Method of predicting the likelihood of wound healing

Abstract

This disclosure relates to the use of inter-alpha-trypsin inhibitor heavy chain 2 (ITIH2) as a biomarker for wound healing, specifically for distinguishing healing from non-healing wounds. Also provided are methods for treating, predicting responsiveness to a wound treatment, and prognosing wound progression based on biomarker levels.

Description

[0001] METHOD OF PREDICTING THE LIKELIHOOD OF WOUND HEALING

[0002] Technical field

[0003] The present invention relates generally to the field of wound healing. In particular, the invention teaches a method of predicting the likelihood of wound healing in a subject by detecting levels of inter-alpha-trypsin inhibitor heavy chain 2 (ITIH2) in the subject.

[0004] Background

[0005] Non-healing chronic wounds result in frequent hospitalisations, increased morbidity, diminished quality of life, and substantial healthcare costs. They are a significant healthcare burden globally. Chronic venous leg ulcers (VLUs) and diabetic foot ulcers (DFUs) are particularly problematic, with Singapore having one of the highest rates of DFU-related amputations in the world. There is thus a critical need for improved chronic wound assessment and management strategies.

[0006] While measurement of wound surface area provides an objective assessment of healing status, this approach has several limitations. Accurate determination of wound healing trajectory based on wound area measurements requires weeks of monitoring. This is due to a critical time lag between the activation of molecular' healing pathways and their manifestation as measurable reductions in wound area. The need for prolonged monitoring can delay timely interventions that may improve recovery. Furthermore, wound area measurement is a gross parameter that lacks the granularity needed for more precise assessment of healing status.

[0007] Wound exudate reflects the metabolic and degradative activities within the wound or ulcer microenvironment, and may contain valuable diagnostic and prognostic biomarkers that can provide more precise, near real-time insight into wound healing status. However, there are currently no molecular diagnostic methods available for routine wound management in the clinic. Accordingly, it would be desirable to overcome or alleviate at least one of the abovedescribed problems, or at least to provide a useful alternative. Summary

[0008] Disclosed herein is a method of predicting the likelihood of wound healing in a subject with a wound, the method comprising determining a level of inter-alpha-trypsin inhibitor heavy chain 2 (ITIH2) in a sample obtained from the subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of wound healing in the subject.

[0009] Disclosed herein is a method of determining the likelihood of the presence of a healing or non-healing wound in a subject, the method comprising determining a level of ITIH2 in a sample obtained from a subject, wherein the level of 1T1H2, or a value derived therefrom, as compared to a reference predicts the likelihood of the presence of a healing or non-healing wound in the subject.

[0010] Disclosed herein is a method of predicting the prognosis of wound healing in a subject with a wound, the method comprising determining a level of TTTH2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference indicates the likely prognosis of wound healing in the subject.

[0011] Disclosed herein is a method of monitoring wound healing in a subject with a wound, the method comprising determining a level of ITIH2 in respective samples obtained from the subject at two different time points during wound healing, wherein a change in the level of ITTH2 in the two samples predicts the likelihood of a healing wound or non-healing wound in the subject.

[0012] Disclosed herein is a method of predicting responsiveness to a wound treatment in a subject who is undergoing or has undergone the treatment, the method comprising determining a level of ITIH2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject, thereby predicting whether the subject is likely or unlikely to respond to the treatment.

[0013] Disclosed herein is a method of stratifying a subject who is undergoing or has undergone a wound treatment as a likely responder or non-rcspondcr to the treatment, the method comprising determining a level of ITIH2 in a sample obtained from a subject, wherein the level of TTTH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject, thereby stratifying the subject as a likely responder or non-responder to the treatment.

[0014] Disclosed herein is a method of treating a subject suffering from a wound, the method comprising the steps of: a) determining a level of ITIH2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject; and b) treating a subject found likely to have a non-healing wound.

[0015] Disclosed herein is a method of treating a subject suffering from a wound, the method comprising the steps of: a) selecting a subject found likely to have a non-healing wound, wherein a level of ITIH2 in a sample obtained from the subject, or a value derived therefrom, as compared to a reference us used to predict the likelihood of a healing wound or nonhealing wound in the subject; and b) treating the subject found likely to have a non-healing wound.

[0016] Disclosed herein is a kit for predicting the likelihood of wound healing in a subject, comprising a reagent for determining a level of intcr-alpha-trypsin inhibitor heavy chain 2 (ITIH2) in a sample obtained from the subject.

[0017] Disclosed herein is a composition comprising a) a sample obtained from a subject, and b) a reagent for determining a level of inter- alpha- trypsin inhibitor heavy chain 2 (ITIH2) in the sample.

[0018] Brief description of the drawings

[0019] Embodiments of the present invention will now be described, by way of non-limiting example, with reference to the drawings in which:

[0020] Figure 1 provides a definition of healing (H, gradient < -0.1) and non-healing (NH, gradient > 0) segments spanning 3 time points based on their best-fitted gradient of the slope. Figure 2 shows (A) Area-under-curve of Inter-alpha-trypsiu inhibitor heavy chain 2 (TTTH2). (B) Abundance of ITTH2 protein in healing and non-healing patients.

[0021] Figure 3 shows time plots of four representative patients of (A) wound area against time, and (B) Inter-alpha-trypsin inhibitor heavy chain 2 (ITIH2) against time.

[0022] Figure 4 shows protein abundances box plots of inter-alpha-trypsin inhibitor heavy chain H2 (ITIH2) showing superior differentiation of healing and non-healing wounds, in comparison to two wound biomarkers (matrix metalloproteinase-9, MMP9; tissue inhibitor of metalloproteinase 1, TIMP1) previously reported to be predictive of non-healing wounds.

[0023] Figure 5 shows a calibration curve using known concentrations of ITIH2 standards to quantify the amount of ITIH2 protein. Calibration standards arc indicated in grey squares, while unknown samples (6 samples from 3 DFU patients) are indicated in crosses.

[0024] Figure 6 shows that ITTH2 can be used in the development of assay kits for the prediction and monitoring of wound healing outcomes, for eventual guidance in the management of VLU and DFU.

[0025] Detailed description

[0026] The present specification teaches a method of predicting the likelihood of wound healing in a subject based on the level of inter-alpha-trypsin inhibitor heavy chain 2 (ITTH2) in a sample obtained from the subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of wound healing in the subject.

[0027] The inventors screened wound exudates from healing and non-healing venous leg ulcers (VLUs) and diabetic foot ulcers (DFUs) for biomarkers that can be used to assess wounds and monitor wound healing. Unlike previous studies that relied on end-point assessment, such as whether there is complete wound closure within 12 weeks, the inventors performed weekly wound area measurements to capture rates of healing over time. Periods of healing (reduction in wound area) and non-healing (slow reduction or increase in wound area) were assigned based on the gradient of wound area change in each segment. This longitudinal assessment allows for more robust correlation of biomarkcr expression with wound healing trajectory.

[0028] A mass spectrometry (MS)-based proteomic study of the samples found that the level of ITIH2, a secreted protein, differs between healing and non-healing ulcers. It was also found that changes in ITIH2 levels correlated inversely with changes in wound area during wound healing. ITIH2 is therefore suitable as both a diagnostic biomarkcr to differentiate healing from non-healing wounds, and as a prognostic biomarker to indicate if a wound is on a healing or non-healing trajectory. Monitoring ITIH2 levels can help determine the efficacy of ongoing wound management and help to guide clinical intervention (see Figure 6). Furthermore, ITIH2 can be used to guide the development of chronic wound models, and the screening of wound treatment therapies.

[0029] Accordingly, this disclosure provides methods for predicting wound healing, treating wounds, predicting responsiveness to wound treatment, and prognosing wound progression based on detecting levels of ITIH2 in patient samples.

[0030] Disclosed herein is a method of predicting the likelihood of wound healing in a subject with a wound, the method comprising determining a level of inter-alpha-trypsin inhibitor heavy chain 2 (ITIH2) in a sample obtained from the subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of wound healing in the subject.

