Methods of detecting or diagnosing a tubular interstitial disease

Abstract

The present invention relates generally to methods of detecting or diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease, using a combination of biomarkers. The present invention further relates to methods of monitoring tubular interstitial disease, or the efficacy of a treatment of tubular interstitial disease, as well as a panel of binding agents, kits and other diagnostic products used to carry out the methods disclosed herein.

Description

[0001] METHODS OF DETECTING OR DIAGNOSING A TUBULAR INTERSTITIAL DISEASE

[0002] CROSS-REFERENCE TO RELATED APPLICATION

[0003]

[0001] This application makes reference to and claims the benefit of priority of the Singapore Patent Application No. 10202500800Y filed on 26 March 2025, the content of which is incorporated herein by reference for all purposes.

[0004] FIELD OF THE INVENTION

[0005]

[0002] The present invention relates generally to methods of detecting or diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease, using a combination of biomarkers. The present invention further relates to methods of monitoring tubular interstitial disease, orthe efficacy of a treatment of tubular interstitial disease, as well as a panel of binding agents, kits and other diagnostic products used to carry out the methods disclosed herein.

[0006] BACKGROUND OF THE INVENTION

[0007]

[0003] Chronic kidney disease (CKD), also known as chronic kidney failure, is marked by structural and functional abnormalities of the kidney that leads to loss of kidney function and subsequently endstage renal failure. Although CKD is on the rise globally, a subgroup of CKDu has particularly shown prevalence among agricultural communities in certain tropical countries. This subgroup named the chronic kidney disease of uncertain etiology (CKDu), was first identified as localised outbreaks without known attributable etiology, and since then has been largely associated with high exposures of environmental and occupational hazards. Among all the CKDu endemic regions, Sri Lanka reports the highest cases of CKDu especially in the rural dry zone. While the diagnosis of classic CKD is routinely carried out with tests evaluating kidney function / damage such as serum creatinine and proteinuria using urine dipstick, sensitive and specific tests to detect CKDu in the early stage and to predict its progression before renal failure is currently unavailable.

[0008]

[0004] CKDu primarily presents as an interstitial disease with tubular atrophy and fibrosis that leads to renal function impairment, howeverwith no or minimal proteinuria in early phases of the disease. Serum creatinine which is popular for assessing renal function shows an increase only after sufficient renal parenchymal damage (40-50%) has set it, rendering its unusable for evaluating renal damage with CKDu. In the lack of success of these rapid point-of-care diagnostics using serum creatinine or urine dipstick in diagnosing creatinine normal non-proteinuric CKDu cases, currently kidney biopsy to confirm CKDu remains the gold standard of diagnosis. This method is both expensive and invasive making it impractical to implement any large community screening to detect CKDu at the early stage.

[0009]

[0005] Clinically, CKDu often remains minimally symptomatic until advanced stages and typically presents with absent or minimal proteinuria during the early phase of disease progression. Conventional kidney health diagnostics therefore frequently fail to detect early disease, as serum creatinine levels may remain within normal levels during initial stages despite ongoing renal injury. As a result, CKDu isoften characterised by reduced estimated glomerular filtration rate (eGFR) or mild albuminuria in the absence of traditional risk factors. Suspected or probable CKDu cases are generally defined by reduced eGFR (for example <60 ml / min / 1.73 m2) and / or low-level albuminuria or proteinuria in individuals lacking diabetes, severe hypertension, prior dialysis-requiring acute kidney injury, or other known causes of chronic kidney disease. However, definitive diagnosis frequently requires renal biopsy to confirm the characteristic tubulointerstitial pathology. Without early detection and intervention, CKDu may progressively advance to end-stage renal failure (ESRF), often presenting only at late stages when therapeutic options are limited.

[0010]

[0006] CKDu represents a major clinical and public health challenge due to its unclear origin and atypical disease presentation. Unlike conventional chronic kidney disease, CKDu occurs in individuals without established risk factors such as diabetes or hypertension and is predominantly observed in agricultural, rural communities where it appears endemic. The disease progresses silently, with patients remaining largely asymptomatic until advanced stages, making timely clinical intervention difficult. Early detection is particularly challenging because conventional diagnostic markers fail to capture the disease in its initial phases. Proteinuria is often minimal or absent, and serum creatinine levels do not rise significantly until substantial and often irreversible kidney damage has occurred. Consequently, a large proportion of affected individuals remain in subclinical stages that go undetected, leading to delayed diagnosis, disease progression, and poor clinical outcomes.

[0011]

[0007] A key limitation in CKDu management is the absence of reliable, non-invasive biomarkers for early diagnosis and disease monitoring. Current clinical criteria can only suggest the presence of CKDu, and definitive diagnosis is not possible without renal biopsy, which remains the gold standard. This reliance on invasive procedures further limits early detection and large-scale screening efforts. Compounding these challenges is the limited understanding of CKDu etiology and disease mechanisms in humans. The lack of identifiable risk factors and mechanistic insights has significantly hindered the development of targeted therapeutics. As a result, CKDu remains a disease with no specific treatment options, highlighting a critical unmet need for improved biomarkers, deeper biological understanding, and effective intervention strategies.

[0012]

[0008] Therefore, there exists a need for an improved method that addresses the above limitations in detecting and diagnosing tubular interstitial disease, such as CKDu, in a subject.

[0013] SUMMARY OF THE INVENTION

[0014]

[0009] The present invention satisfies the aforementioned need in the art by providing the methods, uses, and kits described herein.

[0015]

[0010] In one aspect there is provided, an in vitro method of diagnosing a tubular interstitial disease in a subject, comprising: measuring, in a urine sample obtained from the subject, an amount of each biomarker in a biomarker combination, wherein the biomarker combination comprises at least twobiomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D, comparing the measured amount of each biomarker in the biomarker combination to a corresponding reference amount for that biomarker to determine a differential amount for each biomarker, wherein the differential amount of each biomarker in the biomarker combination are collectively indicative of the subject having a tubular interstitial disease.

[0016]

[0011] In various embodiments, the biomarker combination comprises at least three biomarkers in the urine sample.

[0017]

[0012] In various embodiments, the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu).

[0018]

[0013] In various embodiments, the CKDu is advanced or late-stage CKDu.

[0019]

[0014] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , and FCN2.

[0020]

[0015] In various embodiments, the biomarker combination comprises CELA2A.

[0021]

[0016] In various embodiments, an increased amount of CELA2A, BGLAP, MLN, SPINK1 , and / or FCN2, in the urine sample relative to the corresponding reference amount of that biomarker; and / or a decreased amount of HSPE1 , ENPP5, PLTP, and / or CTSD, in the urine sample relative to the corresponding reference amount of that biomarker, is indicative of the subject having CKDu, or being at risk of developing CKDu.

[0022]

[0017] In various embodiments, the biomarker combination comprises or consists of HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, and FCN2.

[0023]

[0018] In various embodiments, the tubular interstitial disease is Acute Interstitial Nephritis (AIN).

[0024]

[0019] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2.

[0025]

[0020] In various embodiments, the biomarker combination comprises KRT18, LY6D, CRABP2, REGIB and IGKV1-5.

[0021] In various embodiments, an increased amount of KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and / or FCN2, in the urine sample relative to the corresponding reference amount of that biomarker; and / or a decreased amount of CRABP2, in the urine sample relative to the reference amount of CRABP2, is indicative of the subject having AIN, or being at risk of developing AIN.

[0026]

[0022] In various embodiments, the biomarker combination comprises or consists of KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2.

[0027]

[0023] In various embodiments, the biomarker combination comprises FCN2, and the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu) or Acute Interstitial Nephritis (AIN).

[0028]

[0024] In various embodiments, the step of measuring comprises quantifying the amount of a gene product of each biomarker in the urine sample, wherein the gene product is a protein, or a fragment of the protein, preferably protein expression level or abundance of each biomarker in the biomarker combination is measured in the urine sample, using an antibody, interacting protein, ligand, nanoparticle or aptamer that specifically bind to the protein or fragment of each biomarker.

[0029]

[0025] In various embodiments, the differential amount of each biomarker in the biomarker combination collectively define a biomarker profile, and wherein the biomarker profile is indicative of the subject having a tubular interstitial disease.

[0030]

[0026] In various embodiments, the reference amount is derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD), and / or non-diseased healthy individuals.

[0031]

[0027] In various embodiments, the differential amount comprises at least a fold change of about 1.3 relative to the reference amount.

[0032]

[0028] In various embodiments, the subject is a human, preferably the subject is selected from a population residing in or originating from an endemic region for CKDu, preferably the endemic region for CKDu comprises regions of South Asia and Central America.

[0033]

[0029] In various embodiments, the method further comprises classifying the likelihood of the subject being responsive or non-responsive to tubular interstitial disease therapies based on the differential amount of each biomarker in the biomarker combination, and optionally selecting a treatment for tubular interstitial disease to be administered to the subject.

[0030] In various embodiments, the step of measuring comprises quantifying the amount of a gene product of each biomarker in the urine sample, wherein the gene product is a protein, or a fragment of the protein, wherein an increased amount of CELA2A, BGLAP, MLN, SPINK1, KRT18, CTSG, MMP9, REG1A, REG1 B, IGKV1-27, IGKV1-5, S100A12, LY6D or FCN2, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount; and / or a decreased amount of HSPE1 , ENPP5, PLTP, CRABP2 or CTSD, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount, are collectively indicative of the subject having a tubular interstitial disease, and wherein the reference amount is derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD), and / or non-diseased healthy individuals.

[0034]

[0031] In various embodiments, the step of measuring comprises quantifying, in the urine sample, a protein abundance of each biomarker of the biomarker combination, and the step of comparing comprises comparing the quantified protein abundance of each biomarkerto a corresponding reference abundance forthat biomarker, to determine a relative protein abundance, expressed as a fold change, for each biomarker in the biomarker combination, wherein an increased protein abundance of CELA2A, BGLAP, MLN, SPINK1, KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D or FCN2, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference abundance; and / or a decreased protein abundance of HSPE1 , ENPP5, PLTP, CRABP2 or CTSD, in the urine sample by at least a fold change of about 1 .3 relative to the corresponding reference abundance, are collectively indicative of the subject having a tubular interstitial disease, and wherein the reference abundance is derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD).

[0035]

[0032] In another aspect there is provided an in vitro method of predicting a subject's risk of developing a tubular interstitial disease, comprising: measuring, in a urine sample obtained from the subject, an amount of each biomarker of a biomarker combination, wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D, comparing the measured amount of each biomarker in the biomarker combination to a corresponding reference amount forthat biomarker, to determine a differential amount for each biomarker, wherein the differential amount of each biomarker in the biomarker combination, are collectively indicative of the subject being at risk of developing a tubular interstitial disease.

[0036]

[0033] In another aspect, there is provided an in vitro method of monitoring the efficacy of a treatment for tubular interstitial disease (TID) in a subject, comprising: measuring, in a urine sample obtained from the subject, an amount of each biomarker of a biomarker combination, wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D, wherein the treatment fortubular interstitial diseasehas been administered to the subject prior to the measuring step, comparing the measured amount of each biomarker of the biomarker combination to a corresponding reference amount forthat biomarker, wherein a differential amount of each biomarker in the biomarker combination, are collectively indicative of the efficacy of the treatment in treating tubular interstitial disease in the subject.

[0037]

[0034] In various embodiments, the method further comprises measuring, in a reference urine sample obtained from the subject before administration of the treatment, an amount of each biomarker in the biomarker combination, and comparing the amount of each biomarker measured in the urine sample following administration of the treatment to the amount of the corresponding biomarker measured in the reference urine sample.

[0038]

[0035] In various embodiments, the steps of the method are repeated at two or more time points within a predetermined time frame before, during, and / or after administration of the treatment, and wherein the amount of each biomarker in the biomarker combination at each time point is compared to the amount of the corresponding biomarker at one or more other time points to assess progression of the tubular interstitial disease and / or efficacy of the treatment.

[0039]

[0036] In another aspect, there is provided amethod of predicting the efficacy of a treatment for tubular interstitial disease (TID) in a subject having TID, comprising: obtaining a diagnosis of TID in the subject according to the method disclosed herein; and predicting if the subject is likely to exhibit a clinical response to the administration of one or more treatments for TID, based on the diagnosis step.

[0040]

[0037] In another aspect, there is provided amethod for treating tubular interstitial disease (TID) in a subject, comprising selecting a subject having TID for administration of a treatment for TID, wherein the selecting is based on the method disclosed herein; and administering the treatment for TID to the selected subject.

[0041]

[0038] In another aspect, there is provided a panel of binding agents for use in the method disclosed herein, the panel comprising a binding agent directed against each biomarker in the biomarker combination, preferably the binding agent is selected from a polypeptide, polynucleotide probe, or fragment thereof, optionally the binding agent further comprises a detectable reagent or dye, preferably the polypeptide is an antibody that specifically binds to the biomarker, optionally the antibody may be an antibody-based conjugate.

[0042]

[0039] In another aspect, there is provided a kit for use in the method disclosed herein, comprising: the panel of binding agents disclosed herein; and instructions for use.

[0043]

[0040] In another aspect, there is provided the use of a biomarker combination in the preparation of a diagnostic product for (i) diagnosing a TID in a subject, (ii) predicting a subject's risk of developing a TID, (iii) predicting the efficacy of a TID treatment, and / or (iv) monitoring the efficacy of a TID treatment,wherein the biomarker combination comprises at least two biomarkers selected from HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D.

[0044]

[0041] In various embodiments, the diagnostic product is selected from the group consisting of a kit, a diagnostic device and a computer system.

[0045]

[0042] It is understood that all embodiments disclosed herein in relation to one aspect of the invention are similarly applicable to all other aspects of the invention.

