WO2017157893A1 - Diagnosis of urogenital tract infections - Google Patents

Diagnosis of urogenital tract infections Download PDF

Info

Publication number
WO2017157893A1
WO2017157893A1 PCT/EP2017/055918 EP2017055918W WO2017157893A1 WO 2017157893 A1 WO2017157893 A1 WO 2017157893A1 EP 2017055918 W EP2017055918 W EP 2017055918W WO 2017157893 A1 WO2017157893 A1 WO 2017157893A1
Authority
WO
WIPO (PCT)
Prior art keywords
metabolites
level
group
patient
metabolite
Prior art date
Application number
PCT/EP2017/055918
Other languages
French (fr)
Inventor
Nicolas Schauer
Ivana BLAZENOVIC
Martina JAHN
Dieter Jahn
Original Assignee
Technische Universität Braunschweig
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Technische Universität Braunschweig filed Critical Technische Universität Braunschweig
Publication of WO2017157893A1 publication Critical patent/WO2017157893A1/en

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/5308—Immunoassay; Biospecific binding assay; Materials therefor for analytes not provided for elsewhere, e.g. nucleic acids, uric acid, worms, mites
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/34—Genitourinary disorders
    • G01N2800/348—Urinary tract infections
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7057—(Intracellular) signaling and trafficking pathways
    • G01N2800/7066—Metabolic pathways

Definitions

  • the present invention relates to a method for diagnosing a urogenital tract infection (UTI) in a patient and to a method for determining whether a patient responds to a therapeutic treatment of a urogenital tract infection.
  • the present invention further relates to a kit for diagnosing a urogenital tract infection or for determining whether a patient responds to a therapeutic treatment of a urogenital tract infection.
  • Urinary tract infections are considered to be the most common bacterial infection and are responsible for 155 000 cases per year in Germany alone, where each UTI leads to costs of 1.000 €, making it one of the biggest health concerns of today. For women, the lifetime risk of having a UTI is greater than 50% and only 20% of all UTI occur in men. Escherichia coli still remains most common infecting organism in patients with uncomplicated UTI, although other pathogens are present as: Aerococci, Proteus, Staphylococci, Pseudomonas, Enterococci and Klebsiella. Positive urine culture combined with patients' symptoms has been a gold standard for diagnosis, however, it is not uncommon to be treated for UTI based on the symptoms alone.
  • diagnostic/prognostic models play a major role in recent years and are frequently being used to estimate an outcome of patients with a disease or health condition.
  • prognostic model usually involves two main steps: model building and validation.
  • the prognostic model is built based on a training or learning sample set after which the model is validated using test or validation samples.
  • the quality of a prognostic model is measured through its performance, i.e. the ability of the model to correctly predict the prognosis of a patient based on his observed predictors.
  • prognostic models to make predictions for individual patients is more accurate and so is often preferred (L. Holmberg and A. Vickers, PLoS medicine, 2013, 10, el001491).
  • Some examples of prognostic models include: Nottingham Prognostic Index, which is used to determine prognosis following breast cancer surgery (J. L.
  • the inventors of the present invention created a diagnostic model for UTI. This statistical model was successful in infection diagnosis on a randomized and unlabelled sample set with the help of UPLC-MS-based urine metabolomics. The obtained results confirm the potential of this method in quick and accurate clinical diagnostics.
  • the inventors of the present patent application found that the detection of the level of specific metabolites allow the diagnosis of UTI in a patient.
  • the present invention relates to a method of diagnosing a urinary tract infection (UTI) in a patient comprising the step of:
  • the present invention relates to a method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
  • the present invention relates to the use of one or more metabolites for diagnosing a urinary tract infection in a patient or for determining whether a patient responds to a therapeutic treatment of a urinary tract infection, wherein said one or more metabolites are selected from the group consisting of:
  • the present invention relates to a kit comprising means for determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
  • a method of treatment is provided based on the diagnosis of UTI or on determining whether a patient responds to a therapeutic treatment of a UTI.
  • the terms used herein are defined as described in "A multilingual glossary of biotechno logical terms: (IUPAC Recommendations)", Leuenberger, H.G.W, Nagel, B. and Kolbl, H. eds. (1995), Helvetica Chimica Acta, CH-4010 Basel, Switzerland).
  • urinary tract infection refers to an infection that affects part of the urinary tract. When it affects the lower urinary tract, it is known as a bladder infection (cystitis) and when it affects the upper urinary tract, it is known as kidney infection.
  • Symptoms from the lower urinary tract include pain, frequent urination, and the feeling the need to urinate despite having an empty bladder.
  • Symptoms of a kidney infection include fever and pain usually in addition to the symptoms of a lower urinary tract infection. Rarely the urine may appear bloody. In the very old and the very young, symptoms may be vague or non-specific.
  • a urinary tract infection is caused by bacteria, e.g. E. coli.
  • diagnosing a urinary tract infection means determining whether a patient shows signs of or suffers from a urinary tract infection.
  • patient refers to any subject for whom it is desired to know whether she or he suffers from a urinary tract infection.
  • the term "patient”, as used herein, refers to a subject suspected to be affected by a urinary tract infection.
  • the patient may be diagnosed to be affected by a urinary tract infection, i.e. diseased, or may be diagnosed to be not affected by a urinary tract infection, i.e. healthy.
  • the patient may further be prognosed to develop a urinary tract infection.
  • patient also refers to a subject which is affected by a urinary tract infection, i.e. diseased.
  • the patient may be retested for a urinary tract infection and may be diagnosed to be still affected by a urinary tract infection, i.e. diseased, or not affected by a urinary tract infection anymore, i.e. healthy, for example after therapeutic intervention. It should be noted that a patient that is diagnosed as being healthy, i.e. not suffering from a urinary tract infection, may possibly suffer from another disease not tested/known.
  • the patient may be any mammal, including both a human and another mammal, e.g. an animal such as a rabbit, mouse, rat, or monkey. Human patients are particularly preferred.
  • control subject refers to a subject known to be affected by a urinary tract infection (positive control), i.e. diseased.
  • control subject also refers to a subject known to be not affected by a urinary tract infection (negative control), i.e. healthy.
  • healthy subject means a subject which is known to be not affected by a urinary tract infection.
  • a (control) subject which is known to be healthy i.e. not suffering from a urinary tract infection, may possibly suffer from another disease not tested/known.
  • the (control) subject may be any mammal, including both a human and another mammal, e.g. an animal such as a rabbit, mouse, rat, or monkey. Human (control) subjects are particularly preferred.
  • treatment refers to any therapy which improves the health status and/or prolongs (increases) the lifespan of a patient.
  • Said therapy may eliminate the disease in a patient, arrest or slow the development of a disease in a patient, inhibit or slow the development of a disease in a patient, decrease the frequency or severity of symptoms in a patient, and/or decrease the recurrence in a patient who currently has or who previously has had a disease.
  • the treatment of a urinary tract infection encompasses the administration of a drug (e.g. an approved drug or a developmental drug).
  • Antibiotics e.g. nitrofurantoin or trimethoprim/sulfamethoxazole, are most frequently used to treat a urinary tract infection.
  • biological sample refers to any biological sample from a patient or (control) subject containing one or more metabolites selected from the group consisting of: heneicosanoyi-glycero-phospho-(glyceroi), thicnopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group.
  • the biological sample may be a body fluid sample or a body gas sample.
  • biological samples encompassed by the present invention are blood (e.g.
  • a sample may be a mixture of a blood sample and a urine sample.
  • Said biological samples may be provided by removing a body fluid from a patient or (control) subject, but may also be provided by using a previously isolated sample.
  • a blood sample may be taken from a patient or (control) subject by conventional blood collection techniques.
  • the biological sample e.g. urine sample or blood sample, may be obtained from a patient or (control) subject prior to the initiation of a therapeutic treatment, during the therapeutic treatment, and/or after the therapeutic treatment.
  • the biological sample is obtained from one or more (control) subjects, e.g. from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000 or 10.000 (control) subject(s), it is designated as "reference biological sample".
  • the reference biological sample is from the same source than the biological sample of the patient to be tested, e.g. both are blood samples or urine samples. It is further preferred that both are from the same species, e.g. from a human. It is also (alternatively or additionally) preferred that the measurements of the reference biological sample and the biological sample of the patient to be tested are identical, e.g. both have an identical volume. It is particularly preferred that the reference biological sample and the biological sample are from patients/(control) subjects of the same sex and age.
  • body fluid sample refers to any liquid sample derived from the body of a patient or (control) subject containing one or more metabolites selected from the group consisting of: heneicosanoyi-glycero-phospho-(glyceroi), thicnopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group.
  • Said body fluid sample may be a urine sample, blood sample, sputum sample, breast milk sample, cerebrospinal fluid (CSF) sample, cerumen (earwax) sample, gastric juice sample, mucus sample, endolymph fluid sample, perilymph fluid sample, peritoneal fluid sample, pleural fluid sample, saliva sample, sebum (skin oil) sample, semen sample, sweat sample, tears sample, cheek swab, vaginal secretion sample, liquid biopsy, or vomit sample including components or fractions thereof.
  • body fluid sample also encompasses body fluid fractions", e.g. blood fractions, urine fractions or sputum fractions.
  • the body fluid samples may be mixed or pooled.
  • a body fluid sample may be a mixture of a blood and a urine sample or a mixture of a blood and cerebrospinal fluid sample.
  • Said body fluid sample may be provided by removing a body liquid from a patient or (control) subject, but may also be provided by using previously isolated body fluid sample material.
  • the body fluid sample allows for a non-invasive analysis of a patient. It is further preferred that the body fluid sample has a volume of between 0.01 and 20 ml, more preferably of between 0.1 and 10 ml, even more preferably of between 0.5 and 8 ml, and most preferably of between 1 and 5 ml. If the body fluid sample is obtained from one or more control subjects, e.g.
  • blood sample encompasses a whole blood sample or a blood fraction sample such as a blood cell fraction, blood serum, or blood plasma sample.
  • Blood cells also known as hemopoietic cells, may be used. Said blood cells may be erythrocytes, leukocytes, and/or thrombocytes, e.g. mixtures thereof.
  • PBMCs Peripheral blood mononuclear cells
  • the blood serum or plasma sample has a volume of between 0.01 and 20 ml, more preferably of between 0.1 and 10 ml, even more preferably of between 0.5 and 8 ml and most preferably of between 1 and 5 ml.
  • body gas sample refers to any gas sample derived from the body of a patient or (control) subject containing one or more metabolites selected from the group consisting of: heneicosanoyi-glycero-phospho-(glyceroi), thicnopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group.
  • Said body gas sample encompasses exhaled condensate and exhaled gas. If the body gas sample is obtained from one or more control subjects, e.g.
  • reference body gas sample from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000 or 10.000 control subject(s), it is designated as "reference body gas sample”.
  • level refers to an amount (measured for example in grams, mole, or ion counts) or concentration (e.g. absolute or relative concentration) of one or more metabolites selected from the group consisting of: heneicosanoy!-g!ycero-phospho-(glyceroi), thienopyndine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C- H group.
  • level also comprises scaled, normalized, or scaled and normalized amounts or values.
  • sensitivity refers to the number of true positive patients (%) with regard to the number of all patients (100%).
  • the patients may be individuals having a urinary tract infection.
  • the result of each analysis group is usually calculated from a plurality of isolated samples, i.e. from at least 2 isolated samples, preferably from between 2 and 20, more preferably from between 10 and 60, and even more preferably from between 50 and 100 isolated samples, selected from the group consisting of healthy subjects and patients having a urinary tract infection.
  • the methods of the present invention can be carried out in combination with other diagnostic methods for the detection of a urinary tract infection to increase the overall sensitivity and/or specificity.
  • the detection of the metabolites of the present invention allows a detection of a urinary tract infection.
  • AUC relates to an abbreviation for the area under a curve. In particular, it refers to the area under a Receiver Operating Characteristic (ROC) curve.
  • ROC Receiver Operating Characteristic
  • ROC Receiver Operating Characteristic
  • the area under an ROC curve is a measure for the accuracy of a diagnostic test (the larger the area the better, optimum is 1, a random test would have a ROC curve lying on the diagonal with an area of 0.5 (see, for reference, for example, JP. Egan. Signal Detection Theory and ROC Analysis).
  • mass spectrometry refers to the use of an ionization source to generate gas phase ions from a sample on a surface and detecting the gas phase ions with a mass spectrometer.
  • the mass spectrometry may be laser desorption mass spectrometry.
  • laser desorption mass spectrometry refers to the use of a laser as an ionization source to generate gas phase ions from a sample on a surface and detecting the gas phase ions with a mass spectrometer.
  • the mass spectrometry may be a matrix-assisted laser desorption/ionization mass spectrometry or MALDI.
  • the analyte is typically mixed with a matrix material that, upon drying, co-crystallizes with the analyte.
  • the matrix material absorbs energy from the energy source which otherwise would fragment the labile bio molecules or analytes.
  • the mass spectrometry may also be a surface-enhanced laser desorption/ionization mass spectrometry or SELDI.
  • SELDI the surface on which the analyte is applied plays an active role in the analyte capture and/or desorption.
  • tandem mass spectrometry refers to multiple rounds of mass spectrometry, usually separated by some form of molecule fragmentation.
  • one mass analyzer can isolate one analyte from many entering a mass spectrometer.
  • a second mass analyzer then stabilizes the analyte ions while they collide with a gas, causing them to fragment by collision- induced dissociation (CID).
  • CID collision- induced dissociation
  • a third mass analyzer sorts the fragments produced from the analytes. Tandem MS can also be done in a single mass analyzer over time, as in a quadrupole ion trap.
  • CID collision-induced dissociation
  • ECD electron capture dissociation
  • ETD electron transfer dissociation
  • IRMPD infrared multiphoton dissociation
  • BIRD blackbody infrared radiative dissociation
  • EDD electron-detachment dissociation
  • SID surface-induced dissociation
  • kit of parts in short: kit
  • kit is understood to be any combination of at least some of the components identified herein, which are combined, coexisting spatially, to a functional unit, and which can contain further components.
  • the inventors of the present invention developed a diagnostic model for UTI.
  • This statistical model was e.g. successful in infection diagnosis on a randomized and unlabelled sample set with the help of UPLC-MS-based urine metabolomics.
  • the obtained results confirm the potential of this method in quick and accurate clinical diagnostics.
  • the inventors of the present patent application found that the detection of the level of specific metabolites allow the diagnosis of UTI in a patient.
  • the present invention relates to a method of diagnosing a urinary tract infection (UTI) in a patient comprising the step of:
  • the level of one or more metabolites is compared to a reference level of one or more metabolites.
  • the level of one or more metabolites is compared to a reference level of said one or more metabolites.
  • the present invention relates to a method of diagnosing a urinary tract infection in a patient comprising the steps of:
  • thienopyridine and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group, and
  • the above comparison allows to diagnose a urinary tract infection in the patient.
