WO2025008465A2

WO2025008465A2 - Use of a biomarker for determining the child-pugh class into which an individual is to be classified

Info

Publication number: WO2025008465A2
Application number: PCT/EP2024/068882
Authority: WO
Inventors: Eric SCHIFFER; Rudolf JAGDHUBER; Sebastian DE JEL; Frank STÄMMLER; Sebastian RÖTZER; Andrew Robertson; Johannes EIGLSPERGER
Original assignee: Numares AG
Current assignee: Numares AG
Priority date: 2023-07-06
Filing date: 2024-07-04
Publication date: 2025-01-09
Anticipated expiration: 2026-01-06
Also published as: WO2025008465A3

Abstract

The present invention relates, amongst others, to the use of a marker chosen from the group consisting of high-density lipoprotein, apolipoprotein A1, valine, lactate, and pyruvate in an in vitro method for determining a Child-Pugh class into which an individual is to be classified.

Description

Use of a biomarker for determining the Child-Pugh class into which an individual is to be classified

Description

The present invention relates to the in-vitro use of a marker for determining the Child-Pugh class into which an individual is to be classified according to the preamble of claim 1 , to the further medical use of such a marker according to the preamble of claim 13, and to an analysis method for determining the Child-Pugh class into which an individual is to be classified according to the preamble of claim 14.

More generally, the present invention relates to biomarkers that are applicable for accurately estimating liver function. The term “liver function” is broad, given the vast array of physiological and biochemical functions the organ carries out, chiefly biliary synthesis for fatty acid digestion, metabolism of carbohydrate, protein and lipids, synthesis of protein and detoxification. Thus, hepatocyte function is paramount to physiological survival.

Given the broad range of functions the liver carries out, it is challenging to accurately define liver function numerically, as opposed to the glomerular filtration rate of the kidney or the ejection fraction of the heart. A number of blood tests are available which reflect the general damage to hepatocytes - mostly products of hepatic metabolic pathways and enzymes - the most common in clinical practice being serum aminotransferases, bilirubin, alkaline phosphatase, albumin, and prothrombin time. These tests are commonly grouped together under the umbrella term ‘Liver Function Tests’ which is misleading, since most are unable to reflect how well the liver is functioning and abnormal values can be due to diseases unrelated to the liver. Moreover, these tests may be normal in patients with advanced liver disease yet abnormal in asymptomatic healthy individuals [1 -3]. The combination of detection of serial changes in this test panel interpreted together with patient symptomatology can assist in monitoring progression or remission of disease and can trigger subsequent and more advanced diagnostic testing.

Current specific liver function tests are limited. The dye indocyanine green has been in practice for decades for liver function monitoring. The so-called indocyanine green tracer test (ICG) measures hepatic elimination of the dye and is hence a diagnostic tool of overall liver function and a prognostic predictor of mortality. For example, it has utility in peri-operative liver function monitoring during liver surgery, an assessment of liver failure acuity, and as a prognostic tool for critically ill patients. Adverse reactions are rare although it is contraindicated in known iodine allergy. Despite its utility, strong levels of evidence are lacking, and it is therefore not recommended for routine liver function assessment [4, 5].

Image-based modalities assessing liver function exist but are limited in scope and resource. Nuclear medicine scans metastable technetium-99 (99mTc) galactosyl and mebrofenin using plain scintigraphy and single-photon emission computed tomography (SPECT-CT) have been utilized, however these modalities deliver significant radiation exposure to the patient and user. Gadolinium enhanced MRI (Gd-EOB) can show high spatial and temporal metabolic resolution in the liver, yet is strictly limited by magnetic resonance imaging (MRI) availability and cost [6, 7]-

A simpler measure of global liver function lies with the Child-Pugh score, particularly in patients having cirrhosis. Originally designed to assess the risk of non-shunt operations in patients with cirrhosis (namely transection of the esophagus for bleeding esophageal varices), it was further validated to stratify the risk of portacaval shunt surgery in patients with cirrhosis. It was also later demonstrated to correlate with survival in patients not undergoing surgery. Additionally, Child-Pugh class is also associated with the likelihood of developing complications of cirrhosis, such as variceal hemorrhage. Components of the modified Child-Pugh classification of the severity of liver disease are degree of ascites, the serum concentrations of bilirubin and albumin, the prothrombin time, and the degree of encephalopathy.

A total Child-Pugh score (sometimes also referred to as Child-Turcotte-Pugh score) of 5 to 6 is considered Child-Pugh class A (well-compensated disease, 10 % postoperative mortality risk), a score of 7 to 9 is class B (significant functional compromise, 30 % postoperative mortality risk), and a score of 10 to 15 is class C (decompensated disease, 82 % postoperative mortality risk). These classes correlate with one- and two-year patient survival: class A: 100 and 85 %; class B: 80 and 60 %; and class C: 45 and 35 % [8-11 ]. As such, an accurate Child- Pugh score as an estimation of global liver function has significant utility in perioperative planning of patients with cirrhosis, resource management, prioritization of liver treatment, identification of those at risk of decompensated liver failure, and general prognostication. The Child-Pugh score is currently embedded in the treatment algorithm for hepatocellular carcinoma [12], and is a feature of liver transplant eligibility [13].

Measurement of an accurate Child-Pugh score however can be challenging, given the difficult nature of ascites and hepatic encephalopathy diagnosis and severity scaling. An accurate diagnosis of ascites is typically dependent on ultrasound imaging and an invasive paracentesis procedure [14], while hepatic encephalopathy has no standardized diagnostic method and depends on subjective assessment from a clinician and questionable insensitive psychometric testing [15, 16].

In summary, there is a clear need for non-invasive biomarkers enabling a quantitative test which combines a highly accurate estimation of liver function yet as simple as performing a standard liver function panel, obliviating the need for invasive tracer application, avoiding ionizing radiation and associated labor, and the negating the cost of high-resolution imaging.

