WO2018193250A1

WO2018193250A1 - Biomarkers for glucocorticoid action

Info

Publication number: WO2018193250A1
Application number: PCT/GB2018/051019
Authority: WO
Inventors: Brian Robert Walker; Roland Howard STIMSON; Ruth Andrew; David Watson; Mohammed ALWASHIH
Original assignee: University of Edinburgh
Current assignee: University of Edinburgh
Priority date: 2017-04-18
Filing date: 2018-04-18
Publication date: 2018-10-25
Anticipated expiration: 2019-10-18
Also published as: GB201706064D0

Abstract

Methods of detecting supra-physiological levels of glucocorticoid in a biological sample and associated diseases are described. The concentration of at least one biomarker from a group of biomarkers is determined and significant differences in the concentration compared to reference values is indicative of supra-physiological levels of glucocorticoid in the biological sample, and therefore of such levels in the subject from whom the sample originated.

Description

Biomarkers for Glucocorticoid Action Field of the Invention

The invention relates to methods of detecting levels of glucocorticoid in a biological sample, specifically methods of detecting supra-physiological levels of glucocorticoid in a biological sample, and biomarkers for use in the same.

Background of the Invention

Glucocorticoid replacement therapy is the mainstay of treatment for congenital adrenal hyperplasia (CAH) ¹ and both primary and secondary adrenal insufficiency ². Glucocorticoids are also employed commonly in a variety of inflammatory diseases such as rheumatoid arthritis, obstructive lung diseases, inflammatory bowel disease, skin diseases and asthma ³. Although highly efficacious, treatment with glucocorticoids is generally associated with adverse effects such as obesity, hyperglycaemia, hypertension, cardiovascular disease ⁵ and osteoporosis ⁶ and in children, retarded linear growth. These dose-related adverse effects are observed even amongst CAH patients when the goal is physiological replacement rather than pharmacological anti-inflammatory therapy ^4J'⁸. The efficacy of glucocorticoid therapy can be assessed with disease-related endpoints, including adrenal androgen levels in CAH. However, given the narrow therapeutic index, objective monitoring of glucocorticoid toxicity would also be valuable to assist with dose optimisation; unfortunately, the pharmacokinetics of oral glucocorticoids preclude maintenance of blood steroid concentrations with physiological reference ranges, and no sensitive pharmacodynamic biomarkers exist with which to assess glucocorticoid toxicity. Therefore, it would be advantageous to provide a method for detecting supra-physiological levels of glucocorticoid in a subject.

Anti-inflammatory corticosteroids (i.e. glucocorticoids) are the second most commonly prescribed drug class, behind non-steroidal anti-inflammatories. For example, in adults over the age of 40, 48% were prescribed glucocorticoids in a 4-year period. In this setting, glucocorticoids have the side effects listed above and in addition cause suppression of the body's own steroid production ('adrenal insufficiency'), which puts people at risk of morbidity and mortality when they are stressed by illness or injury, or when the glucocorticoid dose is either intentionally or inadvertently reduced. This risk is dependent on dose and duration of exposure to glucocorticoids but it exists even for low dose glucocorticoids such as are commonly used in inhalers for asthma or in creams for people with extensive inflammatory skin diseases. If detected, adrenal insufficiency can be averted by advice about steroid replacement therapy, for example doubling the dose at time or intercurrent illness, and monitoring adrenal function during gradual dose reduction. However, detecting adrenal insufficiency is difficult and expensive: it relies on measuring blood levels of Cortisol after stimulation by injection of adrenocorticotropic hormone (ACTH) in a SynACTHen test; this procedure is rarely performed and there is evidence of substantial underdiagnosis of adrenal insufficiency in glucocorticoid-treated patients with asthma, inflammatory bowel disease, rheumatoid arthritis and other conditions.

Accordingly, there is a need for more effective methods of diagnosing adrenal insufficiency.

Therefore, at least one object of the invention is to provide a more effective method of diagnosing adrenal insufficiency.

Less commonly, glucocorticoids are prescribed as replacement therapy in patients with diseases causing adrenal insufficiency, such as congenital adrenal hyperplasia, Addison's disease, and hypopituitarism. There is evidence that these patients are often overreplaced with glucocorticoids - leading to osteoporosis, obesity, high blood pressure, and cardiovascular disease - because no test exists to detect over-replacement and optimise the glucocorticoid dose regime for each patient.

Therefore, there is a need for a way to test whether a patient is being exposed to supra- physiological levels of glucocorticoids.

Accordingly, at least one object of the invention is to provide a way to test for overreplacement of glucocorticoids in patients.

Finally, there is a group of patients in whom excessive glucocorticoid production ('Cushing's syndrome', usually due to a curable tumour of the pituitary gland) is suspected. Current testing is complicated, involving 24-hour urine collection and/or administration of a dexamethasone tablet at night and measurement of plasma Cortisol the next day.

Therefore, there is a need for a way to test whether a patient has supra-physiological levels of glucocorticoids due to underlying Cushing's syndrome. Accordingly, at least one object of the invention is to provide a way to test for over-production of glucocorticoids in patients due to Cushing's syndrome or similar. Summary of the Invention

According to a first aspect of the invention there is provided a method of detecting supra- physiological levels of glucocorticoid in a biological sample, the method comprising the steps: a) providing a biological sample from a subject;

b) determining the concentration in the biological sample of one or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)- hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 3-(4-hydroxyphenyl)pyruvic acid, alpha-N-phenylacetyl-L- glutamine, 4-hydroxy-2-oxopentanoate, asparagine, threonine, keto-glutaramic acid, N-methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate; and

c) comparing the concentration of the biomarkers in the biological sample to reference values,

wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of supra-physiological levels of glucocorticoid in the biological sample. A large number of patients are prescribed glucocorticoid for inflammatory conditions and in inhalers etc. Subjects that take a course of glucocorticoids may have their own natural production of glucocorticoid production suppressed. As a result, if a subject stops taking prescribed glucocorticoids or is going to be taken off their prescribed course of glucocorticoids they may have a low level of glucocorticoids and therefore be vulnerable to disease or injury.

Current tests to detect whether a subject is taking doses of glucocorticoid which is supra- physiological and hence sufficient to suppress their endogenous production of glucocorticoids involve blood tests using the SynACTHen test that relies on measuring blood levels of Cortisol after stimulation by injection of adrenocorticotropic hormone (ACTH). This test is difficult and expensive and therefore is not often used and as a result some subjects that may be at risk of adrenal insufficiency are not detected.