[0031] Disclosed herein is a method of determining the likelihood of the presence of a healing or non-healing wound in a subject, the method comprising determining a level of ITIH2 in a sample obtained from a subject, wherein the level of 1T1H2, or a value derived therefrom, as compared to a reference predicts the likelihood of the presence of a healing or non-healing wound in the subject.

[0032] Disclosed herein is a method of predicting the prognosis of wound healing in a subject with a wound, the method comprising determining a level of TTIH2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference indicates the likely prognosis of wound healing in the subject. Disclosed herein is a method of monitoring wound healing in a subject with a wound, the method comprising determining a level of TTIH2 in respective samples obtained from the subject at two different time points during wound healing, wherein a change in the level of ITIH2 in the two samples predicts the likelihood of a healing wound or non-healing wound in the subject.

[0033] Disclosed herein is a method of predicting responsiveness to a wound treatment in a subject who is undergoing or has undergone the treatment, the method comprising determining a level of IT1H2 in a sample obtained from a subject, wherein the level of 1TIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject, thereby predicting whether the subject is likely or unlikely to respond to the treatment.

[0034] Disclosed herein is a method of stratifying a subject who is undergoing or has undergone a wound treatment as a likely responder or non-responder to the treatment, the method comprising determining a level of TTIH2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject, thereby stratifying the subject as a likely responder or non-responder to the treatment.

[0035] Disclosed herein is a method of treating a subject suffering from a wound, the method comprising the steps of: a) determining a level of ITIH2 in a sample obtained from a subject, wherein the level of TTTH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject; and b) treating a subject found likely to have a non-healing wound.

[0036] Disclosed herein is a method of treating a subject suffering from a wound, the method comprising the steps of: a) selecting a subject found likely to have a non-healing wound, wherein a level of ITIH2 in a sample obtained from the subject, or a value derived therefrom, as compared to a reference us used to predict the likelihood of a healing wound or nonhealing wound in the subject; and b) treating the subject found likely to have a non-healing wound. General definitions

[0037] The term “ITIH2” refers to inter-alpha-trypsin inhibitor heavy chain 2. ITIH2 is a secreted hyaluronan- binding protein of the inter-alpha-trypsin inhibitor family. As used herein, ITIH2 encompasses full-length mammalian ITIH2, including human and functionally equivalent non-human variants, and any fragments thereof. Functionally equivalent variants of full-length ITIH2 encompass full-length ITIH2 orthologues, isoforms and variants thereof which may contain alterations, such as, but not limited to, minor amino acid deletions, additions or substitutions which do not significantly adversely affect 1TIH2 activity. Transcript and protein sequences of various forms of full-length ITIH2 are known and readily accessible on sequence databases, such as NCBI and Uniprot databases, by reference to gene accession numbers, e.g., human ITIH2 (GenBank NM_002216) and mouse ITIH2 (GcnBank NM_010582). ITIH2 amino acid sequences arc also known such as human (NP_002207, P19823), mouse (NP_034712, Q61703).

[0038] As used herein, the term “wound” refers to an injury to a tissue. Wounds include both open wounds (in which the underlying tissue is exposed to the outside environment, such as ulcers, open sores, abrasions, lacerations, burns and surgical incisions) and closed wounds (in which the underlying tissue is not exposed to the outside environment, such as pressure sores and blunt trauma wounds). The term “wound” encompasses both acute and chronic wounds.

[0039] The term “likelihood of wound healing” refers to how likely it is for a wound in a subject to progress along a healing or a non-healing trajectory. The likelihood may be expressed as a percentage or probability score. For example, the likelihood may be a 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or greater probability of the wound progressing along a healing or a non-healing trajectory. A wound which is progressing or which is expected to progress along a healing trajectory is also referred to as a “healing wound”. A wound which is progressing or which is expected to progress along a non-healing trajectory is also referred to as a “non-healing wound”.

[0040] Wound healing trajectory may be determined using quantitative, semi-quantitative or qualitative methods known in the art, including but not limited to clinical measurements (e.g., changes in wound area or volume), histological assessment (e.g., extent of re- cpithclialisation or wound closure), and tracking of molecular or cellular signatures (e.g., changes in levels of inflammatory cytokines or growth factors). The trajectory' is typically assessed within a specified timeframe, which may vary' depending on wound type. For example, the timeframe may be 1 to 12 weeks for most wounds, or longer than 12 weeks for chronic wounds.

[0041] In a non-limiting example, wound area measurements taken at the beginning and at the end of the timeframe, and optionally at additional time points within the timeframe, are used to calculate a rate of change in wound area to determine wound healing trajectory. In such an embodiment, a healing trajectory may be a rate of change in wound area that is < -0.1 cm2 / week (i.e., a reduction in wound area), and a non-healing trajectory may be a rate of change in wound area that is > 0 cm2 / week over the timeframe (i.e., no change or increase in wound area).

[0042] The terms “increased” and “increase” are used herein to mean an increase by a statistically significant amount. In some embodiments, the terms “increased” and “increase” can mean an increase of at least 10% as compared to a reference level, for example an increase of at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90% or up to and including a 100% increase or any increase between 10-100% as compared to a reference level, or at least about a 2-fold, at least about a 3 -fold, at least about a 4-fold, at least about a 5-fold or at least about a 10-fold increase, or any increase between 2-fold and 10-fold or greater as compared to a reference level.

[0043] The terms “decreased” and “decrease” are used herein to mean a decrease by a statistically significant amount. In some embodiments, the terms “decreased” and “decrease” can mean a decrease of at least 10% as compared to a reference level, for example a decrease of at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90% or up to and including a 100% decrease or any decrease between 10-100% as compared to a reference level.

[0044] The term “sample” herein is used in its broadest sense. In one sense, it is meant to include a specimen or culture obtained from any source, including both biological and environmental sources. A “biological sample” includes within its scope a collection of similar fluids, cells, or tissues isolated from a biological source, such as a whole organism or in vitro culture. Samples include but are not limited to tissue biopsies, tissue resections, tissue aspirates, swabs (e.g., buccal swabs), whole blood, plasma, serum, urine, saliva, cerebrospinal fluid, and cell cultures, and may be obtained using any suitable method known in the art. Archival tissues, such as those having treatment or outcome history may also be used for sample extraction. The sample may be pooled from multiple aliquots. Samples include untreated, treated, diluted and concentrated samples.

[0045] A “biological fluid” herein includes, but is not limited to, wound fluid, intravascular fluid (e.g., blood, plasma, serum, lymph), urine, saliva, sputum, cerebrospinal fluid, pleural fluid, fluid of the respiratory, intestinal, and genitourinary tracts, synovial fluid, vaginal secretion, tear fluid, pus, breast milk, semen, fluid from ascites, cyst or tumour, amniotic fluid, or combinations thereof.

[0046] As used herein, “obtained” is meant to come into possession. For example, reference to obtaining a biomarker profile can include coming into possession of an already generated profile, such as by accessing the profile from a computer or database, as well as generating the profile by evaluating the relevant biomarkers. In another example, obtaining a sample, such as a biological sample, can include coming into the possession of a sample that has already been taken from a subject, as well as actively taking a sample from a subject.

[0047] As used herein, the terms “nucleic acid”, “nucleic acid molecule”, “nucleic acid sequence”, “polynucleotide”, or “oligonucleotide” can comprise a polymeric form of nucleotides of any length, can comprise DNA and / or RNA, and can be single-stranded, double- stranded, or multiple stranded. One strand of a nucleic acid also refers to its complement.

[0048] The terms “polypeptide”, “proteinaceous molecule”, “peptide” and “protein” are used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same. Thus, these terms apply to amino acid polymers in which one or more amino acid residues is a synthetic non-naturally-occurring amino acid, such as a chemical analogue of a corresponding naturally-occurring amino acid, as well as to naturally-occurring amino acid polymers. These terms do not exclude modifications, for example, glycosylations, acetylations, phosphorylations and the like. Soluble forms of the subject proteinaceous molecules are particularly useful. Included within the definition are, for example, polypeptides containing one or more analogues of an amino acid including, for example, unnatural amino acids or polypeptides with substituted linkages.