US 2020/0378991 A1 describes biomarkers and biomarker panels useful for diagnostic methods evaluating liver disease status in a subject, monitoring liver disease, distinguishing between liver diseases, treating subjects evaluated by diagnostic methods of the invention, providing diagnostic tests for evaluating liver disease status in a subject, and kits therefor. The biomarkers are chosen from bile acids, free fatty acids, amino acids, and carbohydrates listed in Table 1 of this U.S. patent application. A specific example relates to a biomarker panel comprising palmitic acid (C16:0), palmitic acid (C16:0)/palmitoleic acid (C16:1 n7) ratio, tyrosine, fructose, fructose/glucose ratio, glycochenodeoxycholic acid (GCDCA), and glycocholic acid (GCA).

US 2017/0370954 A1 describes biomarkers of nonalcoholic steatohepatitis (NASH), nonalcoholic fatty liver disease (NAFLD), and fibrosis and methods for diagnosis (or aiding in the diagnosis) of NAFLD, NASH and/or fibrosis. Additionally, methods of distinguishing between NAFLD and NASH, methods of classifying the stage of fibrosis, methods of determining the severity of liver disease, methods of determining the severity of liver disease or fibrosis, and methods of monitoring progression/regression of NASH, NAFLD, and/or fibrosis are described. In this context, this U.S. patent application lists scores of various substances. A specific example is a biomarker or biomarker set selected from the group consisting of 5-methylthioadenosine (5-MTA), glycine, serine, leucine, 4-methyl-2- oxopentanoate, 3-methyl-2-oxovalerate, valine, 3-methyl-2-oxobutyrate, 2-hydroxybutyrate, prolylproline, lanosterol, tauro-beta-muricholate, and deoxycholate.

It is an object of the present invention to provide novel methods and biomarkers for a highly accurate estimation of liver function.

This object is achieved with the in-vitro use of a marker having the claim elements of claim 1 . The marker is chosen from the group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, lactate, and pyruvate. The marker is used for determining a Child- Pugh class into which an individual is to be classified. Expressed in other words, the marker is used for classifying a patient into the appropriate Child-Pugh class.

For this purpose, the concentration of the marker is determined in a body fluid obtained from a patient. This concentration determination can be carried out by any appropriate measuring or analysis method, such as nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, high-performance liquid chromatography (HPLC), infrared spectroscopy such as Fourier-transform infrared (FT-IR) spectroscopy, clinical chemistry, and immunodiagnostics.

An alteration of concentration of an individual marker with respect to the concentration in a control group or an alteration of a concentration ratio between at least two markers with respect to the concentration ratio of the same markers in a control group was correlated in a statistically significant way with the Child-Pugh classification at the time of analysis (i.e., enabling a classification of an individual into a Child-Pugh class).

Already upon testing individual biomarkers, highly significant results could be obtained for allowing a classification of an individual into a Child-Pugh class. The area under the curve (AUC) values of receiver operating characteristic (ROC) plots showed values lying at 0.75, at 0.80 or at even higher values. The AUC value of ROC plots is an aggregated metric that evaluates how well a logistic regression model classifies positive and negative outcomes at all possible cut-offs. It can range from 0 to 1 .0. An AUC value of 0 represents a prediction of the opposite of the trained correlation. An AUC value of 0.5 represents a random prediction. An AUC value of higher than 0.5 represents a classification of an event as fulfilling the trained correlation, wherein higher values represent better classification.

Upon testing a combination of at least two biomarkers, the resulting AUC values were significantly above 0.8, in most cases even higher than 0.85.

In an embodiment, at least two biomarkers are used for determining the Child-Pugh class (e.g., at least or exactly 2, 3, 4, or 5 substances). Such a combination of biomarkers can also be denoted as marker set or biomarker set.

The individual biomarkers and combinations of the biomarkers were tested against a training dataset and a test dataset. After all training and test processes, high-density lipoprotein (HDL), apolipoprotein A1 , valine, lactate, and pyruvate turned out to be valid biomarkers for the underlying question (i.e., classifying an individual or patient into an appropriate Child-Pugh class being indicative for the liver function of the individual or patient). In this context, these biomarkers were very well appropriate biomarkers if used individually. In addition, these biomarkers turned out to be particularly valid biomarkers for the underlying question, provided that the concentration of at least two of these biomarkers was determined at the same time (i.e., in one or more body fluid samples from the same patient obtained at the same time point).

The concentration determination can be made with a method being able to determine the concentration of the substances by a single measurement or by a method requiring more than one measurement for such determination. NMR spectroscopy is particularly appropriate for such a concentration determination since it enables a highly accurate concentration determination of multiple substances in a body fluid by a single measurement in a very short measuring time.

In contrast to all known prior art solutions for determining the Child-Pugh class, the presently claimed use and methods require only a single test for determining the applicable Child-Pugh class. In contrast, prior art solutions require different tests resulting in different values that sum up to a Child-Pugh score which is then indicative for the Child-Pugh class, as already explained above. It was very surprising to learn that the concentration of individual biomarkers is predictive for the Child-Pugh class in a highly accurate manner since according to a manifest prejudice in prior art different tests are required for determining the Child-Pugh class.

The presently claimed and described invention significantly facilitates the determination of the Child-Pugh class and enables such determination also in the course of other examinations, i.e., without any additional steps. Consequently, the Child-Pugh class can be determined faster and easier than according to prior art solutions, but still with very high accuracy. The present invention significantly advances liver function determination. Various medical applications are particularly appropriate for the present invention. To name only a few of them, the presently claimed and described use of the biomarkers is particularly helpful in: risk stratification of patients having an impaired liver function, in particular cirrhotic patients, requiring abdominal surgery; risk stratification of patients having an impaired liver function, in particular cirrhotic patients, requiring esophageal varices therapy; prognostication of survival of patients having an impaired liver function, in particular cirrhotic patients; determination of hepatocellular carcinoma treatment algorithm; determination of liver transplant eligibility, e.g., of patients grouped into Child-Pugh class B and having portal hypertension but a Model of End Stage Liver Disease (MELD) score of less than between 12 and 18, in particular less than between 13 and 17, in particular less than between 14 and 16, in particular less than 15. In an embodiment, the concentration of the marker is standardized to the concentration of another substance that is naturally present in the sample. This other substance may also be listed in the group of markers. In an embodiment, this other substance does not belong to the group of markers as defined above.