The inventors have surprisingly found that the concentration of the group of biomarkers changes significantly for subjects that have above physiological ("supra-physiological") levels of glucocorticoid in their system when compared to those subjects that have physiological or sub physiological levels of glucocorticoids. Therefore, the present aspect of the invention allows supra-physiological levels of glucocorticoid to be detected within the system of a subject.

Prolonged exposure to supra-physiological levels of glucocorticoid increases the risk of suppression of the subject's production of glucocorticoids, and therefore, increases the risk of adrenal insufficiency in the subject.

Accordingly, the invention extends in a second aspect to a method of predicting risk of adrenal insufficiency in a subject, the method comprising the steps:

a) providing a biological sample from a subject;

wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of a risk of adrenal insufficiency. Adrenal insufficiency is a dose-related phenomenon and those subjects that have been exposed to supra physiological levels of glucocorticoid are at higher risk of adrenal insufficiency than those subjects that have not. Where glucocorticoid or glucocorticoid replacement is given to a subject in a course of treatment, such as for anti-inflammatory therapy using oral, topical or respiratory delivery, for example, it is difficult to predict the dose of glucocorticoid that reaches systemic circulation. Therefore, the methods of the invention allow the level of glucocorticoid that is in systemic circulation for a subject to be detected, and as a result allow a prediction of whether that subject is likely to have adrenal insufficiency.

Therefore, the method of the present aspect of the invention may provide a cheaper and simpler test for predicting adrenal insufficiency than standard methods in the art. Furthermore, the method of the present aspect of the invention may provide a tool to allow a health care practitioner to distinguish between subjects with a normal production of glucocorticoids and those whose production of glucocorticoids has yet to reach normal levels after treatment with glucocorticoids. Accordingly, using the method of the present aspect subjects can be tested using a cheap and simple test to determine whether they are at risk of adrenal insufficiency.

According to a third aspect of the invention there is provided a method of diagnosing Cushing's syndrome in a subject, the method comprising the steps:

a) providing a biological sample from a subject;

wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of Cushing's syndrome in the subject.

Cushing's syndrome is a result of a subject producing glucocorticoids at supra-physiological levels, often due to the presence of a tumour in the pituitary gland. Therefore, if the subject is known to not be taking a course of glucocorticoids and it is determined using the methods of the invention that they have supra-physiological levels of glucocorticoids in their system, a diagnosis of Cushing's syndrome may be given without needing to use the lengthy tests that are currently available.

Therefore, the method of the present aspect of the invention may allow Cushing's syndrome to be diagnosed more effectively than using conventional methods of the art. According to a fourth aspect of the invention there is provided a method of diagnosing over or under replacement of glucocorticoids in a subject, the method comprising the steps:

a) providing a biological sample from a subject; b) determining the concentration in the biological sample of one or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)- hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 3-(4-hydroxyphenyl)pyruvic acid, alpha-N-phenylacetyl-L- glutamine, 4-hydroxy-2-oxopentanoate, asparagine, threonine, keto-glutaramic acid, N-methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate; and

wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of over or under treatment of glucocorticoids in the subject.

Corticosteroids, typically glucocorticoids, are often given to subjects as anti-inflammatories for a variety of conditions. As a result of this treatment, the subjects may be exposed to supra- physiological levels of glucocorticoids and therefore, the method of the present aspect may be able to identify such subjects to ensure that such exposure is taken into consideration when the treatment has run its course and the subject would otherwise stop taking glucocorticoids.

Typically, the concentration of the biomarkers in the group of biomarkers is determined in the methods of the invention using mass spectrometry. For example, the concentration of the biomarkers in the group of biomarkers may be determined in the methods of the invention using Gas or Liquid Chromatography Mass Spectrometry (GC- of LC-MS).

In alternative embodiments, the concentration of biomarkers may be determined in the methods of the invention using immunoassays. In further alternative embodiments, the concentration of biomarkers may be determined in the methods of the invention using any conventional method in the art such as enzymatic assays or colorimetric assays, for example.

The concentration in the biological sample may be determined of two or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 3-(4-hydroxyphenyl)pyruvic acid, alpha-N-phenylacetyl-L- glutamine, 4-hydroxy-2-oxopentanoate, asparagine, threonine, keto-glutaramic acid, N- methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate. The concentration in the biological sample may be determined of three, four, five, six or seven or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L- glutamyl-taurine, tryptophan, dehydroquinate, 3-(4-hydroxyphenyl) pyruvic acid, alpha-N- phenylacetyl-L-glutamine, 4-hydroxy-2-oxopentanoate, asparagine, threonine, keto- glutaramic acid, N-methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate.

The concentration in biological sample may be determined of one or more biomarkers selected from a first group consisting of asparagine, tryptophan, 4-hydroxyphenyl pyruvate, palmitoleic acid, chenodeoxyglycocholate, N-methylnicotinamide, and hypoxanthine, and one or more biomarkers selected from a second group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, methionine, 5-L-glutamyl-taurine, dehydroquinate, alpha- N-phenylacetyl-L-glutamine, 4-hydroxy-2-oxopentanoate, threonine, keto-glutaramic acid, and octanoylcarnitine.

Accordingly, in some embodiments the concentration of at least one biomarker from each of the first and second groups of biomarkers is determined. In embodiments, the concentration of one or more biomarkers selected from the group consisting of palmitoleic acid, hypoxanthine, tryptophan, 3-(4-hydroxyphenyl)pyruvic acid, 4- hydroxy-2-oxopentanoate, asparagine, N-methylnicotinamide, and chenodeoxyglycocholate may be determined. In embodiments, the concentration of one or more biomarkers selected from the group consisting of palmitoleic acid, uridine, hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, 3-(4-hydroxyphenyl)pyruvic acid, 4-hydroxy-2-oxopentanoate, asparagine, threonine, N-methylnicotinamide, and chenodeoxyglycocholate may be determined. In embodiments, the concentration of one or more biomarkers selected from the group consisting of palmitoleic acid, uridine, hypoxanthine, methionine, 3-(4-hydroxyphenyl)pyruvic acid, asparagine, and chenodeoxyglycocholate may be determined. The concentration of two, three, four, five or six biomarkers selected from the group consisting of asparagine, tryptophan, 4-hydroxyphenyl pyruvate, palmitoleic acid, chenodeoxyglycocholate, N-methylnicotinamide, and hypoxanthine may be determined.

The concentration of each biomarker selected from the group consisting of asparagine, tryptophan, 4-hydroxyphenyl pyruvate, palmitoleic acid, chenodeoxyglycocholate, N- methylnicotinamide, and hypoxanthine may be determined. In some embodiments the methods of the invention may determine the concentration of at least chenodeoxyglycocholate. The concentration of at least tryptophan may be determined. The concentration of at least hypoxanthine may be determined. The concentration of at least N-methylnicotinamide may be determined.

