Improved diagnosis of metabolic dysfunction-associated fatty liver disease

Abstract

The present invention provides an improved method for diagnosing risk MASH.

Description

Background Technology

[0001] Metabolic Dysfunction-Associated Steatohepatitis (MASH) is a chronic liver disease characterized by fat accumulation, hepatocellular damage, and inflammation that are histologically similar to alcoholic hepatitis. MASH can lead to liver fibrosis, cirrhosis, liver failure, and / or hepatocellular carcinoma (HCC).

[0002] Until recently, MASH has largely been underdiagnosed because it does not involve obvious symptoms in the early stages and there is a lack of widely available non-invasive tests specifically developed to diagnose the disease. The "risk MASH" status, defined as having MASH with a MAS score ≥4 and fibrosis stage F≥2, represents an important MASH subgroup that needs to be identified. Indeed, these patients are associated with an increased risk of disease progression, particularly cirrhosis, as well as a higher risk of liver-related mortality and all-cause mortality. Due to the technical limitations and risks associated with biopsy, which serves as the clinical reference standard for diagnosing MASH and fibrosis, the development of blood-based non-invasive tests (NITs) is critical. Different NITs have been developed primarily to cater to fibrosis stages. However, there remains an unmet medical need to provide more efficient and robust blood-based NITs to address the combined fibrosis x NAS endpoint when detecting risk MASH patients. In this context, we evaluated whether it is possible to provide NITs that are improved over conventional NITs. means of solving the problem

[0003] The present invention is based on a detailed analysis of a substantial dataset of clinical trials. A non-invasive method for identifying MASH subjects at risk is provided herein.

[0004] Accordingly, the present invention relates to a method for diagnosing dangerous metabolic dysfunction-associated steatohepatitis (MASH) in a subject, wherein the method comprises quantifying levels of miR-34a-5p, particularly hsa-miR-34a-5p, and thrombospondin 2 (TSP2 or TSP-2) in a biological fluid sample of said subject.

[0005] In particular, the present invention relates to a method for the diagnosis, screening, monitoring, or prognosis of risky MASH in a subject, the method comprising the steps of: quantifying the levels of miR-34a-5p and thrombospondin 2 (TSP2) in a biological fluid sample of said subject; assigning a score by combining the quantified levels in a mathematical function; and determining whether said subject is at risk of having risky MASH by comparing said score with a cutoff value.

[0006] In a specific embodiment, the score is compared with several cutoff values ​​to determine whether the subject has a high, low, or uncertain risk of having a risk MASH.

[0007] More specifically, the present invention relates to a method for diagnosing, screening, monitoring, or determining the prognosis of risk MASH in a subject, said method comprising:

[0008] - A step of quantifying the levels of miR-34a-5p and TSP-2 in a biological fluid sample of the subject;

[0009] - A step of assigning scores by combining quantified levels in a mathematical function; and

[0010] - A step of determining whether the subject has a risk of having a risky MASH by comparing the above score with a cutoff value.

[0011] In a specific embodiment, the method of the present invention is for diagnosing risk MASH in an object.

[0012] In a specific implementation, the mathematical function includes a logistic regression equation.

[0013] In another embodiment, the biological fluid sample is a blood, serum, or plasma sample, preferably a serum sample.

[0014] In another embodiment, the subject suffers from obesity, insulin resistance, glucose intolerance, type 2 diabetes (T2DM), prediabetes, dyslipidemia, hypertriglyceridemia, or hypertension.

[0015] According to another aspect, the present invention relates to a computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to perform the following:

[0016] - Receive quantified levels of miR34a-5p and TSP-2;

[0017] - Calculate a score from these quantified levels of the object from a mathematical function as described herein;

[0018] - Subjects are assigned to the high-risk or low-risk group based on a comparison of the calculated score with a predetermined cutoff value.

[0019] In another aspect, the present invention relates to a computer-readable medium comprising a computer program disclosed herein. In a specific embodiment, the computer-readable medium is a non-transient medium or a storage medium.

[0020] Furthermore, the present invention relates to a specific anti-MASH or anti-fibrotic agent for use in the treatment of dangerous MASH in subjects requiring this, wherein the subjects are classified as having dangerous MASH by the method disclosed herein.

[0021] In another aspect, the present invention relates to a kit for the diagnosis, screening, monitoring, or prognosis of risk MASH in a subject, said kit comprising means for determining the levels of TSP-2 and miR-34a-5p.

[0022] In a specific embodiment, the kit includes an antibody or aptamer or peptide for TSP-2 and a primer useful for quantifying miR-34a-5p. Brief explanation of the drawing

[0023] Figure 1 shows the modeling of the combination of TSP-2 and miR-34a-5p (TMIR) and single biomarkers (TSP-2 and miR-34a-5p) in sex-matched subgroups, and a box plot. The Y-axis is log. 10 Amount of TSP-2 expressed as (TSP-2 (ng / mL)) (Fig. 1A), log 10 Shown are the amount of miR-34a-5p expressed as (miR-34a-5p (multiple)) (Fig. 1B) and the score calculated by modeling TMIR (Fig. 1C). Box plots are presented along with relevant Student t-tests for mean comparison, where *** p < 0.001, * p < 0.05, and p ≥ 0.05 is NS. Figure 2 shows a box plot of modeling TMIR and single biomarkers (TSP-2 and miR-34a-5p) in age-matched subgroups. * p < 0.05 and NS: p ≥ 0.05. The Y-axis is log. 10 Amount of TSP-2 expressed as (TSP-2 (ng / mL)) (Fig. 2A), log 10 It shows the amount of miR-34a-5p expressed as (miR-34a-5p (multiple)) (Fig. 2B) and the score calculated by modeling TMIR (Fig. 2C). Figure 3 shows a box plot of modeled TMIR and single biomarkers (TSP-2 and miR-34a-5p) in subgroups matched according to T2D status. NS: p ≥ 0.05. The Y-axis is log.10 Amount of TSP-2 expressed as (TSP-2 (ng / mL)) (Fig. 3A), log 10 It shows the amount of miR-34a-5p expressed as (miR-34a-5p (multiple)) (Fig. 3B) and the score calculated by modeling TMIR (Fig. 3C). Figure 4 shows the Roc curves for the risk MASH evaluation variables obtained from the validation cohort using different NITs. Specific details for implementing the invention