In an embodiment, HDL and apolipoprotein A1 are not the only markers if more than a single marker is used for determining the Child-Pugh class. The concentrations of HDL and apolipoprotein A1 in a body fluid are typically closely interrelated with each other so that both substances can be exchanged against each other for many applications. Therefore, the accuracy of prediction of the Child-Pugh class to be assigned to the individual can be even increased if HDL and apolipoprotein A1 are not used as the only markers in case that more than a single marker is used. However, as will be shown below in more detail, HDL and apolipoprotein A1 are already as individual biomarkers particularly appropriate for determining the Child-Pugh class in a highly reliable manner.

In an embodiment, the marker is HDL or apolipoprotein A1 . HDL as biomarker showed an AUC value of 0.88 in the training dataset (confer Figure 1A) and of 0.87 in the test dataset (confer Figure 1 B). Thus, HDL and consequently the closely related apolipoprotein A1 are a particularly appropriate single biomarker for determining the Child-Pugh class into which an individual is to be classified. The accuracy of such classification can even be increased if HDL is combined with at least one other biomarker, as will be explained in later sections of the present disclosure.

In an embodiment, the marker is lactate. Such a biomarker showed an AUC value of 0.80 in the training dataset (confer Figure 2A) and of 0.78 in the test dataset (confer Figure 2B). Thus, also lactate is a particularly appropriate single biomarker for determining the Child-Pugh class into which an individual is to be classified. The accuracy of such determination can even be increased if lactate is combined with at least one other biomarker, as will be explained in later sections of the present disclosure.

In an embodiment, the marker is valine. Such a biomarker showed an AUC value of 0.80 in the training dataset (confer Figure 3A) and of 0.77 in the test dataset (confer Figure 3B). Thus, also valine is a particularly appropriate single biomarker for determining the Child-Pugh class into which an individual is to be classified. The accuracy of such determination can even be increased if valine is combined with at least one other biomarker, as will be explained in later sections of the present disclosure. In an embodiment, the marker is pyruvate. Such a biomarker showed an ALIC value of 0.75 in the training dataset (confer Figure 4A) and of 0.72 in the test dataset (confer Figure 4B). Thus, also pyruvate is a particularly appropriate single biomarker for determining the Child-Pugh class into which an individual is to be classified. The accuracy of such determination can even be increased if pyruvate is combined with at least one other biomarker, as will be explained in later sections of the present disclosure.

In an embodiment, a marker set comprising at least two substances is used, wherein the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and pyruvate as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.89 in the training dataset (confer Figure 5A) and of 0.88 in the test dataset (confer Figure 5B). Thus, combining HDL and pyruvate as biomarkers increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using any of these substances as individual biomarkers.

In an embodiment, a marker set comprising at least two substances is used, wherein the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and valine as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.89 in the training dataset (confer Figure 6A) and of 0.88 in the test dataset (confer Figure 6B). Thus, combining HDL and valine as biomarkers in the marker set increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using any of these substances as individual biomarkers.

In an embodiment, a marker set comprising at least two substances is used, wherein the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and lactate as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.90 in the training dataset (confer Figure 7A) and of 0.88 in the test dataset (confer Figure 7B). Thus, combining HDL and lactate as biomarkers increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using any of these substances as individual biomarkers.

In an embodiment, a marker set comprising at least two substances is used, wherein the marker set comprises or consists of lactate as a first of the at least two substances and valine as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.86 in the training dataset (confer Figure 8A) and of 0.84 in the test dataset (confer Figure 8B). Thus, combining lactate and valine as biomarkers in the marker set increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to any of these substances as individual biomarkers.

In an embodiment, a marker set comprising at least two substances is used, wherein the marker set comprises or consists of valine as a first of the at least two substances and pyruvate as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.84 in the training dataset (confer Figure 9A) and of 0.81 in the test dataset (confer Figure 9B). Thus, combining valine and pyruvate as biomarkers in the marker set increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using any of these substances as individual biomarkers.

In an embodiment, a marker set comprising at least two substances is used, wherein the marker set comprises or consists of lactate as a first of the at least two substances and pyruvate as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.82 in the training dataset (confer Figure 10A) and of 0.81 in the test dataset (confer Figure 10B). Thus, using a combination of the lactate and pyruvate as biomarkers in the marker set significantly increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using any of these substances as individual biomarkers.

In an embodiment, a marker set comprising at least three substances is used, wherein the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least three substances and valine and pyruvate as further substances of the at least three substances. Such a biomarker set showed an AUC value of 0.90 in the training dataset (confer Figure 1 1 A) and of 0.89 in the test dataset (confer Figure 11 B). Thus, using a combination of the three substances HDL, valine, and pyruvate still slightly increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using only two of these substances.

In an embodiment, a marker set comprising at least three substances is used, wherein the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least three substances and lactate and valine as further substances of the at least three substances. Such a biomarker set showed an AUC value of 0.90 in the training dataset (confer Figure 12A) and of 0.89 in the test dataset (confer Figure 12B). Thus, using a combination of the three substances HDL, lactate, and valine still slightly increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using only two of these substances. In an embodiment, a marker set comprising at least three substances is used, wherein the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least three substances and lactate and pyruvate as further substances of the at least three substances. Such a biomarker set showed an AUC value of 0.90 in the training dataset (confer Figure 13A) and of 0.89 in the test dataset (confer Figure 13B). Thus, using a combination of the three substances HDL, lactate, and pyruvate still slightly increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using only two of these substances.