  • the reference level may be any level which allows to determine whether a patients suffers from a urinary tract infection or not. It is preferred that the reference level is the level determined by measuring one or more reference biological samples from one or more healthy subjects. Preferably said reference biological samples are at least two reference biological samples, more preferably at least 2 to 100 reference biological samples, even more preferably at least 10 to 500 reference biological samples, and most preferably at least 50 to 10.000 reference biological samples, e.g.
  • said healthy subjects are at least two healthy subjects, more preferably at least 2 to 100 healthy subjects, even more preferably at least 10 to 500 healthy subjects, and most preferably at least 50 to 10.000 healthy subjects, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
  • reference biological sample per subject for analysis. If additional reference biological samples are required, e.g. to determine the reference level in different reference biological samples, the same subject may be (re)tested.
  • Said reference level may be an average reference level. It may be determined by measuring reference levels and calculating the "average" value (e.g. mean, median or modal value) thereof.
  • the level of one or more metabolites is compared to a reference level of (said) one or more metabolites.
  • Said reference level is the level determined by measuring a reference biological sample. For example, if the level of the metabolite heneicosanoyl-glycero- phospho-i glycerol) is determined in a biological sample from a patient, it is compared to a reference level of the metabolite heneicosanoyi-giycero-phospho-(giyceroi) determined in a reference biological sample.
  • the level of the metabolite heneicosanoyl-glycero-phospho-(glycerol) is compared to the reference level o f heneicosanoyi-glycero-phospho-(glycerol ) and the level of the metabolite thienopyridine is compared to the reference level of the metabolite thienopyridine determined in a reference biological sample.
  • the level of one or more metabolites above the reference level indicates that the patient has a urinary tract infection.
  • the level of one or more metabolites is at least 1.2 fold or at least 1.5-fold above the reference level, more preferably at least 2-fold above the reference level, even more preferably at least 3-fold or at least 4-fold above the reference level, and most preferably at least 4.5-fold above the reference level.
  • the level is at least 1 .2 fold above the reference level. It is more preferred that the level is at least 1.5-fold above the reference level. It is even more preferred that the level is at least 2-fold above the reference level.
  • the level is at least 1 .2 fold above the reference level. It is more preferred that the level is at least 1.5-fold above the reference level. It is even more preferred that the level is at least 2-fold above the reference level.
  • the metabolite heneicosanoyl-glycero-phospho- (glycerol ) it is preferred that the level is at least 2-fold above the reference level, it is more preferred that the level is at least -fold above the reference level. It is even more preferred that the level is at least 4 -fold above the reference level. It is most preferred that the level is at least 4.5-fold above the reference level.
  • an algorithm or mathematical function is applied to the level of one or more metabolites.
  • the present invention relates to a method of diagnosing a urinary tract infection in a patient comprising the steps of:
  • the above application allows to diagnose a urinary tract infection in the patient.
  • the algorithm or mathematical function is any algorithm or mathematical function which allows to decide if a urinary tract infection is present in the patient or not. It is preferred that the algorithm or mathematical function is obtained from
  • a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects.
  • reference biological samples are at least two reference biological samples, more preferably at least 2 to 100 reference biological samples, even more preferably at least 10 to 500 reference biological samples, and most preferably at least 50 to 10.000 reference biological samples, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97
  • said healthy subjects are at least two healthy subjects, more preferably at least 2 to 100 healthy subjects, even more preferably at least 10 to 500 healthy subjects, and most preferably at least 50 to 10.000 healthy subjects, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98
  • said subjects having a urinary tract infection are at least two subjects having a urinary tract infection, more preferably at least 2 to 100 subjects having a urinary tract infection, even more preferably at least 10 to 500 subjects having a urinary tract infection, and most preferably at least 50 to 10.000 subjects having a urinary tract infection, e.g.
  • machine learning approach involves the following steps:
  • the reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection and the reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects
  • the inventors of the present invention found that the application of a machine learning approach leads to the obtainment of an algorithm or a mathematical function that is trained by the reference level mentioned above which allows a better discrimination between healthiness and a urinary tract infection. In this way, the performance of patient's diagnosis can be improved.
  • Machine learning approaches may include, but are not limited to, supervised or unsupervised analysis: classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches), Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal probit regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression), Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.
  • classification techniques e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches
  • Regression techniques e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal
  • support vector machines are a set of related supervised learning methods which are preferably used for classification and regression. For example, given a set of training examples, each marked as belonging to one of two categories (e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection), an SVM algorithm builds a model that predicts whether a new example (e.g. sample to be tested) falls into one category or the other (e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection).
  • a new example e.g. sample to be tested
  • a SVM model is a representation of the training examples as points in space, mapped so that the training examples of the separate categories (e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection) are divided by a clear gap that is as wide as possible. New examples (e.g. samples to be tested) are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on (e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection).
  • the separate categories e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection
  • a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks.
  • a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.
  • the reference level of a subject having a urinary tract infection is higher than the reference level of a healthy subject.
  • the present invention relates to a method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
  • the patient is a patient to whom at least once (e.g. once, twice, or thrice/1 , 2, 3, 4, or 5 times) a drug to be used in said therapeutic treatment is administered or has been administered.
  • the drug to be used in said therapeutic treatment may be an antibiotic.
  • Antibiotics e.g. nitrofurantoin or trimethoprim/sulfamethoxazole, are most frequently used to treat a urinary tract infection.
  • the way of administration may be oral, nasal, rectal, parenteral, vaginal, or topical. Parental administration includes subcutaneous, intracutaneous, intramuscular, intravenous or intraperitoneal administration.
  • the biological sample is isolated from the patient after at least the first (e.g. first, second, third, fourth, or fifth) administration of said drug. It is particularly preferred that the biological sample is isolated from the patient in a time period of between 1 month and 1 day after at least the first (e.g. first, second, third, fourth, or fifth) administration of said drug. It is particularly more preferred that the biological sample is isolated from the patient in a time period of between 2 weeks and 1 day after at least the first (e.g. first, second, third, fourth, or fifth) administration of said drug. It is particularly even more preferred that the biological sample is isolated from the patient in a time period of between 1 week and 1 day after at least the first (e.g.
  • the level of one or more metabolites is compared to a reference level of one or more metabolites.
  • the level of one or more metabolites is compared to a reference level of said one or more metabolites.
  • the present invention relates to a method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the steps of:
  • a metabolite with the sum formula C9H17NO wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group, and
  • the above comparison allows to determine whether the patient responds to a therapeutic treatment of a urinary tract infection or not.
  • the reference level may be any level which allows to determine whether a patients response to a therapeutic treatment of a urinary tract infection or not.
  • the reference level is the level determined by measuring one or more reference biological samples from one or more subjects having a urinary tract infection.
  • said reference biological samples are at least two reference biological samples, more preferably at least 2 to 100 reference biological samples, even more preferably at least 10 to 500 reference biological samples, and most preferably at least 50 to 10.000 reference biological samples, e.g.
  • said subjects having a urinary tract infection are at least two subjects having a urinary tract infection, more preferably at least 2 to 100 subjects having a urinary tract infection, even more preferably at least 10 to 500 subjects having a urinary tract infection, and most preferably at least 50 to 10.000 subjects having a urinary tract infection, e.g.
  • reference biological sample per subject for analysis. If additional reference biological samples are required, e.g. to determine the reference level in different reference biological samples, the same subject may be (re)tested.
  • Said reference level may be an average reference level. It may be determined by measuring reference levels and calculating the "average" value (e.g. mean, median or modal value) thereof.
  • the reference level is the level determined in a reference biological sample isolated from the (same) patient, particularly prior to the administration of said drug. It is particularly preferred that the reference sample is isolated from the (same) patient in a time period of between 2 weeks and immediately prior to the administration of said drug.
  • the reference sample is isolated from the (same) patient in a time period of between 1 week and immediately prior to the administration of said drug. It is particularly even more preferred that the reference sample is isolated from the (same) patient in a time period of between 2 days and immediately prior to the administration of said drug. It is particularly most preferred that the reference sample is isolated from the (same) patient in a time period of between
  • the level of one or more metabolites is compared to a reference level of (said) one or more metabolites.
  • Said reference level is the level determined by measuring a reference biological sample. For example, if the level of the metabolite heneicosanoyl-glycero- phospho-(glyceroi) is determined in a biological sample from a patient, it is compared to a reference level of the metabolite heneicosanoyi-giycero-phospho-(glycerol) determined in a reference biological sample.
  • both lev els are compared to the respectiv e reference lev els, i.e.
  • the lev el o the metabolite heneicosanoyl-glycero-phospho-(glycerol) is compared to the reference level of heneicosanoyl-glycero-phospho-(glycerol) and the lev el of the metabolite thienopyridine is compared to the reference level of the metabolite thienopyridine determined in a reference biological sample.
  • the level of one or more metabolites below the reference level indicates that the patient response to said treatment of a urinary tract infection.
  • the urinary tract infection is improving or cured in the patient due to the therapeutic treatment.
  • the level of one or more metabolites is at least 1.2 fold or at least 1.5-fold below the reference level, more preferably at least 2-fold below the reference level, even more preferably at least 3 -fold or at least 4-fold below the reference level, and most preferably at least 4.5-fold below the reference level.
  • the level is at least 1.2 fold below the reference level. It is more preferred that the level is at least 1.5-fold below the reference level.
  • the level is at least 2-fold below the reference level.
  • the level is at least 1 .2 fold below the reference level. It is more preferred that the level is at least 1.5-fold below the reference level. It is even more preferred that the level is at least 2 -fold below the reference level.
  • the metabolite heneicosanoyl- glycero-phospho-(glycerol) it is preferred that the level is at least 2 -fold below the reference level . It is more preferred that the level is at least 3-fold below the reference level. It is even more preferred that the level is at least 4-fold below the reference level. It is most preferred that the level is at least 4.5-fold below the reference level.
  • the level of one or more metabolites may be comparable with the reference level. This indicates that the patient does not respond to said treatment of a urinary tract infection. Thus, the urinary tract infection is persistent in the patient.
  • “comparable with” means that the level of one or more metabolites varies between 0 and ⁇ 20%, e.g. 0, 0.1 , 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 19.9, 19.99, or 19.999%.
  • “Comparable with” in this respect alternatively means that the detected level variation is within the accuracy of a measurement. The accuracy of a measurement depends on the measurement method used.
  • the level of one or more metabolites may be above the reference level. This indicates that the patient does not respond to said treatment of a urinary tract infection. Thus, the urinary tract infection is progressing or worsening in the patient.
  • the level of one or more metabolites is at least 1.2 fold or at least 1.5-fold above the reference level, more preferably at least 2-fold above the reference level, even more preferably at least 3-fold or at least 4-fold above the reference level, and most preferably at least 4.5-fold above the reference level.
  • the level is at least 1 .2 fold above the reference level. It is more preferred that the level is at least 1.5-fold above the reference level.
  • the level is at least 2-fold above the reference level.
  • the level is at least 1 .2 fold above the reference level. I t is more preferred that the level is at least 1.5-fold above the reference level. It is even more preferred that the level is at least 2-fold above the reference level.
  • the metabolite heneicosanoyi-glycero-phospho- (glycerol) it is preferred that the level is at least 2 -fold above the reference level. It is more preferred that the level is at least 3-fold above the reference level. It is even more preferred that the level is at least 4-fold above the reference level. It is most preferred that the level is at least 4.5-fold above the reference level.
  • a renewed dose of the drug which is decreased compared to the previously administered dose of the drug may be administered, when the patient responds to the therapeutic treatment.
  • the administration of the drug may be stopped.
  • a renewed dose of the drug which is increased compared to the previously administered dose of the drug may be administered, when the patient does not respond to a therapeutic treatment.
  • another drug may be administered.
  • an algorithm or mathematical function is applied to the level of one or more metabolites.
  • the present invention relates to a method for determining whether a patient response to a therapeutic treatment of a urinary tract infection comprising the steps of:
  • a metabolite with the sum formula C9H17NO wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group, and
  • the above application allows to determine whether the patient response to a therapeutic treatment of a urinary tract infection or not.
  • the algorithm or mathematical function is any algorithm or mathematical function which allows to decide if a urinary tract infection is still present or not.
  • reference biological samples are at least two reference biological samples, more preferably at least 2 to 100 reference biological samples, even more preferably at least 10 to 500 reference biological samples, and most preferably at least 50 to 10.000 reference biological samples, e.g.
  • said healthy subjects are at least two healthy subjects, more preferably at least 2 to 100 healthy subjects, even more preferably at least 10 to 500 healthy subjects, and most preferably at least 50 to 10.000 healthy subjects, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
  • said subjects having a urinary tract infection are at least two subjects having a urinary tract infection, more preferably at least 2 to 100 subjects having a urinary tract infection, even more preferably at least 10 to 500 subjects having a urinary tract infection, and most preferably at least 50 to 10.000 subjects having a urinary tract infection, e.g.
  • the algorithm or mathematical function is obtained using a machine learning approach.
  • machine learning approach involves the following steps:
  • the reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection and the reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects, and (ii) computing an algorithm or a mathematical function based on said reference levels that is suitable to determine whether the patient responds to the therapeutic treatment of a urinary tract infection or not.
  • the reference level of a subject having a urinary tract infection is higher than the reference level of a healthy subject.
  • Machine learning approaches may include, but are not limited to, supervised or unsupervised analysis: classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches), Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal probit regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression), Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.
  • classification techniques e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches
  • Regression techniques e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal
  • the level of one or more metabolites in a biological sample from a patient is determined, wherein said one or more metabolites are selected from the group consisting of:
  • the metabolite heneieosanoy!-glycero-phospho-(glyecro!) encompasses heneicosanoyl-glycero-3-phospho-( 1 ' -glycerol )
  • the metabolite thienopyridine encompasses thieno-(2,3-c)pyridine, thieno-(2,3-b)pyridine, thieno-(3,2-c)pyridine, thieno-(3.2- b)pyridine, thieno[3,4-c]pyridine, and thieno[3,4-b]pyridine
  • the metabolite with the sum formula C9H17NO wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group encompasses 5 -( d i met hy 1 am i no )-3
  • the one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-3-phospho-( 1 " -glycerol ), thieno-(2,3-c)pyridine, and 5- (dimethylamino)-3 ,4-dimethyl- 1 -pentyn-3-ol.
  • the diagnostic power of a single biomarker is not sufficient to reach high AUC values for discrimination between healthiness and a urinary tract infection.
  • the inventors of the present invention surprisingly found that any of the metabolites heneicosanoy -glycero-3- phospho-( 1 ' -glycerol), t h ieno-( 2 ,3 -c )pyr idinc, or 5-(dimethylamino)-3.4-dimethyl- l -pentyn-3-ol provides high diagnostic power in the determination of a urinary tract infection in human patients.
  • Heneicosanoyl-glycero-3-phospho-(l '-glycerol) showed a AUC value of 0.9259 in the validation set ( Figure 4B).