[0046] BRIEF DESCRIPTION OF THE DRAWINGS

[0047]

[0043] The invention will be better understood with reference to the detailed description when considered in conjunction with the non-limiting examples and the accompanying drawings.

[0048]

[0044] FIG. 1A-1B shows receiver operating characteristic (ROC) analysis of the Sym-CKDu (AIN) classification model, and for distinguishing AIN from non-endemic kidney disease, in training (FIG. 1 A) and test (FIG. 1B) datasets.

[0049]

[0045] FIG. 2A-2K shows the distribution of biomarker expression levels across Sym-CKDu (AIN) and non-endemic chronic kidney disease cohorts; FIG. 2A A0A075B6S5 (IGKV1-27); FIG. 2B P01602 (IGKV1-5); FIG. 2C P05451 (REG1A); FIG. 2D P05783 (KRT18); FIG. 2E P08311 (CTSG); FIG. 2F P14780 (MMP9); FIG. 2G P29373 (CRABP2); FIG. 2H P48304 (REG1B); FIG. 2I P80511 (S100A12); FIG. 2J Q14210 (LY6D); and FIG. 2K Q15485 (FCN2).

[0050]

[0046] FIG. 3A-3B shows receiver operating characteristic (ROC) analysis of the CKDu classification model in training (FIG. 3A) and test (FIG. 3B) datasets.

[0051]

[0047] FIG. 4A-4I shows the distribution of biomarker expression levels across CKDu and non-endemic chronic kidney disease cohorts. FIG. 4A P00995 (SPINK1); FIG. 4B P02818 (BGLAP); FIG.

[0052] 4C P07339 (CTSD); FIG. 4D P08217 (CELA2A); FIG. 4E P12872 (MLN); FIG. 4F P55058 (PLTP); FIG. 4G P61604 (HSPE1); FIG. 4H Q9UJA9 (ENPP5); and FIG. 4I Q15485 (FCN2).

[0053] DETAILED DESCRIPTION OF THE INVENTION

[0054]

[0048] The following detailed description refers to, by way of illustration, specific details and embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. Other embodiments may be utilized and structural and logical changes may be made without departing from the scope of the invention. Embodiments described below in context of the methods and biomarker combination are analogously valid for the respective panel of binding agents, kits, uses, and vice versa. The various embodimentsare not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments.

[0055]

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The singular terms "a," "an," and "the" include plural referents unless context clearly indicates otherwise. Similarly, the word "or" is intended to include "and" unless the context clearly indicates otherwise. The term "comprises" means "includes." In case of conflict, the present specification, including explanations of terms, will control.

[0056]

[0050] The invention relates to the identification of a high-performing biomarker panel instead of single biomarkers. While single markers have been routinely tested previously, the efficacy of these falls short in large patient cohorts due to variabilities occurring with physiological and biological changes as well as with other co-morbidities.

[0057]

[0051] From the protein profiles of urine from defined patient cohorts, both healthy control NEC (nonendemic control) and disease states, NECKD (non-endemic CKD), symptomatic CKDu (AIN) and advanced CKDu (CKDu), have identified distinct protein marker panels with high predictive performance and hence diagnostic capability. More importantly, these protein markers are found in urine, making their transformation into rapid point-of-care tests more straightforward, precluding the need for renal biopsy to confirm diagnosis of CKDu. With clinical translation, the proposed marker panel can be utilized for large community-screening based diagnosis of CKDu which will identify early disease groups before the disease progresses into renal failure. This will enable proper clinical triage and follow-up for susceptible groups and will reduce health burden associated with late-stage disease.

[0058]

[0052] The novel protein biomarker panels disclosed herein enable the diagnosis of CKDu, an emerging environmental nephropathy, which has no specific biomarkers thus far. The inventors present urinary protein signature biomarker panels specific to CKDu (panel 1) as well as the early acute stage of the disease marked with Acute Interstitial Nephritis (AIN) (panel 2). These biomarkers were discovered through comprehensive proteomic mass spectrometry analysis of patient urine from defined disease cohorts followed by in-depth analysis using machine learning. These panels with high predictive performance for identifying distinct disease groups enable early diagnosis of patients advancing into CKDu and clinical management of the disease.

[0059]

[0053] Accordingly, in one aspect, there is provided a method of detecting or diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease, comprising: detecting the presence, absence, and optionally quantity, of a biomarker combination in a sample obtained from the subject, wherein the biomarker combination comprises at least two biomarkers selected from those listed in Table 1 below, wherein the presence, absence, and optional quantity, of the biomarker combination in the sample is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease. In this regard, it will beappreciated that there is also provided the biomarker combination disclosed herein for use in detecting or diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease. The biomarker combination may be interchangeably referred to as a biomarker panel.

[0060] Table 1 : List of Biomarkers

[0061]

[0062]

[0063]

[0064]

[0054] The term "biomarker" includes organic biomolecules such as polypeptides or nucleic acids (e.g., mRNA, etc.), lipids, glycolipids, glycoproteins, sugars (monosaccharides, disaccharides, oligosaccharides, etc.) and the like, which show a significantly or statistically significant increased or decreased pattern of the gene expression level or protein expression level in a subject with tubular interstitial disease as compared to a control group (e.g. a subject with no tubular interstitial disease), and / or a suitable reference.

[0065]

[0055] The term "diagnosis" as used herein refers to methods by which the skilled artisan can estimate and / or determine whether or not a subject is suffering from a given disease or condition. The skilled artisan often makes a diagnosis on the basis of the biomarkers, the presence, absence, amount, or change in amount of which is indicative of the presence, severity, or absence of the condition. The term "prognosis" as used herein refers to is used herein to refer to the likelihood of a disease or condition progression, including recurrence of a disease or condition.

[0066]

[0056] In various embodiments, the methods described herein may be used to assist in the diagnosis. The methods may provide information that may support a clinical decision regarding whether a subject is likely to have, develop, or be at risk of tubular interstitial disease. The results generated by the methods described herein may therefore be used by a healthcare professional as one factor among other clinical, laboratory, or imaging findings in determining a diagnosis. In various embodiments, the present disclosure also encompasses methods that comprise analysing the sample in vitro to determine the presence, absence, or amount of the biomarker combination associated with TID. In various embodiments, the method comprises generating, reporting, or outputting biomarker combination data, without requiring the step of making a medical diagnosis. In various embodiments, the method comprises providing the measured biomarker data or analysis results to a clinician, laboratory, or healthcare system for interpretation.

[0067]

[0057] The term ‘‘tubular interstitial disease” (TID) refers to a group of kidney disorders characterized by damage and inflammation primarily affecting the renal tubules (the structures responsible for filtering and reabsorbing substances during urine formation) and the interstitial tissue (the space between thetubules). Unlike glomerular diseases, TID involves the non-glomerular structures of the kidney. This damage can impair kidney function, leading to chronic kidney disease (CKD) or acute kidney injury (AKI), depending on the underlying cause and progression. Types and Conditions covered by Tubular Interstitial Diseases include but are not limited to Chronic Kidney Disease (CKD), Chronic Kidney Disease of Unknown or Uncertain Etiology (CKDu), Acute Tubulointerstitial Nephritis (ATIN) (interchangeably used with Acute Interstitial Nephritis (AIN)), Chronic Tubulointerstitial Nephritis (CTIN), Pyelonephritis (Acute and Chronic), Drug-Induced Interstitial Nephritis, transplant rejections, Toxic Nephropathy, Obstructive Nephropathy, Hereditary and Genetic Disorders such as Polycystic kidney disease (PKD) (progresses to involve the interstitium) and Medullary cystic kidney disease (MCKD), Autoimmune and Systemic Inflammatory Diseases such as Sarcoidosis or Sjogren's syndrome, Renal Tubular Acidosis (RTA),

[0068]

[0058] In various embodiments, the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu).

[0069]

[0059] The term “Chronic Kidney Disease of uncertain etiology” is a form of chronic tubulointerstitial nephritis (CTIN) and fits within the chronic tubulointerstitial disease spectrum. Unlike other chronic kidney diseases (CKDs), CKDu does not have an identifiable underlying cause such as diabetes, hypertension, or glomerular disease, and the disease affects individuals without known traditional CKD risk factors. CKDu first emerged observed in agricultural workers in tropical and subtropical regions (e.g., Central America, Sri Lanka, India) with likely contributors include environmental exposures (e.g., pesticides, heat stress, and dehydration), though the exact cause remains unclear. CKDu progresses insidiously, with early-stage disease being largely asymptomatic. The disease often manifests as chronic kidney dysfunction, presenting with fatigue, and signs of kidney failure in later stages. CKDu may be further characterised as acute CKDu (Acute presentations of CKDu often involve sudden worsening of renal function, which can occur over days or weeks. If detected early, it may be partially reversible, though often it leads to chronic impairment) or advanced or late-stage CKDu (CKDu cases with more gradual onset, often detected at later stages. Often diagnosed late, leading to end-stage kidney disease (ESKD) requiring dialysis or transplantation and is considered irreversible). Advanced CKDu is considered irreversible, particularly in Stage 4 and Stage 5 of chronic kidney disease (CKD). CKDu may also be called Chronic Interstitial Nephritis in Agricultural Communities (CINAC), and previously described as Mesoamerican Nephropathy (See Ben Khadda, Z. et al. Chronic Kidney Disease of Unknown Etiology: A Global Health Threat in Rural Agricultural Communities Prevalence, Suspected Causes, Mechanisms, and Prevention Strategies. Pathophysiology 2024, 31, 761-786).

[0070]

[0060] In various embodiments, the CKDu is advanced or late-stage CKDu.

[0071]

[0061] In various embodiments, the method is able to differentiate CKDu associated with endemic regions from CKD more commonly found in non-endemic regions.

[0062] As used herein, the term “endemic region" refers to a geographic area or population in which a particular disease, condition, or disorder occurs at a consistently elevated prevalence or incidence relative to global background levels, and where the disease is regularly observed within the resident population over a sustained period of time. In the context of the present invention, an endemic region refers to a geographic area in which chronic kidney disease of uncertain etiology (CKDu) is reported to occur with increased frequency and is considered a regional public health concern. Endemic regions may include tropical or subtropical areas associated with agricultural or environmental exposures, including but not limited to regions of Central America, South Asia, and other regions where CKDu has been reported among agricultural or manual labour populations. In various embodiments, an endemic region may be defined by epidemiological evidence demonstrating a higher prevalence of CKDu within a defined geographic population compared with populations outside that region.

[0072]

[0063] In various embodiments, the endemic regions may comprise Asia, Central America, Africa, and the Middle East. In various embodiments, the endemic regions may comprise Central America and / or South Asia. In various embodiments, endemic regions comprise one or more countries selected from Nicaragua, El Salvador, Guatemala, Costa Rica, Mexico, Sri Lanka, India, Egypt, Tunisia, Brazil, Peru, Ecuador, Indonesia, the Philippines, Japan, and South Africa. Published reviews and multinational kidney-health reports identify CKDu hotspot clusters in Central America, India and Sri Lanka most consistently, and also report country-level or subnational hotspot evidence in Mexico, Egypt, Tunisia, Brazil, Peru, Ecuador, Indonesia, the Philippines, Japan, and South Africa (Ref See Ben Khadda, Z. et al. Chronic Kidney Disease of Unknown Etiology: A Global Health Threat in Rural Agricultural Communities Prevalence, Suspected Causes, Mechanisms, and Prevention Strategies. Pathophysiology 2024, 31, 761-786).

[0073]

[0064] As used herein, the term "non-endemic region” refers to a geographic area or population in which a particular disease or condition does not occur at elevated prevalence relative to global background levels, and where the disease is not regularly observed as a regionally characteristic health condition. In the context of the present invention, a non-endemic region refers to a geographic area in which CKDu is not known to occur at elevated prevalence, and where chronic kidney disease is more commonly attributable to established etiologies such as diabetes mellitus, hypertension, autoimmune disease, glomerular disease, hereditary kidney disorders, or other recognized causes. Non-endemic regions may include urban or rural populations in regions where CKDu has not been reported as a significant public health issue, including many regions of Europe, North America, East Asia, and other areas lacking epidemiological evidence of CKDu clusters.

[0074]

[0065] In various embodiments, the tubular interstitial disease is Acute Interstitial Nephritis (AIN).

[0075]

[0066] The term “Acute Interstitial Nephritis” (AIN) refers to acute tubulointerstitial nephritis (ATIN) and falls within the broader category of tubulointerstitial kidney diseases. AIN is characterised by an abrupt onset of inflammation of the renal interstitium and tubules and may be associated with impaired renalfunction. AIN may arise from a variety of causes including drug exposure (for example non-steroidal anti-inflammatory drugs (NSAIDs), antibiotics, or proton pump inhibitors), infections including viral, bacterial, or fungal infections, autoimmune disorders such as sarcoidosis, systemic lupus erythematosus, or Sjogren’s syndrome, or may in some cases be idiopathic. Clinically, AIN may present with symptoms including fever, rash, eosinophilia, or elevated serum creatinine, and may lead to acute kidney injury (AKI) if untreated. In many cases, AIN may be reversible if the underlying cause is identified and addressed. In various embodiments, acute interstitial nephritis may represent a symptomatic manifestation, acute presentation, or clinical complication associated with chronic kidney disease of uncertain etiology (CKDu). Without being bound by theory, inflammatory processes affecting the renal interstitium and tubules may occur during the progression of CKDu orduring episodes of renal injury in subjects predisposed to CKDu.

[0076]

[0067] In various embodiments, the method is able to differentiate AIN from CKDu and CKD more commonly found in non-endemic regions. In various embodiments, the method is able to identify CKDu and AIN distinctly from other non-endemic diseases.