In an embodiment, a marker set comprising at least three substances is used, wherein the marker set comprises or consists of lactate, valine, and pyruvate as the at least three substances. Such a biomarker set showed an AUC value of 0.87 in the training dataset (confer Figure 14A) and of 0.86 in the test dataset (confer Figure 14B). Thus, using a combination of the three substances lactate, valine, and pyruvate still slightly increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using only two of these substances.

In an embodiment, a marker set comprising at least four substances is used, wherein the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least four substances and valine, pyruvate, and lactate as further substances of the at least four substances. Such a biomarker set showed an AUC value of 0.91 in the training dataset (confer Figure 15A) and of 0.90 in the test dataset (confer Figure 15B). Thus, using a combination of the four substances HDL, valine, pyruvate, and lactate still slightly increases the sensitivity of determining the Child-Pugh class into which an individual is to be classified with respect to using only three of these substances.

In an aspect, the present invention relates to the further medical use of a marker chosen from the group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, lactate, and pyruvate for in-vivo diagnostics of the liver function of an individual by determining a Child- Pugh class into which the individual is to be classified.

In an aspect, the present invention relates to a method for analyzing an isolated body fluid sample in vitro, comprising the steps explained in the following. This method is carried out on an isolated body fluid sample originating from an individual. In a first step, the concentration of at least a single substance is determined by analyzing the body fluid sample with a suited measuring technique. The substance is chosen from the group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, lactate, and pyruvate.

Afterwards, a score is calculated from the determined concentration, wherein the score is indicative for the Child-Pugh class into which an individual is to be classified. This score is a different measure than the Child-Pugh score referred to in other sections of the present disclosure. If the present disclosure refers to the Child-Pugh score, the entire term “Child-Pugh score” is used.

The score can be calculated by taking into consideration the concentrations measured or expected in a body fluid sample from a control group. To give a simple example, the score can be the median of the concentration ratios of the at least two substances between the body fluid test sample of the patient and corresponding control values of a body fluid control sample that have been measured in the past. If the score is above or below a predetermined threshold value, a significant increase or decrease of the marker substances is present in the body fluid test sample that is indicative for the Child-Pugh class to be determined. It should be noted that other calculation methods as well as a weighting of individual marker concentrations with respect to other marker concentrations can also be performed in an embodiment.

A suited way to calculate the score is disclosed on pages 25 to 27 of WO 2012/045773 A9. Another suited way to calculate the score is the following:

wherein n co = a + > b_x • I_x

X=1 a = const. b_x = substance specific coefficient

I = parameter being indicative for the concentration of substance x

Thereby, the individual factors a, b need to be adjusted according to the underlying model and can vary in dependence on the specific substance used as marker or the specific substances considered in the marker set. Parameter “I” can be, e.g., the signal intensity or signal integral of an according signal observed in the evaluated measuring result. To give an example, “I” can be the signal intensity or signal integral of an NMR signal in an NMR spectrum if NMR spectroscopy is used as measuring technique.

In an embodiment, “I” is a ratio between two signal intensities or two signal integrals. In such a case, it is, e.g., possible to standardize the concentration of a first substance (or a plurality of substances) by the concentration of a second substance.

The score is a (semi-)quantitative measure for the likelihood that the determined Child-Pugh class is an accurate estimation of the Child-Pugh class as determined by classic methods and into which the individual is to be classified according to his or her liver status and thus an accurate estimation of the factual liver function of the individual. Thus, the score serves for (semi-)quantitatively determining the liver function of the individual.

Calculating the score comprises multiplying each of the concentrations of the considered substances by a substance-specific weighting factor to provide a plurality of weighted values and combining the weighted values into a risk equation. Afterwards, an output of the risk equation is compared to a predefined threshold. If the score is above the threshold, there is a likelihood that the determined Child-Pugh class is an accurate estimation. In an embodiment, the likelihood is higher, the higher the score is (i.e., the likelihood increases with increasing distance of the score from the threshold).

In an embodiment, the calculated score is output and presented to the individual and/or to a third person such as a physician or medical staff. The output can be performed on a display (i.e., in an electronic way) or in printed form. Thereby, it is also possible to generate a report indicating the score, optionally in combination with a comparative scale of possible scores and their meaning with respect to the determined Child-Pugh class.

In an embodiment, the method is a computer-implemented method. In particular, all steps of spectral analysis and concentration determination as well as of score calculation are performed on a computer. Such steps are far too complex to be done in a manual way. The computer- implemented concentration determination is, in an embodiment, based on a spectral analysis, such as an analysis of NMR spectra. The spectral analysis and the further required steps until the score can be output can be done on the same computer that is used for controlling a spectrometer performing the spectral analysis or on a different computer.

In an embodiment, the body fluid sample is a urine sample or a blood sample. In an embodiment, the blood sample is a whole blood sample, a blood serum sample, a blood plasma sample, or any other blood preparation derivable from whole blood or from other blood preparations. Blood serum is a particularly appropriate body fluid for carrying out the method or for the above-mentioned in vitro or in vivo uses of the biomarkers. Blood plasma is also an appropriate body fluid. In an embodiment, lactate is used as marker if blood plasma is used as body fluid to be analyzed.

In an embodiment, the body fluid sample (and therewith the patient from whom the body fluid sample originates) is grouped into one of at least two predefined groups based on the calculated score. Typically, one group encompasses patients that are to be classified into Child-Pugh class A, wherein the other group encompasses patients that are to be classified into Child-Pugh class B or C. The resulting grouping can also be indicated on an according report. In an embodiment, the grouping encompasses more than two groups (yes/no). To give an example of such grouping, a first group encompasses patients that are to be classified into Child-Pugh class A, a second group encompasses patients that are to be classified into Child- Pugh class B, and a third group encompasses patients that are to be classified into Child-Pugh class C.