[0049] The terms “expression product”, “gene expression product” and “gene product” are used interchangeably herein to refer to the RNA transcription products (transcripts) of a gene, including mRNA, and the polypeptide translation products of such RNA transcripts. An expression product can be, for example, an unspliced RNA, an mRNA, a splice variant mRNA, a microRNA, a fragmented RNA, a polypeptide, a post-translationally modified polypeptide, a splice variant polypeptide, etc.

[0050] By “antigen-binding molecule” is meant a molecule that has binding affinity for a target antigen. It will be understood that this term extends to aptamers, immunoglobulins, antigenbinding fragments of immunoglobulins, and non-immunoglobulin protein frameworks that exhibit antigen-binding activity. Representative antigen-binding molecules include antibodies and their antigen-binding fragments.

[0051] The terms “treating”, “treatment” and the like include relieving, reducing, alleviating, ameliorating or otherwise inhibiting the effects of a disease or condition for at least a period of time. It is also to be understood that terms “treating”, “treatment” and the like do not imply that the disease or condition, or a symptom thereof, is permanently relieved, reduced, alleviated, ameliorated or otherwise inhibited and therefore also encompasses the temporary relief, reduction, alleviation, amelioration or otherwise inhibition of the disease, or of a symptom thereof.

[0052] By “therapeutically effective amount” or “effective amount”, in the context of treating a disease or condition, is meant the administration of an amount of active agent to a subject, either in a single dose or as part of a series or slow-release system, which is effective for the treatment of that disease or condition. The effective amount will vary depending upon the health and physical condition of the subject and the taxonomic group of subject to be treated, the severity of the disease or condition, the formulation of the active agent or pharmaceutical composition, the assessment of the medical situation, and other relevant factors. The skilled person would be able to determine an effective amount of an active agent with consideration of, for example, a subject’s age, weight, and clinical condition. The term “subject” as used throughout the specification is to be understood to mean a mammal, including both human and non-human mammals. While it is particularly contemplated that the methods herein are for treatment of humans, they are also applicable to veterinary treatments, including treatment of companion animals such as dogs and cats, and domestic animals such as horses, cattle and sheep, or zoo animals such as primates, fclids, canids, bovids, and ungulates. The “subject” may include a person, a patient or individual, and may be of any age or gender. In one embodiment, the subject is a human.

[0053] As used herein, “and / or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).

[0054] As used in this application, the singular form “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “an agent” includes a plurality of agents, including mixtures thereof.

[0055] Throughout this specification and the claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

[0056] Throughout this specification and the claims which follow, unless the context requires otherwise, the phrase “consisting essentially of’, and variations such as “consists essentially of’ will be understood to indicate that the recited element(s) is / are essential i.e. necessary elements of the invention. The phrase allows for the presence of other non-recited elements which do not materially affect the characteristics of the invention but excludes additional unspecified elements which would affect the basic and novel characteristics of the method defined.

[0057] Wounds

[0058] Wounds herein may be acute or chronic wounds. Acute wounds typically show substantial healing and wound area reduction within 4 weeks, while chronic wounds often fail to improve significantly in that time period. In one embodiment, the wound is a skin wound. Skin wounds include but are not limited to traumatic wounds such as lacerations, abrasions and punctures; burns; surgical wounds; wounds caused by chronic skin or epithelial conditions such as acne, psoriasis, atopic dermatitis or keratitis; and ulcers, such as arterial ulcers, venous ulcers, pressure ulcers, diabetic ulcers, vasculitic ulcers and pyoderma gangrenosum ulcers. The wound may be an infected wound.

[0059] In one embodiment, the wound is an ulcer. In a preferred embodiment, the ulcer is a skin or cutaneous ulcer. The ulcer may be caused by vascular inflammation (e.g., vasculitic ulcers); by poor local circulation often in the limbs or a peripheral part of the body (such as arterial ulcers caused by peripheral artery disease, venous ulcers caused by venous insufficiency, or pressure ulcers caused by prolonged, unrelieved pressure restricting circulation to an area of the body); or by a combination of peripheral neuropathy and circulatory insufficiency (e.g., diabetic ulcers). In one embodiment, the ulcer is an arterial ulcer (e.g. an arterial leg ulcer), a venous ulcer (e.g. a venous leg ulcer), a pressure, or a diabetic ulcer (e.g. a diabetic foot ulcer).

[0060] The subject with the wound may be suffering from a disease or condition that compromises wound healing, such as diabetes, venous insufficiency, peripheral artery disease, peripheral neuropathy, cancer, an immunodeficiency disorder, an autoimmune disorder, malnutrition, or reduced mobility. Alternatively or additionally, the subject may be undergoing treatment which impairs wound healing, such as anti-inflammatory drugs, immunosuppressants, angiogenesis inhibitors, chemotherapy or radiotherapy. In some embodiments, the subject with the wound is suffering from diabetes, chronic venous insufficiency, peripheral artery disease and / or peripheral neuropathy. hr one embodiment, the subject is undergoing or has undergone treatment for the wound. Wound treatments are described herein below.

[0061] In one embodiment, the subject is a human.

[0062] Biomarker detection Methods provided herein generally comprise detecting the level of biomarkers in a sample from the subject and comparing the biomarker levels to a reference to predict whether the wound is following a healing or non-healing trajectory. In some embodiments, multiple biomarkers are assessed, and the trajectory prediction is based on a composite score, algorithm, or multivariate analysis incorporating the levels of all assessed biomarkers. Machine learning algorithms, statistical models, or weighted scoring systems may be employed to integrate multiple biomarker measurements into a single trajectory prediction. The prediction may be used to monitor wound healing in the subject, to assess the efficacy of ongoing treatment, or to guide the generation and implementation of an appropriate treatment plan.

[0063] In one embodiment, methods herein comprise detecting the level of ITIH2 and optionally MMP9 or TIMP1 in the sample. In one embodiment, methods herein comprise detecting the level of ITIH2 and MMP9 in the sample. In one embodiment, methods herein comprise detecting the level of ITIH2 and TIMP1 in the sample. In one embodiment, methods herein comprise detecting the level of ITIH2, MMP9 and TIMP1 in the sample.

[0064] In some embodiments, methods herein may further comprise the use of additional demographic or clinical factors for predicting wound healing trajectory, non-limiting examples of which include gender, age, physiological parameters (c.g., body mass index, blood pressure), and clinical history of diabetes, chronic venous insufficiency, peripheral artery disease, peripheral neuropathy, and / or other comorbid diseases or conditions.

[0065] The expression level of ITIH2 and additional biomarkers such as MMP9 and TIMP1 may be determined in a wound sample or fluid sample obtained from the subject. In one embodiment, the sample is a blood, plasma or serum sample. In one embodiment, the sample is a wound sample.

[0066] Wound samples can be obtained using established techniques, for example, by swabbing, blotting, scraping or aspirating fluid or tissue from a wound site, or by performing a biopsy. In a preferred embodiment, the sample is a wound fluid, such as wound exudate. Advantageously, 1T1H2 can be detected in wound exudates, which enables minimally invasive sample collection and facilitates longitudinal monitoring without the need for repeated tissue biopsies. The Levine technique is a particularly effective method for obtaining deep wound exudates from a tissue swab. The method involves rotating a sterile swab over an area of clean wound tissue (avoiding necrotic areas) while applying sufficient downward pressure to express fluid from the wound bed.

[0067] Once a wound tissue or fluid sample is obtained, biomarkers including ITIH2, MMP9 and TIMP1 may be detected as RNA or protein. Methods of detecting expression products such as RNA and proteins are well known to a person skilled in the art. Exemplary nucleic acid detection methods include blotting techniques (e.g., Northern blots), probe hybridisationbased methods, amplification-based methods, microarrays, and nucleic acid sequencing. Exemplary protein detection methods include gel electrophoresis (e.g., 2D electrophoresis), Western blotting, immunoassays, aptamer-based detection assays, protein activity assays, high performance liquid chromatography, and mass spectrometry. The sample may be further processed to isolate nucleic acids and / or polypeptides for analysis.

[0068] In one embodiment, the method comprises detecting a ITIH2 polypeptide. In another embodiment, the method comprises detecting a nucleic acid expression product (e.g. mRNA) from the ITIH2 gene.