[0077]

[0068] The term "subject", as used herein in the context of the methods, refers to a warm-blooded animal, preferably a mammal, more preferably a human. Said subject may be awaiting or receiving medical treatment for TID, oris, or will become the subject of a medical procedure, oris being monitored for the development of TID. Subjects include those already being afflicted by TID as well as subjects susceptible to the progression of the TID or for whom TID or progression of TID should be prevented or delayed. The subject may not have previously received treatment for the TID; and / or has not been diagnosed as having TID, or does not exhibit symptoms that are diagnostic of TID. A "patient", "subject", or "individual" are used interchangeably and may refer to either a human or a non-human animal.

[0078]

[0069] In various embodiments, the subject is selected from a population residing in or originating from an endemic region for CKDu. In various embodiments, the subject is of an ethnicity associated with a population residing in, originating from, or ancestrally linked to a CKDu endemic region.

[0079]

[0070] In various embodiments, the subject may be an agricultural worker or individuals engaged in occupations involving prolonged outdoor labour, including but not limited to farmers, plantation workers, sugarcane cutters, rice farmers, construction workers, and other manual labourers exposed to heat stress or environmental toxicants.

[0080]

[0071] In various embodiments, the subject may be an individual exposed to environmental risk factors associated with CKDu, including but not limited to chronic heat stress, recurrent dehydration, agrochemical exposure, pesticide exposure, heavy metal exposure, contaminated drinking water, or combinations thereof.

[0072] In various embodiments, the subject may be an individual residing in rural or agricultural communities within endemic regions where CKDu prevalence exceeds that of urban populations.

[0081]

[0073] In various embodiments, the subject may be an individual who has resided in an endemic region for a defined period, such as at least 1 year, at least 5 years, at least 10 years, or throughout their lifetime. In various embodiments, the subject may be an individual who was previously resident in an endemic region but is currently residing in a non-endemic region, for example, migrant workers or relocated individuals.

[0082]

[0074] In various embodiments, the subject may be an individual from endemic regions who do not yet exhibit clinical symptoms of kidney disease but may be at elevated risk of CKDu, for example individuals exhibiting subclinical reductions in estimated glomerular filtration rate (eGFR) or elevated tubular injury biomarkers.

[0083]

[0075] In various embodiments, the subject may be individuals irrespective of geographic location, including those residing within or outside of a CKDu endemic region. Such subjects may include members of the general population who do not originate from, nor reside in, endemic regions, including individuals in non-endemic, urban, or industrialised environments. In this regard, the subject may comprise any individual who may be at risk of developing CKDu due to environmental, occupational, genetic, lifestyle, or idiopathic factors, including exposures not limited to those characteristic of endemic regions. Accordingly, the methods described herein are applicable to both endemic and non-endemic populations and subjects, including subjects with known or suspected risk factors, as well as those without identifiable risk factors.

[0084]

[0076] In various embodiments, the subject may be an individual residing in non-endemic regions, including regions where CKDu prevalence is low or absent and where chronic kidney disease is predominantly attributable to conventional causes such as diabetes mellitus, hypertension, or glomerular disease.

[0085]

[0077] In various embodiments, the subject may be presenting with suspected tubulointerstitial disease but without a confirmed diagnosis, for example subjects presenting with elevated serum creatinine, abnormal urinary biomarkers, or reduced eGFR in the absence of clear etiological factors.

[0086]

[0078] The term "sample,” as used herein, refers to a composition that is obtained or derived from the subject that contains a cellular and / or other molecular entity (i.e. biomarkers) that is to be characterized and / or identified, for example, based on physical, biochemical, chemical, and / or physiological characteristics. For example, the phrase “disease sample" and variations thereof refers to any sample obtained from a subject of interest that would be expected or is known to contain the cellular and / or molecular entity that is to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreousfluid, lymph fluid, synovial fluid, follicular fluid, seminal fluid, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebro-spinal fluid, saliva, sputum, tears, perspiration, mucus, tumour lysates, and tissue culture medium, tissue extracts such as homogenized tissue, tumour tissue, cellular extracts, and combinations thereof.

[0087]

[0079] In various embodiments, the method is an in vitro method and the sample has been previously obtained from the subject.

[0088]

[0080] In various embodiments, the sample is a urine sample. The urine sample used in the present invention may be urine itself, a urine sediment fraction obtained by centrifuging urine, or a supernatant fraction thereof. The measurement method of the present invention can not only directly measure a urine sample without any treatment, but also can centrifuge urine to fractionate urine sediment, and remove substances present on the cell surface of the fraction. The amount of urine for measurement may be about 5-100mL.

[0089]

[0081] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D.

[0090]

[0082] In various embodiments, the biomarker combination comprises FCN2. As shown in the working examples, there is a shared marker, FCN2, between the diagnostic panel of CKDu and AIN. This will prove to be a robust CKDu marker irrespective of the disease stage (early or late, symptomatic or asymptomatic). This is the first time to report such a common marker for both stages. In various embodiments, the biomarker combination comprises FCN2, and the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu) or Acute Interstitial Nephritis (AIN).

[0091]

[0083] In various embodiments, the biomarker combination comprises at least 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18 or 19 biomarkers listed in Table 1.

[0092]

[0084] In various embodiments, the biomarker combination comprises at least three biomarkers. As shown in the working examples, a combination of 3 markers for both CKDu and AIN showed high diagnostic performance for CDKu and AIN (e.g. AUC >0.9).

[0093]

[0085] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , and FCN2. In various embodiments, the biomarker combination comprises FCN2, and at least one biomarker selected from the group consisting of: HSPE1, CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, and SPINK1. In various embodiments, the biomarker combination comprises CELA2A, and at least one biomarker selected from the group consisting of: HSPE1 , ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 and FCN2.

[0086] In various embodiments, the biomarker combination comprises FCN2 and CELA2A, and optionally at least one biomarker selected from the group consisting of: HSPE1 , ENPP5, BGLAP, PLTP, MLN, CTSD, and SPINK1.

[0094]

[0087] In various embodiments, the biomarker combination comprises CELA2A and CTSD. In various embodiments, the biomarker combination comprises CELA2A, FCN2, and HSPE1. In various embodiments, the biomarker combination comprises CELA2A, FCN2, HSPE1 and SPINK1. In various embodiments, the biomarker combination comprises CELA2A, FCN2, HSPE1, CTSD and SPINK1. In various embodiments, the biomarker combination comprises HSPE1 , CELA2A, ENPP5, MLN, CTSD, and FCN2. In various embodiments, the biomarker combination comprises HSPE1 , CELA2A, ENPP5, PLTP, MLN, SPINK1, and FCN2. In various embodiments, the biomarker combination comprises HSPE1 , CELA2A, ENPP5, PLTP, MLN, CTSD, SPINK1, and FCN2.

[0095]

[0088] In various embodiments, the biomarker combination comprises or consists of HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, and FCN2.

[0096]

[0089] In various embodiments, the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu), and the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1, CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, and FCN2. In various embodiments, the CKDu is advanced or late-stage CKDu.

[0097]

[0090] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2. In various embodiments, the biomarker combination comprises FCN2, and at least one biomarker selected from the group consisting of: KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D.

[0098]

[0091] In various embodiments, the biomarker combination comprises KRT18, LY6D, CRABP2, REG1B or IGKV1-5. In various embodiments, the biomarker combination comprises KRT18, LY6D, CRABP2, REG1 B and IGKV1 -5.

[0099]

[0092] In various embodiments, the biomarker combination comprises MMP9 and LY6D. In various embodiments, the biomarker combination comprises MMP9, KRT18 and IGKV1-5. In various embodiments, the biomarker combination comprises CRABP2, REG1 B, REG1A, and KRT18. In various embodiments, the biomarker combination comprises CRABP2, REG1B, IGKV1-5, KRT18 and S100A12. In various embodiments, the biomarker combination comprises CRABP2, REG1 B, KRT18, LY6D, FCN2 and MMP9. In various embodiments, the biomarker combination comprises CRABP2, CTSG, REG1A, REG1B, IGKV1-5, S100A12, and LY6D. In various embodiments, the biomarker combination comprises KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-5, S100A12, and LY6D. Invarious embodiments, the biomarker combination comprises KRT18, CRABP2, CTSG, MMP9, REG1 B, IGKV1-5, S100A12, LY6D, and FCN2. In various embodiments, the biomarker combination comprises KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, LY6D, and FCN2.

[0100]

[0093] In various embodiments, the biomarker combination comprises or consists of KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2.

[0101]

[0094] In various embodiments, the tubular interstitial disease is Acute Interstitial Nephritis (AIN), and the biomarker combination comprises at least two biomarkers selected from the group consisting of: KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2.

[0102]

[0095] In various embodiments, the step of detecting the presence, absence, and optionally quantity, of the biomarker combination may comprise measuring and quantifying the amount of a gene product of each biomarker in the sample.

[0103]

[0096] In various embodiments, the gene product may include a protein or RNA transcript encoded by the biomarker gene, or a fragment of the protein or RNA transcript. In various embodiments, the detection of the biomarker expression may comprise obtaining the quantitative gene expression level of biomarker. In various embodiments, the gene product may include the biomarker protein expressed and present in the sample.

[0104]

[0097] In various embodiments, the step of detecting comprises measuring and quantifying the amount of each biomarker expressed and present in the sample.

[0105]

[0098] In various embodiments, the step of detecting the biomarker combination comprises measuring / quantifying the amount (for example, absolute amount, relative amount, or concentration) of a gene product of each biomarker in the sample, wherein the measured / quantified amount of each biomarker in the biomarker combination is used as an evaluation index.

[0106]

[0099] The term "evaluation index” refers to a quantitative or qualitative value, score, or parameter derived from one or more measured or quantified variables, such as biomarker levels, that is used to assess, classify, or interpret a specific biological or clinical condition. The evaluation index may represent a direct measurement, a combination of multiple measurements, or a derived value based on statistical models, algorithms, or predetermined thresholds. In the context of the present invention, the evaluation index is generated based on the measured levels of a combination of biomarkers and is utilized to provide an objective indication of a biological state, such as the presence, progression, or severity of the TID, orthe efficacy of a therapeutic intervention. The evaluation index may be expressed as a numerical score, category, or probability that facilitates clinical interpretation and decision-making. This approach enables enhanced diagnostic accuracy and consistency by converting raw biomarker data into a standardized assessment format.

[0100] In various embodiments, the step of detecting the biomarker combination comprises: measuring the mRNA amounts of each biomarker in the biomarker combination using a primer pair, probe or antisense nucleotide that specifically binds to a gene of each biomarker; and / or measuring protein amounts of each biomarker in the biomarker combination using an antibody, interacting protein, ligand, nanoparticle or aptamer that specifically binds to a protein or peptide fragment of each biomarker.

[0107]

[0101] The term “amount” may generally refer to the quantity of a gene or gene product in a sample, and may be used interchangeably with “level” or “expression level". “Expression" generally refers to the process by which information (e.g., gene-encoded and / or epigenetic information) is converted into the structures present and operating in cells. Therefore, as used herein, “expression” may refer to transcription into a polynucleotide, translation into a polypeptide, or even polynucleotide and / or polypeptide modifications (e.g., posttranslational modification of a polypeptide). Fragments of the transcribed polynucleotide, the translated polypeptide, or polynucleotide and / or polypeptide modifications (e.g., posttranslational modification of a polypeptide) shall also be regarded as expressed whether they originate from a transcript generated by alternative splicing or a degraded transcript, or from a posttranslational processing of the polypeptide, e.g., by proteolysis. “Expressed genes” include those that are transcribed into a polynucleotide as mRNA and then translated into a polypeptide, and also those that are transcribed into RNA but not translated into a polypeptide (for example, transfer and ribosomal RNAs). The “amount” or “level" of a gene or protein is a detectable level in a sample. The "level" or “amount" may refer to an indication of the concentration of a substance in a sample, often measured in units such as nanograms per milliliter (ng / mL) or picograms per milliliter (pg / mL); or may refer to the total quantity of the proteins in a given sample, often measured in absolute numbers or mass units. These can be measured by methods known to one skilled in the art and also disclosed herein; or may refer to the abundance of the protein, whereby a relative measure of the protein present in the sample is obtained as compared to a matched reference (control) abundance of that protein. Protein abundance may be expressed, for example, as relative expression units, normalized signal intensity, fold-change, ratio, or percentage, as determined by quantitative analytical techniques. In particular embodiments, protein abundance may be determined using proteomic or immunoassaybased methods, including but not limited to mass spectrometry (e.g., label-free quantification, SRM / PRM), Western blotting, ELISA, or multiplexed affinity-based assays, optionally with normalization to internal standards, housekeeping proteins, or total protein levels to account for sample-to-sample variation.

[0108]

[0102] In various embodiments, the protein amount refers to a protein abundance of the biomarker in the urine sample. In various embodiments, “protein abundance" may comprise the protein being quantified based on a detection signal (for example, ion intensity, peak area, spectral counts, optical density, fluorescence intensity, chemiluminescence, or band density) and then compared to a referenceor control abundance, with any relative change (i.e. differential amount) being represented as a foldchange.

[0109]

[0103] In various embodiments, the amount of each biomarker of the combination may be detected and measured by protein expression analysis or mRNA expression analysis.