In an embodiment, the individual of whom the body fluid is analyzed, is a healthy individual. In an embodiment, the individual is a risk patient, i.e., an individual belonging to a group that has an increased risk of impaired liver function with respect to the risk of a healthy standard population. In an embodiment, the individual suffers from (etiology-independent) cirrhosis. In an embodiment, the individual is a patient having cirrhosis but being asymptomatic. In an embodiment, the individual suffers from a (in particular chronic) viral liver infection and has a cirrhosis. In an embodiment, the individual suffers from chronic hepatitis B and has a cirrhosis. In an embodiment, the individual suffers from chronic hepatitis C and has a cirrhosis. In an embodiment, the individual suffers from non-alcoholic fatty liver disease (NAFLD), in particular from non-alcoholic steatohepatitis (NASH), and has a cirrhosis. In an embodiment, the individual suffers from alcoholic liver disease and has a cirrhosis. In an embodiment, the individual has a cryptogenic cirrhosis. In an embodiment, the individual suffers from a (in particular chronic) viral liver infection without having a cirrhosis. In an embodiment, the individual suffers from chronic hepatitis B without having a cirrhosis. In an embodiment, the individual suffers from grade IV fibrosis (i.e., a fibrosis that led to cirrhosis). All of the precedingly mentioned embodiments can be particularly well combined so that the individual may be chosen from any of the beforementioned groups of patients/healthy individuals. In an embodiment, the score is calculated by not only considering the concentration of the at least one marker but also includes the age and/or the gender of the individual who donated the body fluid sample.

In an embodiment, calculating the score involves calculating a ratio between at least two concentration values. To give an example, a ratio between HDL and valine or between pyruvate and valine is calculated in an embodiment. Generally, individual ratios of pairs of two of all marker substances the concentrations of which have been determined upon carrying out the method can be calculated for calculating the score.

In an aspect, the present invention relates to a medical method for diagnosing the liver function of individual by determining a Child-Pugh class into which the individual is to be classified. The method comprises the steps explained in the following.

In a first step, a body fluid sample is gathered from an individual. In a second step, the concentration of at least a single marker is determined by analyzing the body fluid sample with a suited measuring technique. The marker is chosen from the group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, lactate, and pyruvate.

Afterwards, a score is calculated from the determined concentrations, wherein the score is indicative for determining the Child-Pugh class into which the individual is to be classified and thus for the liver function of the individual.

In a further aspect, the present invention relates to a decision support system for analyzing an isolated body fluid sample in vitro, the decision support system comprising: a) a unit for providing a body fluid sample from an individual; b) a unit for determining the concentration of at least one marker with a suited measuring technique, wherein the marker is chosen from the group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, lactate, and pyruvate; and c) a unit for calculating a score from the determined concentration, the score being indicative for determining the Child-Pugh class into which the individual is to be classified. In an embodiment, the unit for determining the concentration of the at least one marker is configured to determine the concentration of any exemplary substance or substance combination of the embodiments explained above.

While some of the explained uses and methods are described as in vitro uses and methods and some of the explained uses and methods are described as in vivo uses and methods, it should be noted that each in vitro use or method can also be carried out as in vivo use or method, and vice versa.

In an independently claimed aspect, an alteration of concentration of an individual marker with respect to the concentration in a control group or an alteration of a concentration ratio between at least two markers with respect to the concentration ratio of the same markers in a control group was correlated in a statistically significant way with the Child-Pugh score (i.e., the underlying value for classifying an individual into a specific Child-Pugh class) at the time of analysis (i.e., enabling an assignment of a specific Child-Pugh score to an individual and thus to allow a determination of the liver function of the individual on an even finer scale than in case of grouping the individual into a specific Child-Pugh class). Confer in this respect Figures 1 E to 15E and 1 F to 15F illustrating the suitability of the individual markers or marker sets for determining the Child-Pugh score. All explanations given within the present disclosure thus do not only relate to determining the Child-Pugh class into which an individual is to be grouped, but also to the Child-Pugh score which can be assigned to the individual. Thus, the term “Child- Pugh class” of the present disclosure can be exchanged by “Child-Pugh score” without departing from the presently claimed and described invention.

In an embodiment of this independently claimed aspect, the assigned Child-Pugh score is a Child-Pugh score lying within a range of from 5 to 15, in particular from 6 to 14, in particular from 7 to 13, in particular from 8 to 12, in particular from 9 to 11 .

All embodiments of the use of the marker can be combined in any desired way and can be transferred either individually or in any arbitrary combination to the further medical use of the marker as well as to the different methods and to the decision support system. Likewise, all embodiments of the further medical use of the marker can be combined in any desired way and can be transferred either individually or in any arbitrary combination to the use of the marker, to the different methods, and to the decision support system. Finally, all embodiments of the different methods can be combined in any desired way and can be transferred either individually or in any arbitrary combination to the use of the marker, to the further medical use of the marker, to any other of the described methods, and to the decision support system. Further details of aspects of the present invention will be explained in the following making reference to exemplary embodiments and accompanying Figures. In the Figures:

Figures 1A to 15A and show ROC plots illustrating the ability of different markers

Figures 1 B to 15B or marker combinations for determining the Child-Pugh class into which an individual is to be classified upon evaluating a training dataset (always in Figure A) or upon evaluating a test dataset (always in Figure B);

Figures 1 C to 15C and show confusion matrices illustrating the distributions of

Figures 1 D to 15D calculated scores from the determined concentrations of the individual biomarkers or the biomarker combinations with respect to a determination of Child-Pugh class B/C upon evaluating a training dataset (always in Figure C) or upon evaluating a test dataset (always in Figure D); and

Figures 1 E to 15E and show plots illustrating the distributions of calculated scores

Figures 1 F to 15F from the determined concentrations of the individual biomarkers or the biomarker combinations with respect to the Child-Pugh score determined by prior art methods upon evaluating a training dataset (always in Figure E) or upon evaluating a test dataset (always in Figure F).