[0069] Nucleic acids such as RNA may be analysed directly without an amplification step, using labelled oligonucleotide probes that specifically hybridise with a target RNA. Alternatively, RNA may be reverse-transcribed into complementary DNA (cDNA) for analysis. Such reverse transcription may be performed alone or in combination with an amplification step. One example of a method combining reverse transcription and amplification steps i reverse transcription polymerase chain reaction (RT-PCR), which may be further modified to be quantitative, e.g., quantitative RT-PCR (qRT-PCR). Methods for reverse transcription with and without amplification axe generally known in the ail.

[0070] Nucleic acid amplification methods include, without limitation, polymerase chain reaction (PCR) and its variants such as in situ PCR and quantitative PCR, and isothermal amplification, i.e., amplification of DNA that occurs at substantially the same temperature. A number of isothermal amplification methods are known in the art, including but not limited to transcription mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), signal mediated amplification of RNA technology (SMART), strand displacement amplification (SDA), nicking enzyme amplification reaction (NEAR), rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), isothermal multiple displacement amplification (MDA), helicase-dependent amplification (HDA), single primer isothermal amplification (SPIA), and cross-primed amplification (CPA). Especially useful are detection schemes designed for the detection of nucleic acid molecules that are present in very low numbers.

[0071] Methods for assessing RNA levels that do not require conversion of the RNA to cDNA are also known in the art and are suitable for use in the methods herein. For example, the nCounter™ Analysis system from Nanostring Technologies uses a digital molecular barcoding technology for multiplex measurement of RNA levels. The RNA sample is mixed with pairs of capture and reporter probes, tailored to each RNA sequence of interest. After hybridisation and washing, probe-bound target nucleic acids axe immobilised to a surface to detect the fluorescent barcodes of the reporter probes. This allows for up to 1000-plcx measurement with high sensitivity and without amplification bias.

[0072] RNA expression levels can also be detected and quantified using various sequencing-based approaches, including but not limited to RNA sequencing (RNA-seq) and targeted RNA sequencing methods. These techniques typically involve reverse transcription of RNA to complementary DNA (cDNA), followed by high-throughput sequencing using platforms such as Illumina, Oxford Nanoporc, or Pacific Bioscicnccs systems. RNA-scq provides comprehensive transcriptome-wide expression profiling, while targeted approaches such as amplicon sequencing or capture-based methods focus on specific biomarkers. The sequencing data is processed through bioinformatics pipelines to quantify transcript abundance, typically expressed as counts per million (CPM), fragments per kilobase of transcript per million mapped reads (FPKM), or transcripts per million (TPM).

[0073] Oligonucleotide probes and primers for use in detection are readily designed based on the known sequences of genes or transcripts encoding ITIH2, and may comprise about 15-40 nucleotides. Software from Thermo Fisher, Integrated DNA Technologies, GenScript and LCG Biosearch Technologies have been developed for this purpose. Suitable labels for oligonucleotide detection are well known in the art and include, for example, fluorescent, chemiluminescent and radioactive labels. In one embodiment, an oligonucleotide comprising a nucleic acid sequence having at least 70% sequence identity (such as about 70%, about 75%, about 80%, about 85%, about 90%, about 95%, or 100% sequence identity) to a nucleic acid sequence in SEQ ID NO: 1 or 2 is used to detect the level of ITIH2 RNA.

[0074] Forward: 5’-ACC AGG TCT CCA CTC CAT TG-3’ (SEQ ID NO: 1) Reverse: 5’-ATC CTG CAA GTC GTC CAT CT-3’ (SEQ ID NO: 2)

[0075] The level of ITIH2 polypeptide may be determined by mass spectrometry or immunoassay.

[0076] Antigen-binding molecules that bind specifically to ITIH2 protein or a fragment thereof may be used in immunoassays. The antigen-binding molecules, such as antibodies, may bind to any region of the ITIH2 polypeptide. Various such antibodies are available commercially, for example, from Atlas Antibodies (HPA059150), R&D Systems (MAB 101993), Novus Biologicals (1023637) and Creative BioLabs (ABP-S-03335). In one embodiment, the antigen-binding molecule binds specifically to an epitope comprised in the amino acid sequence set forth in SEQ ID NO: 3. As one of skill in the art will appreciate, antibodies to ITIH2 may also be raised using techniques conventional in the art. For example, antibodies may be made by injecting a non-human host animal, e.g., a mouse, rabbit, goat or camel, with full-length ITIH2 or an immunogenic fragment thereof (such as the fragment with amino acid sequence of SEQ ID NO: 3), then isolating anti-ITIH2 antibodies by affinity chromatography from a biological sample taken from the host animal.

[0077] Human ITTH2 fragment

[0078] LPGAKVQFELHYQEVKWRKLGSYEHRIYLQPGRLAKHLEVDVWVIEPQGLRFLH VPDTFEGHFDGVPVISKGQQKAHVSFKPTVAQQRICPNCRET (SEQ ID NO: 3)

[0079] The antibody or an antigen- binding fragment thereof may contain a detectable label for detection. Detectable labels include but are not limited to radioactive, fluorescent, chemiluminescent or colorimetric compounds or dyes, nanoparticles such as colloidal gold or semiconductor nanocrystals (e.g., quantum dots), and enzyme labels. Examples of suitable enzymes include, but are not limited to, horseradish peroxidase (HRP), alkaline phosphatase (AP), p-galactosidase, acetylcholinesterase and catalase. Useful substrates for enzyme-based detection are well known in the ail and may be selected based on the level of detection required and the detection instrumentation used, e.g., spectrophotometer, fluorometer or luminometer.

[0080] Different types of immunoassays may be employed to measure the expression level of target proteins, non-limiting examples of which include enzyme-linked immunosorbent assays (ELISA), chemiluminescent immunoassays (CLIA), electrochemiluminescent assays (ECLIA), fluorescence polarisation immunoassays (FPIA), and bead-based assays such as Luminex bead arrays. These assays may be conducted, for example, in sandwich format employing capture and detection antibodies, or in competitive format using substrate-bound target proteins as a competitive binder for the antibody. Digital ELISA may be used to detect target proteins present at very low concentrations or in small sample volumes.

[0081] In some embodiments, mass spectrometry (MS) is used to detect ITIH2 in the samples. A typical analytical workflow for MS involves protein extraction from the sample, followed by enzymatic digestion (e.g., trypsin digest) to generate peptides that serve as markers for the parent protein. Data can be collected using various acquisition methods, including, for example, selected reaction monitoring (SRM), multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), or data-independent acquisition (D1A), often coupled with liquid chromatography for peptide separation (LC-MS / MS). MS analysis generates characteristic mass-to-chargc (m / z) ratios and fragmentation patterns that allow protein identification and quantification. For absolute quantification, peptide ion intensities may be compared to those of isotopically-labelled internal standards (e.g., SILAC, iTRAQ, TMT) or synthetic peptide standards. The results may be expressed as protein concentrations or relative abundance compared to reference standards.

[0082] The detected level of ITIH2 may first be normalised before comparison with a reference. Normalisation can be used to control for unwanted biological variation. In a non-limiting example, biological variation can result from some feature of the patient or the sample collection that is not relevant to the methods of the present disclosure, such variations created by collecting samples at different times of the day or due to patient age or patient gender.

[0083] Normalisation can be performed using methods known in the art. In a non-limiting example, normalisation can be achieved by dividing the detected level of RNA or protein by the expression level of a reference gene. Useful reference genes arc genes that show a low variation in their expression level across a variety of different samples and patients. For example, a useful reference gene will show the same expression level in samples from healing and non-healing wounds. The variation in expression level of a candidate reference gene can be quantified by different methods known in the art, for example, by calculating the coefficient of variation in the expression level of the gene across a set of different samples.