  • Thieno-(2,3-c)pyridine showed a AUC value of 0.8882 in the validation set ( Figure 4A).
  • 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.8017 in the training set ( Figure 4C).
  • the level of the metabolites is even more preferred that the level of the metabolites
  • the level of the metabolites is determined. In particular, it is even most preferred that the level of the metabolites
  • the inventors of the present invention further employed a biomarker combination to further increase and/or improve the performance for diagnosing individuals suffering from a urinary tract infection.
  • the combination of the metabolites heneicosanoyl-glycero-3-phospho-(l '-glycerol), thieno-(2,3-c)pyridine, and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.9311 in the training set (Figure 5A) and a AUC value of 0.9656 in the validation set ( Figure 5B).
  • the patient may be a mammal.
  • the patient is a human.
  • the biological sample is a body fluid sample.
  • the body fluid sample is selected from the group consisting of a blood sample, a urine sample, and a combination thereof.
  • the blood sample is a whole blood sample or a blood fraction sample.
  • the blood fraction sample is a blood cell fraction sample, a blood serum sample, or a blood plasma sample.
  • the aforementioned biological samples are pre-treated before they are used in the methods of the first and second aspect of the present invention.
  • Said pre-treatment may include treatments required to separate the one or more metabolites described herein, or to remove excessive material or waste.
  • pre-treatments may aim at sterilizing biological samples and/or removing contaminants such as undesired cells, bacteria or viruses. Suitable techniques comprise centrifugation, extraction, fractioning, ultrafiltration, protein precipitation followed by filtration and purification and/or enrichment of compounds.
  • other pre-treatments are carried out in order to provide the one or more metabolites described herein in a form or concentration suitable for analysis.
  • gas-chromatography coupled mass spectrometry it will be required to derivatize the one or more metabolites described herein prior to said gas chromatography, or if liquid chromatography coupled mass spectrometry is used, it may be required to derivatize the one or more metabolites described herein prior to said liquid chromatography.
  • the level of the one or more metabolites described herein may be determined by spectrometry, chromatography, an enzymatic method, an immunochemical method, a gravimetric method, a chemosensoric method, or a combination thereof.
  • Suitable techniques include all chromatographic separation techniques such as liquid chromatography (LC), high performance liquid chromatography (HPLC), gas chromatography (GC), thin layer chromatography, or size exclusion or affinity chromatography. These techniques are well known in the art and can be applied by the person skilled in the art without further ado. Suitable devices for such determination are also well known in the art.
  • mass spectrometry is used in particular gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), direct infusion mass spectrometry or Fourier transform ion-cyclotrone-resonance mass spectrometry (FT-ICR-MS), capillary electrophoresis mass spectrometry (CE-MS), quadrupole mass spectrometry, any sequentially coupled mass spectrometry, such as MS-MS or MS-MS-MS, inductively coupled plasma mass spectrometry (ICP-MS), pyro lysis mass spectrometry (Py-MS), ion mobility mass spectrometry or time of flight mass spectrometry (TOF).
  • GC-MS gas chromatography mass spectrometry
  • LC-MS liquid chromatography mass spectrometry
  • FT-ICR-MS Fourier transform ion-cyclotrone-resonance mass spectrometry
  • CE-MS capillary electrophoresis mass spectrome
  • the following techniques may be used for determination: nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier transform infrared analysis (FT-IR), ultraviolet (UV) spectroscopy, refraction index (Rl), fluorescent detection, radiochemical detection, electrochemical detection, light scattering (LS), dispersive Raman spectroscopy or flame ionisation detection (FID).
  • NMR nuclear magnetic resonance
  • MRI magnetic resonance imaging
  • FT-IR Fourier transform infrared analysis
  • UV ultraviolet
  • Rl refraction index
  • fluorescent detection radiochemical detection
  • electrochemical detection electrochemical detection
  • light scattering LS
  • dispersive Raman spectroscopy or flame ionisation detection FID
  • the methods of the present invention shall be, preferably, assisted by automation.
  • biological sample processing or pre-treatment can be automated by robotics.
  • Data processing and comparison is, preferably, assisted by suitable computer programs and databases. Automation allows using the methods of the present invention in high-throughput approaches.
  • the level of the one or more metabolites described herein may also be determined by a specific chemical or biological assay.
  • Said assay shall comprise means which allow to specifically detect the level of the one or more metabolites described herein in the biological sample.
  • said means are capable of specifically recognizing the chemical structure of the one or more metabolites described herein, or are capable of specifically identifying the one or more metabolites described herein on its capability to react with other compounds or its capability to elicit a response in a biological read out system (e.g., induction of a reporter gene).
  • Means which are capable of specifically recognizing the chemical structure of the one or more metabolites described herein are, preferably, antibodies or other proteins which specifically interact with chemical structures, such as receptors or enzymes.
  • Said antibodies include both polyclonal and monoclonal antibodies, as well as fragments thereof, such as Fv, Fab and F(ab)2 fragments that are capable of binding the antigen or hapten.
  • Suitable proteins which are capable of specifically recognizing the one or more metabolites described herein are, preferably, enzymes which are involved in the conversion of said one or more metabolites. Said enzymes may use the one or more metabolites described herein as a substrate.
  • said antibodies may be used as a basis to generate oligopeptides which specifically recognize the one or more metabolites described herein. These oligopeptides shall, for example, comprise the enzyme s binding domains or pockets for the one or more metabolites described herein.
  • Suitable antibody and/or enzyme based assays may be RIA (radioimmunoassay), ELISA (enzyme- linked immunosorbent assay), sandwich enzyme immune tests, electrochemiluminescence sandwich immunoassays (ECLIA), dissociation- enhanced lanthanide fluoro immuno assay (DELFIA) or solid phase immune tests.
  • the one or more metabolites described herein may also be determined based on its capability to react with other molecules, i.e. by a specific chemical reaction. Further, the one or more metabolites described herein may be determined in a biological sample due to its capability to elicit a response in a biological read out system.
  • the biological response shall be detected as read out indicating the presence and/or the amount of the one or more metabolites described herein comprised in the biological sample.
  • the determination of the one or more metabolites described herein is a quantitative process, e.g. allowing also the determination of the amount of the one or more metabolites described herein in the sample. How to apply these techniques is well known to the person skilled in the art. Moreover, suitable devices are commercially available.
  • the level of one or more metabolites described herein may be determined by spectrometry and/or chromatography.
  • the level of one or more metabolites described herein may be determined by spectrometry and/or chromatography.
  • the spectrometry is mass spectrometry (MS), more preferably tandem mass spectrometry (MS/MS),
  • the chromatography is liquid chromatography (LC), gas chromatography (GC), or affinity chromatography, or
  • the chromatography is combined with spectrometry, more preferably mass spectrometry (MS), and is even preferably liquid chromatography-mass spectrometry (LC-MS) and most preferably liquid chromatography-tandem mass spectrometry (LC-MS/MS).
  • MS mass spectrometry
  • LC-MS liquid chromatography-mass spectrometry
  • LC-MS/MS liquid chromatography-tandem mass spectrometry
  • the biological sample used according to the present invention may have undergone chromatographic or other chemical processing before entering the mass spectrometer.
  • the level of one or more metabolites may also be determined by an immunoassay.
  • the immunoassay is an enzyme immunoassay, preferably an enzyme-linked immunosorbent assay (ELISA), a Western Blot (immunoblot), a radio immunoassay (RIA), or a luminescence immunoassay (LIA).
  • ELISA enzyme-linked immunosorbent assay
  • RIA radio immunoassay
  • LIA luminescence immunoassay
  • the present invention relates to the use of one or more metabolites for diagnosing a urinary tract infection in a patient or for determining whether a patient responds to a therapeutic treatment of a urinary tract infection, wherein said one or more metabolites are selected from the group consisting of:
  • the metabolite heneicosanoyi-glycero-phospho-(glyceroi) encompasses heneicosanoyl-glycero-3-phospho-(l '-glycerol)
  • the metabolite thienopyridine encompasses thieno-(2,3-c)pyridine, thieno-(2,3-b)pyridine, thieno-(3,2-c)pyridine, thieno-(3,2- b)pyridine, thieno[3,4-c]pyridine, and thieno [3, 4-b] pyridine
  • the metabolite with the sum formula C9H17NO wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group encompasses 5-(dimethylamino)-3,4- dimethyl- 1 -pentyn-3
  • the one or more metabolites are selected from the group consisting of: heneicosanoyi-giycero-3-phospho-(l '-glycerol), thieno-(2,3-c)pyridine, and 5- (dimethylamino)-3.4-dimethyl- ! -pentyn-3-oi.
  • Heneicosanoyl-glycero-3-phospho-(l '-glycerol) showed a AUC value of 0.9259 in the validation set ( Figure 4B).
  • Thieno-(2,3-c)pyridine showed a AUC value of 0.8882 in the validation set ( Figure 4A).
  • 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.8017 in the training set ( Figure 4C).
  • the level of the metabolites is even more preferred that the level of the metabolites
  • the patient may be a mammal.
  • the patient is a human.
  • the diagnosis of a urinary tract infection in a patient it is referred to the first aspect of the present invention.
  • determination whether a patient responds to a therapeutic treatment of a urinary tract infection it is referred to the second aspect of the present invention.
  • the present invention relates a kit comprising means for determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
  • thienopyndine and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group.
  • the metabolite heneicosanoyl-glycero-phospho-(glycerol) encompasses heneicosanoyl-glycero-3-phospho-( 1 ' -glycerol )
  • the metabolite thienopyridine encompasses thieno-(2,3-c)pyridine, t h ieno-( 2,3-b )pyrid i ne.
  • the one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-3-phospho-( 1 ' -glycerol ), thieno-(2.3-c)pyridine, and 5-
  • Heneicosanoyl-glycero-3-phospho-(l '-glycerol) showed a AUC value of 0.9259 in the validation set ( Figure 4B).
  • Thieno-(2,3-c)pyridine showed a AUC value of 0.8882 in the validation set ( Figure 4A).
  • 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.8017 in the training set ( Figure 4C).
  • the level of the metabolites is even more preferred that the level of the metabolites
  • the kit may be used for conducting the methods according to the first and/or second aspect of the present invention.
  • diagnosis of a urinary tract infection in a patient it is referred to the first aspect of the present invention.
  • determination whether a patient responds to a therapeutic treatment of a urinary tract infection it is referred to the second aspect of the present invention.
  • the means for determining the level of one or more metabolites in a biological sample from a patient encompass dipstrips or dipsticks, e.g. urine or blood dipstrips or dipsticks.
  • Said means are diagnostic tools used to determine changes in patient ' s urine or blood.
  • a dipstrip or dipst ick comprises different chemical pads or regents which react (e.g. change color, in particular by applying an immune assay) when immersed in (e.g. blood or urine), and then removed from the biological sample (e.g. urine or blood sample). The result can be read after a few minutes, preferably after a few seconds.
  • the patient may be a mammal.
  • the patient is a human.
  • the biological sample is a body fluid sample.
  • the body fluid sample is selected from the group consisting of a blood sample, a urine sample, and a combination thereof.
  • the blood sample is a whole blood sample or a blood fraction sample.
  • the blood fraction sample is a blood cell fraction sample, a blood serum sample, or a blood plasma sample.
  • the kit may further comprise
  • Said data carrier may be a non-electronical data carrier, e.g. a graphical data carrier such as an information leaflet, an information sheet, a bar code or an access code, or an electronical data carrier such as a floppy disk, a compact disk (CD), a digital versatile disk (DVD), a microchip or another semiconductor-based electronical data carrier.
  • the access code may allow the access to a database, e.g. an internet database, a centralized, or a decentralized database.
  • the access code may also allow access to an application software that causes a computer to perform tasks for computer users or a mobile app which is a software designed to run on smartphones and other mobile devices.
  • Said data carrier may further comprise a reference level of the one or more metabolites referred to herein.
  • the data carrier comprises an access code which allows the access to a database
  • said reference level is deposited in this database.
  • the data carrier may comprise information or instructions on how to carry out the methods according to the first and second aspect of the present invention.
  • Said kit may also comprise materials desirable from a commercial and user standpoint including a buffer(s), a reagent(s) and/or a diluent(s) for determining the level mentioned above.
  • the present invention relates to a method of treatment comprising the diagnosis of UTI or determining whether a patient responds to a therapeutic regimen of UTI and based on the level or amount determined treating the subject with a suitable treatment.
  • said treatment is administration of antibiotics.
  • Suitable antibiotics include trimethoprim/sulfamethoxazole, fosfomycin, nitrofurantoin, ciprofloxacin, levofloxacin, cephalexin, ceftriaxone, azithromycin, doxycyclinc.
  • a method of diagnosing a urinary tract infection (UTI) in a patient comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
  • the method of item 2, wherein the reference level is the level determined by measuring one or more reference biological samples from one or more healthy subjects.
  • a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects.
  • the reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection and the reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects
  • a method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
  • the method of item 10 wherein the patient is a patient to whom at least once a drug to be used in said therapeutic treatment is administered or has been administered.
  • the reference level is the level determined by measuring one or more reference biological samples from one or more subjects having a urinary tract infection.
  • a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects.
  • the reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection and the reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects
  • the blood fraction sample is a blood cell fraction sample, a blood serum sample, or a blood plasma sample.
  • the spectrometry is mass spectrometry (MS), preferably tandem mass spectrometry (MS/MS),
  • the chromatography is liquid chromatography (LC), gas chromatography (GC), or affinity chromatography, or
  • the chromatography is combined with spectrometry, preferably mass spectrometry (MS), and is more preferably liquid chromatography-mass spectrometry (LC-MS) and most preferably liquid chromatography-tandem mass spectrometry (LC-MS/MS).
  • MS mass spectrometry
  • LC-MS liquid chromatography-mass spectrometry
  • LC-MS/MS liquid chromatography-tandem mass spectrometry
  • a metabolite with the sum formula C9H17NO wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group.
  • the one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-3-phospho-( 1 ' -glycerol), thieno-(2,3-c)pyridine, and
  • a kit comprising means for determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
  • a metabolite with the sum formula C9H17NO wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group.
  • the kit of item 33 wherein the one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-3-phospho-( 1 " -glycerol), thieno-(2,3-e)pyndine, and
  • kit of items 33 or 34 wherein the kit is useful for conducting the methods according to any one of items 1 to 30.
  • kit of any one of items 33 to 35, wherein the kit further comprises
  • a method of treating urinary tract infection (UTI) in a patient comprising the step of: diagnosing UTI in said patient or determining whether said patient responds to a therapeutic treatment of UTI comprising the steps of:
  • a metabolite with the sum formula C9H17NO wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C ⁇ C-H group; diagnosing UTI when the level of one or more of said metabolites is above the reference lev el or determining that the patient does not respond to a therapeutic treatment of UTI when the lev el of said one or more metabolites is above the reference lev el;
  • any one of items 38 to 40 wherein the drug for treatment is selected from trimethoprim sulfamethoxazole, phosphomycin, nitrofurantoin, ciprofloxacin, levofloxacin, cephalexin, ceftriaxone, azithromycin, doxycycline.