All ROC plots shown in Figures 1 A to 15A (in the A Figures), the corresponding confusion matrices shown in Figures 1 C to 15C (in the C Figures), and the corresponding plots shown in Figures 1 E to 15E (in the E Figures) as well as all ROC plots shown in Figures 1 B to 15B (in the B Figures), the corresponding confusion matrices shown in Figures 1 D to 15D (in the D Figures), and the corresponding plots shown in Figures 1 F to 15F (in the F Figures) were obtained by analyzing blood serum samples of individuals assigned to Child-Pugh classes A, B, or C. The recruiting of patients for the analyzed samples as well as the sample preparation and measuring will be explained in the following in more detail.

Study design

A retrospective case-control design using banked serum samples from individuals assigned to Child-Pugh classes A, B, or C was chosen. The samples were either banked or retained samples from routine clinical care collected for other research studies or non-research purposes, or samples collected as part of a clinical study. Serum cohorts were obtained from six different study sites, namely five in Europe and one in the U.S. Thus, this study was a retrospective, multi-centric, cross-sectional case-control study.

Group assignment and reference standard

The case group consisted of patients that were classified into Child-Pugh class B or C, wherein the control group consisted of individuals that were classified into Child-Pugh class A. Cohort details

Details of the tested patient cohort are listed in the following Table A:

Table A: Details of the tested patient cohort.

Characteristic N Training, N = 709 Test, N = 300

Center, n / N (%) 1 ,009

Center 1 A 225 / 709 (32%) 98 / 300 (33%)

Center 1 B 40 / 709 (5.6%) 17 / 300 (5.7%)

Center 2 229 / 709 (32%) 100 / 300 (33%)

Center 3 85 / 709 ( 12%) 33 / 300 (11 %)

Center 4 82 / 709 (12%) 23 / 300 (7.7%)

Center 5 48 / 709 (6.8%) 29 / 300 (9.7%)

Age, Mean (SD) 1 ,009 61.42 (11.08) 61.05 (10.63)

Age Group, n / N (%) 1 ,009

<63 350 / 709 (49%) 165 / 300 (55%)

>=63 359 / 709 (51 %) 135 / 300 (45%)

Sex, n / N (%) 1 ,009 female 194 / 709 (27%) 82 / 300 (27%) male 515 / 709 (73%) 218 / 300 (73%)

HCC Stage (Milan)**, n / N 1 ,009

(%) negative 426 / 709 (60%) 170 / 300 (57%) early 149 / 709 (21 %) 74 / 300 (25%) late 134 / 709 ( 19%) 56 / 300 ( 19%)

Child-Pugh score, n / N (%) 902 Characteristic N Training, N = 709 Test, N = 300

5 230 / 632 (36%) 103 / 270 (38%)

6 115 / 632 (18%) 47 / 270 (17%)

7 85 / 632 ( 13%) 49 / 270 ( 18%)

8 76 / 632 (12%) 26 / 270 (9.6%)

9 59 / 632 (9.3%) 17 / 270 (6.3%)

10 32 / 632 (5.1%) 16 / 270 (5.9%)

11 20 / 632 (3.2%) 6 / 270 (2.2%)

12 13 / 632 (2.1%) 5 / 270 (1.9%)

13 2 / 632 (0.3%) 1 / 270 (0.4%)

(Missing) 77 30

Child-Pugh Class, n / N (%) 1 ,009

Class A 415 / 709 (59%) 176 / 300 (59%)

Class B 224 / 709 (32%) 95 / 300 (32%)

Class C 70 / 709 (9.9%) 29 / 300 (9.7%)

Child-Pugh Class B/C*: yes, 1 ,009 294 / 709 (41 %) 124 / 300 (41 %) n / N (%)

* The target variable used in modelling for this use case.

** Milan criteria stratifies HCC into early-stage disease (transplant-appropriate, curative intent) and late-stage (transplant inappropriate) based on lesion size (one lesion smaller than 5 cm; alternatively, up to three lesions, each smaller than 3 cm), no extrahepatic manifestations and no gross vascular invasion.

Sample preparation

Samples were prepared with reagents of the AXINON® serum kit 2.0 offered by numares AG.

During sample preparation, all reagents were used at ambient temperature (15-30°C). a) Calibration samples

The AXINON® serum calibrator 2.0 was filled into an NMR tube. A cap for NMR tubes was placed onto the NMR tube. Subsequently, the calibration sample was placed in a defined position of an NMR rack. b) Control samples

The AXINON® serum control 2.0 was filled into an NMR tube. A cap for NMR tubes was placed onto the NMR tube. Subsequently, two control samples were placed in defined positions of the same NMR rack. c) Analytical samples

The AXINON® serum additives solution 2.0 was combined with the blood serum to be analyzed in a ratio of 1 :10 in a suitable reagent container. The liquid was gently mixed, wherein foaming was avoided. The volume of the mixture required for the NMR measurement was transferred into an NMR tube. A cap for NMR tubes was placed onto the NMR tube. Each analytical sample was placed at a defined position into the same NMR rack according to a rack list (as defined by the AXINON® Sample Wizard, see AXINON® Sample Wizard User manual). Attention was paid to ensure proper positioning of the samples. The analysis results of samples that were not clearly assignable were discarded.

Measurement

All measurements were carried out on a Broker Avance II+ 600MHz NMR spectrometer with an UltraShield 600 Plus NMR Magnet System, or a Broker Avance III HD 600MHz NMR spectrometer with an UltraShield 600 Plus NMR Magnet System, or a Broker Avance III HD 600MHz NMR spectrometer with an Ascend NMR Magnet System using a PATXI 1 H/D- 13C/15N Z-GRD probe. All samples were kept at 5-7°C in the SampleJet and brought to the target temperature in the integrated preheating block before measurement.

Two measurement sequences were used for all samples:

• on the one hand, a standard pulse program with 30-degree excitation pulse and presaturation for water suppression was used (zgpr30);

• on the other hand, a pulse program to measure T2-weighted spectra without J modulation using refocusing pulses between double spin echoes (project = Periodic Refocusing Of J Evolution by Coherence Transfer) was used.

Samples were measured in batches of up to 93 analytical samples per run. In addition to the analytical samples, each run included one AXINON® blood serum calibrator sample and two AXINON® blood serum control samples (before and after the analytical blood serum samples, respectively) to assure ideal measurement conditions throughout the run.