[0024] The present invention relates to a non-invasive method that can be used to assist in distinguishing between hazardous MASH and non-hazardous MASH in a subject from a biological fluid sample of the subject.

[0025] A histological scoring / staging system was developed to assess the activity level and fibrosis stage of metabolic dysfunction-associated fatty liver disease (MASLD) and to estimate the risk of progression to clinical hepatic outcomes. The MASLD-Activity-Score (MAS) was developed to assess the severity of MASLD. MAS is the sum of three histological scores determined from liver biopsy sections:

[0026] - S: Steatosis score: 0: <5%; 1: 5-33%; 2: 34-66% and 3: >66%;

[0027] - LI: Lobular inflammation score (20x lesions per field): 0: None; 1: <2 lesions; 2: 2-4 lesions and 3: >4 lesions; and

[0028] - HB: Balloonization Degeneration Score: 0: None; 1: Small number; 2: Numerous cells / significant balloonization.

[0029] Using this scoring system, a "MASH patient" has at least 1 point for steatosis, at least 1 point for lobular inflammation, and at least 1 point for hepatocyte ballooning, and has MAS≥3. A "non-MASH" patient is (i) a patient with at least one of the scores for steatosis, lobular inflammation, and hepatocyte ballooning being 0 and having MAS≥3; or (ii) a patient with MAS<3. Additionally, in the context of the present invention, if the patient has viral hepatitis, autoimmune liver disease, alcohol-related liver disease, drug-induced liver disease, or a congenital cause of chronic liver disease such as hereditary hemochromatosis, Wilson's disease, alpha-1-antitrypsin deficiency, and polycystic ovary syndrome, the patient is excluded from being a MASH patient.

[0030] In histological examination, the location and extent of fibrosis (F) indicate the severity (progression) of MASH. The NASH-CRN (Non-alcoholic Steatohepatitis Clinical Research Network) developed a dedicated fibrosis staging system (Kleiner, DE et al , Hepatology, 2005 Jun; 41(6):1313-21).

[0031] Definition of the MASH Clinical Research Network Scoring System F score

[0032] Perisinusoidal or periportal fibrosis 1

[0033] Mild perisinusoidal fibrosis (zone 3) 1a

[0034] Moderate perisinusoidal fibrosis (zone 3) 1b

[0035] Portal / Periodontal Fibrosis 1c

[0036] Perisinusoidal and portal / peris-portal fibrosis 2

[0037] Bridging fibrosis 3

[0038] Liver cirrhosis 4

[0039] Using this fibrosis staging system, patients with no or minimal fibrosis (F=0-1) are generally not considered to be at risk of cirrhosis, liver failure, HCC (hepatocellular carcinoma), or liver-related death. Patients with significant (F=2) and advanced fibrosis (F=3) have an increased risk of developing cirrhosis, liver failure, HCC, and liver-related death. Patients with compensated cirrhosis have severe fibrosis (F=4) and a high risk of liver failure (decompensated cirrhosis), HCC, and liver-related death. Identifying patients at risk of developing HCC, cirrhotic complications, and liver-related death is the ultimate reason for liver evaluation. As defined by the FDA and EMA, patients at risk of liver outcomes requiring pharmacological treatment are those with MAS ≥ 4 (scores ≥ 1 for steatosis, lobular inflammation, and ballooning, respectively) and a NASH-CRN fibrosis score (F) ≥ 2.

[0040] Accordingly, in the context of the present invention, a “risk MASH” patient, otherwise referred to as a “risk patient” or “patient at risk of liver outcome,” is a patient having an MAS score of 4 or higher, an S score of 1 or higher, an LI score of 1 or higher, an HB score of 1 or higher, and an F score of 2 or higher. This defines a subgroup of MASH patients having a high risk of developing at least one life-threatening liver outcome, such as cirrhosis, liver failure, HCC, and liver-related death.

[0041] The terms “subject” and “patient” may be used interchangeably herein and refer to a human subject. As mentioned above, MASH occurs more frequently in patients with metabolic disorders. Additionally, MASH is known to be associated with comorbidities, such as metabolic disorders. Therefore, the method of the present invention may be of particular benefit to patients exhibiting such comorbidities. Common comorbidities of MASH include obesity, insulin resistance, glucose intolerance, T2DM, prediabetes, dyslipidemia, hypertriglyceridemia, hypertension, and cardiovascular disease. Old age may also be a predisposition to HCC in MASH patients. Accordingly, in certain embodiments, the patient suffers from metabolic disorders, such as obesity, insulin resistance, glucose intolerance, T2DM, prediabetes, dyslipidemia, hypertriglyceridemia, and hypertension.

[0042] In certain embodiments, the term “screening” as used herein refers to the selection of patients to be treated or not to be treated in a cohort or clinical study. Screening of patients may include a diagnostic phase and subsequently assigning patients to a treatment group and a non-treatment group.