  • Figure 1 Core plot of the PCA prognostic regression model built on spectral data from urine training set, coloured by infectious status.
  • First principal component accounts for 52.57 % of overall variability while the second principal component explains 27.76 % of overall variability.
  • Figure 2 Score plot of the PCA prognostic regression model built on spectral data from urine validation set, coloured by infectious status. First two principal components explain 83.93 % of overall variability.
  • Figure 3 Box plot for the levels of representative metabolites.
  • A Heneicosanoyl-glycero- 3-phospho-(l '-glycerol),
  • B thieno-(2,3-c)pyridine, and
  • C 5-(Dimethylamino)-3,4-dimethyl-l- pentyn-3-ol.
  • Figure 4 ROC curves of the biomarkers.
  • A Shows the ROC curve for Thieno-(2,3- c)pyridine in the validation set
  • B Shows the ROC curve for Heneicosanoyl-glycero-3-phospho- (1 '-glycerol) in the validation set
  • C shows the ROC curve for 5-(Dimethylamino)-3,4- dimethyl-l-pentyn-3-ol in test set.
  • FIG. 5 ROC curves of the biomarkers.
  • A Shows the ROC curve for the biomarker combination Thieno-(2,3-c)pyridine, Heneicosanoyl-glycero-3-phospho-(l '-glycerol), and 5- (Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol in the training set
  • B shows the ROC curve for the biomarker combination Thieno-(2,3-c)pyridine, Heneicosanoyl-glycero-3-phospho-(l '- glycerol), and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol in the validation set.
  • Urine samples were collected from February until March 2015 in a Urology and Urooncology clinic in Braunschweig, Germany. Clean mid-stream catch urine sample from patients was collected, 15 mL was taken for urine analysis performed by the clinic, part of it was stored at 4 °C for microbiology - related experiments at the University and the rest was stored at - 80 °C within 1 hour from the collection for further analysis. Samples were labelled as infected if the colony forming unit (CFU) number was >10 3 per mL of urine. All patients provided written informed consent for the collection of samples and subsequent analysis.
  • CFU colony forming unit
  • Urine samples were thawed, mixed by shaking and creatinine concentration in all samples was determined using Creatinine urinary Colorimetric Assay Kit (Cayman Chemical Company, Ann Arbor, USA). Urine samples were diluted with HPLC grade water (Sigma Aldrich, USA) to the lowest creatinine level measured in a sample, 50 mg/dL. After all the samples were normalized on creatinine level, 300 of urine was incubated at 37 °C while shaking at 1000 rpm for 10 min, mixed by shaking, 100 of urine was then added to 300 LC-MS grade methanol (Sigma Aldrich, USA) containing internal standards. Samples were then incubated at 37 °C while shaking at 1000 rpm for 15 min followed by centrifugation at 13500 rpm for 15 min. An aliquot of 200 was taken and stored at - 80 °C until LC-MS analysis.
  • Metabolite extracts were analysed on a UPLC-QTOF/MS system. Chromatography was performed using a ZIC®-pHILIC column 3.5 ⁇ , 200 A, 100 x 2.1 mm (Merck Sequant, Germany), operated by an Agilent 1290 UPLC system (Agilent, Santa Clara, USA).
  • the LC mobile phase was: A) 20 mM ammonium formate (Sigma- Aldrich, USA) in Water (Thermo, USA) with 5% Acetonitrile (Thermo, USA) (pH 9) and B) acetonitrile with 5% 20 mM ammonium formate in water with a gradient from 5% A to 35% at 0.5 min, to 75% at 1 min, to 85% at 1.3 min, to 95% at 2 min until 2.3 min, to 5% at 3 min until 4 min.
  • the flow rate was 300 with an injection volume of 1 ⁇ .
  • Mass spectrometry was performed using a high-resolution 6540 QTOF/MS Detector (Agilent, Santa Clara, USA) with a mass accuracy of ⁇ 2 ppm. Spectra were recorded in a mass range from 50 m/z to 1700 m/z at 2 GHz in extended dynamic range in both positive and negative ionization mode.
  • Candidate compounds were analysed in targeted manner by performing MS/MS analysis on the UPLC-QTOF/MS.
  • Candidate compounds were fragmented with the collision energies of 10 V, 20 V and 40 V respectively.
  • Spectra were recorded with a rate of 3 spectra/s. Obtained spectra were deconvoluted using decoMS2 and our in house MS/MS analysis pipeline.
  • the LC- MS data files were analysed using the untargeted approach described elsewhere ( B. Brand, et al, PloS one, 2015, 10, e0125044).
  • the peak picking and raw data processing was performed with Mass Hunter software package (Agilent, USA).
  • Targeted feature extraction (recursive workflow) and feature alignment and identification of the compounds was performed in Mass Profiler Professional.
  • the generated data matrix was then normalized on internal standards using R software package (R, Auckland, New Zealand).
  • Each collected sample was labelled as infected, healthy or blank.
  • Levels of 1783 metabolites in positive and 993 metabolites in negative mode were measured. In order to determine which metabolites can predict the infection status of the sample relation between the metabolite level and the infection status for each metabolite separately was considered. For each metabolite multiple linear regressions were performed, with metabolite level as an outcome. The task was to separate whether an infected status of a sample has an effect on the metabolite level, while accounting for the "blank" (extraction solvent, methanol containing internal standards) status of the sample as the confounder.
  • bladenk extraction solvent, methanol containing internal standards
  • Boolean infected status of a sample was used as one independent predictor and Boolean "blank" status of a sample was used as the second independent predictor (F. Eduati, et al, PloS one, 2010, 5).
  • the effect of the infected status on the metabolite level was analysed, accounting for the blank samples as potential confounders.
  • the strength of association between the metabolite level and the infected status was examined, by assessing the statistical significance of the coefficient for the infected status in the regression equation for the metabolite.
  • PCA principal component analysis
  • One of the biomarkers of this research is metabolite heneicosanoyl-glycero-3-phospho-(l '- glycerol) that is significantly increased in the infected samples as shown in Figure 3A.
  • Another interesting compound is thieno-(2,3-c)pyridine.
  • Figure 3B shows our findings that relative concentration of this compound is increased by more than 2 fold in the urine of the infected group.
  • h.3o7alnk Another interesting compound responsible for clustering infected samples from the healthy ones is 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol. Our findings show that its concentration is significantly elevated in the infected group as shown in Figure 3C.
  • Receiver operating characteristic (ROC) curve analysis is a standard method for assessing performance, and it is widely accepted as a statistically valid and object ive means for determining the clinical utility of a bio marker in metabolomic studies where area under the curve (AUC ) of the ROC curve facilitates the identificat ion of sensit ive and specific biological markers (J.
  • the binary logistic regression model was establ ished to obtain the receiver operating characterist ic (ROC ) curve of the combinatorial bio markers.
  • the AUC for thieno-(2,3-c)pyridine was 0.8882 in the validat ion set, as shown in Figure 4 A.
  • the AUC for heneicosanoyl-glycero-3-phospho-( 1 ' -glycerol ) was 0.9259 in the validation set, as shown in Figure 4B.
  • the AUC for 5-(Dimethylamino)-3,4-dimethyl-l-pentyn- 3-ol AUC was 0.8017 in the training set, as shown in Figure 4C.
  • the AUC for the combination of the metabolites thieno-(2,3-c)pyridine, heneicosanoyl- glycero-3-phospho-(l '-glycerol), and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol was also determined.
  • the AUC for thieno-(2,3-c)pyridine, heneicosanoyl-glycero-3-phospho-(l '-glycerol), and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol was 0.931 1 in the training set, as shown in Figure 5A
  • the AUC for thieno-(2,3-c)pyridine, heneicosanoyl-glycero-3-phospho-(l '- glycerol), and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol was 0.9656 in the validation set, as shown in Figure 5B.
  • biomarkers obtained with our UPLC-MS platform are biologically meaningful and suitable as non-invasive clinical biomarkers that can readily dist inguish UTI subjects from the healthy ones.
  • the results of the prediction confirm that the built model has good sensitivity for all measured samples, regardless of the pathogen origin.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Immunology (AREA)
  • Urology & Nephrology (AREA)
  • Hematology (AREA)
  • Biomedical Technology (AREA)
  • Chemical & Material Sciences (AREA)
  • Biochemistry (AREA)
  • Physics & Mathematics (AREA)
  • Microbiology (AREA)
  • Pathology (AREA)
  • Cell Biology (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Biotechnology (AREA)
  • General Health & Medical Sciences (AREA)
  • Endocrinology (AREA)
  • Biophysics (AREA)
  • Tropical Medicine & Parasitology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

The present invention relates to a method for diagnosing a urogenital tract infection in a patient and to a method for determining whether a patient responds to a therapeutic treatment of a urogenital tract infection. The present invention further relates to a kit for diagnosing a urogenital tract infection or for determining whether a patient responds to a therapeutic treatment of a urogenital tract infection.

Description

DIAGNOSIS OF UROGENITAL TRACT INFECTIONS
The present invention relates to a method for diagnosing a urogenital tract infection (UTI) in a patient and to a method for determining whether a patient responds to a therapeutic treatment of a urogenital tract infection. The present invention further relates to a kit for diagnosing a urogenital tract infection or for determining whether a patient responds to a therapeutic treatment of a urogenital tract infection.
BACKGROUND OF THE INVENTION
Urinary tract infections (UTIs) are considered to be the most common bacterial infection and are responsible for 155 000 cases per year in Germany alone, where each UTI leads to costs of 1.000€, making it one of the biggest health concerns of today. For women, the lifetime risk of having a UTI is greater than 50% and only 20% of all UTI occur in men. Escherichia coli still remains most common infecting organism in patients with uncomplicated UTI, although other pathogens are present as: Aerococci, Proteus, Staphylococci, Pseudomonas, Enterococci and Klebsiella. Positive urine culture combined with patients' symptoms has been a gold standard for diagnosis, however, it is not uncommon to be treated for UTI based on the symptoms alone. Contamination of the collected urine and misinterpretation of asymptomatic bacteriuria remain pitfalls in clinical diagnosis (M. Franz and W. H. Horl, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 1999, 14, 2746-2753) which often results in patients' unnecessary antibiotic exposure and overtreatment indicating that novel methods and approaches are much needed (B. Mishra, et al, International journal of clinical practice, 2012, 66, 493-498). In addition, in the very old and the very young, symptoms may be vague or non-specific.
To overcome some of the clinical bottlenecks, diagnostic/prognostic models play a major role in recent years and are frequently being used to estimate an outcome of patients with a disease or health condition.
The development of a prognostic model usually involves two main steps: model building and validation. The prognostic model is built based on a training or learning sample set after which the model is validated using test or validation samples. The quality of a prognostic model is measured through its performance, i.e. the ability of the model to correctly predict the prognosis of a patient based on his observed predictors. Using prognostic models to make predictions for individual patients is more accurate and so is often preferred (L. Holmberg and A. Vickers, PLoS medicine, 2013, 10, el001491). Some examples of prognostic models include: Nottingham Prognostic Index, which is used to determine prognosis following breast cancer surgery (J. L. Hybittle, et al, British journal of cancer, 1982, 45, 361-366), an Apgar score for new born babies wellbeing assessment and validated biomarkers used to predict future cardiovascular events (O. Melander et al, JAMA, 2009, 302, 49- 57). Application of mass spectrometers in clinical laboratories is broad because it is an accessible, versatile, and powerful technology that is best suited to solve research and analytical problems in a large number of scientific fields.
To overcome the above-described disadvantages, novel methods and approaches are much needed. The inventors of the present invention created a diagnostic model for UTI. This statistical model was successful in infection diagnosis on a randomized and unlabelled sample set with the help of UPLC-MS-based urine metabolomics. The obtained results confirm the potential of this method in quick and accurate clinical diagnostics. In particular, the inventors of the present patent application found that the detection of the level of specific metabolites allow the diagnosis of UTI in a patient.
SUMMARY OF THE INVENTION
In a first aspect, the present invention relates to a method of diagnosing a urinary tract infection (UTI) in a patient comprising the step of:
determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
In a second aspect, the present invention relates to a method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-glycero-phospho-(glycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
In a third aspect, the present invention relates to the use of one or more metabolites for diagnosing a urinary tract infection in a patient or for determining whether a patient responds to a therapeutic treatment of a urinary tract infection, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
In a fourth aspect, the present invention relates to a kit comprising means for determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyi-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
Further, a method of treatment is provided based on the diagnosis of UTI or on determining whether a patient responds to a therapeutic treatment of a UTI.
This summary of the invention does not necessarily describe all features of the present invention. Other embodiments will become apparent from a review of the ensuing detailed description. DETAILED DESCRIPTION OF THE INVENTION
Definitions
Before the present invention is described in detail below, it is to be understood that this invention is not limited to the particular methodology, protocols and reagents described herein as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention which will be limited only by the appended claims. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art.
Preferably, the terms used herein are defined as described in "A multilingual glossary of biotechno logical terms: (IUPAC Recommendations)", Leuenberger, H.G.W, Nagel, B. and Kolbl, H. eds. (1995), Helvetica Chimica Acta, CH-4010 Basel, Switzerland).
Several documents are cited throughout the text of this specification. Each of the documents cited herein (including all patents, patent applications, scientific publications, manufacturer's specifications, instructions, GenBank Accession Number sequence submissions etc.), whether supra or infra, is hereby incorporated by reference in its entirety. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention. In the event of a conflict between the definitions or teachings of such incorporated references and definitions or teachings recited in the present specification, the text of the present specification takes precedence.
The term "comprise" or variations such as "comprises" or "comprising" according to the present invention means the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers. The term "consisting essentially of according to the present invention means the inclusion of a stated integer or group of integers, while excluding modifications or other integers which would materially affect or alter the stated integer. The term "consisting of or variations such as "consists of according to the present invention means the inclusion of a stated integer or group of integers and the exclusion of any other integer or group of integers.
The terms "a" and "an" and "the" and similar reference used in the context of describing the invention (especially in the context of the claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.
The term "urinary tract infection", as used herein, refers to an infection that affects part of the urinary tract. When it affects the lower urinary tract, it is known as a bladder infection (cystitis) and when it affects the upper urinary tract, it is known as kidney infection. Thus, the term "urinary tract infection", as used herein, encompasses a bladder infection (cystitis) and a kidney infection (cystitis or pyelonephritis). Symptoms from the lower urinary tract include pain, frequent urination, and the feeling the need to urinate despite having an empty bladder. Symptoms of a kidney infection include fever and pain usually in addition to the symptoms of a lower urinary tract infection. Rarely the urine may appear bloody. In the very old and the very young, symptoms may be vague or non-specific. A urinary tract infection is caused by bacteria, e.g. E. coli.
