2, or 3 liver fibrosis; and (ii) the age of the subjects. - SF scores are calculated from the levels of these measurements using mathematical functions; - Compare the score SF with the predetermined cutoff value for the score SF, and -Specify the subject as: When the score SF is higher than the cutoff value, the liver fibrosis will progress in the subject; When the score SF is below the cutoff value, the liver fibrosis of the subject will regress; When the SF score is neither higher nor lower than the cutoff value, the liver fibrosis of the subject will be stable.

23. The computer-aided program according to any one of claims 19 to 22, wherein the score is calculated by a mathematical function as defined in claims 2, 8, 11 and 17.

24. A data processing apparatus comprising tools for performing the method according to any one of claims 1 to 13, 16-18.

25. 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 13, 16-18.

26. A computer-readable storage medium comprising instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 13, 16-18.

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