[0043] As used herein, the term "monitoring" refers to the comparison of scores calculated at two or more points in time. Monitoring is the continuous and systematic collection and analysis of data as a project or condition progresses, such as in clinical studies or treatment protocols. According to the present invention, if the score increases over time, the pathological condition progresses, whereas if the score decreases over time, the pathological condition regresses. Monitoring is possible due to the non-invasiveness of the method according to the present invention. In practice, the ease of use of this test enables repeated measurements and the tracking of the patient over time of the pathological condition. In conclusion, monitoring is the application of a diagnostic method to the patient over time.

[0044] As used herein, the expression "biological fluid sample" refers to a blood, serum, or plasma sample, preferably a serum sample.

[0045] The analysis of biological fluid samples can be performed using various analytical methods depending on the type of biomarker to be quantified. Such analytical methods include quantitative RT-PCR, mass spectrometry, immunoPCR, and immunodetection. The use of biochips to implement the simultaneous analysis of multiple biomarkers may also be mentioned.

[0046] In a specific implementation, miR34a-5p is has-miR34a-5p.

[0047] In a specific embodiment, the level of miR-34a-5p is measured as Cq (amplification cycle) or a change in the number of cycles, preferably in the number of cycles. Suitable methods are readily available to those skilled in the art. Specific RNA can be quantified after extracting total RNA from a biological fluid sample, for example, using a commercial kit. Extraction efficiency can be monitored to minimize variation between samples. Such monitoring can be performed by adding an internal process control (IPC) to the sample prior to total RNA extraction. Such an IPC may correspond to a miRNA molecule of a known sequence that is heterogeneous to the sample. For example, miRNA molecules found in species different from humans, such as Caenorhabditis elegansmiRNA molecules, particularly Cel-miR-40-3p (SEQ ID NO:1: UCACCGGGUGUACAUCAGCUAA), may be used. Such IPCs may be contained in synthetic vesicles. To quantify the level of miR-34a-5p, positive controls having known miR-34a-5p Cq values, such as three positive controls having known miR-34a-5p Cq values, may be used. These positive controls may cover the range of miR-34a-5p levels in the MASH population. When three positive controls are used, one corresponds to a low miR-34a-5p level, one corresponds to an intermediate level, and the other corresponds to a high level. The intermediate level may also be referred to as a calibrator and serves to calculate the fold change value in the test. The positive controls are processed simultaneously with the subject's test sample. For example, using a commercial reverse transcription kit, reverse transcription is performed simultaneously on i) total RNA extracted from samples supplemented with IPC and ii) total RNA from a positive control supplemented with IPC. Subsequently, quantification for cDNA can be implemented, for example, using a commercial quantitative PCR kit. The Cq determination mode may be regression. Additionally, for each patient sample, transcriptome abundance can be expressed as a fold change using both IPC and calibrator Cq according to the equation used to normalize the calibrator with respect to IPC, normalize the sample with respect to IPC, and quantify miR-34a-5p levels as a fold change.

[0048] The following paragraph describes specific but non-limiting methods for achieving miR34a-5p quantification. Briefly, total RNA is extracted from patient serum samples using the Promega magnetic bead-based extraction Maxwell® Plasma and Serum Kit (AS1680, Promega) and the RCS48 Instrument (AS8500, Promega) according to the manufacturer's instructions. To monitor extraction efficiency and minimize sample-to-sample variability, Caenorhabditis elegansA synthetic vesicle containing Cel-miR-40-3p (mature miRNA sequence UCACCGGGUGUACAUCAGCUAA-3', Integrated DNA Technologies, purified RNAse-free HPLC) is used as an IPC and added to each sample prior to RNA extraction. Three positive controls with known miR-34a Cq values ​​covering the range of miR-34a expression in the MASH population (low [C1=32 Cq], intermediate [C2=30.7 Cq] and high [C3=28 Cq] has-miR34a-5p levels, also known as calibrators) are processed concurrently with the test samples, and the intermediate standard also acts as a calibrator for the assay to calculate the fold change value. Total RNA from the serum samples containing the IPC and total RNA from the positive controls likewise containing the IPC are reverse transcribed simultaneously using the TaqMan MicroRNA Reverse Transcription Kit (4366597, Applied Biosystems, Thermo Fisher Scientific). Reverse transcription is performed in a final 24 μL mixture containing 3 μL of TaqMan MicroRNA Assay 5X and incubated in a Thermal Cycler T100 (Biorad). The cDNA is stored in low-binding tubes at -20°C until further use. The expression of mature miRNA is quantified using TaqMan miRNA RT-qPCR Assay 20X and TaqMan Universal Master Mix II, no Uracil-N-Glycosilase (UNG) (4440040, Applied Biosystems, ThermoFisher Scientific) according to the manufacturer's instructions. A total fixation volume of 5 μL of cDNA is used as a template for qPCR assays using the CFX96 Real-Time PCR detection System.The miR-34a TaqMan assay (sequence of mature hsa-miR-34a-5p = UGGCAGUGUCUUAGCUGGUUGU (SEQ ID NO: 2), miR-base number = MIMAT0000255; and sequence of mature Cel-miR-40-3p = UCACCGGGUGUACAUCAGCUAA (SEQ ID NO: 1), miR-base number = MIMAT0000011) is used. The Cq determination mode is regression. For each patient sample, miRNA levels are expressed as a fold change using both IPC and calibrator Cq according to the following equation:

[0049] - Step 1: Normalization of Calibrator C2 for the internal process control

[0050] ΔCq C2 miR-34 = Cq C2 miR-34a - Cq C2 miR-40 in C2

[0051] - Step 2: Normalization of samples against the internal process control group