[0084] In one embodiment, there is provided a composition for predicting the likelihood of wound healing in a subject. Provided herein is also a method for preparing a composition for predicting the likelihood of wound healing in a subject. The composition may comprise a sample obtained from a subject, and a reagent for determining a level of IT1H2 in the sample. The reagent may be an antigen-binding molecule (such as an antibody) or antigen-binding fragment thereof that binds specifically to a ITIH2 polypeptide, or an oligonucleotide that hybridises with a ITIH2 RNA. The antigen-binding molecule or fragment thereof or the oligonucleotide may be conjugated to a detectable label (such as an enzyme or fluorescent label) for detection. The sample may be mixed with the reagent (e.g., antigen-binding molecule or oligonucleotide) to obtain the composition. For example, a wound exudate sample may be mixed with a labelled antibody that binds to 1TIH2 to obtain a composition as described herein.

[0085] Also provided herein is a kit for predicting the likelihood of wound healing in a subject. Also provided herein is the use of a reagent for determining a level of ITIH2 in a sample obtained from a subject in the manufacture of a kit for predicting the likelihood of wound healing in a subject.

[0086] The kit may comprise a reagent for determining a level of ITIH2 in the sample. In one embodiment, the reagent comprises an antigen- binding molecule or antigen- binding fragment thereof that binds specifically to a ITIH2 polypeptide. Alternatively, the reagent may comprise an oligonucleotide primer or probe that hybridises with a ITIH2 RNA for detection or amplification-based detection of ITIH2 RNA. The antigen-binding molecule, antibody, oligonucleotide primer, or oligonucleotide probe may be conjugated to a detectable label (such as enzyme or a fluorescent label). The kit may additionally contain detection reagents (such as substrates for the conjugated enzymes, or labelled secondary antibodies) to generate a detectable signal. In some embodiments, the kit further comprises a device for testing ITTH2 levels in the sample. In one embodiment, the device is a lateral flow device. The lateral flow device may utilise paper-based immunochromatography for detection of ITIH2.

[0087] Such a device typically comprises several functional zones arranged in scries: a sample application pad, a conjugate pad containing labelled detection antibodies (e.g., gold nanoparticle-conjugated anti-ITIH2 antibodies), a porous membrane (e.g., nitrocellulose membrane) with immobilised capture antibodies (for binding 1T1H2 -detection antibody complexes), and an absorbent pad at the distal end to maintain fluid flow. Upon application of the wound fluid sample to the sample pad, the fluid travels via capillary action across the conjugate pad and membrane towaids the absorbent pad. As the sample migrates through the conjugate pad, ITIH2 present in the sample binds to the labelled detection antibodies, forming antibody-ITIH2 complexes. These complexes continue to migrate along the nitrocellulose membrane until they encounter the immobilised capture antibodies in a test zone. Where the sample contains ITIH2, binding of detection antibody-ITIH2 complexes to the immobilised capture antibodies generates a detectable colorimetric signal, typically visible as a coloured line or spot. The intensity of the signal may correlate with the concentration of ITIH2 in the sample, allowing for semi-quantitative or quantitative assessment. The lateral flow device may further comprise a control zone containing immobilised antibodies that bind to the labelled detection antibodies regardless of ITIH2 presence, providing an internal control to verify proper flow and device functionality.

[0088] The intensity of the colorimetric signal generated by a sample and by a reference may be compared to predict the likelihood of a healing wound or a non-healing wound. The reference may be, e.g., a sample from non-wounded skin from the same subject, or a skin or plasma sample from a subject without a wound. A darker (more intense) colorimetric signal compared to the reference predicts that the wound is likely a healing wound, while a lighter (less intense) colorimetric signal predicts that the wound is likely a non-healing wound. The comparison may be semi-quantitative or quantitative. For semi-quantitative assessment, the colorimetric signals may be visually compared. A densitometer or spectrophotometer may be used for quantitative assessment. Wound classification and monitoring

[0089] The level of ITIH2 in the sample, or a value derived therefrom, is compared to a reference to determine the likelihood of wound healing.

[0090] In one embodiment, the reference is an average level of ITIH2 from a population of nonwounded tissue, e.g., healthy skin samples from a group of subjects. In another embodiment, the reference is an average level of ITIH2 from a population of healing wounds, e.g., wound samples from healing wounds in a group of subjects. The healing wound can be a healing chronic wound (e.g., a chronic ulcer that is being treated), or a healing acute wound (e.g., surgical wound, traumatic wound, ulcer or bum). The reference may also be subject- specific and established from the subject’s own tissue or wound samples. In one embodiment, the reference is a level of ITIH2 in a non- wounded tissue sample from the subject (e.g., healthy skin from the subject, or a plasma sample from an earlier non-wounded state). In another embodiment, the reference is a ITIH2 level in a wound sample obtained at an earlier time point from the same wound in the subject. A person skilled in the art will appreciate that the reference may be a single value, or a range of values derived from analysing multiple samples.

[0091] The level of ITIH2, or a value derived therefrom, may be an increase or a decrease over the reference level, or may be within a reference range of values. In some embodiments, an increase or decrease in ITIH2 level relative to the reference indicates the likelihood of the wound being a healing wound or a non-healing wound.

[0092] In one embodiment, an increased level of ITIH2, or a value derived therefrom, as compared to a reference indicates the likelihood of a healing wound in the subject. The increase may be an increase of at least 1.1 times, 1.2 times, 1.3 times, 1.4 times, 1.5 times, 1.6 times, 1.7 times, 1.8 times, 1.9 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times, or anywhere in between as compared to the reference. In one embodiment, an increase of at least 1.1 times in the level of ITIH2, or a value derived therefrom, as compared to a reference predicts that the wound is likely a healing wound.

[0093] In one embodiment, a decreased level of ITIH2, or a value derived therefrom, as compared to a reference indicates the likelihood of a non-healing wound in a subject. The decrease in level may refer to a biomarker having 0.9 times or less, 0.8 times or less, 0.7 times or less, 0.6 times or less, 0.5 times or less, 0.4 times or less, 0.3 times or less, 0.2 times or less, 0.1 times or less or anywhere in between as compared to the reference. In one embodiment, a decrease by at least 0.3 times or less in the level of ITIH2, or a value derived therefrom, as compared to a reference predicts that the wound is likely a non-healing wound. hr some embodiments, the ITIH2 level in the sample is compared to a population-average level of ITIH2 to predict the likelihood of wound healing. For example, the ITIH2 level in a sample may be expressed as a standardised t-score. A t-score of zero represents a populationaverage level of ITIH2 (e.g., in healthy non-wounded skin samples from a group of subjects). A sample with a t-score falling between between -1 and +1 is within normal deviation (i.e., within one standard deviation below or above the average, respectively). A sample with a t- scorc above +1 or below -1 predicts that the wound is likely a healing wound or a nonhealing wound, respectively.

[0094] In some embodiments, the level of TTIH2 in the sample is used to calculate a value predictive of wound healing. This value may be, for example, a probability score representing the likelihood of wound healing. A score that is higher than a set threshold (e.g., 0.5) may predict the wound as a likely healing wound, and a score that is lower than the threshold may predict the wound as a likely non-hcaling wound.

[0095] In one embodiment, a machine learning model is used to derive the predictive value. The machine learning model may be a model trained to predict wound healing status based on the level of ITIH2 in healing and non-healing wounds. In one embodiment, the machine learning model is trained on a dataset comprising the level of 1TIH2 in samples (such as wound samples or blood samples) from subjects with healing wounds and subjects with nonhealing wounds.

[0096] The machine learning model may rely on a classifier algorithm. For example, the machine learning model may rely on a logistic regression classifier, linear regression classifier, decision tree classifier, nearest neighbour classifier, neural network classifier, Gaussian mixture model (GMM), Support Vector Machine (SVM) classifier, nearest centroid classifier, linear' discriminant analysis (LDA) classifier, quadratic discriminant analysis (QDA) classifier, LogitBoost classifier, rotation forest classifier, random forest classifier, extreme gradient boosting (XGBoost) classifier, linear mixed effects model classifier, or a variation or combination thereof. Tn one embodiment, the machine learning model is a logistic regression classifier model.

[0097] Samples may be collected at various time points during a wound care process, including at initial presentation, at regular intervals during treatment (c.g., daily, every 2-3 days, weekly), or when clinical assessment suggests a change in healing status. In one embodiment, samples are collected at consistent time points to enable longitudinal monitoring of biomarker levels and healing trajectory.