[0110]

[0104] In various embodiments, the protein expression analysis may comprise Western blotting, Mass Spectrometry (MS), enzyme-linked immunosorbent assay (ELISA), immunofluorescence, flow cytometry, immunohistochemistry (IHC), immunocytochemistry (ICC), multiplex immunoassays, beadbased immunoassays, protein microarray analysis, electrochemiluminescence immunoassays, lateral flow immunoassays, radioimmunoassays, immunoprecipitation-based assays, selected reaction monitoring (SRM), multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), aptamerbased protein detection assays, proximity extension assays, proximity ligation assays, surface plasmon resonance (SPR), biosensor-based detection assays, nephelometry, turbidimetry, and combinations thereof.

[0111]

[0105] In various embodiments, protein expression level may be measured using antibody-based, affinity-based, spectrometric, chromatographic, or biosensor-based methods. In various embodiments, the protein expression analysis may comprise Western blotting, Mass Spectrometry (MS), ELISA, immunofluorescence, or flow cytometry.

[0112]

[0106] In various embodiments, the protein abundance for each biomarker may be measured by mass spectrometry. In various embodiments, the protein intensity and / or protein density for each biomarker may be measured using one or more immunoassays, including but not limited to enzyme-linked immunosorbent assays (ELISA), multiplex immunoassays, Western blotting, immunohistochemistry, or flow cytometry-based detection methods. In various embodiments, the measured protein values may be normalised to internal standards, housekeeping proteins, total protein content, or reference controls to enable comparative analysis across samples.

[0113]

[0107] In various embodiments, the amount of each biomarker of the combination may be detected and measured in the sample using suitable binding agents. In various embodiments, the amount of each biomarker of the combination may be detected and measured in the sample using antibody-based conjugates or other molecular probes specific for each biomarker that are linked to imaging agents (e.g., contrast agents, nanoparticles, radioactive tracers, fluorescent dyes) and detecting the signal by imaging modalities, or optical imaging.

[0114]

[0108] The term "antibody-based conjugates” refers to antibody conjugates used for in vitro detection or quantification of one or more biomarkers of the biomarker combination described herein, comprising a molecular complex in which an antibody or antigen-binding fragment thereof is chemically linked (conjugated) to a detectable label. The detectable label may comprise, for example, a fluorescent dye,enzyme label, chemiluminescent label, electrochemiluminescent label, nanoparticle, radioactive label, affinity tag, or other detectable reporter moiety, which permits detection of the antibody-biomarker complex in a biological sample. The antibody is configured to specifically bind to one or more biomarkers of the biomarker combination present in a sample obtained from a subject, thereby enabling detection or measurement of the biomarker. Following binding of the antibody conjugate to the biomarker in the sample, the attached detectable label allows visualisation or quantification of the biomarker using suitable analytical techniques, including but not limited to flow cytometry, immunoassays, fluorescence-based detection, chemiluminescence-based detection, electrochemiluminescence-based detection, or other antibody-based detection platforms. In various embodiments, the antibody-based conjugate may comprise an antibody-nanoparticle conjugate, wherein the nanoparticle functions as a detectable label in an analytical assay.

[0115]

[0109] In various embodiments, the mRNA expression analysis may comprise quantitative reverse transcription polymerase chain reaction (qRT-PCR), reverse transcription PCR (RT-PCR), digital PCR, droplet digital PCR (ddPCR), RNA sequencing (RNA-seq), bulk RNA-seq, single-cell RNA sequencing, microarray analysis, NanoString analysis, Northern blotting, in situ hybridisation (ISH), fluorescence in situ hybridisation (FISH), RNase protection assays, nucleic acid hybridisation assays, branched DNA assays, ligation-based amplification assays, transcription-mediated amplification assays, loop-mediated isothermal amplification (LAMP), CRISPR-based nucleic acid detection assays, and combinations thereof. In various embodiments, the mRNA expression analysis may comprise qRT-PCR or RNA-seq.

[0116]

[0110] In various embodiments, measurement of the biomarker combination may be performed using multiplexed detection or staining approaches to simultaneously detect, measure, or quantify the amount of the at least two biomarkers of the biomarker combination in a single assay. Such multiplex analyses may comprise, for example, multiplex immunostaining, multiplex immunoassays, multiplex flow cytometry, bead-based assays, or multiplex nucleic acid detection platforms.

[0117]

[0111] In various embodiments, the measuring step comprises multiplex detection of the at least two biomarkers present in the sample. In various embodiments, multiplex detection comprises simultaneously or sequentially detecting / measuring the two or more biomarkers within a single sample using a multiplex assay platform. In various embodiments, the multiplex assay platform comprises one or more techniques selected from multiplex immunoassays, bead-based assays, microarrays, quantitative PCR panels, next-generation sequencing, mass spectrometry-based proteomic analysis, or combinations thereof.

[0118]

[0112] A general principle of the method disclosed herein is the detection and measuring of biomarkers including preparing the sample that may contain the biomarkers (e g., one or more of DNA, RNA, protein, polypeptide, carbohydrate, lipid, metabolite, and the like) and a binding agent under appropriate conditions and for a time sufficient to allow the biomarker and binding agent to interact and bindtogether, thus forming a complex that can be removed and / or detected in the sample mixture. For example, one method to conduct such an assay would involve anchoring the biomarker or binding agent onto a solid phase support, also referred to as a substrate, and detecting target biomarker / binding agent complexes anchored on the solid phase at the end of the reaction. In various embodiments of such a method, a sample from the subject, which is to be assayed for presence and / or amount of each biomarker, can be anchored onto a carrier or solid phase support. In another embodiment, the reverse situation is possible, in which the binding agent can be anchored to a solid phase and the sample from a subject can be allowed to react as an unanchored component of the assay. Solid phase supports suitable for such assays include, without limitation, glass, polystyrene, nylon, polypropylene, polyethylene, polyacrylamides, modified celluloses, dextran, magnetic particles, or other materials capable of binding biomolecules. In various embodiments, immobilization maybe achieved using biotinstreptavidin interactions or other affinity coupling systems, and assay components may be preimmobilized on substrates such as microplates, beads, microarrays, or other analytical platforms. Following formation of biomarker-probe complexes, unbound components may be removed, for example by washing, and the bound complexes detected using detectable labels or label-free detection methods. Detectable labels may include fluorescent, chemiluminescent, enzymatic, electrochemiluminescent, radioactive, or nanoparticle labels, enabling quantification of biomarker levels. In other embodiments, binding events may be detected using label-free techniques, including fluorescence energy transfer (FRET), surface plasmon resonance (SPR), or other biomolecular interaction analysis technologies, which allow real-time detection of biomarker-probe interactions. Such detection approaches enable qualitative or quantitative determination of the biomarker combination in samples, thereby facilitating diagnosis, classification, or monitoring of tubular interstitial disease in the subject.

[0119]

[0113] In various embodiments, the measuring step comprises multiplexed quantitative proteomic analysis. In various embodiments, proteins in the sample are extracted and subjected to enzymatic digestion to generate peptides, followed by labelling of the peptides with isobaric tags suitable for multiplexed quantification, optionally Tandem Mass Tags (TMT). In various embodiments, the labelled peptides are analysed using mass spectrometry, optionally tandem mass spectrometry (MS / MS), to determine the abundance of each protein biomarker in the sample. In various embodiments, the multiplexed analysis may be performed using TMT, optionally across a plurality of experimental sets, thereby enabling simultaneous quantification of protein abundances across multiple samples. In various embodiments, the method may further comprises normalising the measured protein biomarker amount within and across multiplexed TMT sets to account for inter-experiment variation.

[0120]

[0114] In various embodiments, the method disclosed herein may further comprise the step of: comparing the amount of each biomarker of the biomarker combination to a reference amount of that biomarker; and detecting or diagnosing the tubular interstitial disease in the subject, or predicting the subject's risk of developing the tubular interstitial disease based on a differential amount of each biomarker in the biomarker combination being detected compared to the reference amount.

[0115] In various embodiments, the measured amount of each biomarker in the biomarker combination is compared to a corresponding reference amount of that biomarker. This refers to performing a comparison on a per-biomarker basis, such that each biomarker measured in the subject's sample is evaluated against a matched reference value specific to that same biomarker. The term "corresponding” denotes a one-to-one relationship between each measured biomarker and its matched reference amount, meaning that each biomarker is compared only to its own reference value and not to that of a different biomarker. For example, where the biomarker combination comprises KRT18 and MMP9, the measured amount of KRT 18 in the subject’s urine sample is compared to a reference amount of KRT 18, and the measured amount of MMP9 is compared to a reference amount of MMP9. By way of illustration, in embodiments where the measurement is expressed as a relative protein abundance, the comparison may be represented as a fold-change (increase or decrease) relative to the corresponding reference amount (for example, a 1 3-fold increase for KRT18 and a 0.5-fold increase for MMP9). In each case, the comparison is performed independently for each biomarker, and the resulting deviations (i.e. differential amount) from the corresponding reference amounts may be used, collectively (for example, as a biomarker profile or signature), to indicate whether the subject has a tubular interstitial disease.

[0121]

[0116] The term “reference amount” refers to baseline or control data against which results of the method (i.e. measured amount of each biomarker) are compared. These controls and reference data may serve as a standard for normal (i.e. healthy) or known levels of each biomarker, allowing for the assessment of deviations or changes or similarities that may indicate a diagnosis or risk in the subject of tubular interstitial disease. The reference amount may be derived from a reference group or control group that may refer to a group of subjects or samples used as a baseline or reference for comparison against, for example, when measuring mRNA or protein levels of biomarkers, the control group provides a standard or expected range of gene or protein expression. The reference amount may also be obtained from an online medical database. The term “reference sample,” “control sample,” as used herein, refers to a sample, cell, tissue, standard, or level that is used for comparison purposes. A reference sample or control sample, may be obtained from a healthy and / or non-diseased individual, or a non-healthy and / or diseased individual, or a cell line, or the same subject undergoing the method of the present invention at one or more time points prior to or during diagnosis or treatment. In various embodiments, the "reference amount” may be obtained from a control sample that has not been treated or exposed to any experimental conditions, treatments, or interventions, such as drugs, chemicals, or other stimuli, and in particular has received treatment for a tubular interstitial disease to obtain a baseline biomarker amount in determining for comparison and determination of any differential amount. The "reference amount” may also be used interchangeably with “reference expression level”.

[0122]

[0117] In various embodiments, the reference amount may be obtained and derived from a control subject. A control subject refers to any individual that has not been diagnosed as having the disease or condition being assayed. The terms "normal control", "healthy control", likewise mean a sample (e.g., urine sample) taken from a source (e.g., subject, control subject) that does not have the condition ordisease being assayed and therefore may be used to determine the baseline for the condition or disorder being measured. It is also understood that the control subject, normal control, and healthy control, include data obtained and used as a standard, i.e. it can be used over and over again for multiple different subjects. In other words, for example, when comparing a subject sample to a control sample, the data from the control sample could have been obtained in a different set of experiments, for example, it could be an average obtained from a number of healthy subjects and not actually obtained at the time the data for the subject was obtained.

[0123]

[0118] In various embodiments, the reference amount of the biomarker in the biomarker combination may be derived from non-diseased healthy individuals, optionally individuals without clinical or laboratory evidence of kidney disease. In various embodiments, the reference amount may be derived from (i) non-diseased healthy individuals and / or (ii) individuals residing in non-endemic regions where CKDu prevalence is low or absent.

[0124]

[0119] In various embodiments, the reference amount may be derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD). In various embodiments, the reference amount corresponds to a mean, median, or statistically derived value obtained from a plurality of reference samples obtained from NEC and / or NECKD cohorts. In various embodiments, the reference amount is a non-endemic (NEC) control and / or a non-endemic CKD (NECKD) control.

[0125]

[0120] In various embodiments, the method may comprise obtaining a reference baseline sample from the subject, and subsequently obtaining one or more additional samples from the subject at later time points. The measured expression level / amounts of each biomarker in the biomarker combination in the subsequent sample(s) may be compared to the baseline reference sample, thereby enabling assessment of disease onset, progression, or response to clinical management.

[0126]

[0121] In various embodiments, samples may be obtained from the subject at a baseline and at one or more predefined time points, for example during clinical monitoring ordisease surveillance. Changes or trends in the amounts (i.e. expression levels) of the biomarkers in the biomarker combination may be used to identify early tubular injury, monitor disease progression, distinguish CKDu from other forms of chronic kidney disease, or differentiate acute interstitial nephritis (AIN) from CKDu or non-endemic CKD. Accordingly, the method may comprise serial monitoring of biomarker expression levels in a subject over time to support diagnosis, disease stratification, or longitudinal assessment of tubular interstitial disease.

[0127]

[0122] Accordingly, there is provided an in vitro method of diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease, comprising: measuring, in a urine sample obtained from the subject, an amount of each biomarker of a biomarker combination, wherein the biomarker combination comprises at least two biomarkers selected from thegroup consisting of: HSPE1, CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D, comparing the measured amount of each biomarker of the biomarker combination to a corresponding reference amount of that biomarker, wherein a differential amount of each biomarker in the biomarker combination, are collectively indicative of the subject having a tubular interstitial disease.

[0128]

[0123] The term ‘‘differential amount” or “differential expression level” refers to a significant variation in the level of a biomarker, relative to a reference amount / level. Biomarkers identified as being present in a differential amount may be upregulated (higher / increased expression) or downregulated (lower / d ecreased expression) relative to the reference / control amount. Statistically significant differences may be determined by applying statistical tests well-known to those skilled in the art. The differential amount may include “aberrant amount / level” which refers to any deviation from the normal, physiological amount / levels of the biomarker within a biological system. A suitable fold change, concentration change, signal intensity change, band intensity change, statistical values (p-values), Iog2 fold change (Log2FC) value or TPM value may indicate a differential amount.