The term "diagnosing a urinary tract infection", as used herein, means determining whether a patient shows signs of or suffers from a urinary tract infection.
The term "patient", as used herein, refers to any subject for whom it is desired to know whether she or he suffers from a urinary tract infection. In particular, the term "patient", as used herein, refers to a subject suspected to be affected by a urinary tract infection. The patient may be diagnosed to be affected by a urinary tract infection, i.e. diseased, or may be diagnosed to be not affected by a urinary tract infection, i.e. healthy. The patient may further be prognosed to develop a urinary tract infection. The term "patient", as used herein, also refers to a subject which is affected by a urinary tract infection, i.e. diseased. The patient may be retested for a urinary tract infection and may be diagnosed to be still affected by a urinary tract infection, i.e. diseased, or not affected by a urinary tract infection anymore, i.e. healthy, for example after therapeutic intervention. It should be noted that a patient that is diagnosed as being healthy, i.e. not suffering from a urinary tract infection, may possibly suffer from another disease not tested/known. The patient may be any mammal, including both a human and another mammal, e.g. an animal such as a rabbit, mouse, rat, or monkey. Human patients are particularly preferred.
The term "(control) subject", as used herein, refers to a subject known to be affected by a urinary tract infection (positive control), i.e. diseased. The term "(control) subject", as used herein, also refers to a subject known to be not affected by a urinary tract infection (negative control), i.e. healthy. Thus, the term "healthy subject", as used herein, means a subject which is known to be not affected by a urinary tract infection. It should be noted that a (control) subject which is known to be healthy, i.e. not suffering from a urinary tract infection, may possibly suffer from another disease not tested/known. The (control) subject may be any mammal, including both a human and another mammal, e.g. an animal such as a rabbit, mouse, rat, or monkey. Human (control) subjects are particularly preferred.
The term "treatment", in particular "therapeutic treatment", as used herein, refers to any therapy which improves the health status and/or prolongs (increases) the lifespan of a patient. Said therapy may eliminate the disease in a patient, arrest or slow the development of a disease in a patient, inhibit or slow the development of a disease in a patient, decrease the frequency or severity of symptoms in a patient, and/or decrease the recurrence in a patient who currently has or who previously has had a disease. The treatment of a urinary tract infection encompasses the administration of a drug (e.g. an approved drug or a developmental drug). Antibiotics, e.g. nitrofurantoin or trimethoprim/sulfamethoxazole, are most frequently used to treat a urinary tract infection.
The term "biological sample", as used herein, refers to any biological sample from a patient or (control) subject containing one or more metabolites selected from the group consisting of: heneicosanoyi-glycero-phospho-(glyceroi), thicnopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group. The biological sample may be a body fluid sample or a body gas sample. For example, biological samples encompassed by the present invention are blood (e.g. whole blood or blood fraction such as blood cell fraction, serum or plasma) samples, urine samples, or samples from other peripheral sources. Said biological samples may be mixed or pooled, e.g. a sample may be a mixture of a blood sample and a urine sample. Said biological samples may be provided by removing a body fluid from a patient or (control) subject, but may also be provided by using a previously isolated sample. For example, a blood sample may be taken from a patient or (control) subject by conventional blood collection techniques. The biological sample, e.g. urine sample or blood sample, may be obtained from a patient or (control) subject prior to the initiation of a therapeutic treatment, during the therapeutic treatment, and/or after the therapeutic treatment. If the biological sample is obtained from one or more (control) subjects, e.g. from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000 or 10.000 (control) subject(s), it is designated as "reference biological sample". Preferably, the reference biological sample is from the same source than the biological sample of the patient to be tested, e.g. both are blood samples or urine samples. It is further preferred that both are from the same species, e.g. from a human. It is also (alternatively or additionally) preferred that the measurements of the reference biological sample and the biological sample of the patient to be tested are identical, e.g. both have an identical volume. It is particularly preferred that the reference biological sample and the biological sample are from patients/(control) subjects of the same sex and age.
The term "body fluid sample", as used herein, refers to any liquid sample derived from the body of a patient or (control) subject containing one or more metabolites selected from the group consisting of: heneicosanoyi-glycero-phospho-(glyceroi), thicnopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group. Said body fluid sample may be a urine sample, blood sample, sputum sample, breast milk sample, cerebrospinal fluid (CSF) sample, cerumen (earwax) sample, gastric juice sample, mucus sample, endolymph fluid sample, perilymph fluid sample, peritoneal fluid sample, pleural fluid sample, saliva sample, sebum (skin oil) sample, semen sample, sweat sample, tears sample, cheek swab, vaginal secretion sample, liquid biopsy, or vomit sample including components or fractions thereof. The term "body fluid sample" also encompasses body fluid fractions", e.g. blood fractions, urine fractions or sputum fractions. The body fluid samples may be mixed or pooled. Thus, a body fluid sample may be a mixture of a blood and a urine sample or a mixture of a blood and cerebrospinal fluid sample. Said body fluid sample may be provided by removing a body liquid from a patient or (control) subject, but may also be provided by using previously isolated body fluid sample material. The body fluid sample allows for a non-invasive analysis of a patient. It is further preferred that the body fluid sample has a volume of between 0.01 and 20 ml, more preferably of between 0.1 and 10 ml, even more preferably of between 0.5 and 8 ml, and most preferably of between 1 and 5 ml. If the body fluid sample is obtained from one or more control subjects, e.g. from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000 or 10.000 control subject(s), it is designated as "reference body fluid sample". The term "blood sample", as used herein, encompasses a whole blood sample or a blood fraction sample such as a blood cell fraction, blood serum, or blood plasma sample. Blood cells, also known as hemopoietic cells, may be used. Said blood cells may be erythrocytes, leukocytes, and/or thrombocytes, e.g. mixtures thereof. Peripheral blood mononuclear cells (PBMCs) such as lymphocytes, monocytes, or macrophages may also be used. It is preferred that the blood serum or plasma sample has a volume of between 0.01 and 20 ml, more preferably of between 0.1 and 10 ml, even more preferably of between 0.5 and 8 ml and most preferably of between 1 and 5 ml.
The term "body gas sample", as used herein, refers to any gas sample derived from the body of a patient or (control) subject containing one or more metabolites selected from the group consisting of: heneicosanoyi-glycero-phospho-(glyceroi), thicnopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group. Said body gas sample encompasses exhaled condensate and exhaled gas. If the body gas sample is obtained from one or more control subjects, e.g. from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000 or 10.000 control subject(s), it is designated as "reference body gas sample".
The term "level", as used herein, refers to an amount (measured for example in grams, mole, or ion counts) or concentration (e.g. absolute or relative concentration) of one or more metabolites selected from the group consisting of: heneicosanoy!-g!ycero-phospho-(glyceroi), thienopyndine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C- H group. The term "level", as used herein, also comprises scaled, normalized, or scaled and normalized amounts or values.
The term "sensitivity", as used herein, refers to the number of true positive patients (%) with regard to the number of all patients (100%). The patients may be individuals having a urinary tract infection. The sensitivity is calculated by the following formula: Sensitivity= TP/(TP+FN) (TP= true positives; FN=false negatives).
The term "specificity", as used herein, relates to the number of true negative individuals (%>) with regard to the number of all healthy subjects (100%). The specificity is calculated by the following formula: Specificity= TN/(TN+FP) (TN= true negatives; FP=false positives).
The result of each analysis group is usually calculated from a plurality of isolated samples, i.e. from at least 2 isolated samples, preferably from between 2 and 20, more preferably from between 10 and 60, and even more preferably from between 50 and 100 isolated samples, selected from the group consisting of healthy subjects and patients having a urinary tract infection. The methods of the present invention can be carried out in combination with other diagnostic methods for the detection of a urinary tract infection to increase the overall sensitivity and/or specificity. The detection of the metabolites of the present invention allows a detection of a urinary tract infection.
The term "AUC", as used herein, relates to an abbreviation for the area under a curve. In particular, it refers to the area under a Receiver Operating Characteristic (ROC) curve. The term "Receiver Operating Characteristic (ROC) curve", as used herein, refers to a plot of the true positive rate against the false positive rate for the different possible cut points of a diagnostic test. It shows the trade-off between sensitivity and specificity depending on the selected cut point (any increase in sensitivity will be accompanied by a decrease in specificity). The area under an ROC curve is a measure for the accuracy of a diagnostic test (the larger the area the better, optimum is 1, a random test would have a ROC curve lying on the diagonal with an area of 0.5 (see, for reference, for example, JP. Egan. Signal Detection Theory and ROC Analysis).
The term "mass spectrometry (MS)", as used herein, refers to the use of an ionization source to generate gas phase ions from a sample on a surface and detecting the gas phase ions with a mass spectrometer. The mass spectrometry may be laser desorption mass spectrometry. The term "laser desorption mass spectrometry", as used herein, refers to the use of a laser as an ionization source to generate gas phase ions from a sample on a surface and detecting the gas phase ions with a mass spectrometer. The mass spectrometry may be a matrix-assisted laser desorption/ionization mass spectrometry or MALDI. In MALDI, the analyte is typically mixed with a matrix material that, upon drying, co-crystallizes with the analyte. The matrix material absorbs energy from the energy source which otherwise would fragment the labile bio molecules or analytes. The mass spectrometry may also be a surface-enhanced laser desorption/ionization mass spectrometry or SELDI. In SELDI, the surface on which the analyte is applied plays an active role in the analyte capture and/or desorption.
The term "tandem mass spectrometry (MS/MS)", as used herein, refers to multiple rounds of mass spectrometry, usually separated by some form of molecule fragmentation. For example, one mass analyzer can isolate one analyte from many entering a mass spectrometer. A second mass analyzer then stabilizes the analyte ions while they collide with a gas, causing them to fragment by collision- induced dissociation (CID). A third mass analyzer then sorts the fragments produced from the analytes. Tandem MS can also be done in a single mass analyzer over time, as in a quadrupole ion trap. There are various methods for fragmenting molecules for tandem MS, including collision-induced dissociation (CID), electron capture dissociation (ECD), electron transfer dissociation (ETD), infrared multiphoton dissociation (IRMPD), blackbody infrared radiative dissociation (BIRD), electron-detachment dissociation (EDD) and surface-induced dissociation (SID). In the context of the present invention, the term "kit of parts (in short: kit)" is understood to be any combination of at least some of the components identified herein, which are combined, coexisting spatially, to a functional unit, and which can contain further components. Embodiments of the invention
The present invention will now be further described. In the following passages different aspects of the invention are defined in more detail. Each aspect so defined may be combined with any other aspect or aspects unless clearly indicated to the contrary. In particular, any feature indicated as being preferred or advantageous may be combined with any other feature or features indicated as being preferred or advantageous, unless clearly indicated to the contrary.
The inventors of the present invention developed a diagnostic model for UTI. This statistical model was e.g. successful in infection diagnosis on a randomized and unlabelled sample set with the help of UPLC-MS-based urine metabolomics. The obtained results confirm the potential of this method in quick and accurate clinical diagnostics. In particular, the inventors of the present patent application found that the detection of the level of specific metabolites allow the diagnosis of UTI in a patient.
Thus, in a first aspect, the present invention relates to a method of diagnosing a urinary tract infection (UTI) in a patient comprising the step of:
determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
In one embodiment, the level of one or more metabolites is compared to a reference level of one or more metabolites. In particular, the level of one or more metabolites is compared to a reference level of said one or more metabolites. Thus, in one (particular) embodiment, the present invention relates to a method of diagnosing a urinary tract infection in a patient comprising the steps of:
(i) determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-glycero-phospho-(giycerol),
thienopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, and
(ii) comparing the level of one or more metabolites to a reference level of (said) one or more metabolites.
The above comparison allows to diagnose a urinary tract infection in the patient.
The reference level may be any level which allows to determine whether a patients suffers from a urinary tract infection or not. It is preferred that the reference level is the level determined by measuring one or more reference biological samples from one or more healthy subjects. Preferably said reference biological samples are at least two reference biological samples, more preferably at least 2 to 100 reference biological samples, even more preferably at least 10 to 500 reference biological samples, and most preferably at least 50 to 10.000 reference biological samples, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 reference biological samples. Preferably, said healthy subjects are at least two healthy subjects, more preferably at least 2 to 100 healthy subjects, even more preferably at least 10 to 500 healthy subjects, and most preferably at least 50 to 10.000 healthy subjects, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 healthy subjects. It is practicable to take one reference biological sample per subject for analysis. If additional reference biological samples are required, e.g. to determine the reference level in different reference biological samples, the same subject may be (re)tested. Said reference level may be an average reference level. It may be determined by measuring reference levels and calculating the "average" value (e.g. mean, median or modal value) thereof.
As mentioned above, the level of one or more metabolites is compared to a reference level of (said) one or more metabolites. Said reference level is the level determined by measuring a reference biological sample. For example, if the level of the metabolite heneicosanoyl-glycero- phospho-i glycerol) is determined in a biological sample from a patient, it is compared to a reference level of the metabolite heneicosanoyi-giycero-phospho-(giyceroi) determined in a reference biological sample. Alternat iv ely, if the level of the metabolite hcneicosanoyl-glycero- phospho-(glycerol) and the level of the metabolite thienopyridine is determined in a biological sample from a patient, both levels are compared to the respective reference levels, i.e. the level of the metabolite heneicosanoyl-glycero-phospho-(glycerol) is compared to the reference level o f heneicosanoyi-glycero-phospho-(glycerol ) and the level of the metabolite thienopyridine is compared to the reference level of the metabolite thienopyridine determined in a reference biological sample.
It is further preferred that the level of one or more metabolites above the reference level indicates that the patient has a urinary tract infection. Preferably, the level of one or more metabolites is at least 1.2 fold or at least 1.5-fold above the reference level, more preferably at least 2-fold above the reference level, even more preferably at least 3-fold or at least 4-fold above the reference level, and most preferably at least 4.5-fold above the reference level. In particular, with respect to the metabolite thienopyridine, it is preferred that the level is at least 1 .2 fold above the reference level. It is more preferred that the level is at least 1.5-fold above the reference level. It is even more preferred that the level is at least 2-fold above the reference level. With respect to the metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, it is preferred that the level is at least 1 .2 fold above the reference level. It is more preferred that the level is at least 1.5-fold above the reference level. It is even more preferred that the level is at least 2-fold above the reference level. With respect to the metabolite heneicosanoyl-glycero-phospho- (glycerol ), it is preferred that the level is at least 2-fold above the reference level, it is more preferred that the level is at least -fold above the reference level. It is even more preferred that the level is at least 4 -fold above the reference level. It is most preferred that the level is at least 4.5-fold above the reference level.