The methods of the invention may determine the concentration of at least

chenodeoxyglycocholate and asparagine. The methods of the invention may determine the concentration of at least chenodeoxyglycocholate, asparagine and hypoxanthine. In some embodiments, the methods of the invention may determine the concentration of at least tryptophan, chenodeoxyglycocholate, N-methylnicotinamide, and hypoxanthine

Typically, the biological sample is a blood sample, such as a sample of whole blood or a sample of a blood fraction, such as blood plasma, or blood serum, for example. Alternatively, the biological sample may be urine or saliva.

The methods of the invention may differentiate between subjects that have received less than 5 mg daily of prednisolone equivalent doses of glucocorticoid replacement and subjects that have received 5 mg or more daily of prednisolone equivalent doses of glucocorticoid replacement. This ability to determine accurately the dose of glucocorticoid taken that is not effected by the highly variable pharmacokinetics and/or pharmacodynamics, and that does not have the difficulty associated with estimating administered doses from inhalers and topical therapy is highly advantageous over methods known in the art.

Throughout the term "reference value" may refer to a pre-determined reference value, for instance specifying a confidence interval or threshold value for the diagnosis or prediction of supra-physiological levels of glucocorticoid. Alternatively, the reference value may be derived from the expression level of a corresponding biomarker or biomarkers in a 'control' biological sample, for example a positive (patient determined to have supra-physiological levels of glucocorticoid) or negative (patient with physiological or sub-physiological levels of glucocorticoid) control. Furthermore, the reference value may be an 'internal' standard or range of internal standards, for example a known concentration of the one or more biomarkers. For example, in embodiments where the methods of the invention are carried out using mass spectrometry, the reference value may be a range of standard concentrations of the one or more biomarkers against which biological samples are to be compared. Alternatively, the reference value may be an internal technical control for the calibration of expression values or to validate the quality of the sample or measurement techniques. Accordingly, it would be routine for the skilled person to apply these known techniques alone or in combination in order to quantify the level of biomarker in a sample relative to standards or endogenous compounds or in order to validate the quality of the biological sample, the assay or statistical analysis.

The term "significant difference" refers to a difference between the reference value and the determined concentration of a specific biomarker, optionally after scaling of biomarker levels in relation to sample mean and sample variance, of at least 1.2-fold, 1.3-fold, preferably 1.5- fold.

The term "chenodeoxyglycocholate" refers to chenodeoxygylcocholate and to its hydrolysed form chenodeoxyglycocholic acid.

In embodiments, a significant difference in the determined concentration of pentadecanoic acid, palmitic acid, arachidic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 4-hydroxy-2-oxopentanoate, asparagine, N-methylnicotinamide, octanoylcarnitine, or chenodeoxyglycocholate is an increase of the determined concentration over the reference value.

In embodiments, a significant difference in the determined concentration of tridecanoic acid, palmitoleic acid, inosine, uridine, 3-(4-hydroxyphenyl)pyruvic acid, alpha-N-phenylacetyl-L- glutamine, threonine, or keto-glutaramic acid is a decrease of the determined concentration over the reference value.

In some embodiments, the dose of glucocorticoid that is currently being taken by a subject may be taken into consideration. Accordingly, the determination of a significant difference in the concentration of one or more biomarkers in the biological sample to reference values may be weighted by the dose, or a logistic regression model may include the dose as an independent variable for example.

In some embodiments, the period of time from when the latest dose of glucocorticoid was received by a subject to the time when the biological sample is taken from that subject (the "dose period") may be taken into consideration,

Accordingly, the determination of a significant difference in the concentration of one or more biomarkers in the biological sample to reference values may be weighted by the dose period, or a logistic regression model may include the dose period as an independent variable, for example.

The invention extends in a fifth aspect to a method of treatment for over- or under- administration of corticosteriods in a subject, the method comprising the steps:

a) providing a biological sample from a subject;

wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of over- or under-treatment of glucocorticoids, wherein where a significant difference in the concentration of biomarkers in the biological sample is determined,

d) adjusting the dosage of glucocorticoids for the subject.

Accordingly, where overadministration of glucocorticoids is determined, the dosage of glucocorticoids for that subject may be reduced. Where underadministration of glucocorticoids is determined, the dosage of glucocorticoids for that subject may be increased. According to a sixth aspect of the invention there is provided a method of treatment of adrenal insufficiency in a subject, the method comprising the steps:

a) providing a biological sample from a subject;

wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of adrenal insufficiency, wherein where a significant difference in the concentration of biomarkers in the biological sample is determined, d) prescribing a glucocorticoid replacement therapy for the subject. The invention extends in a seventh aspect to a kit of parts comprising one or more standard solutions and a set of instructions, the one or more standard solutions comprising one or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L- glutamyl-taurine, tryptophan, dehydroquinate, 3-(4-hydroxyphenyl) pyruvic acid, alpha-N- phenylacetyl-L-glutamine, 4-hydroxy-2-oxopentanoate, asparagine, threonine, keto- glutaramic acid, N-methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate, wherein the or each biomarker in the one or more standard solutions is present at a predetermined concentration such that the standard solution can be used to calibrate an assay for the determination of concentration of the one or more biomarkers in a biological sample.

Typically, the kit of parts is provided for use in mass spectrometry assays. In embodiments, the or each standard solution of the kit of parts is run through the mass spectrometer to provide a reference value for the concentration of the one or more biomarkers of the or each standard solution. The or each standard solution may be run through the mass spectrometer before a biological sample from a subject is run through the mass spectrometer to determine the concentration of the one or more biomarkers within the biological sample. Accordingly, the kit of parts may allow differences in the concentration of the one or more biomarkers in a biological sample to be readily determined on a standard mass spectrometer.

Accordingly, the kit of parts may be used to calibrate a mass spectrometer prior to running a biological sample through the mass spectrometer to carry out the methods of the first to sixth aspects of the invention.

The or each standard solution may comprise two, three, four, five six or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 3-(4-hydroxyphenyl)pyruvic acid, alpha-N-phenylacetyl-L- glutamine, 4-hydroxy-2-oxopentanoate, asparagine, threonine, keto-glutaramic acid, N- methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate.