[0129]

[0124] In various embodiments, the differential amount may refer to at least a fold change of 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 or at least a 30% change (increase or decrease) in signal intensity, whereby these were based on mass spectrometry measurements applied and could vary depending on the method of assay implementation such as antibody or ELISA-based. It will be appreciated that the actual fold changes vary for individual markers and the combination used will also influence the actual cut off / scoring used. Since the biomarkers will be used as a combination panel, the optimal cut-point value is dependent on other biomarkers too. In various embodiments, the differential amount may refer to at least a fold change of about 1.3.

[0130]

[0125] In various embodiments, the amount of at least one of the biomarkers is increased or upregulated in the sample compared to the reference amount. In various embodiments, the amount of at least one of the biomarkers is increased or upregulated in the sample compared to the reference amount. In various embodiments, the amount of a first biomarker is increased or upregulated in the sample compared to the reference amount, and the amount of a second biomarker is increased or upregulated in the sample compared to the reference amount. The term “increased amount", or "increased expression level”, may refer to a higher or increased expression level of a biomarker above or greater than a reference amount or expression level. The increased or higher amount or expression level may be above a threshold value. In contrast, the term “decreased amount”, or "decreased expression level” may refer to a lower or decreased amount or expression level of a biomarker below or less than a reference amount or expression level. The decreased or lower amount or expression level may be below a threshold value. A threshold value or level may be associated with a statistic, whereby a threshold value or level is a value or level above or below which the difference in the measured detectable signal is assigned significance (i.e. via a statistical method), for example a p-value <0.05 may be considered as statistically significant.

[0126] In various embodiments, the relative increase or decrease in the amount or expression level of the at least two biomarkers of the biomarker combination in the sample relative to their respective reference amounts collectively defines a biomarker profile of the subject. The biomarker profile may comprise the pattern and / or magnitude of upregulation and / or downregulation of the biomarkers relative to the reference amounts, including simultaneous increases in one or more biomarkers, simultaneous decreases in one or more biomarkers, ora combination of increased and decreased biomarker levels. Such a pattern of relative biomarker changes may be referred to as a disease-associated biomarker signature, which may be used to identify, classify, or monitor tubular interstitial disease in the subject, including differentiation from non-diseased controls or other kidney diseases.

[0131]

[0127] In this regard, a detected differential amount of each biomarker in the biomarker combination are collectively considered in which the deviation of each individual biomarker from its corresponding reference amount is not interpreted in isolation, but rather integrated across the full combination of biomarkers to generate a combined diagnostic indication. In this context, a “differential amount" denotes a relative change in the measured level of a given biomarker compared to its corresponding reference amount. Each biomarker therefore contributes a respective relative amount, and the collection of these relative amounts across the biomarker combination defines a composite biomarker profile or signature. The term “collectively” refers to the diagnostic determination being based on the overall pattern, magnitude, and / or direction of these relative changes considered together, rather than on any single biomarker alone. Accordingly, the method relies on the integration of the determined relative amounts of each biomarker in the biomarker combination as a collective to provide an indication of disease rather than each biomarker individually.

[0132]

[0128] In various embodiments, the biomarker profile may be referred to and include a protein biomarker profile, wherein the protein biomarker profile of the subject is compared with a reference protein biomarker profile, and a detected / measured "difference" between these profiles is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease. Accordingly, the method may comprise generating a biomarker profile of the subject’s urine sample, wherein the biomarker profile comprises the relative increases and / or decreases in the measured amount of each biomarker in the biomarker combination compared to its corresponding reference amount. The biomarker profile may then be used to determine whether the subject has, or is at risk of developing, a tubular interstitial disease. In this regard, differential amounts of each biomarker in the biomarker combination collectively define a biomarker profile, and wherein the biomarker profile is indicative of the subject having a tubular interstitial disease.

[0133]

[0129] In various embodiments, the biomarker combination comprises CTSD, wherein a decreased amount of CTSD in the sample relative to a reference amount of CTSD is indicative ofthe subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0130] In various embodiments, the biomarker combination comprises SPINK1, wherein an increased amount of SPINK1 in the sample relative to a reference amount of SPINK1 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0134]

[0131] In various embodiments, the biomarker combination comprises FCN2, wherein an increased amount of FCN2 in the sample relative to a reference amount of FCN2 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0135]

[0132] In various embodiments, the biomarker combination comprises BGLAP, wherein an increased amount of BGLAP in the sample relative to a reference amount of BGLAP is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0136]

[0133] In various embodiments, the biomarker combination comprises PLTP, wherein a decreased amount of PLTP in the sample relative to a reference amount of PLTP is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0137]

[0134] In various embodiments, the biomarker combination comprises CELA2A, wherein an increased amount of CELA2A in the sample relative to a reference amount of CELA2A is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0138]

[0135] In various embodiments, the biomarker combination comprises MLN, wherein an increased amount of MLN in the sample relative to a reference amount of MLN is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0139]

[0136] In various embodiments, the biomarker combination comprises ENPP5, wherein a decreased amount of ENPP5 in the sample relative to a reference amount of ENPP5 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0140]

[0137] In various embodiments, the biomarker combination comprises HSPE1 , wherein a decreased amount of HSPE1 in the sample relative to a reference amount of HSPE1 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0141]

[0138] In various embodiments, the biomarker combination comprises REG1A, wherein an increased amount of REG1A in the sample relative to a reference amount of REG1A is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0142]

[0139] In various embodiments, the biomarker combination comprises REG1 B, wherein an increased amount of REG1B in the sample relative to a reference amount of REG1B is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0140] In various embodiments, the biomarker combination comprises MMP9, wherein an increased amount of MMP9 in the sample relative to a reference amount of MMP9 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0143]

[0141] In various embodiments, the biomarker combination comprises LY6D, wherein an increased amount of LY6D in the sample relative to a reference amount of LY6D is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0144]

[0142] In various embodiments, the biomarker combination comprises IGKV1-5, wherein an increased amount of IGKV1-5 in the sample relative to a reference amount of IGKV1-5 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0145]

[0143] In various embodiments, the biomarker combination comprises CRABP2, wherein a decreased amount of CRABP2 in the sample relative to a reference amount of CRABP2 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0146]

[0144] In various embodiments, the biomarker combination comprises IGKV1-27, wherein an increased amount of IGKV1-27 in the sample relative to a reference amount of IGKV1-27 is indicative of the subject having a tubular interstitial disease, oris at risk of developing a tubular interstitial disease.

[0147]

[0145] In various embodiments, the biomarker combination comprises CTSG, wherein an increased amount of CTSG in the sample relative to a reference amount of CTSG is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0148]

[0146] In various embodiments, the biomarker combination comprises S100A12, wherein an increased amount of S100A12 in the sample relative to a reference amount of S100A12 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0149]

[0147] In various embodiments, the biomarker combination comprises KRT18, wherein an increased amount of KRT18 in the sample relative to a reference amount of KRT18 is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease.

[0150]

[0148] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , and FCN2, wherein an increased amount of CELA2A, BGLAP, MLN, SPINK1 , and / or FCN2 in the sample relative to the reference amount is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease, and / or a decreased amount of HSPE1 , ENPP5, PLTP, and / or CTSD, in the sample relative to the reference amount is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease. In variousembodiments, the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu), optionally the CKDu may be advanced or late-stage CKDu.

[0151]

[0149] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2, wherein an increased amount of KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and / or FCN2, in the sample relative to the reference amount is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease, and / or a decreased amount of CRABP2, in the sample relative to the reference amount is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease. In various embodiments, the tubular interstitial disease is Acute Interstitial Nephritis (AIN).

[0152]

[0150] In various embodiments, the amount or expression level of the biomarker may be upregulated (i.e., increased) or, downregulated (i.e. decreased) by a Iog2(fold-change) that is equal to or greater than 0.1, 0.2, 0.3, 0.4, 0.5, 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10. The Log2FC value for up-regulation of the biomarker may be at least 0.5, at least 1 , at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7. Similarly, the Log2FC value fordownregulation of the biomarker may be less than -0.1 , -0.2 or lower, -0.3 or lower, -0.4 or lower, -0.5 or lower, -1 or lower, -2 or lower, -3 or lower, -4 or lower, -5 or lower, -6 or lower, or -7 or lower.

[0153]

[0151] In various embodiments, the differential amount of at least one of the biomarkers is at least 1.05-fold, 1.1 -fold, 1.2- fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold, 2-fold, 2.1-fold, 2.2-fold, 2.3-fold, 2.4-fold, 2.5-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7- fold, 8-fold, 9-fold, or 10-fold difference relative to the reference amount.

[0154]

[0152] In various embodiments, the differential amount of at least one of the biomarkers may be as shown in the below Table 2.

[0155] Table 2: Biomarker expression levels

[0156]

[0157]

[0158]

[0153] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , and FCN2, wherein an increased amount of at least 1.3 fold change of CELA2A, BGLAP, MLN, SPINK1 , and / or FCN2 in the sample relative to the reference amount is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease, and / or a decreased amount of at least 1.3 fold change of HSPE1 , ENPP5, PLTP, and / or CTSD, in the sample relative to the reference amount is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease. In various embodiments, the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu), optionally the CKDu may be advanced or latestage CKDu.

[0159]

[0154] In various embodiments, the biomarker combination comprises at least two biomarkers selected from the group consisting of: KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2, wherein an increased amount of at least 1.3 fold change of KRT18, CTSG, MMP9, REG1A, REG1 B, IGKV1-27, IGKV1-5, S100A12, LY6D, and / or FCN2, in the sample relative to the reference amount is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease, and / or a decreased amount of at least 1.3 fold change of CRABP2, in the sample relative to the reference amount is indicative of the subject having a tubular interstitial disease, or is at risk of developing a tubular interstitial disease. In various embodiments, the tubular interstitial disease is Acute Interstitial Nephritis (AIN).

[0160]

[0155] In various embodiments, the method comprises: measuring the amount of a gene product of each biomarker in the urine sample, wherein the gene product is a protein, or a fragment of the protein, wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1, CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , FCN2, KRT18,CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D, comparing the measured protein amount of each biomarker in the biomarker combination to a corresponding reference amount of that biomarker, wherein the reference amount is derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD), and / or non-diseased healthy individuals, wherein an increased amount of CELA2A, BGLAP, MLN, SPINK1, KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D or FCN2, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount; and / or a decreased amount of HSPE1, ENPP5, PLTP, CRABP2 orCTSD, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount, are collectively indicative of the subject having a tubular interstitial disease, or being at risk of developing a tubular interstitial disease.

[0161]

[0156] In various embodiments, the method comprises: measuring, in a urine sample obtained from the subject, a protein abundance of each biomarker in the biomarker combination, wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D; comparing the protein abundance of each biomarker to a corresponding reference abundance of that biomarker, to determine a relative protein abundance for each biomarker, expressed as a fold change relative to the corresponding reference abundance, wherein an increased protein abundance of CELA2A, BGLAP, MLN, SPINK1, KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D or FCN2, in the urine sample, of at least a fold change of about 1.3; and / or a decreased protein abundance of HSPE1 , ENPP5, PLTP, CRABP2 or CTSD, in the urine sample, of at least a fold change of about 1.3, are collectively indicative of the subject having a tubular interstitial disease, or being at risk of developing a tubular interstitial disease.

[0162]

[0157] In various embodiments, the method comprises: measuring the amount of a gene product of each biomarker in the urine sample, wherein the gene product is a protein, or a fragment of the protein, wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , and FCN2, comparing the measured protein amount of each biomarker in the biomarker combination to a corresponding reference amount of that biomarker, wherein the reference amount is derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD), and / or non-diseased healthy individuals, wherein an increased amount of CELA2A, BGLAP, MLN, SPINK1 , or FCN2, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount; and / or a decreased amount of HSPE1, ENPP5, PLTP, orCTSD, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount, are collectively indicative of the subject having CKDu, or being at risk of developing CKDu.

[0163]

[0158] In various embodiments, the method comprises: measuring the amount of a gene product of each biomarker in the urine sample, wherein the gene product is a protein, or a fragment of the protein,wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: KRT18, CRABP2, CTSG, MMP9, REG1A, REG1 B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2, comparing the measured protein amount of each biomarker in the biomarker combination to a corresponding reference amount of that biomarker, wherein the reference amount is derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD), and / or non-diseased healthy individuals, wherein an increased amount of KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, or FCN2, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount; and / or a decreased amount of CRABP2, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount, is indicative of the subject having AIN, or being at risk of developing AIN.

[0164]

[0159] In various embodiments, the method disclosed herein may further comprise classifying the likelihood of the subject being responsive or non-responsive to tubular interstitial disease therapies based on the detected presence / absence, optional quantity, of the combination of biomarkers. The rationale is that expression, detection and the measurement of the biomarkers provide predictive value for treatment outcome, and may allow stratification of subjects into groups more or less likely to benefit from tubular interstitial disease therapies. Accordingly, classification of a subject based on these combined biomarkers provides a framework for personalised treatment selection, early identification of non-responders, and the development of combination regimens.

[0165]

[0160] The term "tubular interstitial disease therapies” or "tubular interstitial disease treatments" (TID treatments) refer to the use of one or more treatment modalities aimed at managing the underlying causes, reducing inflammation, slowing disease progression, and addressing associated complications. These may include antimicrobial agents such as antibiotics for infection control; discontinuation of nephrotoxic agents and the administration of corticosteroids or immunosuppressants (e g., prednisone, azathioprine, or mycophenolate mofetil) for drug-induced or autoimmune-related TID; and chelating agents for cases involving heavy metal toxicity. Supportive therapies include maintaining hydration, correcting electrolyte imbalances, and treating metabolic acidosis with sodium bicarbonate. Renal protective strategies include blood pressure control using ACE inhibitors or angiotensin receptor blockers (ARBs), dietary protein and sodium restriction, and the avoidance of nephrotoxic substances such as NSAIDs. In advanced disease, management of proteinuria, anemia using erythropoiesisstimulating agents, and phosphate binders for hyperphosphatemia are implemented. For patients with end-stage renal disease (ESRD), renal replacement therapies, such as dialysis or kidney transplantation, may be necessary. Preventive measures in heat-stress-related nephropathy include hydration protocols and minimizing environmental heat exposure for high-risk populations. Together, these therapies aim to preserve renal function, mitigate disease progression, and enhance patient outcomes.