[0052] ΔCq Sample miR-34a = Cq sample miR-34a - Cq sample miR-40

[0053] - Step 3: Calculation of sample miR-34a delta expression at Cq

[0054] ΔΔCq sample miR-34 = ΔCq Sample - ΔCq C2

[0055] - Step 4: Sample delta expression of miR-34a expressed as a divisor

[0056] hsa-miR-34a multiple change (FC) = 2 -ΔΔCq

[0057] The circulating level of TSP-2 can be measured by any conventional methodology widely known in the art, such as immunoassays (e.g., ELISA (enzyme-linked immunosorbent assay), immunoturbidimetry, immunocytography, protein arrays). For example, the level of TSP-2 can be determined by antibodies, aptamers, or peptides against the marker.

[0058] In a specific embodiment, the level of TSP-2 is measured in ng / mL. Specific TSP-2 can be quantified by the Quantikine® ELISA for Human Thrombospondin-2 Immunoassay from R&D Systems (Minneapolis, USA). A quantitative sandwich enzyme immunoassay technique is used for the assay. Monoclonal antibodies specific to human TSP-2 are pre-coated on a microplate. Standards (0.313 ng / mL to 20 ng / mL) and serum (15-fold dilution) are pipetted into wells, and any TSP-2 present binds to the immobilized antibody. After unbound material is washed away, an enzyme-conjugated polyclonal antibody (human TSP-2 conjugated to horseradish peroxidase with a preservative) is added to the wells. After washing to remove unconjugated antibody-enzyme reagents, a substrate solution is added to the wells, and a color is expressed in proportion to the amount of TSP-2 bound in the initial step. Color development is stopped and the color intensity is measured. A standard curve is generated by processing the data using computer software capable of generating a 4-parameter logistic (4-PL) curve fit.

[0059] In a specific embodiment, circulating levels of miR-34a-5p and TSP-2 are measured from one or more blood-derived sample(s) from a subject. In that case, the same type of sample is used whenever a measurement is to be performed. For clarity, this means that if a previous measurement was performed from a serum sample, a subsequent measurement is performed from a serum sample of the same subject. Likewise, if a previous measurement was performed from a blood or plasma sample, a subsequent measurement is performed from a blood or plasma sample of the same subject, respectively. In a specific embodiment, circulating levels of the markers are measured from one or more serum sample(s) from a subject.

[0060] According to another embodiment, the selection of at-risk MASH patients among MASLD patients depends on the score calculated based on the patient's miR-34a-5p and TSP-2 levels. Further comparison of the score and cutoff value may be used to assign the patient to a treatment group or a non-treatment group.

[0061] According to the present invention, each of the above cutoff values ​​may be a specific value or a range of values.

[0062] Accordingly, preferably, each biomarker level of the subject can be introduced into a mathematical function to generate an output value correlated with the risk MASH status. Thus, the method can be used to distinguish whether the subject has risk MASH or not. Those skilled in the art know a number of suitable methods for developing mathematical functions, all of which are within the scope of the present invention. In a specific embodiment, the mathematical function comprises a logistic regression equation.

[0063] In an additional embodiment, the method of the present invention implements the following expression:

[0064]

[0065] Here

[0066] y= β0 + β1 * log 10 (TSP-2 (ng / mL)) + β2* log 10 (miR-34a-5p (multiple)).

[0067] In a specific embodiment, β0 is between -11.3 and 2.5, particularly between -9.859 and 1.124, and more particularly equal to -5.2897.

[0068] In a specific embodiment, β1 is between 1.5 and 6.5, particularly between 1.824 and 6.1574, and more particularly equal to 3.8547.

[0069] In a specific embodiment, β2 is between 0.7 and 3.4, particularly between 0.985 and 2.9841, and more particularly equal to 1.8733.

[0070] For example, the following equation can be used to diagnose risk MASH.

[0071] y= -5.2897+ 3.8547 * log10(TSP2 (ng / mL)) + 1.8733 * log10(miR-34a-5p (multiple)).

[0072] Next, the score calculated from the mathematical function can be compared with a predetermined cutoff value. According to an embodiment, the cutoff is between 0.13 and 0.92. In a specific embodiment, the cutoff is between 0.2806 and 0.7337. In a more specific embodiment, the cutoff is equal to 0.5526.

[0073] According to another embodiment, a score calculated from a mathematical function can be compared with predetermined cutoff values, such as a low cutoff value and a high cutoff value. In this context, if the calculated S value is lower than the low cutoff, it indicates that the subject does not have a risky MASH, and if the calculated S value is greater than or equal to the high cutoff value, it indicates that the subject has a risky MASH.

[0074] In a specific embodiment, the lower cutoff is between 0.28 and 0.48, particularly between 0.37 and 0.39. In an additional specific embodiment, the lower cutoff is equal to 0.3806. In another specific embodiment, the higher cutoff is between 0.59 and 0.89, particularly between 0.62 and 0.65. In an additional specific embodiment, the higher cutoff is equal to 0.6337.

[0075] The present invention also relates to a computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to perform the following:

[0076] - Receive quantified levels of miR-34a-5p and TSP-2;

[0077] - Calculate a score from these quantified levels of the object using the mathematical function as described above;

[0078] - Subjects are assigned to the high-risk or low-risk group based on a comparison of the calculated score with a predetermined cutoff value.

[0079] The present invention further provides a computer-readable medium comprising a computer program described therein. According to a specific embodiment, the computer-readable medium is a non-transient medium or a storage medium.