The kit of parts may comprise a plurality of standard solutions. Each standard solution within the plurality of standard solutions may comprise one or more biomarkers. Typically, each standard solution within the plurality of standard solutions comprise the same one or more biomarker. Preferably, each standard solution within the plurality of standard solutions comprises the same one or more biomarkers at a different predetermined concentration than the other standard solutions within the plurality of standard solutions.

In embodiments, the plurality of standard solutions may comprise at least two, three or four standard solutions, wherein each standard solution comprises the same one or more biomarkers at different concentrations than the other standard solutions of the plurality of standard solutions.

In some embodiments, the one or more standard solutions comprise one or more biomarkers selected from the group comprising asparagine, tryptophan, 4-hydroxyphenyl pyruvate, palmitoleic acid, chenodeoxyglycocholate, N-methylnicotinamide, and hypoxanthine. For example, the one or more standard solutions may comprise one, two, three, four, five, six or all seven of the biomarkers selected from the group consisting of asparagine, tryptophan, 4- hydroxyphenyl pyruvate, palmitoleic acid, chenodeoxyglycocholate, N-methylnicotinamide, and hypoxanthine.

In one embodiment the one or more standard solutions comprise at least tryptophan, chenodeoxyglycocholate, N-methylnicotinamide and hypoxyanthine. The kit of the invention allows biological samples to be processed using a standard laboratory mass spectrometer. The standard solutions may be run through the mass spectrometer to provide standard curves for the one or more biomarkers at one or more concentrations. As a result, in embodiments where the kit comprises a plurality of standard solutions, each of which comprise the one or more biomarkers at different concentrations, the concentration of the one or more biomarkers in a biological sample may be readily determined by comparing the results of standard solutions and the results of the biological sample. According to an eighth aspect of the invention there is provided a kit of parts comprising at least one binder and a suitable solvent, wherein the at least one binder is configured to selectively bind to the one or more biomarker selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)- hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 3-(4- hydroxyphenyl) pyruvic acid, alpha-N-phenylacetyl-L-glutamine, 4-hydroxy-2-oxopentanoate, asparagine, threonine, keto-glutaramic acid, N-methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate, such that during use the at least one binder selectively binds to the one or more biomarkers. Typically, the kit of parts comprises at least one binder for each of the one or more biomarkers to be detected.

The or each binder selectively binds to a single target. Therefore, where three biomarkers are to be detected, for example, the kit of parts comprises three types of binder, each of which selectively binds to one of the three biomarkers to be detected.

The kit of parts may comprise two, three, four, five, six, seven or more binders, each of which bind to a different biomarker. In one embodiment, the kit of parts comprises at least one binder that selectively binds to each of asparagine, tryptophan, 4-hydroxyphenyl pyruvate, palmitoleic acid, chenodeoxyglycocholate, N-methylnicotinamide, and hypoxanthine. As a result, the kit of parts may be operable to detect the concentration of each of asparagine, tryptophan, 4- hydroxyphenyl pyruvate, palmitoleic acid, chenodeoxyglycocholate, N-methylnicotinamide, and hypoxanthine in a biological sample that is contacted to the binders.

The kit of parts may comprise a substrate. The at least one binder may be anchored to the substrate. The substrate may be suitable for use in an assay where the binding of the at least one binder to the one or more biomarkers in a biological sample is detectable. Typically, the kit of parts is suitable to be used in an assay wherein the binding of the one or more biomarkers to the at least one binder is detected. For example, the at least one binder may comprise a label that is activated upon binding of a biomarker to the at least one binder. The at least one binder may be an antibody. The at least one binder may be suitable for use in an immunoassay. At least one epitope of the binder may selectively bind to one biomarker to be detected.

A solution comprising the at least one binder may change colour when the at least one binder binds to the one or more biomarker.

Brief Description of the Figures

Embodiments of the present invention will now be described, by way of non-limiting example, with reference to the accompanying drawings.

Figure 1 : Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) score plots showing 117 patients with CAH grouped based on their daily doses of glucocorticoid. (A) Patients divided into 4 groups by daily prednisolone equivalent dose: 1) patients having 1-2.5 mg (green), 2) >2.5-5 mg (blue), 3) >5-7.5 mg (plum) and 4) >7.5 -15 mg (orange). (B) Patients divided into 2 groups: 1) 1-5 mg (green-64 samples) and 2) >5-15 mg (blue-53 samples). The later model consists of one predictive x-score components; component t [1] and three orthogonal x-score components to[1-3]. t [1] explains 4.8% of the predictive variation in x, to[1] explains 45.7% of the orthogonal variation in x, R²X (cum) = 0.506, R²Y (cum) = 1 , R² (cum) = 0.829, Goodness of prediction Q² (cum) = 0.657;

Figure 2: Bars plot shows 24 metabolites (Table 3). Each bar represents a metabolite on y- axis, and its area under ROC curve (AUROCC) value on x-axis. Each metabolite' bar comprises of two segments; VIPpred (predictive value of variable importance in the projection) (blue) and VIPortho (orthogonal value of variable importance in the projection) (red), their values presented as percentages (white), FDR adjusted p-value presented for each metabolite at the right of each bar (dark blue). Metabolites arranged based on their statistical significance from top to down. Metabolite was included if it had a FDR corrected pvalue < 0.01 and VIPpred ≥ 2*VIPortho. 7 metabolites only passed the filter;

Figure 3: OPLS-DA score plot was comprised 7 biomarkers (table 4) quantified in 117 patients. Green observations (64 samples) represent patients receiving a GC dose of 1-5 prednisolone equivalent and the blue observations (53 samples) represent patients receiving GC dose > 5-15 mg prednisolone equivalent. The model consists of one predictive x-score components; component t[1] and one orthogonal x-score components to[1]. t[1] explains 33.7 % of the predictive variation in x, to[1] explains 23% of the orthogonal variation in x, R²X (cum) = 0.57, R²Y (cum) = 1 , R² (cum) = 0.535, Goodness of prediction Q² (cum) = 0.497; and

Figure 4: plot showing area under the ROC curve (AUROCC) of the two groups, x-axis showing (FPR) false positive rate (1 -specificity), y-axis showing true positive rate (sensitivity). AUROCC for 1) 1-5 = 0.92 and 2) >5-15 = 0.92.

Detailed Description

While the making and using of various embodiments of the present invention are discussed in detail below, it should be appreciated that the present invention provides many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed herein are merely illustrative of specific ways to make and use the invention and do not delimit the scope of the invention.

To facilitate the understanding of this invention, a number of terms are defined below. Terms defined herein have meanings as commonly understood by a person of ordinary skill in the areas relevant to the present invention. Terms such as "a", "an" and "the" are not intended to refer to only a singular entity, but include the general class of which a specific example may be used for illustration. The terminology herein is used to describe specific embodiments of the invention, but their usage does not delimit the invention, except as outlined in the claims.