[0098] In some embodiments, two or more samples obtained from the subject at different time points are used to prognose the wound healing trajectory or to monitor wound healing in the subject. The samples may be obtained at a suitable duration apart or at a frequency suitable for monitoring wound healing progress. For example, the samples may be obtained 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 9 weeks, 10 weeks, 11 weeks, or 12 weeks apart, or at 1 week, 2 week, 3 week, 4 week, 5 week, 6 week, 7 week, 8 week, 9 week, 10 week, 11 week, or 12 week intervals for wound monitoring.

[0099] In one embodiment, the subject is undergoing or has undergone a wound treatment, and method is used to monitor treatment progress, to predict responsiveness to the treatment, or to stratify the subject as a likely responder or non-responder to the treatment. Thus, for example, a first wound sample may be obtained prior to starting the treatment, and second and subsequent wound samples may be obtained during or following treatment, after a period of time sufficient for the treatment to have an effect. The ITIH2 levels in the two or more samples may be compared to determine whether the wound is progressing along a healing trajectory (i.e., the subject is responding to the treatment) or a non-healing trajectory (i.e., the subject is not-responding to the treatment). The method is thus useful to determine whether or not a treatment, such as a newly developed treatment or medication, is effective to treat a wound.

[0100] In one embodiment, an increase in the level of ITIH2 in the sample obtained at a later time point, as compared to the level of ITIH2 in the sample obtained at an earlier time point indicates that the wound is likely progressing along a healing trajectory. In a non-limiting example, an increase of at least 1.1 times in the level of ITIH2 at the later time point compared to the earlier time point indicates that the wound is likely along a healing trajectory.

[0101] In one embodiment, a decrease in the level of ITIH2 in the sample obtained at a later time point as compared to the level of ITIH2 in the sample obtained at an earlier time point indicates that the wound is likely progressing along a non-healing trajectory. In a nonlimiting example, a decrease of at least 0.3 times in the level of ITIH2 at the later time point compared to the earlier time point indicates that the wound is likely progressing along a nonhealing trajectory.

[0102] Alternatively, the IT1H2 levels in the two samples, or a value derived therefrom, may be compared to a reference to predict the likelihood of a healing or non-healing wound at the respective time points, to determine whether the wound is progressing along a healing or non-healing trajectory.

[0103] Wound treatment

[0104] Methods herein may be used to guide wound intervention strategies. In some embodiments, methods herein comprise generating a treatment plan for the subject based on the predicted likelihood of wound healing. The treatment plan may comprise: a) initiation of therapeutic intervention; b) continuation of current therapy; c) monitoring of wound healing trajectory; d) escalation of intervention; and / or e) de-escalation of intervention.

[0105] Disclosed herein is a method of treating a subject suffering from a wound, the method comprising the steps of: a) determining a level of 1TIH2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject; and b) treating a subject found likely to have a non-healing wound.

[0106] Disclosed herein is a method of treating a subject suffering from a wound, the method comprising the steps of: a) selecting a subject found likely to have a non-healing wound, wherein a level of IT1H2 in a sample obtained from the subject, or a value derived therefrom, as compared to a reference us used to predict the likelihood of a healing wound or non- healing wound in the subject; and b) treating the subject found likely to have a non-healing wound.

[0107] Therapeutic interventions for wounds include standard wound care such as wound cleaning and dressing; treatment with an antimicrobial agent (e.g., an antibacterial, antifungal or antiviral agent); treatment with an anti-inflammatory agent such as but not limited to a nonsteroidal anti-inflammatory agent (NSAID) such as ibuprofen, aspirin or naproxen, or non- NSAID agents such as acetaminophen; and compression therapy for venous and diabetic ulcers. In some cases, more advanced therapies such as the use of growth factors (e.g., EGF, FGF) or other pharmacological interventions, wound debridement, skin grafts, xenografts, dermal substitutes, negative pressure wound therapy (NPWT), hyperbaric oxygen therapy, electrical stimulation, ultrasound, shock wave therapy, or other therapies or surgical interventions, may be utilised. The treatment may depend on a clinical assessment of the type and stage of the wound, availability of a wound product, the patient’s behaviour / reception and consent, etc.

[0108] In a non-limiting example, the treatment plan may comprise maintaining or de-escalating a current course of wound treatment when the subject is found likely to have a healing wound or whose wound is found likely to be progressing along a healing trajectory. De-escalation may involve, e.g., a reduction in drug dosage, or moving to more conservative or standard- of-care wound management. In such cases, a monitoring protocol may also be implemented, such as continued periodic assessment of biomarker levels to confirm sustained healing trajectory.

[0109] Alternatively, for a subject who is found likely to have a non-healing wound or whose wound is found likely to be progressing along a non-healing trajectory, the treatment plan may comprise the initiation of therapeutic intervention (for previously untreated wounds), or escalation to more aggressive interventions. Escalation may involve, e.g., increasing drug dosages, administering an alternative therapy (such as a therapy suited for chronic nonhealing wounds), or administering a combination of therapies. Specific interventions for non-healing wounds include but are not limited to advanced wound dressings (e.g., for exudate management), more frequent dressing changes and / or cleaning, application of topical growth factors, infection control, negative pressure wound therapy, hyperbaric oxygen therapy, electrical stimulation, and surgical intervention such as debridement, skin grafting or skin implants. Non-healing wounds may also be caused by a non-healthy lifestyle, such that appropriate changes, e.g., to diet, physical activity, or hygiene, may be initiated to assist in wound healing. hr some embodiments, methods herein include iterative assessment of biomarker levels following treatment initiation or escalation. This iterative, biomarkcr-guidcd approach enables optimised and personalised treatment and prevents prolonged use of ineffective therapies. For example, a subject initially predicted as having a non-healing wound may, following treatment, exhibit 1TIH2 levels above the reference, indicating transition to a healing trajectory and enabling de-escalation of therapy. Conversely, a subject whose ITIH2 levels decline below the reference during follow-up may be escalated to more intensive therapy.

[0110] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

[0111] Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications, which fall within the spirit and scope. The invention also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features.

[0112] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary' skill in the art to which this invention belongs.

[0113] Certain embodiments of the invention will now be described with reference to the following examples which are intended for the purpose of illustration only and are not intended to limit the scope of the generality hereinbefore described. EXAMPLES

[0114] Methods

[0115] Study design

[0116] A prospective two-centre study (as part of the WCIT programme) was performed at two tertiary hospitals in Singapore (National University Hospital Singapore; Tan Tock Seng Hospital). In Singapore, the majority of VLU compression bandages are performed by specialist wound nurses in a hospital outpatient setting. Patient inclusion and exclusion criteria are listed in Table 1. All patients were given information about the study and patients provided informed consent to participate. Study patients were assigned a unique identifier to ensure anonymity. The institutional ethics committee also granted ethical approval (DSRB Ref: 2017 / 00886, DSRB 2018 / 00566).

[0117] Patients were monitored over a 12-week period to assess wound healing, with weekly or fortnightly wound sample collection. This study period was chosen as most wounds heal within 8-12 weeks. Samples were collected from the time the patients presented with an open wound until the skin healed or until the end of 12 weeks in the case of non-healing wounds. A total of 467 fortnightly samples from 142 patients with DFU, and 639 weekly samples from 99 patients with VLU were included in this analysis.

[0118] Table 1. Inclusion and exclusion criteria for the recruitment of diabetic foot ulcers (DFU) and venous leg ulcers (VLU).

[0119] Wound area measurement

[0120] Baseline and follow-up wound area measurements were determined by digital planimetry and photography and digitalised to area readouts as a photograph series per subject to reduce bias. Week-to-week wound area trajectories were charted. Periods of healing (reduction of wound area) and non-healing (slow reduction or increase of wound area) along each patient’ s wound trajectory were assigned based on the gradient of each segment (Figure 1).