[0161] In various embodiments, the tubular interstitial disease therapies may be CKDu therapies or AIN therapies.

[0166]

[0162] The term “CKDu therapies” or “CKDu treatments” refers to therapeutic interventions and clinical management strategies directed toward slowing progression, mitigating renal injury, managing complications, and reducing environmental or occupational risk factors associated with CKDu. Because CKDu lacks a clearly defined primary cause, treatment strategies typically focus on supportive care, renal protective measures, and mitigation of suspected environmental contributors. Such therapies may include hydration protocols to prevent recurrent dehydration, reduction of occupational heat exposure, modification of work practices, and avoidance of suspected nephrotoxic agents such as agrochemicals, heavy metals, or non-steroidal anti-inflammatory drugs (NSAIDs).

[0167]

[0163] In various embodiments, CKDu therapies may comprise renal protective pharmacological treatments, including angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) for blood pressure control and reduction of intraglomerular pressure. Supportive metabolic management may include correction of metabolic acidosis using sodium bicarbonate, management of electrolyte disturbances, and treatment of anemia using erythropoiesis-stimulating agents. Dietary interventions may include protein restriction, sodium restriction, and nutritional management to reduce kidney workload.

[0168]

[0164] In more advanced stages of CKDu, therapies may include management of complications associated with chronic kidney disease, including phosphate binders for hyperphosphatemia, vitamin D supplementation, and treatment of secondary hyperparathyroidism. In subjects progressing to endstage kidney disease (ESKD), treatment may include renal replacement therapies such as hemodialysis, peritoneal dialysis, or kidney transplantation. Preventive interventions may further include community-level measures such as improved access to clean drinking water, workplace hydration programs, heat stress mitigation strategies, and reduction of environmental exposures linked to CKDu risk.

[0169]

[0165] The term “Acute Interstitial Nephritis therapies” or “AIN treatments” refers to therapeutic interventions directed toward reducing renal interstitial inflammation, removing the causative trigger, and restoring kidney function in subjects diagnosed with acute interstitial nephritis. In many cases, treatment involves identification and discontinuation of the causative agent, which may include nephrotoxic drugs such as antibiotics (e.g., beta-lactams, rifampin), proton pump inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDs), or other medications associated with hypersensitivity reactions.

[0170]

[0166] In various embodiments, AIN therapies may comprise anti-inflammatory or immunomodulatory treatments, including systemic corticosteroids such as prednisone, particularly in cases where renal function does not improve following removal of the offending agent. In certain embodiments,immunosuppressive agents, including azathioprine, mycophenolate mofetil, cyclophosphamide, or other immunomodulatory therapies, may be used in cases associated with autoimmune or systemic inflammatory diseases.

[0171]

[0167] Supportive management may include fluid and electrolyte management, monitoring of renal function, and avoidance of further nephrotoxic exposure. In severe cases of acute kidney injury associated with AIN, temporary renal replacement therapy such as dialysis may be required until renal function recovers. Treatment of underlying infections or systemic diseases contributing to AIN may also be performed using appropriate antimicrobial or disease-specific therapies.

[0172]

[0168] In various embodiments, the method may further comprise stratifying the subject into treatment-responsive or treatment-non-responsive categories based on the detected presence, absence, or measured amount of biomarkers in the biomarker combination in the sample. In such embodiments, changes in the biomarker combination are indicative of improvement, stabilisation, or progression of tubular interstitial disease (TID) following administration of the TID treatment.

[0173]

[0169] The efficacy of tubular interstitial disease treatments may vary among subjects due to a variety of factors including, but not limited to, the underlying disease etiology (for example CKDu, acute interstitial nephritis, drug-induced nephritis, or autoimmune-associated TID), disease stage, degree of tubular injury or fibrosis, environmental exposures, and variability in patient response to antiinflammatory, immunosuppressive, antimicrobial, or renal protective therapies. Accordingly, monitoring the amounts or expression levels of the biomarker combination in the sample may provide objective indicators of renal tubular injury, inflammatory activity, or therapeutic response.

[0174]

[0170] It will be appreciated that the detection and measurement of the biomarker combination in the sample (i.e. urine sample), and their relative change in amounts, may enable minimally invasive monitoring of disease status and treatment response overtime.

[0175]

[0171] Thus, the present invention also provides a method for evaluating subject response to a tubular interstitial disease treatment, guiding therapeutic decision-making, and identifying early signs of treatment failure or disease progression. In various embodiments, the method comprises monitoring expression levels of the biomarker combination in a sample obtained from a subject before and after administration of a TID treatment, wherein changes in the biomarker combination relative to a reference level are indicative of treatment efficacy, disease stabilisation, or continued disease progression. Such monitoring may facilitate personalised management of tubular interstitial diseases, including CKDu and acute interstitial nephritis, by enabling early modification of therapeutic strategies where insufficient response to treatment is detected.

[0176]

[0172] Accordingly, there is also provided a method of monitoring the efficacy of an TDI treatment in treating tubular interstitial disease in a subject, comprising: detecting the presence, absence, andoptionally quantity, of a biomarker combination in a sample obtained from the subject, wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D, wherein the presence, absence, and optional quantity, of the biomarker combination in the sample is indicative of the efficacy of the TID treatment in treating tubular interstitial disease in the subject.

[0177]

[0173] In various embodiments, the method of monitoring comprises: measuring the amount of a biomarker combination in a urine sample obtained from the subject, wherein the treatment for tubular interstitial disease has been administered to the subject prior to the measuring step, and comparing the measured amount of each biomarker in the biomarker combination to a reference amount of that biomarker, wherein a differential amount of each biomarker in the biomarker combination compared to the reference amount is indicative of the efficacy of the treatment in treating tubular interstitial disease in the subject.

[0178]

[0174] In various embodiments, the method of monitoring may further comprise administering the TDI treatment to the subject.

[0179]

[0175] In various embodiments, the method of monitoring may further comprise obtaining a sample from the subject.

[0180]

[0176] In various embodiments, the method may further comprise measuring the amount of the biomarker combination in a reference sample obtained from the subject prior to administration of the TDI treatment, wherein the amount of the each biomarker in the biomarker combination in the detection step is compared to the amount of the each biomarker in the biomarker combination in the reference sample obtained prior to the administration step.

[0181]

[0177] In various embodiments, the steps of the method of monitoring may be repeated two or more times within a predetermined time-frame pre-treatment, during and post-treatment, and the amount of the each biomarker in the biomarker combination at each time point is compared against each other to assess the progression ofthe TID and efficacy ofthe TID treatment based on the detection, and optional quantification, of each biomarker in the biomarker combination in the sample at each time point. The method may comprise serial monitoring of the biomarker combination in the subject over the course of TID therapy.

[0182]

[0178] In various embodiments, the method may further comprise selecting a TID treatment regimen for the subject based on the changes in the biomarker combination detected in the sample.

[0183]

[0179] In another aspect, there is also provided a method of predicting the efficacy of a TID treatment in a subject having TID, comprising: obtaining a diagnosis of TID in the subject according to the methoddisclosed herein; and predicting if the subject is likely to exhibit a clinical response to the administration of one or more treatments forTID, based on the diagnosis of step.

[0184]

[0180] In another aspect, there is also provided a method of treating TID in a subject, comprising: selecting a subject having TID, for administration of a therapy for treating the TID, wherein the selecting is based on the method of diagnosing a TID in the subject, or predicting the subject's risk of developing TID; and administering the TID treatment to the selected subject.

[0185]

[0181] As used herein, the terms "treating" and "treatment" refer to reduction in severity and / or frequency of symptoms, elimination of symptoms and / or underlying cause, prevention of the occurrence of symptoms and / or their underlying cause, and improvement or remediation of damage. As used herein, the term "preventing” refers to the prophylactic or preventative measures that prevent and / or slow the development of a targeted pathologic condition or disorder. Thus, those in need of treatment include those already with the disorder; those prone to have the disorder; and those in whom the disorder is to be prevented and those in whom reoccurrence of the disorder needs to be prevented. In various embodiments, the terms "treating”, “ameliorating”, “delaying" or “preventing”, as used herein refer to achieving one or more of the following in the subject: (a) reducing the severity of a given condition; (b) limiting or preventing the development of a condition; (c) removing a given condition; (d) limiting or preventing the recurrence of a given condition; (e) alleviation of the condition and / or its symptoms; and (f) delay the onset of a condition. Any one or more of these effects may be achieved in a subject who previously had or currently has or is suspected to have TID.

[0186]

[0182] As will be appreciated, one or more steps of the methods disclosed above may be carried out using one or more computational methods / models, software / programs or algorithms, as well as any subsequent interpretation of the output of the analysis steps to provide the classification. As such, the method disclosed herein may apply the use of a computer-processing device to perform one or more of the steps of the methods disclosed herein, optionally connected to a computer network. Accordingly, there is also provided a computer-implemented method for diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease, wherein the computer and its components may carry out one or more steps and aspects of the method disclosed herein. In various embodiments, an electronic memory may be used for capturing and storing the measured parameters of biomarkers. A software module executed by the computer-processing device may be used to generate a read-out on the measurements of each biomarker in the biomarker combination. The software module executed by the computer-processing device may also analyse the measurements using suitable statistical or analytical methods / programs / algorithms / software and transmit such an analysis, and / or any interpretation and / or prediction to the subject or a medical professional treating the subject.

[0187]

[0183] It is also contemplated that a panel of binding agents is provided for use in carrying out the methods disclosed herein. All embodiments disclosed above in relation to the methods similarly applyto the panel of binding agents and vice versa. In particular, the panel may be used for (I) diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease; (ii) monitoring the efficacy of an TDI treatment in treating tubular interstitial disease in a subject; (ill) predicting the efficacy of a TID treatment in a subject having TID; or (iv) selecting a subject for administration of a therapy for treating the TID.

[0188]

[0184] In various embodiments, the panel may comprise binding agents each directed against a biomarker of the biomarker combination. For example, the binding agents may be selected from those listed as non-limiting and representative examples in Table 3 below.

[0189]

[0190]

[0191]

[0185] In various embodiments, the binding agent may be selected from a polypeptide, polynucleotide probe, or fragment thereof, optionally the binding agent further comprises a detectable reagent or dye (e.g. fluorescent reagent or dye detectable by flow cytometry or other microscopy techniques). In various embodiments, the polypeptide is an antibody that specifically binds to the biomarker. In various embodiments, the antibody may be an antibody-based conjugate.

[0192]

[0186] In various embodiments, the binding agents are selected and designed for use in lateral flow assays or dipsticks for detection of the biomarker combination in a liquid sample, such as urine. The term “antibody-based conjugates” refers to antibody conjugates used for imaging as a molecular complex comprising of an antibody that is chemically linked (conjugated) to a reporter molecule, such as a coloured nanoparticle (e.g., gold nanoparticles or latex beads), an enzyme (e.g., horseradish peroxidase), a fluorescent dye, or another detectable label that is detectable by one or more imagingtechniques. These conjugates may specifically bind to the target protein biomarker in the sample. In the case of lateral flow assays, this binding results in a visual signal, typically appearing as a test line.

[0193]

[0187] The present invention also contemplates the use of the biomarker combination disclosed herein in the preparation or manufacture of a diagnostic product for (I) diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease; (ii) monitoring the efficacy of an TDI treatment in treating tubular interstitial disease in a subject; (ill) predicting the efficacy of a TID treatment in a subject having TID; or (iv) selecting a subject for administration of a therapy for treating the TID.

[0194]

[0188] In various embodiments, the diagnostic product may be selected from the group consisting of a kit, a diagnostic device and a computer system.

[0195]

[0189] It is also contemplated that a kit is provided for use in carrying out the method disclosed herein. All embodiments disclosed above in relation to the methods, and panel similarly apply to the kit and vice versa. In particular, the kit may be used for (i) diagnosing a tubular interstitial disease in a subject, or predicting a subject's risk of developing a tubular interstitial disease; (ii) monitoring the efficacy of an TDI treatment in treating tubular interstitial disease in a subject; (iii) predicting the efficacy of a TID treatment in a subject having TID; or (iv) selecting a subject for administration of a therapy for treating the TID.

[0196]

[0190] In various embodiments, the kit may comprise the panel of binding agents disclosed herein, and instructions for use.

[0197]

[0191] The kit may further include a detectable label. The term "detectable label" refers to an atom or molecule that specifically detects a molecule including a label among the same type of molecules without the label. The detectable label may be one attached to an antibody, interacting protein, ligand, nanoparticle, or aptamer that specifically binds to the protein or fragment thereof. The detectable label may include a radionuclide, a fluorophore or an enzyme. The kit may be used according to various immunoassays or immunostaining methods known in the art. The immunoassays or immunostaining may include radioimmunoassay, radioimmunoprecipitation, immunoprecipitation, ELISA, capture-ELISA, inhibition or competition assays, sandwich assays, flow cytometry, immunofluorescence and immunoaffinity purification. Preferably, the kit may be a lateral flow test kit, or a dipstick test kit, reverse transcription polymerase chain reaction (RT-PCR) kit, a DNA chip kit, an enzyme-linked immunosorbent assay (ELISA) kit, a protein chip kit, a rapid kit or a multiple reaction monitoring (MRM) kit.