Example 1 Patient recruitment

The UK Congenital adrenal Hyperplasia Adult Study Executive (CaHASE) cohort is a cross- sectional study of adult CAH patients (aged≥18 years) recruited from 17 specialized endocrine centres across the UK. The study protocol was approved by West Midlands research ethics committee (MR EC/03/7/086) and registered with ClinicalTrials.gov (NCT00749593) and has been previously published in detail¹⁰. All participants gave written informed consent. Procedures

Participants attended the research unit of their respective centre after an overnight fast having taken their regular medication, followed by medical history, physical examination (height, weight, blood pressure) and blood sampling (including for 17-hydroxyprogesterone (170HP), androstenedione). All laboratories participate in the UK NEQAS scheme for quality control of steroid assays. Inclusion criteria for the metabolomics analysis were as follows: known 21- hydroxylase deficiency; additional serum sample collected at time of recruitment; full anthropometric and biochemical data available for each participant. Samples from 117 patients were used for metabolomics analysis; subjects were treated with hydrocortisone, prednisolone and dexamethasone or combination therapy. Glucocorticoid therapies were converted to daily prednisolone equivalents based on the relative potencies of the steroids reported in the British National Formulary (PredEqBNF)¹¹.

Chemicals and materials

HPLC grade acetonitrile (ACN) was from Fisher Scientific, UK. HPLC grade water was produced by a Direct-Q 3 Ultrapure Water System from Millipore, UK. AnalaR grade formic acid (98%) was from BDH-Merck, UK. Ammonium carbonate and ammonium acetate were from Sigma-Aldrich, UK.

Sample Preparation

Samples were stored at -80°C and thawed at ambient temperature before further preparation. A pooled sample was prepared by taking 20 μΙ of 10 randomly selected samples. Metabolites were extracted by transferring 200 μΙ of sample to an Eppendorf tube with addition of 800 μΙ of ACN. After vortexing the samples were centrifuged at 8000 RPM for 10 min. The supernatant was then collected into a HPLC vial for LC-MS analysis.

LC-MS Analysis

Sample analysis was carried out on an Accela 600 HPLC system combined with an Exactive (Orbitrap) mass spectrometer (Thermo Fisher Scientific, UK). An aliquot of each sample solution (10 μΙ) was injected onto a ZIC-pHILIC column (150 ^χ 4.6 mm, 5 μηι; HiChrom, Reading, UK) with mobile phase A (20 mM ammonium carbonate in HPLC grade water (pH 9.2)), and B (HPLC grade acetonitrile). The gradient was formed by decreasing the percentage of B from 80% to 20% over 30 minutes followed by washing the column at 5% of B for 5 minutes and finally re-equilibrating the column at 80% of B for 10 minutes Samples were submitted in random order for LC-MS analysis, and pooled quality control samples were injected after every 20 samples to monitor the stability of the instrumentation. Standard mixtures containing authentic standards for 220 compounds were run in order to calibrate the columns. The Exactive Orbitrap (Thermo Fisher Scientific, Hemel Hempstead, UK) was operated in both positive and negative modes set at 50,000 resolution and controlled by Xcalibur version 2.1.0 (Thermo Fisher Corporation, UK). The mass scanning range was m/z 75-1200; the capillary temperature was 320 °C; and the sheath and auxiliary gas flow rates were 50 and 17 arbitrary units, respectively. Data Extraction

Raw LC-MS files were converted to mzXML (ProteoWizard) and separated into ESI positive and negative. Converted files were then processed with open source MzMatch

(http://mzmatch.sourceforge.net/) and the identification of putative metabolites was made via the macro-enabled Excel file, Ideom (http://mzmatch.sourceforge.net/ideom.html). Thus metabolites were identified to MSI levels 2 or 3 ² according to either exact mass (< 3 ppm deviation) or exact mass plus retention time matching to a standard.

Data Analysis

Softwares used

All data processing, including data visualisation, biomarker identification, diagnostics and validation was implemented using SIMCA software v.14 (Umetrics AB, Umea, Sweden) for multivariate analysis³. For univariate analysis, Metaboanalyst 3.0 (www.metaboanalyst.ca) ⁴ to get FDR adjusted p-value and AUC for each metabolite. And IBM SPSS Statistics software package version 22.0 (IBM SPSS, Chicago, IL) were employed to test statistical difference between anthropometrics measurements in low compared to high glucocorticoid (GC) dose. The normality of the anthropometrics and clinical data was examined statistically by Shapiro- Wilk test; if this was violated then Mann Whitney test was employed. Data visualisation and biomarkers identification

Orthogonal projections to latent structures-discriminant analysis (OPLS-DA), a supervised model, was employed to examine the differences between groups while neglecting the systemic variation, It links the metabolomic variability among samples either to the intervention (predictive) or to a systematic variation (orthogonal) ⁵. The p values of the biomarkers were corrected using false discovery rate (FDR)^{6, 7}. Variable importance in the projection (VIP) was employed to assess the contribution of each variable in the observed metabolomics change to a given model compared to the rest of variables ^{8 9}. The average VIP is equal to 1 ; thus a variable with VIP larger than 1 has more contribution in explaining y and vice versa ⁸. The 99% confidence interval was calculated for each metabolite based on jack-knife of uncertainty which estimates the prediction error rate based on the cross validation rule used ¹⁰. Correlation coefficient of a metabolite to high dose of GC used to evaluate reliability of a metabolite, reliable metabolites > |5| ¹¹. Metabolites were then filtered based on their corrected p-values and 99% CI so that all metabolites with p-values > 0.05 and/or 99% Cls crossing the zero point were filtered out. Diagnostics and validation

R² and Q² were employed as diagnostic tools for supervised and unsupervised models. The R² represents the percentage of variation explained by the model while Q² indicates cross validated R^{2 5}. Model validity was also assessed using cross validated ANOVA (CV-ANOVA) which corresponds to Ho hypothesis of equal cross validated predictive residual of the supervised model in comparison with the variation around the mean ¹². The area under receiver operating characteristic curve (AUROCC) was used to assess the accuracy of the classifier with a rough guide as follow; 0.9-1.0 = excellent; 0.8-0.9 = good; 0.7-0.8 = fair; 0.6- 0.7 = poor; 0.5-0.6 = fail ¹³.

Results

To examine relationships between glucocorticoid dose and metabolomic profiles, patients were grouped by their daily dose; 1) 1-2.5 mg, 2) >2.5-5 mg, 3) >5-7.5 mg and 4) >7.5-15 mg prednisolone equivalents (Figure 1).