[0121] Sample collection and processing

[0122] Wound fluid was collected by pressing a FLOQSwabs (Copan) into the wound area and rotating with orbital movements ensure partial coverage of the swab sites. Swabs were transported in a clean tube to the analytical lab. where the swab head was removed and sonicated with 500 pL of saline for 10 min at 4°C. After centrifugation, the supernatant was aliquoted and stored at -80°C until ready for batch analysis. An aliquot was used for protein quantification using the BCA protein assay kit.

[0123] Mass spectrometry-based proteomics

[0124] Samples were randomised and precipitated with acetone. Protein pellets were resuspended by vortexing in 25 pL of 100 mM TEAB with 25% v / v TFE. TCEP was added to 10 mM, followed by incubation at 55°C for 20 min. CAA was added to 55 mM, followed by incubation in the dark at room temperature for 20 min. Samples were then diluted with 75 pL of 100 mM TEAB. LysC was added at 1:40 ratio (0.5 pg per 20 pg sample protein), followed by incubated for 2 h at 37°C. Trypsin was added at 1:25 ratio (0.8 pg per 20 pg sample protein), followed by overnight incubation at 37°C. All samples were desalted on Waters Oasis HLB 96-well plates (10 mg resin) on a vacuum manifold. Desalting wells were activated with 250 pL acetonitrile, followed by equilibration with 250 pL 0.1% formic acid in water (buffer A). Samples were added into the wells and diluted in-place to 250 pL with buffer A before being drawn through the resin. Wells were washed another 300 pL buffer A, then eluted with 250 pL 65% ACN with 0.1% formic acid (buffer B) into collection plates.

[0125] All samples were further processed on stage tips packed in-house with 6 mg Dr Maisch Reposil-Pur basic C18 10 pm resin prior to data-independent acquisition (DIA) acquisition. Desalted elutes corresponding to 5 pg digested peptides were dried in a vacuum concentrator for 2 h at room temperature, then resuspended in 80 pL lOmM ammonium formate at pH 10 (basic buffer A). Samples were then rearranged into their final randomised order for injection in blocks of 48. Packed stage tips were activated with 50 pL acetonitrile, followed by equilibration with 50 pL basic buffer A using a deep-well plate centrifuge at 500 g for 2 min. Samples were loaded onto stage-tips and similarly centrifuged to bind peptides. Stage tips were washed with 80 pL basic buffer A, and then eluted with 50 pL 50% ACN with 10 mM ammonium formate at pH 10 directly onto autosampler plates.

[0126] Samples for data-dependent acquisition (DDA) were resuspended in 10 pL A* buffer (0.5% acetic acid, 0.06% TFA, 2% ACN in water) and 0.5 pL of Biognosys iRT standard peptide mixture (prepared according to vendor’s instructions) were spiked into each sample. Four (4) pL of resuspended sample corresponding to 2 pg of digested peptides were injected on column. Fractions for library-building were resuspended in 15 pL A* buffer and spiked with 0.75 pL of iRT standard. 3 pL corresponding to 2 pg per fraction were injected on column. Triplicate injections for each fraction were performed as the stochastic nature of DDA isolates slightly different peptide populations in each run. Pierce HeLa digests were divided beforehand from stock vials and dried to obtain 6.5 pg of digest per tube. Dried digests were resuspended in 16.25 pL of A* buffer and spiked with 1.3 pL of iRT standard as required. 2.5 pL of this resultant HeLa-iRT mix was injected as quality control (QC), giving 1 pg on column.

[0127] The same liquid chromatography (LC) conditions were used for HeLa QC, DIA and DDA library runs. The separation column was Thermo Scientific ES803A 50 cm x 75 pm, C18, 2 pm particle size. The column heater temperature was 50°C. The LC method used a constant flow rate of 0.3 pL / min using 0.1 % formic acid in water (mobile phase A) and 0.1 % formic acid in 95 / 5 acetonitrile / water (mobile phase B) with gradient programmed as the following: 0 min 2%B, 65 min 32%B, 70 min 45%B, 71 min 95%B, 75 min 95%B.

[0128] All mass spectrometry (MS) runs were acquired with 2.1 kV spray voltage and 300°C ion transfer tube temperature. HeLa QC runs were acquired with top 40 DDA, 350-1550 m / z at 60,000 resolution, 3e6 AGC target, 50 ms maximum injection time for MSI; and dynamic mass range at 7,500 resolution, le5 AGC, 15 ms maximum injection for MS2. The isolation width was 1 Da, normalised collision energy was 28, and charge states of only 2-5 were included. DDA library' runs were acquired with top 30 DDA, 390-1010 m / z at 60,000 resolution, 3e6 AGC target, 50 ms maximum injection time for MSI; and dynamic mass range at 7,500 resolution, 2c5 AGC, 25 ms maximum injection time for MS2. The isolation width was 1 Da, normalised collision energy was 30 and charge states of only 2-5 were included. Fractions acquired by DDA for spectra library were searched in Proteome Discoverer against reviewed human proteome database from Uniprot (2019 June 1 1 version) and iRT peptide fasta. DIA runs were acquired with 395-1010 m / z at 60,000 resolution, 3e6 AGC target and 50 ms maximum injection time for MSI. DIA isolation windows for MS2 were calculated in Skyline using a scan range of 400-1000 m / z, 10 Da width and the feature “optimise window placement” enabled. Window centres were imported into acquisition method, with an increased isolation width of 10.5 Da to better capture isotope peaks. A loop count of 60 was applied to cycle through all isolation windows before starting a new duty cycle. DIA MS2 were acquired with 3e6 AGC target, 12 ms maximum injection time and dynamic mass range. Normalised collision energy was set to 30. Acquired DIA files were checked for total ion chromatogram signal and imported into Skyline to monitor retention time behaviour of iRT standard peptides daily.

[0129] Protein quantification and statistics

[0130] Protein normalisation and quantification through the selection of robust peptides and fragments was achieved using mapDIA, with the following inputs: experimental design was set as IndependentDesign, retention time normalization (NORMALIZATION) value was set as 10, standard deviation factor (SDF) was set to 2 and median intra-protein correlation cutoff (MIN_CORREL) was set to 0.5. Minimum number of observations for each group (MIN_OBS) was set to 1, minimum number of fragments per peptide (MIN_FRAG_PER_PEP) was set to 3, and minimum number of peptides per protein (MIN_PEP_PER_PROT) was set to 1. Maximum number of fragments per peptide (MAX_FRAG_PER_PEP) was set to 5, and minimum number of peptides per protein (MAX_PEP_PER_PROT) was set to 5. Protein intensities were log-transformed prior to further statistical analysis. Proteins were imported into Mctabo Analyst for statistical analysis (including Receiver operating curve (ROC) analysis) and Reactome Pathway Knowledgebase for pathway analysis.

[0131] Quantification of potential biomarkers in wound samples

[0132] Following the biomarker identification, targeted quantitative proteomics was performed using a triple-quadruple mass spectrometer. Plasma and Levine swab samples (10-20 pL) collected from the study and protein standards for the ITIH2 calibration curve were processed according to the mass-spectrometry based proteomics protein digestion protocol mentioned above. Peptide fragments obtained via in silico digestion were set as precursor ions for the targeted method. Corresponding product ions were also obtained in silico using MRMAtlas database. Collision energies were obtained using a formula. For liquid chromatography, water with 0.1% fonnic acid was used as mobile phase A and acetonitrile with 0.1% formic acid as mobile phase B. The LC method used a constant flow rate of 0.450 pL / rnin with the following gradient: 0 min 2%> B, 15 min 25% B, 25 min 75% B, 25.1 min 100% B, 27.1 min 2% B. The mass spectrometer parameters were 3500V for positive and 2800V for negative ion spray voltage, 350°C as source temperature, 230°C as vaporiser temperature. The sheath gas, aux gas and sweep gas were set as arbitrary values of 50, 18 and 0 respectively. The acquired data was processed using Skyline. A calibration curve was generated and the amount of ITIH2 was quantitated.

[0133] EXAMPLE 1

[0134] Current management of venous leg ulcers (VLUs) and diabetic foot ulcers (DFU) relies on the experience and expertise of the treating clinician. The assessment criteria are subjective and primarily involve visual assessment of the wound at the time of dressing change. Given that macroscopic visual changes are only evident days or weeks after key cellular' events have occurred, specific biomarkers arc needed to prognosticate identify slow and / or non- healing VLUs and DFUs to guide intervention during clinical wound monitoring. Current biomarkers available commercially, or prospective biomarkers reported in literature, are either inaccurate or require invasive sampling (i.e., biopsies) (see Table 2 below for a comparison).