In an alternative embodiment, an algorithm or mathematical function is applied to the level of one or more metabolites. Thus, in an alternative embodiment, the present invention relates to a method of diagnosing a urinary tract infection in a patient comprising the steps of:
(i) determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
hencicosanoyl-glycero-phospho-(glyccrol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, w herein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, and
(ii) applying an algorithm or mathemat ical funct ion to the level of one or more metabolites. The above application allows to diagnose a urinary tract infection in the patient. The algorithm or mathematical function is any algorithm or mathematical function which allows to decide if a urinary tract infection is present in the patient or not. It is preferred that the algorithm or mathematical function is obtained from
a reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection, and
a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects.
Preferably said reference biological samples are at least two reference biological samples, more preferably at least 2 to 100 reference biological samples, even more preferably at least 10 to 500 reference biological samples, and most preferably at least 50 to 10.000 reference biological samples, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 reference biological samples. Preferably, said healthy subjects are at least two healthy subjects, more preferably at least 2 to 100 healthy subjects, even more preferably at least 10 to 500 healthy subjects, and most preferably at least 50 to 10.000 healthy subjects, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 healthy subjects. Preferably, said subjects having a urinary tract infection are at least two subjects having a urinary tract infection, more preferably at least 2 to 100 subjects having a urinary tract infection, even more preferably at least 10 to 500 subjects having a urinary tract infection, and most preferably at least 50 to 10.000 subjects having a urinary tract infection, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 subjects having a urinary tract infection. It is practicable to take one reference biological sample per subject for analysis. If additional reference biological samples are required, e.g. to determine the reference level in different reference biological samples, the same subject may be (re)tested. It is more preferred that the algorithm or mathematical function is obtained using a machine learning approach.
It is even more preferred that the machine learning approach involves the following steps:
(i) inputting
the reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection, and the reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects, and
(ii) computing an algorithm or a mathematical function based on said reference levels that is suitable to distinguish between a urinary tract infection and healthiness or to decide if a urinary tract infection is present in the patient or not.
The inventors of the present invention found that the application of a machine learning approach leads to the obtainment of an algorithm or a mathematical function that is trained by the reference level mentioned above which allows a better discrimination between healthiness and a urinary tract infection. In this way, the performance of patient's diagnosis can be improved.
Machine learning approaches may include, but are not limited to, supervised or unsupervised analysis: classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches), Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal probit regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression), Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.
In particular, support vector machines (SVMs) are a set of related supervised learning methods which are preferably used for classification and regression. For example, given a set of training examples, each marked as belonging to one of two categories (e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection), an SVM algorithm builds a model that predicts whether a new example (e.g. sample to be tested) falls into one category or the other (e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection). A SVM model is a representation of the training examples as points in space, mapped so that the training examples of the separate categories (e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection) are divided by a clear gap that is as wide as possible. New examples (e.g. samples to be tested) are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on (e.g. diseased, i.e. suffering from a urinary tract infection, or healthy, i.e. not suffering from a urinary tract infection). More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. A good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.
It is also preferred that the reference level of a subject having a urinary tract infection is higher than the reference level of a healthy subject.
In a second aspect, the present invention relates to a method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
It is preferred that the patient is a patient to whom at least once (e.g. once, twice, or thrice/1 , 2, 3, 4, or 5 times) a drug to be used in said therapeutic treatment is administered or has been administered. The drug to be used in said therapeutic treatment may be an antibiotic. Antibiotics, e.g. nitrofurantoin or trimethoprim/sulfamethoxazole, are most frequently used to treat a urinary tract infection. The way of administration may be oral, nasal, rectal, parenteral, vaginal, or topical. Parental administration includes subcutaneous, intracutaneous, intramuscular, intravenous or intraperitoneal administration.
It also preferred that the biological sample is isolated from the patient after at least the first (e.g. first, second, third, fourth, or fifth) administration of said drug. It is particularly preferred that the biological sample is isolated from the patient in a time period of between 1 month and 1 day after at least the first (e.g. first, second, third, fourth, or fifth) administration of said drug. It is particularly more preferred that the biological sample is isolated from the patient in a time period of between 2 weeks and 1 day after at least the first (e.g. first, second, third, fourth, or fifth) administration of said drug. It is particularly even more preferred that the biological sample is isolated from the patient in a time period of between 1 week and 1 day after at least the first (e.g. first, second, third, fourth, or fifth) administration of said drug, e.g. 1, 2, 3, 4, 5, 6, day(s), 1, 2, 3 week(s), or 1 month after at least the first (e.g. first, second, third, fourth, or fifth) administration of said drug. In one embodiment, the level of one or more metabolites is compared to a reference level of one or more metabolites. In particular, the level of one or more metabolites is compared to a reference level of said one or more metabolites. Thus, in one embodiment, the present invention relates to a method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the steps of:
(i) determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyi-glycero-phospho-(glyceroi),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, and
(ii) comparing the level of one or more metabolites to a reference level of (said) one or more metabolites.
The above comparison allows to determine whether the patient responds to a therapeutic treatment of a urinary tract infection or not.
The reference level may be any level which allows to determine whether a patients response to a therapeutic treatment of a urinary tract infection or not.
It is preferred that the reference level is the level determined by measuring one or more reference biological samples from one or more subjects having a urinary tract infection. Preferably said reference biological samples are at least two reference biological samples, more preferably at least 2 to 100 reference biological samples, even more preferably at least 10 to 500 reference biological samples, and most preferably at least 50 to 10.000 reference biological samples, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 reference biological samples. Preferably, said subjects having a urinary tract infection are at least two subjects having a urinary tract infection, more preferably at least 2 to 100 subjects having a urinary tract infection, even more preferably at least 10 to 500 subjects having a urinary tract infection, and most preferably at least 50 to 10.000 subjects having a urinary tract infection, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 subjects having a urinary tract infection. It is practicable to take one reference biological sample per subject for analysis. If additional reference biological samples are required, e.g. to determine the reference level in different reference biological samples, the same subject may be (re)tested. Said reference level may be an average reference level. It may be determined by measuring reference levels and calculating the "average" value (e.g. mean, median or modal value) thereof. It is alternatively preferred that the reference level is the level determined in a reference biological sample isolated from the (same) patient, particularly prior to the administration of said drug. It is particularly preferred that the reference sample is isolated from the (same) patient in a time period of between 2 weeks and immediately prior to the administration of said drug. It is particularly more preferred that the reference sample is isolated from the (same) patient in a time period of between 1 week and immediately prior to the administration of said drug. It is particularly even more preferred that the reference sample is isolated from the (same) patient in a time period of between 2 days and immediately prior to the administration of said drug. It is particularly most preferred that the reference sample is isolated from the (same) patient in a time period of between
1 day and immediately prior to the administration of said drug or between 1 hour and immediately prior to the administration of said drug, e.g. immediately, 10, 20, 30, 40, 50 minutes, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 hour(s), 1 , 2, 3, 4, 5, 6 day(s), 1, or
2 week(s) prior to the administration of said drug.
As mentioned above, the level of one or more metabolites is compared to a reference level of (said) one or more metabolites. Said reference level is the level determined by measuring a reference biological sample. For example, if the level of the metabolite heneicosanoyl-glycero- phospho-(glyceroi) is determined in a biological sample from a patient, it is compared to a reference level of the metabolite heneicosanoyi-giycero-phospho-(glycerol) determined in a reference biological sample. Alternatively, if the level of the metabolite heneicosanoyl-glycero- phospho-(glyceroi) and the level of the metabolite thienopyridine is determined in a biological sample from a patient, both lev els are compared to the respectiv e reference lev els, i.e. the lev el o the metabolite heneicosanoyl-glycero-phospho-(glycerol) is compared to the reference level of heneicosanoyl-glycero-phospho-(glycerol) and the lev el of the metabolite thienopyridine is compared to the reference level of the metabolite thienopyridine determined in a reference biological sample.
It is further preferred that the level of one or more metabolites below the reference level indicates that the patient response to said treatment of a urinary tract infection. Thus, the urinary tract infection is improving or cured in the patient due to the therapeutic treatment. Preferably, the level of one or more metabolites is at least 1.2 fold or at least 1.5-fold below the reference level, more preferably at least 2-fold below the reference level, even more preferably at least 3 -fold or at least 4-fold below the reference level, and most preferably at least 4.5-fold below the reference level. I n particular, with respect to the metabolite thienopyridine, it is preferred that the level is at least 1.2 fold below the reference level. It is more preferred that the level is at least 1.5-fold below the reference level. It is even more preferred that the level is at least 2-fold below the reference level. With respect to the metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C- II group, it is preferred that the level is at least 1 .2 fold below the reference level. It is more preferred that the level is at least 1.5-fold below the reference level. It is even more preferred that the level is at least 2 -fold below the reference level. With respect to the metabolite heneicosanoyl- glycero-phospho-(glycerol), it is preferred that the level is at least 2 -fold below the reference level . It is more preferred that the level is at least 3-fold below the reference level. It is even more preferred that the level is at least 4-fold below the reference level. It is most preferred that the level is at least 4.5-fold below the reference level.
Alternatively, the level of one or more metabolites may be comparable with the reference level. This indicates that the patient does not respond to said treatment of a urinary tract infection. Thus, the urinary tract infection is persistent in the patient. In this respect "comparable with" means that the level of one or more metabolites varies between 0 and < 20%, e.g. 0, 0.1 , 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 19.9, 19.99, or 19.999%. "Comparable with" in this respect alternatively means that the detected level variation is within the accuracy of a measurement. The accuracy of a measurement depends on the measurement method used.
Alternatively, the level of one or more metabolites may be above the reference level. This indicates that the patient does not respond to said treatment of a urinary tract infection. Thus, the urinary tract infection is progressing or worsening in the patient. Preferably, the level of one or more metabolites is at least 1.2 fold or at least 1.5-fold above the reference level, more preferably at least 2-fold above the reference level, even more preferably at least 3-fold or at least 4-fold above the reference level, and most preferably at least 4.5-fold above the reference level. In particular, with respect to the metabolite thienopyridine, it is preferred that the level is at least 1 .2 fold above the reference level. It is more preferred that the level is at least 1.5-fold above the reference level. It is even more preferred that the level is at least 2-fold above the reference level. With respect to the metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, it is preferred that the level is at least 1 .2 fold above the reference level. I t is more preferred that the level is at least 1.5-fold above the reference level. It is even more preferred that the level is at least 2-fold above the reference level. With respect to the metabolite heneicosanoyi-glycero-phospho- (glycerol), it is preferred that the level is at least 2 -fold above the reference level. It is more preferred that the level is at least 3-fold above the reference level. It is even more preferred that the level is at least 4-fold above the reference level. It is most preferred that the level is at least 4.5-fold above the reference level.
If the patient is a patient which receives or has received a drug as therapeutic treatment of a urinary tract infection, a renewed dose of the drug which is decreased compared to the previously administered dose of the drug may be administered, when the patient responds to the therapeutic treatment. Alternatively, the administration of the drug may be stopped.
If the patient is a patient which receives or has received a drug as therapeutic treatment of a urinary tract infection, a renewed dose of the drug which is increased compared to the previously administered dose of the drug may be administered, when the patient does not respond to a therapeutic treatment. Alternatively, another drug may be administered.
In an alternative embodiment, an algorithm or mathematical function is applied to the level of one or more metabolites. Thus, in an alternative embodiment, the present invention relates to a method for determining whether a patient response to a therapeutic treatment of a urinary tract infection comprising the steps of:
(i) determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-glycero-phospho-(glycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, and
(ii) applying an algorithm or mathematical function to the level of one or more metabolites.
The above application allows to determine whether the patient response to a therapeutic treatment of a urinary tract infection or not.
The algorithm or mathematical function is any algorithm or mathematical function which allows to decide if a urinary tract infection is still present or not.
It is preferred that the algorithm or mathematical function is obtained from
a reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection, and
a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects. Preferably said reference biological samples are at least two reference biological samples, more preferably at least 2 to 100 reference biological samples, even more preferably at least 10 to 500 reference biological samples, and most preferably at least 50 to 10.000 reference biological samples, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 reference biological samples. Preferably, said healthy subjects are at least two healthy subjects, more preferably at least 2 to 100 healthy subjects, even more preferably at least 10 to 500 healthy subjects, and most preferably at least 50 to 10.000 healthy subjects, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 healthy subjects. Preferably, said subjects having a urinary tract infection are at least two subjects having a urinary tract infection, more preferably at least 2 to 100 subjects having a urinary tract infection, even more preferably at least 10 to 500 subjects having a urinary tract infection, and most preferably at least 50 to 10.000 subjects having a urinary tract infection, e.g. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 150, 200, 250, 300, 400, 500, 1.000, 2.000, 3.000, 4.000, 5.000, or 10.000 subjects having a urinary tract infection. It is practicable to take one reference biological sample per subject for analysis. If additional reference biological samples are required, e.g. to determine the reference level in different reference biological samples, the same subject may be (re)tested.
It is more preferred that the algorithm or mathematical function is obtained using a machine learning approach.
It is even more preferred that the machine learning approach involves the following steps:
(i) inputting
the reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection, and the reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects, and (ii) computing an algorithm or a mathematical function based on said reference levels that is suitable to determine whether the patient responds to the therapeutic treatment of a urinary tract infection or not.
It is also preferred that the reference level of a subject having a urinary tract infection is higher than the reference level of a healthy subject.
Machine learning approaches may include, but are not limited to, supervised or unsupervised analysis: classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches), Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal probit regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression), Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.
In the methods of the first and second aspect of the present invention, the level of one or more metabolites in a biological sample from a patient is determined, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyi-glycero-phospho-(glyceroi),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
Preferably, the level of the metabolite
(i) heneicosanoyl-glycero-phospho-(giycerol),
(ii) thienopyridine, or
(iii) a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group is determined.
More preferably, the level of the metabolites
(i) heneicosanoyi-glycero-phospho-(glycerol) and thienopyridine,
(ii) heneicosanoyi-glycero-phospho-(giyceroi) and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, or
(iii) th ienopyridine and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group
is determined. Even more preferably, the level of the metabolites
heneicosanoyl-glycero-phospho-(glycerol), thienopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group is determined.
In this respect, it should be noted that the metabolite heneieosanoy!-glycero-phospho-(glyecro!) encompasses heneicosanoyl-glycero-3-phospho-( 1 '-glycerol ), the metabolite thienopyridine encompasses thieno-(2,3-c)pyridine, thieno-(2,3-b)pyridine, thieno-(3,2-c)pyridine, thieno-(3.2- b)pyridine, thieno[3,4-c]pyridine, and thieno[3,4-b]pyridine, and the metabolite with the sum formula C9H17NO , wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group encompasses 5 -( d i met hy 1 am i no )-3 ,4- dimethyl- 1 -pentyn-3-ol, 5-(dimethylamino)-3,4-dimethyl-l-pentyn-5-ol, and 5-( isobutyl-methyl- amino)- 1 -butyn-3-ol.
In particular, it is preferred that the one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-3-phospho-( 1 "-glycerol ), thieno-(2,3-c)pyridine, and 5- (dimethylamino)-3 ,4-dimethyl- 1 -pentyn-3-ol.