Table 1. Data corresponding to figure 1 regarding group assignment plus AUROCC for classification

% of correctly

„ Samples P CV- Group _(n) Distribution of samples classified AUROCC

ANOVA

samples

Comparison A 1 -

>2.5-5 >5-7.5 >7.5

2.5

1 -2.5 18 0* 17 1 0 0.00% 0.75

0 43* 3 0 93.48% 0.89

0

2.5E-06

0 1 40* 0 97.56% 0.9

7.5 ⁴¹

>7.5 12 0 1 1 1 0* 0.00% 0.81

Comparison B 1 -5 >5

1 -5 64 64* 0 100% 0.98

1 .5E-20 >5 53 2 53* 96% 0.98

*Number of samples that correctly assigned to the correct group, AUROCC = area under the ROC curve.

A clear difference in metabolomic profile was found between patients receiving 1-5 mg prednisolone equivalents daily (low GC, 64 patients) compared to patients receiving >5-15 mg (high GC, 53 patients) (Figure 1 B, Table 1). The median (IQR) daily glucocorticoid dose was 3.75 (2.5-5) mg and 7.5 (6.25-7.5) mg for low GC and high GC groups, respectively. There were no statistically significant differences in any of the anthropometric and biochemical measurements between groups (Table 2). Table 2 Comparison of anthropometric and clinical measurements between the low (L) (n=64) and high (H) (n=53) dose glucocorticoid exposed groups; all measurements were similar between the two groups except for glucocorticoid dose.

Glucocorticoid Media FDR-adjusted p

Parameter dose group Mean ± SD Ql n Q3 value

L 36.5 ± 10.8 30.15 35 42.15

Age (y) 0.64

H 35.4 ± 11.7 25.6 34.85 42.25

L 75.7 ± 13.96 65.9 73 83.6

Weight (kg) 0.76

H 77.9 ± 17.4 64.4 74.6 89.63

L 1.56 ± 0.08 1.51 1.57 1.62

Height (m) 0.39

H 1.58 ± 0.08 1.52 1.58 1.64

L 30.9 ± 6.04 26.9 30 33.95

BMI (m/kg²) 0.83

H 30.84 ± 6.5 26.05 29.35 34.9

Systolic blood L 118.7±12.1 110.16 117.33 125.8

0.27

pressure (mmHg)

H 122.7 ± 12.6 112.6 123 131.6

Diastolic blood L 73.53 ± 9.01 68 73.3 79.3

0.21

pressure (mmHg)

H 76.9 ± 8.8 72.6 77 81.08

L 3.68 ± 1.3 2.5 3.75 5

u PredEqBNF 1.4E-19

H 7.52 ± 1.7 6.25 7.5 7.5

u Serum L 8.56 ± 19.1 1.475 3.35 5.85

0.42

androstenedione

H 11.18 ± 15.7 1.7 3.1 15 u Serum 17-OH L 65.3 ± 98.8 3 11.5 92.45

0.81

progesterone

H 82.42 ± 162 4.25 12.55 80.85 ^u p-value based on Mann-Whitney U test (non-parametric), L= 1-5 mg daily prednisolone equivalent, H= >5-15 mg daily prednisolone equivalent, PredEqBNF = daily prednisolone equivalents of glucocorticoids therapies based on British National Formulary.

The OPLS-DA model (Figure 1 B) based on 382 metabolites in 1 17 patients showed a clear separation between low GC and high GC groups with P CV-ANOVA = 7.4E-22. The metabolites which were different between the two groups are shown in Table 3, metabolites refined based on FDR corrected p value <0.05 and AUROCC >0.6. Metabolites in Table 3 (24 metabolites) were then refined by discarding metabolites which did not make a strong individual contribution to predicting glucocorticoid dose, based on their VIPpred versus VIPortho (Figure 2), resulting in a model (Figure 3A) based on only seven metabolites (Table 4). These 7 variables in combination produced an area under ROC curve (AUROCC) ¹² of 0.92 (Figure 3B). The new model (Figure 3A) explained more of the variation between low GC and high GC groups (33%) compared to the earlier model (Figure 1 B) which explained only 4.3% of the variation. The majority of the 7 metabolites were positively correlated to glucocorticoid dose; of those, chenodeoxyglycocholate had the highest correlation value (r= 0.76) while N-methylnicotinamide had the lowest correlation value (r=0.46).

Table 3 Biomarkers significantly different between the low (L) and high (H) glucocorticoid dose groups.

FDR- Metabolites AUROCC corrected L : H VIPpred VIPortho

p value

Straight chain fatty acids

Tridecanoic acid(C13:0) 0.63 0.0162 1:0.9 0.58 0.54

Pentadecanoic acid(C15:0) 0.64 0.0056 1:1.3 0.98 0.78

Palmitic acid *(C16:0) 0.66 0.021 1:1.4 1.06 1.07

Arachidic acid (C20:0) 0.65 0.0117 1:1.4 1.03 0.92

Unsaturated fatty acids

Palmitoleic acid(16:l) 0.77 0.000004 1:0.7 1.55 0.4

15(S)-Hydroxyeicosatrienoic

0.65 0.00201 1:1.3 1.06 0.75

acid(20:3)

Docosahexaenoic acid(22:6) 0.65 0.00298 1:1.2 0.87 0.62

Prostaglandin Bl (C20:2) 0.64 0.00596 1:1.3 1.02 0.8

Nucleotide Metabolism

Inosine * 0.63 0.0102 1:0.9 0.69 0.56

Uridine * 0.75 0.00027 1:0.7 1.21 0.64

Hypoxanthine * 0.73 0.00001 1:2.4 2.11 1.02

Methionine and taurine metabolism

Methionine * 0.73 0.00041 1:1.2 0.87 0.4 5-L-Glutamyl-taurine 0.65 0.0114 1:1.6 1.14 0.85

Aromatic amino acid metabolism

Tryptophan * 0.67 0.0003 1:1.7 1.59 0.97

Dehydroquinate 0.67 0.0059 1:1.3 1.02 0.82

3-(4-Hydroxyphenyl)pyruvic acid

* 0.75 0.00006 1:0.5 1.8 0.6

Alpha-N-Phenylacetyl-L-

0.61 0.0293 1:0.9 0.55 0.43

glutamine

4-Hydroxy-2-oxopentanoate 0.65 0.0003 1:3.5 2.34 1.6

Miscellaneous

Asparagine * 0.72 0.00004 1:2.6 2.29 1.14

Threonine * 0.62 0.0211 1:0.7 1.14 0.56

Keto-glutaramic acid 0.64 0.0103 1:0.8 0.8 0.65

N-Methylnicotinamide 0.69 0.0003 1:2.2 1.85 0.85

Octanoylcarnitine 0.66 0.00319 1:1.4 1.16 0.83

Chenodeoxyglycocholate 0.82 1.9E-10 1:4.8 6.73 1.46

* Retention time matches standard, AUROCC = area under the ROC curve, VIPpred = predictive value of variable importance in the projection, VIPortho = orthogonal value of variable importance in the projection. VIP values represent the contribution of the metabolite in the variability between the two groups compared to the other metabolites.