[0080] In some embodiments, by the method of the present invention, a decision may be made to provide lifestyle recommendations to a subject (e.g., providing dietary recommendations or physical activity recommendations), to medically manage the subject (e.g., by establishing regular visits or examinations by a doctor to regularly monitor liver damage markers), or to administer at least one MASH or liver fibrosis therapy to the patient to treat or prevent risk MASH. In specific embodiments, a decision may be made to provide lifestyle recommendations to a subject or to administer at least one MASH or liver fibrosis therapy. Accordingly, the present invention further relates to an anti-MASH or anti-fibrotic compound for use in a method for treating MASH in a subject who requires it, wherein the subject is identified by the method according to the present invention.

[0081] As used herein, the term "treatment" refers to both therapeutic measures and preventive or protective measures, the purpose of which is to prevent or slow (reduce) unwanted physiological changes or disorders. Beneficial or desired clinical outcomes include, but are not limited to, the alleviation of symptoms, the stabilization of the pathological condition (specifically, without worsening), the slowing or halting of disease progression, and the improvement or alleviation of the pathological condition. In particular, for the purposes of the present invention, treatment is intended to slow the progression of MASH and / or fibrosis and reduce the risk of additional complications. This may also include extending survival compared to the survival expected without treatment.

[0082] In the context of the present invention, the patient to be treated is a high-risk MASH patient, and the patient not to be treated is a non-high-risk MASH patient.

[0083] Anti-MASH or anti-fibrotic agents are administered at a therapeutically effective dose. As used herein, the expression "therapeutically effective dose" refers to the amount of drug effective in achieving a desired therapeutic outcome. The therapeutically effective dose of a drug may vary depending on factors such as the disease state, the individual's age, sex, and weight, and the drug's ability to induce a desired response in the individual. The therapeutically effective dose is also an amount in which the therapeutically beneficial effect outweighs any toxic or adverse effects of the formulation. The effective dose and administration regimen for the drug depend on the disease or condition to be treated and can be determined by a person skilled in the art. A physician with ordinary skill in the art can easily determine and prescribe the effective dose of the necessary pharmaceutical composition. For example, a physician may start the dose 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 dose of the composition of the present invention will be the amount of the compound that is the lowest effective dose to produce a therapeutic effect according to a specific administration regimen. Such an effective dose will generally depend on the factors described above.

[0084] In a specific embodiment, the present invention relates to an anti-MASH compound for use in a method for treating MASH in a subject suffering from dangerous MASH, wherein the subject is classified as having dangerous MASH by the method according to the present invention.

[0085] Exemplary anti-MASH and anti-fibrotic compounds are listed below:

[0086] - Compound of formula (I) or a pharmaceutically acceptable salt thereof:

[0087] (I)

[0088] Here:

[0089] X1 represents a halogen atom, an R1 group, or a G1-R1 group;

[0090] A represents a CH=CH group or a CH2-CH2 group;

[0091] X2 represents the G2-R2 group;

[0092] G1 represents an oxygen atom;

[0093] G2 represents an oxygen or sulfur atom;

[0094] R1 represents an alkyl group substituted with one or more substituents selected from a hydrogen atom, an unsubstituted alkyl group, an aryl group or a halogen atom, an alkoxy group, an alkylthio group, a cycloalkyl group, a cycloalkylthio group, and a heterocyclic group;

[0095] R2 represents an alkyl group substituted with a -COOR3 group, wherein R3 represents an alkyl group substituted or not substituted with one or more substituents selected from a hydrogen atom or a halogen atom, a cycloalkyl group, and a heterocyclic group; and

[0096] R4 and R5 are the same or different and represent an alkyl group that is substituted or not substituted with one or more substituents selected from halogen atoms, cycloalkyl groups and heterocyclic groups;

[0097] - AMP-activated protein kinase stimulants, e.g., PXL-770, MB-11055, Debio-0930B, metformin, CNX-012, O-304, mangiferin calcium salt, eltrombopac, carotuximab, and imeglimin;

[0098] - Bile acids, e.g., obeticholic acid (OCA), ursodeoxycholic acid (UDCA), norursodeoxycholic acid, and ursodiol;

[0099] - CCR antagonists, e.g., cenicriviroc (CCR2 / 5 antagonist), PG-092, RAP-310, INCB-10820, RAP-103, PF-04634817, and CCX-872;

[0100] - Dipeptidyl peptidase IV (DPP4) inhibitors, evogliptin, vidagliptin, potagliptin, alogliptin, saxagliptin, tilogliptin, anagliptin, sitagliptin, retagliptin, melogliptin, sogliptin, trelagliptin, teneligliptin, dutogliptin, linagliptin, gemigliptin, yogliptin, betagliptin, imigliptin, omagliptin, vidagliptin, and denagliptin;

[0101] - Farnesoid X receptor (FXR) agonists, e.g., obeticholic acid (OCA), tropexor (LJN452), cilofexor (GS9674), nidupexor (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)-isooxazole-4-ylmethoxy]-piperidine-1-yl}-1-methyl-1H-indole-3-carboxylic acid), TERN-201, TERN-501, and TERN-301;

[0102] - Fibroblast growth factor 19 (FGF-19) receptor ligand or functionally modified variant of FGF-19;

[0103] - Fibroblast growth factor 21 (FGF-21) agonists, e.g., PEG-FGF21 (Pegbelpermin, formerly BMS-986036), YH-25348, BMS-986171, YH-25723, LY-3025876, and NNC-0194-0499;

[0104] - Engineered fibroblast growth factor 19 (FGF-19) analogs, e.g., NGM-282 (aldafermin);