In particular, it is more preferred that the level of the metabolite
(i) heneicosanoyi-glycero-3-phospho-( 1 '-glycerol),
(ii) thieno-(2,3-c)pyridine, or
(iii) 5-(dimethylamino )-3 ,4-dimethyl- 1 -pentyn-3-o 1
is determined.
Usually the diagnostic power of a single biomarker is not sufficient to reach high AUC values for discrimination between healthiness and a urinary tract infection. However, the inventors of the present invention surprisingly found that any of the metabolites heneicosanoy -glycero-3- phospho-( 1 '-glycerol), t h ieno-( 2 ,3 -c )pyr idinc, or 5-(dimethylamino)-3.4-dimethyl- l -pentyn-3-ol provides high diagnostic power in the determination of a urinary tract infection in human patients. Heneicosanoyl-glycero-3-phospho-(l '-glycerol) showed a AUC value of 0.9259 in the validation set (Figure 4B). Thieno-(2,3-c)pyridine showed a AUC value of 0.8882 in the validation set ( Figure 4A). 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.8017 in the training set (Figure 4C).
In particular, it is even more preferred that the level of the metabolites
(i) heneicosanoy!-glycero-3-phospho-( I '-glycerol) and thieno-(2.3-c)pyridine,
(ii) heneicosanoyl-glycero-3-phospho-(l '-glycerol) and 5-(dimethylamino)-3.4-dimethyl- 1 - pentyn-3-ol, or
(iii) th ieno-( 2,3-c )pyridine and 5-(dimethyiamino)-3,4-dimethyi-l-pentyn-3-ol
is determined. In particular, it is even most preferred that the level of the metabolites
heneicosanoyi-glycero-3-phospho-(l '-glycerol), thieno-(2,3-c)pyridine, and 5-(dimethyiamino)- 3 ,4-dimethyl- 1 -pentyn-3-ol
is determined.
The inventors of the present invention further employed a biomarker combination to further increase and/or improve the performance for diagnosing individuals suffering from a urinary tract infection. The combination of the metabolites heneicosanoyl-glycero-3-phospho-(l '-glycerol), thieno-(2,3-c)pyridine, and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.9311 in the training set (Figure 5A) and a AUC value of 0.9656 in the validation set (Figure 5B).
In the methods of the present invention, the patient may be a mammal. Preferably, the patient is a human.
In the methods of the first and second aspect of the present invention, it is preferred that the biological sample is a body fluid sample. Preferably, the body fluid sample is selected from the group consisting of a blood sample, a urine sample, and a combination thereof. More preferably, the blood sample is a whole blood sample or a blood fraction sample. Even more preferably, the blood fraction sample is a blood cell fraction sample, a blood serum sample, or a blood plasma sample.
Preferably, the aforementioned biological samples are pre-treated before they are used in the methods of the first and second aspect of the present invention. Said pre-treatment may include treatments required to separate the one or more metabolites described herein, or to remove excessive material or waste. Furthermore, pre-treatments may aim at sterilizing biological samples and/or removing contaminants such as undesired cells, bacteria or viruses. Suitable techniques comprise centrifugation, extraction, fractioning, ultrafiltration, protein precipitation followed by filtration and purification and/or enrichment of compounds. Moreover, other pre-treatments are carried out in order to provide the one or more metabolites described herein in a form or concentration suitable for analysis. For example, if gas-chromatography coupled mass spectrometry is used, it will be required to derivatize the one or more metabolites described herein prior to said gas chromatography, or if liquid chromatography coupled mass spectrometry is used, it may be required to derivatize the one or more metabolites described herein prior to said liquid chromatography.
According to the present invention, the level of the one or more metabolites described herein may be determined by spectrometry, chromatography, an enzymatic method, an immunochemical method, a gravimetric method, a chemosensoric method, or a combination thereof. Suitable techniques include all chromatographic separation techniques such as liquid chromatography (LC), high performance liquid chromatography (HPLC), gas chromatography (GC), thin layer chromatography, or size exclusion or affinity chromatography. These techniques are well known in the art and can be applied by the person skilled in the art without further ado. Suitable devices for such determination are also well known in the art. For example, mass spectrometry is used in particular gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), direct infusion mass spectrometry or Fourier transform ion-cyclotrone-resonance mass spectrometry (FT-ICR-MS), capillary electrophoresis mass spectrometry (CE-MS), quadrupole mass spectrometry, any sequentially coupled mass spectrometry, such as MS-MS or MS-MS-MS, inductively coupled plasma mass spectrometry (ICP-MS), pyro lysis mass spectrometry (Py-MS), ion mobility mass spectrometry or time of flight mass spectrometry (TOF). As an alternative or in addition to mass spectrometry techniques, the following techniques may be used for determination: nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier transform infrared analysis (FT-IR), ultraviolet (UV) spectroscopy, refraction index (Rl), fluorescent detection, radiochemical detection, electrochemical detection, light scattering (LS), dispersive Raman spectroscopy or flame ionisation detection (FID). These techniques are well known to the person skilled in the art and can be applied without further ado. The methods of the present invention shall be, preferably, assisted by automation. For example, biological sample processing or pre-treatment can be automated by robotics. Data processing and comparison is, preferably, assisted by suitable computer programs and databases. Automation allows using the methods of the present invention in high-throughput approaches.
Moreover, the level of the one or more metabolites described herein may also be determined by a specific chemical or biological assay. Said assay shall comprise means which allow to specifically detect the level of the one or more metabolites described herein in the biological sample. Preferably, said means are capable of specifically recognizing the chemical structure of the one or more metabolites described herein, or are capable of specifically identifying the one or more metabolites described herein on its capability to react with other compounds or its capability to elicit a response in a biological read out system (e.g., induction of a reporter gene). Means which are capable of specifically recognizing the chemical structure of the one or more metabolites described herein are, preferably, antibodies or other proteins which specifically interact with chemical structures, such as receptors or enzymes. Said antibodies include both polyclonal and monoclonal antibodies, as well as fragments thereof, such as Fv, Fab and F(ab)2 fragments that are capable of binding the antigen or hapten. Suitable proteins which are capable of specifically recognizing the one or more metabolites described herein are, preferably, enzymes which are involved in the conversion of said one or more metabolites. Said enzymes may use the one or more metabolites described herein as a substrate. Moreover, said antibodies may be used as a basis to generate oligopeptides which specifically recognize the one or more metabolites described herein. These oligopeptides shall, for example, comprise the enzyme s binding domains or pockets for the one or more metabolites described herein. Suitable antibody and/or enzyme based assays may be RIA (radioimmunoassay), ELISA (enzyme- linked immunosorbent assay), sandwich enzyme immune tests, electrochemiluminescence sandwich immunoassays (ECLIA), dissociation- enhanced lanthanide fluoro immuno assay (DELFIA) or solid phase immune tests. Moreover, the one or more metabolites described herein may also be determined based on its capability to react with other molecules, i.e. by a specific chemical reaction. Further, the one or more metabolites described herein may be determined in a biological sample due to its capability to elicit a response in a biological read out system. The biological response shall be detected as read out indicating the presence and/or the amount of the one or more metabolites described herein comprised in the biological sample. Preferably, the determination of the one or more metabolites described herein is a quantitative process, e.g. allowing also the determination of the amount of the one or more metabolites described herein in the sample. How to apply these techniques is well known to the person skilled in the art. Moreover, suitable devices are commercially available.
As mentioned above, the level of one or more metabolites described herein may be determined by spectrometry and/or chromatography. Preferably,
(i) the spectrometry is mass spectrometry (MS), more preferably tandem mass spectrometry (MS/MS),
(ii) the chromatography is liquid chromatography (LC), gas chromatography (GC), or affinity chromatography, or
(iii) the chromatography is combined with spectrometry, more preferably mass spectrometry (MS), and is even preferably liquid chromatography-mass spectrometry (LC-MS) and most preferably liquid chromatography-tandem mass spectrometry (LC-MS/MS).
The biological sample used according to the present invention may have undergone chromatographic or other chemical processing before entering the mass spectrometer.
The level of one or more metabolites may also be determined by an immunoassay. Preferably, the immunoassay is an enzyme immunoassay, preferably an enzyme-linked immunosorbent assay (ELISA), a Western Blot (immunoblot), a radio immunoassay (RIA), or a luminescence immunoassay (LIA).
In a third aspect, the present invention relates to the use of one or more metabolites for diagnosing a urinary tract infection in a patient or for determining whether a patient responds to a therapeutic treatment of a urinary tract infection, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
Preferably, the level of the metabolite
(i) heneicosanoyi-glycero-phospho-(glycerol),
(ii) thienopyridine, or
(iii) a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group is determined.
More preferably, the level of the metabolites
(i) heneicosanoyl-glycero-phospho-(glycerol) and thienopyridine,
(ii) heneicosanoyi-giycero-phospho-(giyceroi) and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, or
(iii) thienopyridine and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group
is determined.
Even more preferably, the level of the metabolites
heneicosanoyi-giycero-phospho-(glyceroi), th ienopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group is determined.
In this respect, it should be noted that the metabolite heneicosanoyi-glycero-phospho-(glyceroi) encompasses heneicosanoyl-glycero-3-phospho-(l '-glycerol), the metabolite thienopyridine encompasses thieno-(2,3-c)pyridine, thieno-(2,3-b)pyridine, thieno-(3,2-c)pyridine, thieno-(3,2- b)pyridine, thieno[3,4-c]pyridine, and thieno [3, 4-b] pyridine, and the metabolite with the sum formula C9H17NO , wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group encompasses 5-(dimethylamino)-3,4- dimethyl- 1 -pentyn-3-ol, 5-(dimethylamino)-3,4-dimethyl-l-pentyn-5-ol, and 5-( isobutyl-methyl- amino)- 1 -butyn-3-ol. In particular, it is preferred that the one or more metabolites are selected from the group consisting of: heneicosanoyi-giycero-3-phospho-(l '-glycerol), thieno-(2,3-c)pyridine, and 5- (dimethylamino)-3.4-dimethyl- ! -pentyn-3-oi.
In particular, it is more preferred that the level of the metabolite
(i) heneicosanoyl-giycero-3-phospho-(l '-glycerol),
(ii) thieno-(2,3-c)pyridine, or
(iii) 5-(dimethylamino)-3 ,4-dimethyl- 1 -pentyn-3-o 1
is determined.
Heneicosanoyl-glycero-3-phospho-(l '-glycerol) showed a AUC value of 0.9259 in the validation set (Figure 4B). Thieno-(2,3-c)pyridine showed a AUC value of 0.8882 in the validation set (Figure 4A). 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.8017 in the training set (Figure 4C).
In particular, it is even more preferred that the level of the metabolites
(i) heneicosanoyl-glycero-3-phospho-( 1 '-glycerol) and th ieno-( 2,3-c )pyrid i ne,
(ii) heneicosanoyi-glycero-3-phospho-(l '-glycerol) and 5-(dimethyiamino)-3,4-dimethyl-l- pentyn-3-ol, or
(iii) thieno-(2,3-c)pyridine and 5-(dimethylamino)-3,4-dimethyi-l-pentyn-3-oi
is determined.
In particular, it is even most preferred that the level of the metabolites
heneicosanoyl-glycero-3-phospho-( 1 '-glycerol), thieno-(2,3-c)pyridine, and 5-(dimethyIamino)- 3 ,4-dimethyl- 1 -pentyn-3-ol
is determined.
The combination of the metabolites heneicosanoyl-glycero-3-phospho-(l '-glycerol), thieno-(2,3- c)pyridine, and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.9311 in the training set (Figure 5A) and a AUC value of 0.9656 in the validation set (Figure 5B).
The patient may be a mammal. Preferably, the patient is a human.
Regarding the diagnosis of a urinary tract infection in a patient, it is referred to the first aspect of the present invention. In addition, regarding the determination whether a patient responds to a therapeutic treatment of a urinary tract infection, it is referred to the second aspect of the present invention.
In a fourth aspect, the present invention relates a kit comprising means for determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyi-glycero-phospho-(glycerol),
thienopyndine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
Preferably, the level of the metabolite
(i) heneicosanoyl-glycero-phospho-(glycerol),
(ii) thienopyridine, or
(iii) a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group is determined.
More preferably, the level of the metabolites
(i) heneicosanoyl-glycero-phospho-(glycerol) and th ienopyridine,
(ii) heneicosanoyl-glyccro-phospho-(glycerol) and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group, or
(iii) thienopyridine and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiai amine group, and the metabolite comprises a -C≡C-H group
is determined.
Even more preferably, the level of the metabolites
heneicosanoyl-glycero-phospho-(glycerol), thienopyridine, and a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group is determined.
In this respect, it should be noted that the metabolite heneicosanoyl-glycero-phospho-(glycerol) encompasses heneicosanoyl-glycero-3-phospho-( 1 '-glycerol ), the metabolite thienopyridine encompasses thieno-(2,3-c)pyridine, t h ieno-( 2,3-b )pyrid i ne. th ieno-(3,2-c )pyridine, thieno-(3,2- b)pyridine, thieno[3,4-c]pyridine, and thieno[3,4-b]pyridine, and the metabolite with the sum formula C9H17NO , wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group encompasses 5-(dimethylamino)-3,4- dimethyl- 1 -pentyn-3-ol, 5-(dimethylamino)-3,4-dimethyl-l-pentyn-5-ol, and 5-( isobutyl-methyl- amino)- 1 -butyn-3-ol.
In particular, it is preferred that the one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-3-phospho-( 1 '-glycerol ), thieno-(2.3-c)pyridine, and 5-
(dimethyiamino)-3,4-dimethyi-l-pentyn-3-oi.
In particular, it is more preferred that the level of the metabolite
(i) heneicosanoy -g]ycero-3-phospho-( I '-glycerol),
(ii) thieno-(2,3-c)pyridine, or (iii) 5 -(dimethylamino)-3 ,4-dimethyl- 1 -pentyn-3 -o 1
is determined.
Heneicosanoyl-glycero-3-phospho-(l '-glycerol) showed a AUC value of 0.9259 in the validation set (Figure 4B). Thieno-(2,3-c)pyridine showed a AUC value of 0.8882 in the validation set (Figure 4A). 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.8017 in the training set (Figure 4C).
In particular, it is even more preferred that the level of the metabolites
(i) heneicosanoyi-glycero-3-phospho-(l '-glycerol) and thieno-(2,3-c)pyridine,
(ii) heneicosanoyl-glycero-3-phospho-(l '-glycerol) and 5-(dimethy!amino)-3,4-dimethyl- 1 - pentyn-3-oi, or
(iii) thieno-(2,3-c)pyridine and 5-(dimethylamino)-3,4-dimethyl-l-pentyn-3-ol
is determined.
In particular, it is even most preferred that the level of the metabolites
heneicosanoyl-glycero-3-phospho-(l '-glycerol), thieno-(2,3-c)pyridine, and 5 -( d i meth y 1 am i no )- 3.4-dimethyl- 1 -pentyn-3-ol
is determined.