Table 4. List of significant biomarkers used to build the OPLS-DA model in Figure 2

FDR- IP IP

Biomarker adjusted L: H r 99% CI

pred orth

p value

Asparagine * 4.5E-05 1: 2.6 2.29 1.14 0.52 (0.08, 0.34)

Tryptophan * 3.3E-04 1: 1.6 1.59 0.79 0.53 (0.12, 0.29)

4-Hydroxyphenyl pyruvate * 6.6E-05 1: 0.5 1.8 0.6 -0.57 (-0.37, -0.08)

Palmitoleic acid 4.1E-06 1: 0.7 1.55 0.4 -0.66 (-0.42, -0.1)

Chenodeoxyglycocholate 1.9E-10 1: 4.8 6.73 1.46 0.76 (0.16, 0.44)

N-Methylnicotinamide 3.0E-04 1: 2.2 1.85 0.85 0.46 (0.02, 0.34)

Hypoxanthine * 1.8E-05 1: 2.4 2.11 1.02 0.51 (0.05, 0.34) *Retention time matches standard. r= correlation coefficient of a metabolite to high dose of GC.

These 7 variables in combination produced a ROC curve ¹⁴ with an AUC of 0.92 (Figure 3B). The new model (Figure 3) explained more of the variation between low GC and high GC groups (33%) compared to the earlier model (Figure 2) which explained only 4.3% of the variation. The 7 metabolites were positively correlated to glucocorticoid dose; of those, chenodeoxyglycocholate had the highest correlation value (r= 0.76) while N- methylnicotinamide had the lowest correlation value (r=0.46).

Discussion

Screening a biological sample for one or more of the biomarkers used in the methods of the present invention, the differences between patients receiving ≤5 and >5-15 mg daily prednisolone equivalent doses of glucocorticoid replacement were shown. 5 mg prednisolone equivalent doses of glucocorticoid replacement per day is widely regarded as 'physiological replacement', at 5 mg daily. I.e. the body is considered to typically produce about 5 mg prednisolone equivalent per day glucocorticoid and as a result, the group of patients receiving more than 5 mg daily prednisolone equivalent doses of glucocorticoid replacement are receiving supra-physiological levels of glucocorticoid. Therefore, the methods of the invention are able to differentiate between subjects exposed to supra-physiological glucocorticoid levels and those that are not.

The ability of the methods of the invention to determine whether a subject has a supra- physiological level of glucocorticoid thereby allows those medical conditions that are either caused by exposure to supra-physiological levels of glucocorticoid (e.g. adrenal insufficiency), or that themselves cause supra-physiological levels of glucocorticoid (e.g. Cushing's syndrome) to be predicted and detected respectively.

By selecting individual metabolites which in combination could most reliably predict glucocorticoid dose, seven biomarkers were identified which in combination provide an AUROCC of 0.92 for a ROC curve ("best set").

The biomarkers used in the methods of the invention are substantially more sensitive than the non-specific clinical indicators presently in use, listed in Table 2. This is especially true of the best set of 7 biomarkers that had AUROCC values above 0.7 and a high contribution to the separation between the high GC and low GC groups and low within-group variability. Example 2

Samples from patients suffering from rheumatoid arthritis and that were being treated with prednisolone were also tested using biomarkers according to the invention. Method:

84 serum samples taken from patients with Rheumatoid arthritis treated with prednisolone were analysed by LC- MS/MS. The biomarkers hypoxanthine (HX), chenodeoxyglycocholate, N-methylnicotinamide (N-MNA), Tryptophan, 5-hydroxytryptophan (5-HT) and 4- hydroxyphenylpyruvate (4-HPP) were extracted from plasma (100 μΙ_) and analysed by LC- MS/MS as described below.

Samples were enriched with isotopically labelled standards of the biomarkers. Samples (100 μΙ_) were extracted by protein precipitation using acetonitrile on a PPT+ cartridge (Biotage, UK). The extract was reduced to dryness and resuspended in water/methanol (70:30; 100 μΙ_). 20 μΙ- was injected onto a Nexera UPLC system (Shimadzu, UK), and components separated using a Waters HSS T3 (100x2.1 mm; 1.8μηι) column at 0.4 mL/min, 40C, using a gradient from 0-90% organic mobile phase B over 12 minutes, where B is 0.1 % formic acid in methanol.

Mass analysis was performed on a QTrap 6500+ (Sciex, UK) following electrospray ionisation, in multiple reaction monitoring mode. Linear regression analysis of the peak area ratio of the biomarker to the isotopically labelled internal standards was used to calculate the amount of biomarker in each sample.

The samples were baseline blood samples of patients following omission of prednisolone for >36 hours (median 48 hours, range 36-96 hours), prior to synacthen testing (Borresen et al., 2017). The median dose of prednisolone used by patients was 5mg (range 5-20mg) The following data were made available:

1. Baseline Cortisol (nM)

2. Cortisol 30 mins following injection of synacthen (nM)

3. Pass or fail of synacthen test (binary)

4. Prednisolone dose (mg)

5. Time since last dose of prednisolone (h)

6. Measure of 4 metabolites (nM) assessed by LC-MS/MS from the panel of 7 identified in Table 4:

a. Chenodeoxyglycocholate

b. N-Methylnicotinamide

c. Tryptophan d. Hypoxanthine

Two samples were excluded due to incomplete data set (no baseline Cortisol). Thus, data were included from 40 patients with intact adrenal function and 42 who did not produce adequate Cortisol (>430nM) 30 minutes following administration of 250microg Synacthen.

The values for the metabolites were not normally distributed and were Log transformed to assist with model fitting. Models were fitted using multivariate logistic regression using the glm function (using the binomial distribution) in R. The binary value for the result of the Synacthen test was the dependent variable and models were tested including the individual metabolites and prednisolone dose and withdrawal period as independent variables. Goodness of fit of the model was assessed by maximising Akaike Information Criteria (AIC) and aiming to minimise residual deviance. Models were then fit to the data and this predicted data set used to generate Received Operator Characteristic (ROC) curves and area under the curve values using the pROC package for each combination.