Signal analysis a) Spectrum Qualification (Quality Control for measurement)

NMR spectra underwent automatic referencing, phase correction and baseline correction before further analysis.

Subsequently, the NMR spectra underwent an automatic standardization and calibration procedure to minimize between-device, between-day and between-run effects. The quality of each of these spectra was assessed by a custom spectrum qualification algorithm that analyzes general spectral properties, e.g., offset and tilt of the baseline in selected spectral regions, and properties of selected indicator signals, e.g., signal position, shape and width. Spectra that did not meet the predefined quality criteria were excluded from further analysis. b) Bins

Successfully qualified spectra (typically covering a chemical shift from -5 to 14 ppm) were subjected to further modifications. In particular, broad background signals were separated with a suitable algorithm, e.g., background intensities (such as generated from proteins) were subtracted from the spectra, resulting in spectral intensities devoid of such background signals.

The cohort (i.e., the plurality) of modified spectra was checked for regions in which the cohort did not show a significant number of signals. These regions - like the region of the water signal and the regions in which signals are contained that originate from substances contained in AXINON® serum additives - were ignored in the steps explained in the following.

The remaining spectral regions were subject to an adaptive binning, which divides the spectrum in bins of differing size or extent (typically covering 0.01 to 0.05 ppm, but in extreme cases also covering 0.005 to 0.5 ppm). The resulting boundaries for the bins are tailored to represent signals and/or signal structures in the spectrum as good as possible.

Depending on the cohort of modified spectra, the size and thus the number of bins varies. Typical numbers of bins lie in a range of from 100 to 400. c) Quantifier

Quantification of substances was done by fitting a predefined, characteristic set of PseudoVoigt functions, which represent a linear combination of a Gaussian and a Lorentzian function, to the substance specific signal structure(s). The resulting signal fits were checked for goodness of fit and physical plausibility of properties related to fit parameters in order to reject results of insufficient fit quality.

Alternatively, quantification models making use of the previously assigned bins were applied. After substance identification, substance labels have been assigned to these bins. The quantification was then determined by the bin value, which calculates as [(sum of intensities in bin)/(number of data points in bin)]. The standardization by data points is used to compensate for a varying number of data points in the bins. The number of data points in a bin may vary by one data point due to shifts of the applied discretization grid. In either case, the resulting integral of the quantification is then translated into a substance concentration by applying a conversion factor which has been determined experimentally for each of the substances.

Test of identified marker substances

The identified marker substances were tested in different combinations to assess their suitability determining the Child-Pugh class into which an individual is to be classified. In doing so, the result of the determination based on the marker substances (predicted determination) has been checked against the Child-Pugh classification performed by prior art methods, as already explained above.

The results are summarized in receiver operating characteristic (ROC) plots. In these plots, the area under the curve (AUC) indicates the fitness of the prediction. If the AUC is 0.5, the prediction is to be considered random and thus not well suited. The higher the AUC, the better is the prediction model.

The obtained results will be explained in the following in more detail making reference to Figures 1 A to 15F.

Figures 1A, 2A, 3A, 4A, 5A, 6A, 7A, 8A, 9A, 10A, 11 A, 12A, 13A, 14A, and 15A show ROC plots of different individual markers or marker combinations upon evaluation of a training dataset. Figures 1 C, 2C, 3C, 4C, 5C, 6C, 7C, 8C, 9C, 10C, 1 1 C, 12C, 13C, 14C, and 15C show the corresponding confusion matrices based on the scores assigned to the patient’s liver status (i.e., if the patient is to be classified into Child-Pugh class B or C (yes) or into Child- Pugh class A (no)) confirmed by other tests.

Figures 1 B, 2B, 3B, 4B, 5B, 6B, 7B, 8B, 9B, 10B, 11 B, 12B, 13B, 14B, and 15B show ROC plots of different individual markers or marker combinations upon evaluation of a test dataset. Figures 1 D, 2D, 3D, 4D, 5D, 6D, 7D, 8D, 9D, 10D, 11 D, 12D, 13D, 14D, and 15D show the corresponding confusion matrices based on the scores assigned to the patient’s liver status (i.e., if the patient is to be classified into Child-Pugh class B or C (yes) or into Child-Pugh class A (no)) confirmed by other tests.

Figures 1 E, 2E, 3E, 4E, 5E, 6E, 7E, 8E, 9E, 10E, 1 1 E, 12E, 13E, 14E, and 15E show plots illustrating the dependency of the scores calculated from the training dataset on the underlying Child-Pugh score determined by prior art methods. Here, it can be well seen that the score is higher, the higher the underlying Child-Pugh score is. Thus, a higher score is indicative of a more impaired liver function and thus of a higher likelihood that the individual is to be classified into Child-Pugh class B or C. Figures 1 F, 2F, 3F, 4F, 5F, 6F, 7F, 8F, 9F, 10F, 1 1 F, 12F, 13F, 14F, and 15F show plots illustrating the dependency of the scores calculated from the test dataset on the underlying Child-Pugh score determined by prior art methods. Also here, it can be well seen that the score is higher, the higher the underlying Child-Pugh score is. Thus, a higher score is indicative of a more impaired liver function and thus of a higher likelihood that the individual is to be classified into Child-Pugh class B or C.

The following Table 1 summarizes the results depicted in the Figures.

Table 1 : Summary of biomarkers/biomarker set composition and corresponding AUC values depicted in the Figures.