[0105] - Glucagon-like peptide-1 (GLP-1) analogs, e.g., semaglutide, liraglutide, exenatide, albiglutide, dulaglutide, lixisenatide, loxenatide, effeglenatide, taspoglutide, MKC-253, DLP-205, and ORMD-0901;

[0106] - Nicotinic acid, e.g. niacin and vitamin B3;

[0107] - Nitazoxanide (NTZ), its active metabolite Tizoxanide (TZ), or other precursor drugs of TZ, e.g., RM-5061;

[0108] - PPAR alpha agonists, e.g., fenofibrate, ciprofibrate, pemafibrate, gemfibrozil, clofibrate, vinifibrate, clinofibrate, clopibric acid, nicopybrate, pyrifibrate, flafibride, ronifibrate, theofibrate, tocofibrate, and SR10171;

[0109] - PPAR gamma agonists, e.g., pioglitazone, deuterated pioglitazone, rosiglitazone, efatotazone, ATx08-001, OMS-405, CHS-131, THR-0921, SER-150-DN, KDT-501, GED-0507-34-Levo, CLC-3001, and ALL-4;

[0110] - PPAR delta agonists, e.g., GW501516 (Endurabol or ({4-[({4-methyl-2-[4-(trifluoromethyl)phenyl]-1,3-thiazole-5-yl}methyl)sulfanyl]-2-methylphenoxy}acetic acid)), MBX8025 (Seladelpar or {2-methyl-4-[5-methyl-2-(4-trifluoromethyl-phenyl)-2H-[l,2,3]triazole-4-ylmethylsilphanyl]-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;

[0111] - PPAR alpha / gamma dual agonists (also named glitasar) e.g., saroglitazar, aleglitazar, muraglitazar, tesaglitazar, and DSP-8658;

[0112] - PPAR gamma / delta dual agonists, e.g., conjugated linoleic acid (CLA) and T3D-959;

[0113] - PPAR alpha / gamma / delta pan agonists or PPARpan agonists, e.g., IVA337, TTA (tetradecylthioacetate), babakinine, GW4148, GW9135, bezafibrate, ranifibranor, lobeglitazone, and CS038;

[0114] - Sodium-glucose transporter (SGLT) 2 inhibitors, e.g., ricoglifosin, remogliflozin, dapagliflozin, empagliflozin, ertugliflozin, sotagliflozin, ipragliflozin, tianagliflozin, canagliflozin, topogliflozin, zanagliflozin, bexagliflozin, luceogliflozin, sergliflozin, HEC-44616, AST-1935, and PLD-101.

[0115] - Stearoyl CoA desaturase-1 inhibitors / fatty acid bile acid conjugates, e.g., Aramcol, GRC-9332, Steemcol, TSN-2998, GSK-1940029, and XEN-801;

[0116] - Thyroid receptor β (THR β) agonists, e.g., VK-2809, Resmetirom (MGL-3196), MGL-3745, SKL-14763, Sovetirom, BCT-304, ZYT-1, MB-07811, and Eprothirom;

[0117] - Vitamin E and isoforms; Vitamin E combined with Vitamin C and atorvastatin.

[0118] In certain embodiments, the anti-MASH agonist is selected from pegbelpermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, ranifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, seladelfar, semaglutide, sitagliptin, TERN-101, TERN-201, and tropexor.

[0119] The present invention also relates to a kit for the diagnosis, screening, monitoring, or prognostic determination of risk MASH in subjects. The kit comprises means for determining levels of TSP-2 and miR-34a-5p. In a specific embodiment, the kit comprises a specific antibody, aptamer, or peptide for measuring TSP-2 and a primer useful for quantifying miR-34a-5p.

[0120] The antibody against TSP-2 may be any monoclonal, polyclonal, and / or conjugated antibody against TSP-2 known in the art. For example, the antibody against TSP-2 is the monoclonal antibody Thrombospondin 2 (4) (Ref: sc-136238, Santa Cruz Biotechnology, Texas, USA).

[0121] Primers useful for quantifying miR-34a-5p can be designed according to any conventional method. For example, these primers are TaqMan TM It could be a probe.

[0122] The kit of the present invention may further include immunoassay standards and reagents. In a specific embodiment, the kit includes at least one specific positive control for TSP-2 and / or at least one specific positive control for miR-34a-5p.

[0123] In particular, the positive control for TSP-2 comprises 11 to 300 ng / mL of TSP-2. The positive control for miR-34a-5p comprises 32 Cq to 28 Cq of miR-34a-5p.

[0124] More specifically, the kit of the present invention further comprises three positive controls for TSP-2 and three positive controls for miR-34a-5p, wherein the positive controls for TSP-2 each comprise TSP-2 of 30, 50, and 100 ng / mL; and wherein the positive controls for miR-34a-5p each comprise low [C1=32 Cq], intermediate [C2=30.7 Cq] and high [C3=28 Cq] miR34a-5p levels, also known as calibrators.

[0125] The present invention is further described with reference to the following non-limiting embodiments.

[0126] Examples

[0127] 1. dataset

[0128] Patients were screened to form the study population for potential inclusion in the Resolve-It Phase 3 clinical trial. All patients for whom biopsy results were available, with a time interval of less than 90 days between the biopsy and blood collection, and who had blood samples used for biomarker measurement were selected as potentially useful for this study.

[0129] Next, all patients were selected who had complete data regarding the selected set of biomarkers as well as complete data regarding conventional demographic and clinical parameters (age, sex, type 2 diabetes, dyslipidemia, arterial hypertension [HT], BMI).