[0198]

[0192] The present invention is further illustrated by the following examples. However, it should be understood, that the invention is not limited to the exemplified embodiments.

[0199] EXAMPLESMaterials and Methods

[0200]

[0193] The technology utilised mass spectrometry-based proteome profiling of urine samples from defined disease cohorts, namely non-endemic control (NEC, n=30), non-endemic CKD (NECKD, n=30), symptomatic CKDu (AIN, n=30), and advanced CKDu (CKDu, n=30).

[0201]

[0194] The protein in the urine were extracted and digested and subject to Tandem Mass Tag (TMT)-based mass spectrometry to quantify the protein abundances in each sample in a multiplexed manner. Using a total of 15 TMT 10-plex experiments, 5027 proteins were quantified across all samples. The raw protein abundances were normalised within and across TMT sets, and data was cleaned to include robustly quantified proteins.

[0202]

[0195] Data quality measures were performed for protein abundance and classification (PCA), following which proteins specific for AIN and CKDu were shortlisted. For this a limma-based approach of differential expression was performed. Proteins significantly changing by at least 1.3-fold (p value < 0.05) in either AIN or CKDu with respect to NEC and NECKD groups in the same direction were first shortlisted. In particular, the significant variation and fold change in the level of each biomarker is shown in the following Table 4.

[0203] Table 4: Biomarker expression levels

[0204]

[0205]

[0206]

[0196] Those proteins which showed expression difference between NEC and NECKD by at least 1.1-fold (p value <0.0.5) were excluded from the shortlisted list. Following this, the data was split into 75% training and 25% test set for subsequent machine learning and validation. First multiple algorithms were compared to assess model performance and svm, glmnet, gbm and random forest were identified as high-performing algorithms for CKDu classification, and svm, glmnet, knn, and random forest were identified as high-performing algorithms for AIN based on high accuracy obtained. Among these, glmnet showed the highest kappa, indicative of expected accuracy and observed agreement for both classification models. Rigorous feature selection was carried out on the above-shortlisted markers to prioritise only those markers consistently picked by multiple algorithms. For this recursive feature elimination (logistic and random forest), lasso-based regression, decision tree, and Boruta wrapper using random forest were all implemented to prioritize protein features that were reliably identified across these algorithms. Machine learning models were then trained using the minimal panel of shortlisted protein features using glmnet with 4-fold cross validation and at least 10 iterations, and performance was assessed using reserved test set based on receiver operating characteristic analyses (area under curve). The performance of the model on the training data was also evaluated using receiver operating characteristic analyses (area under curve). Machine learning classifications were done independently for both CKDu and AIN against the non-endemic groups which included both nonendemic CKD (NECKD) disease and non-endemic controls (NEC) from the cohort.

[0207]

[0197] The analyses were independently carried out for both AIN and CKDu to arrive at two distinct panel of markers (9-panel for CKDu and 11 -panel for AIN). The expression plots for individual prioritized markers were plotted against the non-endemic groups to show consistent change of these markers in the urine of the two disease groups.

[0208] Results and Discussion

[0209]

[0198] The overall urinary proteome profiles from the different sample groups revealed that the disease groups were clearly distinguishable from the control NEC group. Among all the disease groups, AIN formed an early transition stage into disease while CKDu and NECKD both appeared distinguished as the advanced stage of CKD. Upon retaining only 75% of the proteins that were consistently quantified across the samples, and imputing for those missing values using KNN imputation, 2017 proteins were used overall to extract biomarkers for different disease states. For marker extraction, the data was firstsplit into 75% training set and 25% test set, and sufficiently trained the model with 4-fold cross validation with 10 repeats to get a robust set of markers.

[0210]

[0199] For AIN, differentially secreted proteins in the AIN were first extracted, changing in the same direction against both NEC and NECKD, excluding those that showed changes between NECKD and NEC. From this analysis, 47 markers were extracted for subsequent machine learning analysis. Then different machine learning models were compared, and it was observed that glmnet was one of the topranking models along with random forest, svm, and knn, and was able to achieve 0.8 accuracy for all the extracted markers. By implementing several feature extraction methods on these markers, markers that were consistently picked among the different methods tested were shortlisted. It was noticed that markers from Boruta based method were overall highly robust and were also shortlisted as features using other methods. From these, the robust set of 11 markers showed a training data accuracy of 0.88 with sensitivity 0.96 and specificity 0.86, with AUC of 0.967 for AIN compared to all non-endemic CKD (NECK + NECKD). On the test data set, these markers showed high performance with accuracy 0.9, sensitivity 1 and specificity 0.86, with AUC of 0.959.

[0211]

[0200] For AIN, the 11-marker panel include proteins KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, FCN2. Forthetraining data, the marker panel displayed a high AUC of 96.7% and for the reserved test data the AUC was 95.9%.

[0212]

[0201] Table 5 presents biomarker panels for AIN. Each row therefore represents a different panel composed of a subset of the listed biomarkers. The column labelled “No. of Biomarkers” indicates the number of biomarkers included in the panel, while the “Testing AUC” column reports the Area Under the Receiver Operating Characteristic Curve (ROC AUC) obtained when the corresponding biomarker panel was evaluated on a test dataset.

[0213] Table 5

[0214]

[0215] 1 = MMP9; 2 = S100A12; 3 = FCN2; 4 = CTSG; 5 =IGKV1-27; 6 = KRT18; 7 = LY6D; 8 = REG1A; 9 = REG1 B; 10 = IGKV1-5; 11 = CRABP2. Shaded boxes indicate the biomarkers included in the biomarker combination.

[0216]

[0202] The AUC is a statistical measure of diagnostic performance that reflects the ability of the biomarker panel to correctly discriminate AIN samples from comparator cohorts, with an AUC value of 1.0 indicating perfect classification and 0.5 indicating no discriminatory ability beyond random chance.

[0217]

[0203] The results demonstrate that multiple biomarker combinations provide strong discriminatory performance, with AUC values ranging from approximately 0.844 for a two-marker panel to about 0.985 for a nine-marker panel, indicating moderate to excellent diagnostic accuracy. Panels comprising larger numbers of biomarkers generally achieve higher AUC values, suggesting that integrating information from multiple biomarkers improves classification performance. Notably, several biomarkers appear repeatedly across the top-performing panels, including MMP9, S100A12, FCN2, CTSG, IGKV1-27, KRT18, LY6D, REG1A, REG1B, IGKV1-5, and CRABP2, indicating that these markers consistently contribute to the discriminatory signal. Collectively, the results demonstrate that combinations of these biomarkers form a robust diagnostic signature for AIN, capable of distinguishing AIN from comparator cohorts with high accuracy.

[0218]

[0204] FIG. 1A-1B presents ROC curves evaluating the performance of the 11 -marker Sym-CKDu (AIN) biomarker panel in distinguishing acute interstitial nephritis (AIN), representing symptomatic CKDu, from non-endemic kidney disease cohorts comprising non-endemic controls (NEC) and nonendemic chronic kidney disease (NECKD). FIG. 1A shows the ROC analysis for the training dataset, where the classifier demonstrates excellent discriminatory performance with an area under the curve (AUC) of 0.967, indicating high sensitivity and specificity for identifying AIN cases. FIG. 1B shows the ROC analysis for an independent test dataset, which confirms strong predictive performance with an AUC of 0.959, demonstrating the robustness and generalisability of the biomarker panel. In both panels, the solid black curve represents the aggregated performance of the biomarker panel, while the dashed curves correspond to individual classifiers or model iterations evaluated during development. The grey diagonal line represents the reference line of no discrimination (AUC = 0.5).

[0219]

[0205] FIG. 2A-2K shows box-and-whisker plots illustrating the distribution of Iog2-transformed foldchange values for each of the eleven biomarkers included in the Sym-CKDu (AIN) marker panel across three subject groups: AIN (representing symptomatic CKDu), non-endemic controls (NEC), and nonendemicchronic kidney disease (NECKD). Each subplot corresponds to a specific biomarker within the panel. The box plots represent the distribution of biomarker expression within each cohort, with the central line indicating the median value and the box boundaries representing the interquartile range (IQR). Whiskers extend to the most extreme values within 1.5x the IQR, and individual points outside this range are shown as outliers. Across multiple markers, the AIN cohort exhibits distinct expression biomarker profiles relative to NEC and NECKD groups, with several biomarkers demonstrating elevated or reduced Iog2 fold-change values that contribute to discrimination between symptomatic CKDu andother renal conditions. These expression patterns support the diagnostic utility of the 11 -marker panel and demonstrate its capacity to distinguish AIN-associated CKDu from other forms of kidney disease in both endemic and non-endemic populations.

[0220]

[0206] For the CKDu group, a similar approach was implemented with differentially secreted protein extraction, this time specific to CKDu. From this analysis, 21 markers were extracted, and the overall performance of these markers were assessed by comparing different machine learning models. This again revealed svm, gbm, glmnet, and rf as top performance algorithms with accuracy reaching 0.9.

[0221]

[0207] Based on features that were consistently picked with multiple feature extraction algorithms, a robust set of 9 markers was found that displayed a high accuracy of 0.91 with sensitivity 0.83 and specificity 0.96, and overall AUC of 0.944 for the training data performance for CKDu as compared to non-endemic groups including NEC + NECKD. On the test data, these same markers displayed an accuracy of 0.81 , sensitivity of 1 , and specificity of 0.71 with an overall AUC of 0.857 for CKD vs non endemic NECKD + NEC. Since CKDu is much more similar in terms of their advanced stage with NECKD, the overall AUC for CKDu is slightly lower than that achieved for AIN. Nevertheless, these markers have high performance in distinguishing early symptomatic stage AIN and advanced stage CKDu from the classic CKD prevalent in non-endemic regions.

[0222]

[0208] For CKDu, a 9-marker panel include proteins HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , FCN2. For the training data, the marker panel displayed a high AUC of 94.4% and for the reserved test data the AUC was 85.7%.

[0223]

[0209] Table 6 presents biomarker panels for CKDu. Each row therefore represents a different panel composed of a subset of the listed biomarkers. The column labelled "No. of Biomarkers’’ indicates the number of biomarkers included in the panel, while the “Testing AUC" column reports the Area Under the Receiver Operating Characteristic Curve (ROC AUC) obtained when the corresponding biomarker panel was evaluated on a test dataset.

[0224] Table 6

[0225]

[0226] 1 = SPINK1 ; 2 = CTSD; 3 = FCN2; 4 = BGLAP; 5 = HSPE1 ; 6 = CELA2A; 7 = PLTP; 8 = MLN; 9 = ENPP5. Shaded boxes indicate the biomarkers included in the biomarker combination.

[0227]

[0210] The results in Table 6 demonstrate that multiple biomarker combinations exhibit strong diagnostic performance, with AUC values ranging from approximately 0.905 for a nine-marker panel to about 0.976 for a seven-marker panel, indicating good to excellent classification accuracy. Panels containing three to seven biomarkers show particularly high performance, with AUC values around 0.964-0.976, suggesting that relatively compact biomarker panels can effectively capture the disease-associated signal. The data further indicate that CELA2A is consistently present in the highest-performing panels, supporting its role as a key contributor to the CKDu biomarker signature. The repeated appearance of CELA2A across multiple high-performing combinations suggests that it provides strong discriminatory information for identifying CKDu when combined with other biomarkers such as SPINK1, CTSD, FCN2, HSPE1, PLTP, MLN, and ENPP5. Collectively, these results demonstrate that combinations of urinary protein biomarkers form a robust diagnostic panel capable of distinguishing CKDu from comparator cohorts with high accuracy.

[0228]

[0211] FIG. 3A shows the ROC curve generated using the training dataset for distinguishing chronic kidney disease of unknown etiology (CKDu) from non-endemic chronic kidney disease (NEC+NECKD). The model demonstrates high discriminatory performance, with an area under the curve (AUC) of 0.944, indicating strong sensitivity and specificity for identifying CKDu cases within the training cohort. FIG.

[0229] 3B shows the ROC curve obtained using an independent test dataset. The model retains robust predictive performance with an AUC of 0.857, demonstrating good generalizability of the classifier to unseen data. In both panels, the solid black curve represents the aggregated model performance, while the dashed curves represent individual classifier or feature contributions evaluated during model development. The diagonal grey line indicates the line of no discrimination (AUC = 0.5). The results demonstrate that the model provides reliable discrimination between CKDu and non-endemic chronic kidney disease across both training and validation datasets.

[0230]

[0212] FIG. 4A-4I presents box-and-whisker plots illustrating the distribution of log2-transformed foldchange values for the 9 panel of candidate biomarkers across three study groups: chronic kidney disease of unknown etiology (CKDu), non-endemic chronic kidney disease (NECKD), and non-endemic controls (NEC). Each subplot corresponds to an individual biomarker, with box plots representing the different classes (CKDu, NEC, and NECKD). The central line within each box indicates the median value, the box boundaries represent the interquartile range (IQR), and the whiskers extend to the most extreme data points within 1.5* the IQR, with outliers shown as individual points. Across multiple biomarkers, CKDu samples exhibit distinct expression patterns relative to both NEC and NECKD groups, with several markers showing increased or decreased log2fold changes that distinguish CKDu from other chronic kidney disease etiologies and from non-endemic controls. These distribution profiles demonstrate the discriminatory potential of the biomarker panel and support their use in differentiating CKDu from other renal conditions in endemic and non-endemic populations.