[0135] The phenotype classification of healing and non-healing wounds is critical to guide downstream statistics to yield useful biomarkers. Previous studies have used complete wound closure at Week 12 as an indicator of wound healing, which fail to account for changing rates of healing across time. In this approach, the rates of wound area change of all patients were measured weekly, which allowed granular segmentation into healing (wound area reduction) and non-healing (slow wound area reduction or wound area increment) periods across time (Figure 1).

[0136] The mass spectrometry-based proteomics yielded a total of 3551 human proteins across all samples. After peptide and fragment selection, 1890 proteins passed quality control filter and were robustly quantifiable. DEFINE (ROC) analysis generated a list of proteins showing significant differences between healing and non-healing trajectories in DFU and VLU patients. The top biomarker in DFU, inter-alpha-trypsin inhibitor heavy chain 2 (1TIH2) demonstrated an area-under-the-curve (AUC) value of 0.762 (confidence interval 0.656- 0.86) (Figure 2). ITIH2 showed an inverse correlation with wound area (Figure 3). The role of ITIH2 in wound healing is currently unknown. The family of inter-alpha-trypsin inhibitors, including ITIH2, are structurally related to plasma serine protease inhibitors involved in extracellular matrix stabilisation and binding to hyaluronic acid. A comparison of ITIH2 to reported singular biomarker and grouped panels is presented in Figure 4 and Table 2.

[0137] A simpler and specialised method was further developed to specifically measure ITIH2 elucidated from our discovery proteomics approach. Known concentrations of ITIH2 standards were processed with plasma and wound fluid samples. After validating the limits of detection of our triple-quadruple mass spectrometry, a calibration curve was generated which allows for quantification of ITIH2 protein (Figure 5). A similar standard curve derived from 1TIH2 levels in wound fluids may be used to develop analytical solutions for wound healing prediction and monitoring, including laboratory testing services and point-of-care diagnostic assays. For wounds that are small or dry and which may not contain sufficient wound fluid for biomarker analysis, hydrophilic microfluidic channels may be used to pull sample fluid via capillary action for detection. Alternatively, ITIH2 DNA or mRNA may be amplified and detected.

[0138] Table 2. Comparison of ITIH2 against two wound biomarker alternatives (WoundChek, WDM). Advantages and disadvantages coloured in green and red respectively.

Claims

CLAIMS1. A method of predicting the likelihood of wound healing in a subject with a wound, the method comprising determining a level of inter-alpha-trypsin inhibitor heavy chain 2 (ITIH2) in a sample obtained from the subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of wound healing in the subject.

2. The method of claim 1, wherein the method comprises determining the level of a 1T1H2 polypeptide in the sample.

3. The method of claim 1 or 2, wherein an increased level of ITIH2, or a value derived therefrom, as compared to a reference, predicts that the wound is likely a healing wound.

4. The method of claim 1 or 2, wherein a decreased level of ITIH2, or a value derived therefrom, as compared to a reference, predicts that the wound is likely a non-healing wound.

5. The method of any one of claims 1 to 4, wherein the reference is an average ITIH2 level in wound samples from healing wounds, or a predetermined value.

6. The method of any one of claims 1 to 5, wherein the method comprises deriving, by a machine learning model for predicting wound healing status, a probability value predictive of whether the wound is a healing wound or a non-healing wound from the level of 1T1H2 in the sample.

7. The method of claim 6, wherein the machine learning model is trained on a dataset comprising the level of ITIH2 in samples from subjects with healing wounds and subjects with non-healing wounds.

8. The method of claim 6 or 7, wherein the machine learning model is a logistic regression model.

9. The method of any one of claims 1 to 8, wherein the method comprises determining the level of TTTH2 in respective samples obtained at two different time points during the wound healing process.

10. The method of claim 9, wherein an increase in the level of ITIH2 in the sample obtained at a later time point as compared to the level of ITIH2 in the sample obtained at an earlier time point predicts that the wound is likely a healing wound.

11. The method of claim 9, wherein a decrease in the level of 1T1H2 in the sample obtained at a later time point as compared to the level of ITIH2 in the sample obtained at an earlier time point predicts that the wound is likely a non-healing wound.

12. The method of any one of claims 1 to 11, wherein the subject is suffering from diabetes, chronic venous insufficiency, peripheral artery disease, and / or peripheral neuropathy.

13. The method of any one of claims 1 to 12, wherein the wound is an ulcer.

14. The method of claim 12 or 13, wherein the ulcer is a venous ulcer, an arterial ulcer, pressure ulcer or a diabetic ulcer.

15. The method of any one of claims 1 to 14, wherein the subject is undergoing or has undergone a treatment for the wound.

16. The method of any one of claims 1 to 15, wherein the sample is a wound sample.

17. The method of any one of claims 2 to 16, wherein the level of the ITIH2 polypeptide is determined by mass spectrometry or immunoassay.

18. The method of any one of claims 1 to 17, wherein the method further comprises determining the level of MMP9 and / or TTMP1 in the sample.

19. The method of any one of claims 1 to 18, wherein the method further comprises generating a treatment plan for the subject based on the predicted likelihood of wound healing.

20. A method of determining the likelihood of the presence of a healing or non-healing wound in a subject, the method comprising determining a level of ITIH2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of the presence of a healing or nonhealing wound in the subject.

21. A method of predicting the prognosis of wound healing in a subject with a wound, the method comprising determining a level of 1T1H2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference indicates the likely prognosis of wound healing in the subject.

22. The method of claim 21, wherein the method comprises determining the level of ITIH2 in respective samples obtained at two different time points during the wound healing process.

23. A method of monitoring wound healing in a subject with a wound, the method comprising determining a level of 1T1H2 in respective samples obtained from the subject at two different time points during wound healing, wherein a change in the level of ITIH2 in the two samples predicts the likelihood of a healing wound or non-healing wound in the subject.

24. The method of claim 23, wherein the subject is undergoing or has undergone treatment for the wound.

25. A method of predicting responsiveness to a wound treatment in a subject who is undergoing or has undergone the treatment, the method comprising determining a level of ITIH2 in a sample obtained from a subject, wherein the level of ITIH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject, thereby predicting whether the subject is likely or unlikely to respond to the treatment.

26. A method of stratifying a subject who is undergoing or has undergone a wound treatment as a likely responder or non-rcspondcr to the treatment, the methodcomprising determining a level of ITIH2 in a sample obtained from a subject, wherein the level of TTTH2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject, thereby stratifying the subject as a likely responder or non-responder to the treatment.

27. A method of treating a subject suffering from a wound, the method comprising the steps of: a) determining a level of ITIH2 in a sample obtained from a subject, wherein the level of 1T1H2, or a value derived therefrom, as compared to a reference predicts the likelihood of a healing wound or non-healing wound in the subject; and b) treating a subject found likely to have a non-healing wound.

28. A method of treating a subject suffering from a wound, the method comprising the steps of: a) selecting a subject found likely to have a non-healing wound, wherein a level of TTIH2 in a sample obtained from the subject, or a value derived therefrom, as compared to a reference is used to predict the likelihood of a healing wound or nonhealing wound in the subject; and b) treating the subject found likely to have a non-healing wound.

29. A kit for predicting the likelihood of wound healing in a subject, comprising a reagent for determining a level of inter-alpha-trypsin inhibitor heavy chain 2 (ITIH2) in a sample obtained from the subject.

30. The kit of claim 29, wherein the kit comprises an antigen-binding molecule or an antigen-binding fragment thereof that binds specifically to a ITIH2 polypeptide, and / or an oligonucleotide that hybridises with a ITIH2 RNA.31 . A composition for predicting the likelihood of wound healing in a subject, comprising a) a sample obtained from a subject, and b) a reagent for determining a level of inter- alpha-trypsin inhibitor heavy chain 2 (TTIH2) in the sample.

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