Results

Results are presented in Table 5 for four of the metabolites for which quantitative data were available. Logistic regression models are presented where the dose of prednisolone or the period of omission of prednisolone for the patient from which a given biological sample was taken was included in the regression model as an additional independent variable.

Table 5: List of biomarkers and combinations of biomarkers and their ability to predict the result of the synacthen test for patients as shown by the area under the curve (AUC) for Received Operator Characteristic curves (ROC) when considered alone and in combination with the prednisolone dose taken by the patient (Dose) or the withdrawal period (period of omission of prednisolone for that patient). Logistic regression models were fit where more than one predictorwas taken into account. Akaike Information Criteria (AIC) and residual deviances for each model are shown to demonstrate the relative goodness-of-fit of each model.

N-methylnicotinomide n n 0.544

(B)

y n 0.745 98 92.1 n y 0.669 1 16 1 10

Tryptophan (C) n n 0.493 - - y n 0.742 98 91 .9 n y 0.680 1 16 109.8

Hypoxanthine (D) n n 0.538 - - y n 0.744 97 91 .61 n y 0.641 1 16 109.7

A+B+C+D n n 0.571 122 1 12

y n 0.760 103 91 n y 0.669 120 108.9

Accordingly, the data confirms that it is possible to use the biomarkers disclosed to predict the outcome of the synacthen test, especially when the dose of steroid such as prednisolone or the period of omission of glucocorticoid prior to the test is taken into account.

Conclusion

Therefore, the inventors have shown that the biomarkers of the invention can be used to predict prednisolone dose in patients with congenital adrenal hyperplasia (CAH), and that they are associated with adrenal function as assessed by the synacthen test to thereby predict the outcome of that test for patients being treated with anti-inflammatory glucocorticoid therapy, for example with rheumatoid arthritis.

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Claims

A method of detecting supra-physiological levels of glucocorticoid in a biological sample, the method comprising the steps:

a) providing a biological sample from a subject;

b) determining the concentration in the biological sample of one or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L- glutamyl-taurine, tryptophan, dehydroquinate, 3-(4- hydroxyphenyl)pyruvic acid, alpha-N-phenylacetyl-L-glutamine, 4- hydroxy-2-oxopentanoate, asparagine, threonine, keto-glutaramic acid, N-methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate; and

wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of supra-physiological levels of glucocorticoid in the biological sample.

The method according to claim 1 , wherein the concentration of two, three, four, five or six biomarkers selected from the group consisting of asparagine, tryptophan, 4- hydroxyphenyl pyruvate, palmitoleic acid, chenodeoxyglycocholate, N- methylnicotinamide, and hypoxanthine are determined.

The method according to either claim 1 or claim 2, wherein the concentration of at least chenodeoxyglycocholate is determined. 4. The method according to claim 3, wherein the concentration of at least chenodeoxyglycocholate and asparagine are determined.

The method according to claim 4, wherein the concentration of at least chenodeoxyglycocholate, asparagine and hypoxanthine are determined.

The method according to any one preceding claim, wherein the biological sample is a blood sample The method according to any one preceding claim, wherein a significant difference in the determined concentration of pentadecanoic acid, palmitic acid, arachidic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 4- hydroxy-2-oxopentanoate, asparagine, N-methylnicotinamide, octanoylcarnitine, or chenodeoxyglycocholate is an increase of the determined concentration over the reference value.

The method according to any one preceding claim, wherein a significant difference in the determined concentration of tridecanoic acid, palmitoleic acid, inosine, uridine, 3- (4-hydroxyphenyl)pyruvic acid, alpha-N-phenylacetyl-L-glutamine, threonine, or keto- glutaramic acid is a decrease of the determined concentration over the reference value.

The method according to any one preceding claim, wherein the is a method of predicting risk of adrenal insufficiency in a subject wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of a risk of adrenal insufficiency.

The method according to any one of claims 1 to 8, wherein the method is a method of diagnosing Cushing's syndrome in a subject, wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of Cushing's syndrome in the subject.

The according to any one of claims 1 to 8, wherein the method is a method of diagnosing over or under replacement of glucocorticoids in a subject, wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of over or under treatment of glucocorticoids in the subject.

A method of treatment for over- or under-administration of corticosteroids in a subject, the method comprising the steps:

a) providing a biological sample from a subject;

d) adjusting the dosage of glucocorticoids for the subject.

A method of treatment of adrenal insufficiency in a subject, the method comprising the steps:

a) providing a biological sample from a subject;

wherein a significant difference in the concentration of the biomarkers in the biological sample compared to the reference values is indicative of adrenal insufficiency, wherein where a significant difference in the concentration of biomarkers in the biological sample is determined,

d) prescribing a glucocorticoid replacement therapy for the subject. A kit of parts comprising one or more standard solutions and a set of instructions, the one or more standard solutions comprising one or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 3-(4-hydroxyphenyl)pyruvic acid, alpha-N-phenylacetyl- L-glutamine, 4-hydroxy-2-oxopentanoate, asparagine, threonine, keto-glutaramic acid, N-methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate, wherein the or each biomarker in the one or more standard solutions is present at a predetermined concentration such that the standard solution can be used to calibrate an assay for the determination of concentration of the one or more biomarkers in a biological sample.

The kit according to claim 14, wherein The or each standard solution comprises two, three, four, five six or more biomarkers selected from the group consisting of tridecanoic acid, pentadecanoic acid, palmitic acid, arachidic acid, palmitoleic acid, 15(S)-hydroxyeicosatrienoic acid, docosahexaenoic acid, prostaglandin B1 , inosine, uridine, hypoxanthine, methionine, 5-L-glutamyl-taurine, tryptophan, dehydroquinate, 3-(4-hydroxyphenyl) pyruvic acid, alpha-N-phenylacetyl-L-glutamine, 4-hydroxy-2- oxopentanoate, asparagine, threonine, keto-glutaramic acid, N-methylnicotinamide, octanoylcarnitine, and chenodeoxyglycocholate.

The kit of parts according to claim 14 or claim 15 comprising a plurality of standard solutions.

17. The kit of parts according to claim 16, wherein each standard solution within the plurality of standard solutions comprise one or more biomarkers.

The kit of parts according to claim 17, wherein each standard solution within the plurality of standard solutions comprises the same one or more biomarkers at a different predetermined concentration than the other standard solutions within the plurality of standard solutions.