[0130] Through this selection, a total of 2,036 patients were obtained. Of these patients, 500 (~25%) were randomly selected, and the remaining population was retained to potentially be included in the validation dataset. To control for potential confounding factors when handling modeling and the selection of related biomarkers, a propensity score matching algorithm was applied to homogenize the following parameters between patients with and without risk MASH: age, sex, type 2 diabetes, dyslipidemia, arterial hypertension, and BMI.

[0131] In this process, 360 patients were selected, consisting of 180 patients with risk MASH and 180 patients without, and were well balanced for the parameters shown in Table 1. In this training cohort, the mean age of the patients was 54 years, 64% were male, 35% had T2D, and 75% were obese. Regarding histology, 49% of patients without risk MASH had fibrosis stage F1, while patients with risk MASH were equally distributed between F2 and F3 (44% each).

[0132] Table 1:

[0133]

[0134] During the training process, a biomarker panel including two biomarkers, TSP-2 and miR-34a-5p, was selected. Based on these results, all patients who were not part of the training dataset were selected, provided they had available measurements for these two biomarkers, satisfied the conditions of biopsy result availability and a time interval of ≤90 days between blood collection and biopsy. As a result, an independent validation dataset containing 1,846 patients was obtained. Key characteristics are reported in Table 1 above.

[0135] In this validation dataset, and as is typically observed in MASH databases, the risk MASH population had significantly higher rates of T2D, dyslipidemia, HT, and obesity. Regarding histology, 85% of patients without risk MASH had MASLD, and 41% had MASH.

[0136] 1. Statistical analysis

[0137] Descriptive statistics for baseline characteristics were generated and reported as mean ± sd or %(n), and patients without risk of MASH and patients with risk were compared using appropriate 2-sample tests (Student t-test for mean comparison and χ² test for percentage comparison).

[0138] The modeling process was performed using a Stepwise algorithm that starts with an empty model, sequentially inserts variables that minimize this criterion, and sequentially compares binomial logistic regression models for the detection of risk MASH based on the relevant BIC (Bayesian Information Criterion). The process was stopped when none of the remaining variables resulted in a decrease in the BIC value. To this end, the stat R package ( step The function was used. The regression coefficients of the selected model were extracted, and the relevant 95% CI was reported.

[0139] Specific cutoff values ​​were derived using the training dataset. A low cutoff for achieving 85% sensitivity (Sen) and a high cutoff for achieving 90% specificity (Spe) were extracted, and the diagnostic performance (Sen, Spe, PPV, NPV, Acc) associated with these cutoffs was calculated for both the training and validation datasets. Additionally, the Youden cutoff that maximizes Sen + Spe was reported.

[0140] The overall diagnostic performance of non-invasive tests and biomarkers was estimated using AUROC values, and significant differences in these statistics were tested using the Delong test. AUROC values ​​were reported along with 95% CIs estimated using 1,000 bootstrap samples. The relevant ROC curves were reported graphically.

[0141] Box plots were presented along with relevant Student t-tests for mean comparison, and p-values ​​were reported graphically as follows: **** if p < 0.0001, *** if p < 0.001, ** if p < 0.01, * if p < 0.05, and NS if p ≥ 0.05.

[0142] All propensity score matching algorithms were executed using the matchIt function of the MatchIt R package, and all statistical analyses were performed using R programming language version 4.3.0.

[0143] When running the stepwise algorithm, the combination of TSP-2 and miR-34a-5p was identified as the optimal combination because it returned the lowest BIC before the addition of random additional biomarkers caused an increase in BIC. Based on this selection of biomarkers, the following equation was obtained using a binomial logistic regression model:

[0144]

[0145] Here

[0146] y = β0 + β1 * log 10 (TSP2 (ng / mL)) + β2* log 10 (miR-34a-5p (multiple)).

[0147] The coefficients of this model were reported in Table 2 along with their corresponding 95% CIs, all of which reached high significance with a corresponding p-value <0.0001.

[0148] Table 2:

[0149]

[0150] Based on this modeling and training dataset, the cutoff values ​​to achieve 85% sensitivity (low cutoff) and 90% specificity (high cutoff) were fixed at 0.3806 and 0.6337, respectively. The Youden cutoff that maximizes the sum of the sensitivity and specificity of this diagnostic method was fixed at 0.5526.

[0151] 1. result

[0152] 3.1 Performance of Combination TSP-2 and miR-34a-5p vs. Single Biomarker

[0153] Next, the performance of the modeling described above (named TMIR) was tested on an independent validation dataset by comparing it with the performance of TSP-2 alone or miR-34a-5p alone. To measure this, AUROC values ​​for TSP2 alone and miR34a alone were calculated and compared to those achieved by the modeling TMIR. The results obtained from the validation dataset, which included 1846 patients, are reported in Table 3.

[0154] Table 3: AUROC values ​​for hazardous MASH detection

[0155]

[0156] Modeling TMIR achieved a significantly higher AUROC than when the two biomarkers were used alone, with a p-value <0.0001, confirming the superior performance of the TSP-2 and miR-34a-5p combination in one model.

[0157] 3.2 Robustness of the combination of TSP-2 and miR-34a-5p

[0158] The potential robustness of the TSP-2 and miR-34a-5p combination was further analyzed for three different parameters of interest in the fields of diagnosis and MASH. These parameters were patient age, sex, and T2D status. In fact, it is important to ensure that any diagnostic test can be interpreted independently of these patient parameters.