AUC value in training AUC value in test Figure Biomarker dataset (415 controls; dataset (176

294 cases) controls; 124 cases)

1A and 1 B HDL 0.88 0.87

2A and 2B Lactate 0.80 0.78

3A and 3B Valine 0.80 0.77

4A and 4B Pyruvate 0.75 0.72

5A and 5B HDL and pyruvate 0.89 0.88

6A and 6B HDL and valine 0.89 0.88

7A and 7B HDL and lactate 0.90 0.88

8A and 8B Lactate and valine 0.86 0.84

9A and 9B Valine and pyruvate 0.84 0.81

10A and 10B Lactate and pyruvate 0.82 0.81

11 A and 11 B ^HDL’ ^valine’ ^and 0.90 0.89 pyruvate

12A and 12B HDL, lactate, and valine 0.90 0.89

13A and 13B ^HDL’ ^lactate’ ^and o.9O 0.89 pyruvate AUC value in training AUC value in test Figure Biomarker dataset (415 controls; dataset (176

294 cases) controls; 124 cases)

14A and 14B Lactate, valine, and

_ . _n „ U.o/ _n U.o _oo o pyruvate

15A and 15B HDL, valine, pyruvate,

■ I . . _n u.yi _n u. _n y_n u and lactate

List of references cited in the preceding sections or otherwise deemed to be relevant

1 . Gowda, S., et al., A review on laboratory liver function tests. Pan Afr Med J, 2009. 3: p. 17.

2. Smith, A., et al., Liver Disease: Evaluation of Patients With Abnormal Liver Test Results. FP Essent, 2021. 511 : p. 11 -22.

3. Sullivan, M.K., H.B. Daher, and D.C. Rockey, Normal or near normal aminotransferase levels in patients with alcoholic cirrhosis. Am J Med Sci, 2022. 363(6): p. 484-489.

4. Kholoussy, A.M., D. Pollack, and T. Matsumoto, Prognostic significance of indocyanine green clearance in critically ill surgical patients. Crit Care Med, 1984. 12(2): p. 115-6.

5. Vos, J.J., et al., Green light for liver function monitoring using indocyanine green? An overview of current clinical applications. Anaesthesia, 2014. 69(12): p. 1364-76.

6. Geisel, D., et al., Imaging-based evaluation of liver function: comparison of (9)(9)mTc- mebrofenin hepatobiliary scintigraphy and Gd-EOB-DTPA-enhanced MRI. Eur Radiol, 2015. 25(5): p. 1384-91.

7. Nilsson, H., et al., Gd-EOB-DTPA-enhanced MRI for the assessment of liver function and volume in liver cirrhosis. Br J Radiol, 2013. 86(1026): p. 20120653.

8. Albers, I., et al., Superiority of the Child-Pugh classification to quantitative liver function tests for assessing prognosis of liver cirrhosis. Scand J Gastroenterol, 1989. 24(3): p. 269-76.

9. Garrison, R.N., et al., Clarification of risk factors for abdominal operations in patients with hepatic cirrhosis. Ann Surg, 1984. 199(6): p. 648-55.

10. Infante-Rivard, C., S. Esnaola, and J.P. Villeneuve, Clinical and statistical validity of conventional prognostic factors in predicting short-term survival among cirrhotics. Hepatology, 1987. 7(4): p. 660-4.

11 . Pugh, R.N., et al., Transection of the oesophagus for bleeding oesophageal varices. Br J Surg, 1973. 60(8): p. 646-9.

12. Reig, M., et al., BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. J Hepatol, 2022. 76(3): p. 681 -693.

13. Dove, L. and R. Brown. Liver transplantation in adults: Patient selection and pretransplantation evaluation. UpToDate 2021. Accessed 20 February 2023; Available from:

14. Simonovsky, V., The diagnosis of cirrhosis by high resolution ultrasound of the liver surface. Br J Radiol, 1999. 72(853): p. 29-34.

15. Ferenci, P., Diagnosis of minimal hepatic encephalopathy: still a challenge. Gut, 2013. 62(10): p. 1394.

16. US 2020/0378991 A1

17. US 2017/0370954 A1

Claims

1. Use of a marker chosen from the group consisting of high-density lipoprotein, apolipoprotein A1 , valine, lactate, and pyruvate in an in vitro method for determining a Child-Pugh class into which an individual is to be classified.

2. Use according to claim 1 , characterized in that the determined Child-Pugh class is class A or class B/C.

3. Use according to claim 1 , characterized in that the determined Child-Pugh class is class A, class B, or class C.

4. Use according to any of the preceding claims, characterized in that at least one of high- density lipoprotein and apolipoprotein A1 is used together with pyruvate as marker.

5. Use according to claim 4, characterized in that at least one of high-density lipoprotein and apolipoprotein A1 is used together with pyruvate and valine as marker.

6. Use according to claim 4, characterized in that at least one of high-density lipoprotein and apolipoprotein A1 is used together with pyruvate and lactate as marker.

7. Use according to any of claims 1 to 3, characterized in that at least one of high-density lipoprotein and apolipoprotein A1 is used together with valine as marker.

8. Use according to any of claims 1 to 3, characterized in that at least one of high-density lipoprotein and apolipoprotein A1 is used together with lactate as marker.

9. Use according to claim 8, characterized in that at least one of high-density lipoprotein and apolipoprotein A1 is used together with lactate and pyruvate as marker.

10. Use according to any of claims 1 to 3, characterized in that lactate is used together with valine as marker.

11 . Use according to any of claims 1 to 3, characterized in that valine is used together with pyruvate as marker.

12. Use according to any of claims 1 to 3, characterized in that lactate is used together with pyruvate as marker.

13. Marker chosen from the group consisting of high-density lipoprotein, apolipoprotein A1 , valine, lactate, and pyruvate for use in in-vivo diagnostics of the liver function of an individual by determining a Child-Pugh class into which the individual is to be classified.

14. Method for analyzing an isolated body fluid sample in vitro, comprising the following steps: a) determining the concentration of at least one substance chosen from the group consisting of high-density lipoprotein, apolipoprotein A1 , valine, lactate, and pyruvate in an isolated body fluid sample from an individual by analyzing the body fluid sample with a suited measuring technique, b) calculating a score from the determined concentrations, the score being indicative for determining a Child-Pugh class into which the individual is to be classified.

15. Method according to claim 14, characterized in that calculating the score involves calculating a ratio between at least two concentration values.