[0213] The current strategy for biomarker assignment to CKDu largely relies on refurbishing existing biomarkers in CKD for applicability in CKDu screening. While this may still be useful within the endemic region, it limits the general applicability of the markers as many of these are not specific for CKD or CKDu. Using an unbiased proteomic screening with defined patient cohorts, we have extracted potential urinary protein markers that has the potential to distinguish definitive disease states of CKD in the endemic region, the symptomatic AIN and the more progressed CKDu disease stage.

[0231]

[0214] The current diagnosis of CKDu is based on clinical exclusion of known causes of CKD. With the limited applicability of routine tests for renal function assessment such as serum creatinine and proteinuria in diagnosis of CKDu due to the minimally proteinuric nature of the disease, renal biopsy remains the current gold standard for diagnosis in identification and quantification of tubular interstitial diseases. Hence, an efficient, rapid, cost-effective, and sensitive method that aids in the early detection of tubular interstitial diseases is urgently required for better management of CKDu in marginalized populations, and also for management of primary and secondary tubular interstitial diseases which is a universal necessity. The invention mainly addresses this gap in CKDu diagnosis, including diagnosing at the reversible early acute AIN stage, using a panel of protein markers detected in the urine in a non-invasive manner.

[0232]

[0215] The present diagnostic approach is based on the use of multi-protein biomarker panels measured in urine, enabling robust and reliable detection of tubular interstitial disease and its associated subtypes. Unlike diagnostic strategies that rely on a single biomarker, the disclosed method utilises combinations of protein markers, thereby improving robustness and reducing the influence of inter-individual variability or transient biological fluctuations that may affect single-marker measurements. The biomarkers are detected in urine samples, providing a non-invasive diagnostic platform suitable for screening, disease typing, and longitudinal monitoring without the need for invasive procedures such as renal biopsy. Importantly, the diagnostic panel is capable of distinguishing between different disease states along the CKDu disease continuum, including the reversible acute phase represented by acute interstitial nephritis (AIN), which may represent a pre-CKDu condition, and the advanced chronic stage of CKDu, thereby enabling detection across multiple stages of disease progression.

[0233]

[0216] The biomarker signatures further demonstrate high specificity for CKDu associated with endemic regions, enabling differentiation from chronic kidney disease more commonly observed in nonendemic regions (NECKD) as well as from non-endemic healthy controls (NEC). This capability is particularly important in clinical settings where conventional kidney disease markers often fail to distinguish CKDu from other forms of CKD. For CKDu diagnosis, a nine-marker protein panel comprising HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , and FCN2 was identified using robust feature selection and cross-validated machine learning approaches. This panel demonstrated strong predictive performance, achieving an AUC of approximately 0.944 in trainingdatasets and 0.857 in independent test datasets, with high accuracy, sensitivity, and specificity. Notably, multiple sub-combinations of these markers also maintained strong diagnostic performance, with panels containing as few as three biomarkers achieving AUC values above 0.9, and panels including CELA2A consistently demonstrating the highest performance.

[0234]

[0217] Similarly, for detection of the acute disease state (AIN), an eleven-marker panel comprising KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2 was identified using a comparable cross-validated selection strategy. This panel achieved excellent classification performance, with an AUC of approximately 0.967 in training datasets and 0.959 in independent test datasets, demonstrating strong sensitivity and specificity for identifying AIN. As with the CKDu panel, several smaller sub-combinations of markers retained high performance, with panels containing three or more biomarkers achieving AUC values exceeding 0.9. Among these, panels including KRT18, LY6D, CRABP2, REG1B, and IGKV1-5 consistently contributed to the highest diagnostic accuracy. Importantly, many of the proteins included within these biomarker panels are biologically linked to kidney function and renal injury pathways, and several have been previously associated with chronic kidney disease in the scientific literature. This biological relevance further strengthens the reliability and translational potential of the disclosed biomarker combinations for diagnosis, disease stratification, and monitoring of CKDu and related tubulointerstitial kidney disorders.

[0235]

[0218] The invention has been described broadly and generically herein. Each of the narrower species and subgeneric groupings falling within the generic disclosure also form part of the invention. This includes the generic description of the invention with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein. Other embodiments are within the following claims. In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0236]

[0219] One skilled in the art would readily appreciate that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. Further, it will be readily apparent to one skilled in the art that varying substitutions and modifications may be made to the invention disclosed herein without departing from the scope and spirit of the invention. The methods, biomarker combination, panels, kits and uses described herein are presently representative of preferred embodiments are exemplary and are not intended as limitations on the scope of the invention. Changes therein and other uses will occur to those skilled in the art which are encompassed within the spirit of the invention are defined by the scope of the claims. The listing or discussion of a previously published document in this specification should not necessarily be taken as an acknowledgement that the document is part of the state of the art or is common general knowledge.

[0220] The invention illustratively described herein may suitably be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, for example, the terms “comprising”, “including," containing”, etc. shall be read expansively and without limitation. The word "comprise" or variations such as "comprises" or "comprising" will accordingly be understood to imply the inclusion of a stated integer or groups of integers but not the exclusion of any other integer or group of integers. Additionally, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by exemplary embodiments and optional features, modification and variation of the inventions embodied therein herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention.

[0237]

[0221] The content of all documents and patent documents cited herein is incorporated by reference in their entirety.

Claims

CLAIMSWhat is claimed is:

1. An in vitro method of diagnosing a tubular interstitial disease in a subject, comprising:measuring, in a urine sample obtained from the subject, an amount of each biomarker of a biomarker combination,wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D, comparing the measured amount of each biomarker of the biomarker combination to a corresponding reference amount for that biomarker, to determine a differential amount for each biomarker,wherein the differential amount of each biomarker in the biomarker combination, are collectively indicative of the subject having a tubular interstitial disease.

2. The method of claim 1, wherein the biomarker combination comprises at least three biomarkers in the urine sample.

3. The method of claim 1 or 2, wherein the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu).

4. The method of claim 3, wherein the CKDu is advanced or late-stage CKDu.

5. The method of claim 3 or 4, wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , and FCN2.

6. The method of claim 5, wherein the biomarker combination comprises CELA2A.

7. The method of claim 5, whereinan increased amount of CELA2A, BGLAP, MLN, SPINK1, or FCN2, in the urine sample relative to the corresponding reference amount forthat biomarker; and / ora decreased amount of HSPE1, ENPP5, PLTP, or CTSD, in the urine sample relative to the corresponding reference amount forthat biomarker,is collectively indicative of the subject having CKDu, or being at risk of developing CKDu.

8. The method of claim any one of claims 2-7, wherein the biomarker combination comprises or consists of HSPEI, CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , and FCN2.

9. The method of claim 1 , wherein the tubular interstitial disease is Acute Interstitial Nephritis (AIN).

10. The method of claim 9, wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2.

11. The method of claim 9 or 10, wherein the biomarker combination comprises KRT18, LY6D, CRABP2, REG1 B and IGKV1 -5.

12. The method of claim 10 or 11 , whereinan increased amount of KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D, or FCN2, in the urine sample relative to the corresponding reference amount forthat biomarker; and / ora decreased amount of CRABP2, in the urine sample relative to a reference amount for CRABP2,is collectively indicative of the subject having AIN, or being at risk of developing AIN.

13. The method of any one of claims 9-12, wherein the biomarker combination comprises or consists of KRT18, CRABP2, CTSG, MMP9, REG1A, REG1 B, IGKV1-27, IGKV1-5, S100A12, LY6D, and FCN2.

14. The method of claim 1 , wherein the biomarker combination comprises FCN2, and the tubular interstitial disease is Chronic Kidney Disease of uncertain etiology (CKDu) or Acute Interstitial Nephritis (AIN).

15. The method of any one of claims 1-14, wherein the step of measuring comprises quantifying the amount of a gene product of each biomarker in the urine sample, wherein the gene product is a protein, or a fragment of the protein, preferably protein expression level or abundance of each biomarker in the biomarker combination is measured in the urine sample, using an antibody, interacting protein, ligand, nanoparticle or aptamer that specifically bind to the protein or fragment of each biomarker.

16. The method of claim 15, wherein the differential amount of each biomarker in the biomarker combination collectively define a biomarker profile, and wherein the biomarker profile is indicative of the subject having a tubular interstitial disease.

17. The method of any one of claims 1-16, wherein the reference amount is derived from nonendemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD), and / or non-diseased healthy individuals.

18. The method of any one of claims 1-17, wherein the differential amount comprises at least a fold change of about 1.3 relative to the reference amount.

19. The method of any one of claims 1-18, wherein the subject is a human, preferably the subject is selected from a population residing in or originating from an endemic region forCKDu, preferably the endemic region for CKDu comprises regions of South Asia and Central America.

20. The method of any one of claims 1-19, further comprising classifying the likelihood of the subject being responsive or non-responsive to tubular interstitial disease therapies based on the differential amount of each biomarker in the biomarker combination, and optionally selecting a treatment for tubular interstitial disease to be administered to the subject.

21. The method of claim 1 , wherein the step of measuring comprises quantifying the amount of a gene product of each biomarker in the urine sample, wherein the gene product is a protein ora fragment of the protein,wherein an increased amount of CELA2A, BGLAP, MLN, SPINK1 , KRT18, CTSG, MMP9, REG1A, REG1 B, IGKV1-27, IGKV1-5, S100A12, LY6D or FCN2, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount; and / or a decreased amount of HSPE1 , ENPP5, PLTP, CRABP2 or CTSD, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference amount, are collectively indicative of the subject having a tubular interstitial disease, andwherein the reference amount is derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD), and / or non-diseased healthy individuals.

22. The method of claim 1, wherein the step of measuring comprises quantifying, in the urine sample, a protein abundance of each biomarker of the biomarker combination, andwherein the step of comparing comprises comparing the quantified protein abundance of each biomarker to a corresponding reference abundance forthat biomarker, to determine a relative protein abundance, expressed as a fold change, for each biomarker in the biomarker combination, wherein an increased protein abundance of CELA2A, BGLAP, MLN, SPINK1 , KRT18, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, LY6D orFCN2, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference abundance; and / or a decreased protein abundance of HSPE1 , ENPP5, PLTP, CRABP2 or CTSD, in the urine sample by at least a fold change of about 1.3 relative to the corresponding reference abundance, are collectively indicative of the subject having a tubular interstitial disease, andwherein the reference abundance is derived from non-endemic controls (NEC) and / or subjects diagnosed with non-endemic chronic kidney disease (NECKD).

23. An in vitro method of predicting a subject's risk of developing a tubular interstitial disease, comprising:measuring, in a urine sample obtained from the subject, an amount of each biomarker of a biomarker combination,wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D, comparing the measured amount of each biomarker in the biomarker combination to a corresponding reference amount for that biomarker, to determine a differential amount for each biomarker,wherein the differential amount of each biomarker in the biomarker combination, are collectively indicative of the subject being at risk of developing a tubular interstitial disease.

24. An in vitro method of monitoring the efficacy of a treatment for tubular interstitial disease (TID) in a subject, comprising:measuring, in a urine sample obtained from the subject, an amount of each biomarker of a biomarker combination,wherein the biomarker combination comprises at least two biomarkers selected from the group consisting of: HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1, FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D,wherein the treatment for tubular interstitial disease has been administered to the subject prior to the measuring step,comparing the measured amount of each biomarker of the biomarker combination to a corresponding reference amount forthat biomarker,wherein a differential amount of each biomarker in the biomarker combination, are collectively indicative of the efficacy of the treatment in treating tubular interstitial disease in the subject.

25. The method of claim 24, further comprises measuring, in a reference urine sample obtained from the subject before administration of the treatment, an amount of each biomarker of the biomarker combination, and comparing the amount of each biomarker measured in the urine sample following administration of the treatment to the amount of the corresponding biomarker measured in the reference urine sample.

26. The method of claim 24, wherein the steps of the method are repeated at two or more time points within a predetermined time frame before, during, and / or after administration of the treatment, and wherein the amount of each biomarker of the biomarker combination at each time point is compared to the amount of the corresponding biomarker at one or more other time points to assess progression of the tubular interstitial disease and / or efficacy ofthe treatment.

27. A method of predicting the efficacy of a treatment for tubular interstitial disease (TID) in a subject having TID, comprising:obtaining a diagnosis of TID in the subject according to the method of any one of claims 1 -22; andpredicting if the subject is likely to exhibit a clinical response to the administration of one or more treatments for TID, based on the diagnosis step.

28. A method for treating tubular interstitial disease (TID) in a subject, comprisingselecting a subject having TID for administration of a treatment for TID, wherein the selecting is based on the method of any one of claims 1-22; andadministering the treatment for TID to the selected subject.

29. A panel of binding agents for use in the method of any one of claims 1-22, the panel comprising a binding agent directed against each biomarker in the biomarker combination,preferably the binding agent is selected from a polypeptide, polynucleotide probe, or fragment thereof, optionally the binding agent further comprises a detectable reagent or dye, preferably the polypeptide is an antibody that specifically binds to the biomarker, optionally the antibody may be an antibody-based conjugate.

30. A kit for use in the method of any one of claims 1 -22, comprising: the panel of binding agents of claim 28; and instructions for use.

31. Use of a biomarker combination in the preparation of a diagnostic product for (i) diagnosing a TID in a subject, (ii) predicting a subject's risk of developing a TID, (ill) predicting the efficacy of a TID treatment, and / or (iv) monitoring the efficacy of a TID treatment, wherein the biomarker combination comprises at least two biomarkers selected from HSPE1 , CELA2A, ENPP5, BGLAP, PLTP, MLN, CTSD, SPINK1 , FCN2, KRT18, CRABP2, CTSG, MMP9, REG1A, REG1B, IGKV1-27, IGKV1-5, S100A12, and LY6D.

32. The use of claim 31 , wherein the diagnostic product is selected from the group consisting of a kit, a diagnostic device and a computer system.

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