[0159] To this end, a propensity score matching algorithm was applied using the validation dataset to extract two subgroups in each case while balancing against the following list of potential confounding factors: age, sex, T2D prevalence, dyslipidemia and HT, BMI, risk MASH prevalence, fibrosis, steatosis, ballooning, and graded lobular inflammation.

[0160] In this process, two subgroups were created for each of the parameters of interest (age, sex, and T2D), and each time, these subgroups were well balanced against the other parameters listed above. For the two age subgroups, one subgroup containing patients aged ≤50 and a second subgroup containing patients aged ≥60 were created to ensure clear age separation between the two subgroups. A total of 1,242 patients (621 in each category), 676 patients (338 in each category), and 1,170 patients (585 in each category) were extracted for sex, age, and T2D, respectively.

[0161] The distribution of modeling TMIRs and their related biomarkers in each relevant category (gender, age, and T2D status) is reported in box plots in Figures 1, 2, and 3.

[0162] As observed in Figure 1, a decrease in TSP-2 expression was observed between men and women, whereas miR-34a expression was higher between men and women, as previously observed in past studies. When the two biomarkers were combined in the modeling, the patient's sex did not have a significant effect on the distribution associated with this test.

[0163] Regarding age, this parameter did not affect TSP-2, whereas miR-34a-5p was significantly reduced in older patients. However, as reported in Figure 2, the magnitude of this reduction was not sufficient to cause a significant age effect on the modeled TMIR when combined with TSP-2.

[0164] Finally, when focusing on and comparing patients with and without T2D, this parameter did not have a significant effect on any biomarker, and both returned similar distributions. As a result, as reported in Figure 3, similar distributions for modeled TMIR were obtained across these two subgroups.

[0165] These results show that the patient's gender, age, and T2D status do not have a significant effect on the diagnostic method of the present invention.

[0166] Overall, the combination of TSP-2 and miR-34a-5p exhibits high robustness and can be used with a fixed cutoff regardless of patient parameters, such as gender, age, and T2D status.

[0167] 3.3 Performance of Combination TSP-2 and miR-34a-5p vs. Other NITs

[0168] Finally, the overall performance of the TSP-2 and miR-34a-5p combination in the modeling TMIR for the detection of risky MASH was compared with other conventional non-invasive tests, namely FIB-4, ELF, NFS, and ALT.

[0169] To this end, a dataset consisting of 1,671 patients was obtained by selecting patients with complete data on these NITs from all patients included in the validation cohort. AUROC values ​​and related Delong tests were derived for the AUROC differences between combinations and different NITs. The results are summarized in Table 4. The related ROC curves are also reported graphically in Figure 4.

[0170] Table 4: AUROC values ​​for hazardous MASH detection

[0171]

[0172] In this population, the modeled TMIR achieved an AUROC of 0.85, which was significantly higher than that achieved by other NITs, and all p-values ​​were <0.0001. These results confirm the high performance of this biomarker combination and, in particular, the benefits obtained by using this new diagnostic test over conventional diagnostic tests.

[0173] Therefore, an efficient and robust NIT was identified. This new diagnostic method requires a limited number of biomarkers (TSP-2 and miR-34a-5p), both of which were quantified in serum.

[0174] Most interestingly, this new model provides high diagnostic performance. Furthermore, it was demonstrated that gender, age, and T2DM status do not affect this new NIT. This provides a useful new tool for diagnosing MASH subjects at risk.

Claims

Claim 1 A method for the diagnosis, screening, monitoring, or prognosis of dangerous metabolic dysfunction-associated steatohepatitis (MASH) in a subject, the method comprising the following steps: - quantifying the levels of miR-34a-5p and TSP-2 in a biological fluid sample of the subject; - assigning a score by combining the quantified levels in a mathematical function; and - determining whether the subject is a dangerous MASH subject by comparing the score with a cutoff value. Claim 2 In claim 1, a method in which the comparison of the scores is performed using a cutoff value. Claim 3 A method according to claim 1 or 2, wherein the mathematical function includes a logistic regression function. Claim 4 A method according to any one of claims 1 to 3, wherein the biological fluid sample is a blood, serum, or plasma sample. Claim 5 A method according to any one of claims 1 to 4, wherein the biological fluid sample is a serum sample. Claim 6 A method according to any one of claims 1 to 5, wherein the subject suffers from obesity, insulin resistance, glucose intolerance, T2DM, prediabetes, dyslipidemia, hypertriglyceridemia, or hypertension. Claim 7 A computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to perform the following: - receive quantified levels of miR-34a-5p and TSP-2; - calculate a score from these quantified levels of the subject using a mathematical function; and - assign the subject to a dangerous subject group or a non-dangerous subject group based on a comparison of the calculated score with a predetermined cutoff value. Claim 8 A computer-readable medium containing a computer program according to claim 7. Claim 9 In claim 8, a computer-readable medium that is a non-transient medium or a storage medium. Claim 10 An anti-MASH or anti-fibrotic agent for use in the treatment of risky MASH in subjects requiring this, wherein the anti-MASH agent is selected from pegbelpermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, ranifibranor, liraglutide, obeticholic acid, pioglitazone, resmetiroc, saroglitazar magnesium, seladelfar, semaglutide, sitagliptin, TERN-101, TERN-201, and tropexor, and the subject is classified as having risky MASH by the method according to any one of claims 1 to 6. Claim 11 A kit for the diagnosis, screening, monitoring, or prognosis of risk MASH in a subject, wherein the kit comprises means for determining the levels of TSP-2 and miR-34a-5p. Claim 12 A kit according to claim 11 comprising an antibody or aptamer or peptide against TSP2 and primers useful for quantifying miR-34a-5p.

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