The combination of the metabolites heneicosanoyl-glycero-3-phospho-(l '-glycerol), thieno-(2,3- c)pyridine, and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol showed a AUC value of 0.9311 in the training set (Figure 5A) and a AUC value of 0.9656 in the validation set (Figure 5B).
The kit may be used for conducting the methods according to the first and/or second aspect of the present invention. Regarding the diagnosis of a urinary tract infection in a patient, it is referred to the first aspect of the present invention. In addition, regarding the determination whether a patient responds to a therapeutic treatment of a urinary tract infection, it is referred to the second aspect of the present invention.
The means for determining the level of one or more metabolites in a biological sample from a patient encompass dipstrips or dipsticks, e.g. urine or blood dipstrips or dipsticks. Said means are diagnostic tools used to determine changes in patient 's urine or blood. A dipstrip or dipst ick comprises different chemical pads or regents which react (e.g. change color, in particular by applying an immune assay) when immersed in (e.g. blood or urine), and then removed from the biological sample (e.g. urine or blood sample). The result can be read after a few minutes, preferably after a few seconds.
The patient may be a mammal. Preferably, the patient is a human.
In the methods of the present invention, it is preferred that the biological sample is a body fluid sample. Preferably, the body fluid sample is selected from the group consisting of a blood sample, a urine sample, and a combination thereof. More preferably, the blood sample is a whole blood sample or a blood fraction sample. Even more preferably, the blood fraction sample is a blood cell fraction sample, a blood serum sample, or a blood plasma sample.
The kit may further comprise
(i) a container, and/or
(ii) a data carrier.
Said data carrier may be a non-electronical data carrier, e.g. a graphical data carrier such as an information leaflet, an information sheet, a bar code or an access code, or an electronical data carrier such as a floppy disk, a compact disk (CD), a digital versatile disk (DVD), a microchip or another semiconductor-based electronical data carrier. The access code may allow the access to a database, e.g. an internet database, a centralized, or a decentralized database. The access code may also allow access to an application software that causes a computer to perform tasks for computer users or a mobile app which is a software designed to run on smartphones and other mobile devices.
Said data carrier may further comprise a reference level of the one or more metabolites referred to herein.
In case that the data carrier comprises an access code which allows the access to a database, said reference level is deposited in this database.
In addition, the data carrier may comprise information or instructions on how to carry out the methods according to the first and second aspect of the present invention.
Said kit may also comprise materials desirable from a commercial and user standpoint including a buffer(s), a reagent(s) and/or a diluent(s) for determining the level mentioned above.
In a further aspect, the present invention relates to a method of treatment comprising the diagnosis of UTI or determining whether a patient responds to a therapeutic regimen of UTI and based on the level or amount determined treating the subject with a suitable treatment. In an embodiment, said treatment is administration of antibiotics. Suitable antibiotics include trimethoprim/sulfamethoxazole, fosfomycin, nitrofurantoin, ciprofloxacin, levofloxacin, cephalexin, ceftriaxone, azithromycin, doxycyclinc. In this connection, it is noted that when the level of the at least one metabolite is higher than the reference level, it is assumed that the pat ient is not responding to the therapeutic regimen and/or UTI can be diagnosed while in case of level or amounts lower than the reference level, it is submitted that a response in the patient occurs.
Finally, die invention is summarized as follows:
1. A method of diagnosing a urinary tract infection (UTI) in a patient comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group. The method of item 1, wherein the level of one or more metabolites is compared to a reference level of one or more metabolites.
The method of item 2, wherein the reference level is the level determined by measuring one or more reference biological samples from one or more healthy subjects.
The method of items 2 or 3, wherein the level of one or more metabolites above the reference level indicates that the patient has a urinary tract infection.
The method of any one of items 1 to 4, wherein an algorithm or mathematical function is applied to the level of one or more metabolites.
The method of item 5, wherein the algorithm or mathematical function is obtained from a reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection, and
a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects.
The method of items 5 or 6, wherein the algorithm or mathematical function is obtained using a machine learning approach.
The method of item 7, wherein the machine learning approach involves the following steps:
(i) inputting
the reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection, and the reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects, and
(ii) computing an algorithm or a mathematical function based on said reference levels that is suitable to distinguish between a urinary tract infection and healthiness or to decide if a urinary tract infection is present in the patient.
The method of any one of items 6 to 8, wherein the reference level of a subject having a urinary tract infection is higher than the reference level of a healthy subject.
A method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group. The method of item 10, wherein the patient is a patient to whom at least once a drug to be used in said therapeutic treatment is administered or has been administered.
The method of item 11, wherein the biological sample is isolated from the patient after at least the first administration of said drug.
The method of any one of items 10 to 12, wherein the level of one or more metabolites is compared to a reference level of one or more metabolites.
The method of item 13, wherein the reference level is the level determined by measuring one or more reference biological samples from one or more subjects having a urinary tract infection.
The method of items 13 or 14, wherein the reference level is the level determined in a reference biological sample isolated from the patient prior to the administration of said drug.
The method of any one of items 13 to 15, wherein the level of one or more metabolites below the reference level indicates that the patient response to said treatment of a urinary tract infection.
The method of any one or items 10 to 16, wherein an algorithm or mathematical function is applied to the level of one or more metabolites.
The method of item 17, wherein the algorithm or mathematical function is obtained from a reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection, and
a reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects.
The method of items 17 or 18, wherein the algorithm or mathematical function is obtained using a machine learning approach.
The method of item 19, wherein the machine learning approach involves the following steps:
(i) inputting
the reference level of one or more metabolites determined in one or more reference biological samples from one or more subjects having a urinary tract infection, and the reference level of one or more metabolites determined in one or more reference biological samples from one or more healthy subjects, and
(ii) computing an algorithm or a mathematical function based on said reference levels that is suitable to determine whether the patient responds to the therapeutic treatment of a urinary tract infection or not.
The method of any one of items 18 to 20, wherein the reference level of a subject having a urinary tract infection is higher than the reference level of a healthy subject.
The method of any one of items 1 to 21, wherein the one or more metabolites are selected from the group consisting of: heneicosanoyi-glycero-3-phospho-(l '-glycerol), thieno-(2,3- c (pyridine, and 5-(dimcthylamino)-3,4-dimethyl- l -pentyn-3-ol.
The method of item 22, wherein
the level of the metabolite
(i) heneicosanoyl-glycero-3-phospho-(l '-glycerol),
(ii) thieno-(2,3-c)pyridine, or
(iii) 5 -(dimethyl amino)-3 ,4-dimethyl- 1 -pentyn-3-o 1
is determined, or
the level of the metabolites
(i) heneicosanoyl-giycero-3-phospho-(l '-glycerol) and thieno-(2,3-c)pyridine,
(ii) heneicosanoyl-glycero-3-phospho-(l '-glycerol) and 5-(dimethylamino)-3,4- dimethyl- 1 -pentyn-3-ol,
(iii) thieno-(2,3-c)pyridine and 5-(dimethylamino)-3,4-dimethyl- l -pentyn-3-oi, or
(iv) heneicosanoyl-glycero-3-phospho-( 1 "-glycerol), th ieno-( 2,3-c )pyrid i ne, and 5- (dimethy1amino)-3,4-dimethyl- i -pentyn-3-ol
is determined.
The method of any one of items 1 to 23, wherein the patient is a mammal, preferably a human.
The method of any one of items 1 to 24, wherein the biological sample is a body fluid sample.
The method of item 25, wherein the body fluid sample is selected from the group consisting of a blood sample, a urine sample, and a combination thereof.
The method of item 26, wherein the blood sample is a whole blood sample or a blood fraction sample.
The method of item 27, wherein the blood fraction sample is a blood cell fraction sample, a blood serum sample, or a blood plasma sample. The method of any one of items 1 to 28, wherein the level of one or more metabolites is determined by spectrometry and/or chromatography.
The method of item 29, wherein
(i) the spectrometry is mass spectrometry (MS), preferably tandem mass spectrometry (MS/MS),
(ii) the chromatography is liquid chromatography (LC), gas chromatography (GC), or affinity chromatography, or
(iii) the chromatography is combined with spectrometry, preferably mass spectrometry (MS), and is more preferably liquid chromatography-mass spectrometry (LC-MS) and most preferably liquid chromatography-tandem mass spectrometry (LC-MS/MS).
Use of one or more metabolites for diagnosing a urinary tract infection in a patient or for determining whether a patient responds to a therapeutic treatment of a urinary tract infection, wherein said one or more metabolites are selected from the group consisting of: heneicosanoyi-glycero-phospho-(glyceroi),
thienopyridine. and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group. The use of item 31, wherein the one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-3-phospho-( 1 '-glycerol), thieno-(2,3-c)pyridine, and
5 -( d i met hyl am i no )-3 ,4-dimethyl- 1 -pentyn-3-o 1.
A kit comprising means for determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group. The kit of item 33, wherein the one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-3-phospho-( 1 "-glycerol), thieno-(2,3-e)pyndine, and
5-(dimethylamino)-3 ,4-dimethyl- 1 -pentyn-3-o 1.
The kit of items 33 or 34, wherein the kit is useful for conducting the methods according to any one of items 1 to 30.
The kit of any one of items 33 to 35, wherein the kit further comprises
(i) a container, and/or (ii) a data carrier.
37. The kit of item 36, wherein the data carrier comprises instructions on how to carry out the methods according to any one of items 1 to 30.
38. A method of treating urinary tract infection (UTI) in a patient comprising the step of: diagnosing UTI in said patient or determining whether said patient responds to a therapeutic treatment of UTI comprising the steps of:
determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-giycero-phospho-(giycerol),
thienopy idine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group; diagnosing UTI when the level of one or more of said metabolites is above the reference lev el or determining that the patient does not respond to a therapeutic treatment of UTI when the lev el of said one or more metabolites is above the reference lev el;
treating said patient with a drug approved for the treatment of UTI or changing the treatment regimen in view of non-responsiveness to the therapeutic treatment by changing the drug approved for UTI.
39. The method according to item 38 wherein the treatment is by antibiotics.
40. The treatment of item 38 or 39 wherein treatment is by nitrofurantoin or trimethoprim, . sul famcthoxazo lc .
41 . The method according to any one of items 38 to 40 wherein the drug for treatment is selected from trimethoprim sulfamethoxazole, phosphomycin, nitrofurantoin, ciprofloxacin, levofloxacin, cephalexin, ceftriaxone, azithromycin, doxycycline.
42. The method according to any one of items 38 to 41 wherein the dosage of drug, in particular antibiotics, is increased when non-responding to the therapeutic treatment.
BRIEF DESCRIPTION OF THE FIGURES
Figure 1: Core plot of the PCA prognostic regression model built on spectral data from urine training set, coloured by infectious status. First principal component accounts for 52.57 % of overall variability while the second principal component explains 27.76 % of overall variability.
Figure 2: Score plot of the PCA prognostic regression model built on spectral data from urine validation set, coloured by infectious status. First two principal components explain 83.93 % of overall variability. Figure 3: Box plot for the levels of representative metabolites. (A) Heneicosanoyl-glycero- 3-phospho-(l '-glycerol), (B) thieno-(2,3-c)pyridine, and (C) 5-(Dimethylamino)-3,4-dimethyl-l- pentyn-3-ol.
Figure 4: ROC curves of the biomarkers. (A) Shows the ROC curve for Thieno-(2,3- c)pyridine in the validation set, (B) Shows the ROC curve for Heneicosanoyl-glycero-3-phospho- (1 '-glycerol) in the validation set, and (C) shows the ROC curve for 5-(Dimethylamino)-3,4- dimethyl-l-pentyn-3-ol in test set.
Figure 5: ROC curves of the biomarkers. (A) Shows the ROC curve for the biomarker combination Thieno-(2,3-c)pyridine, Heneicosanoyl-glycero-3-phospho-(l '-glycerol), and 5- (Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol in the training set, and (B) shows the ROC curve for the biomarker combination Thieno-(2,3-c)pyridine, Heneicosanoyl-glycero-3-phospho-(l '- glycerol), and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol in the validation set.
EXAMPLES
1. Materials and methods
1.1 Study subjects and sampling
Urine samples were collected from February until March 2015 in a Urology and Urooncology clinic in Braunschweig, Germany. Clean mid-stream catch urine sample from patients was collected, 15 mL was taken for urine analysis performed by the clinic, part of it was stored at 4 °C for microbiology - related experiments at the University and the rest was stored at - 80 °C within 1 hour from the collection for further analysis. Samples were labelled as infected if the colony forming unit (CFU) number was >10 3 per mL of urine. All patients provided written informed consent for the collection of samples and subsequent analysis.
1.2 Sample preparation
1.2.1. Sample preparation for LC-MS analysis
Urine samples were thawed, mixed by shaking and creatinine concentration in all samples was determined using Creatinine urinary Colorimetric Assay Kit (Cayman Chemical Company, Ann Arbor, USA). Urine samples were diluted with HPLC grade water (Sigma Aldrich, USA) to the lowest creatinine level measured in a sample, 50 mg/dL. After all the samples were normalized on creatinine level, 300 of urine was incubated at 37 °C while shaking at 1000 rpm for 10 min, mixed by shaking, 100 of urine was then added to 300 LC-MS grade methanol (Sigma Aldrich, USA) containing internal standards. Samples were then incubated at 37 °C while shaking at 1000 rpm for 15 min followed by centrifugation at 13500 rpm for 15 min. An aliquot of 200 was taken and stored at - 80 °C until LC-MS analysis.
1.2.2 Metabolomics measurements
Metabolite extracts were analysed on a UPLC-QTOF/MS system. Chromatography was performed using a ZIC®-pHILIC column 3.5 μΜ, 200 A, 100 x 2.1 mm (Merck Sequant, Germany), operated by an Agilent 1290 UPLC system (Agilent, Santa Clara, USA). The LC mobile phase was: A) 20 mM ammonium formate (Sigma- Aldrich, USA) in Water (Thermo, USA) with 5% Acetonitrile (Thermo, USA) (pH 9) and B) acetonitrile with 5% 20 mM ammonium formate in water with a gradient from 5% A to 35% at 0.5 min, to 75% at 1 min, to 85% at 1.3 min, to 95% at 2 min until 2.3 min, to 5% at 3 min until 4 min. The flow rate was 300
Figure imgf000037_0001
with an injection volume of 1 μί. Mass spectrometry was performed using a high-resolution 6540 QTOF/MS Detector (Agilent, Santa Clara, USA) with a mass accuracy of < 2 ppm. Spectra were recorded in a mass range from 50 m/z to 1700 m/z at 2 GHz in extended dynamic range in both positive and negative ionization mode.
Candidate compounds were analysed in targeted manner by performing MS/MS analysis on the UPLC-QTOF/MS. Candidate compounds were fragmented with the collision energies of 10 V, 20 V and 40 V respectively. Spectra were recorded with a rate of 3 spectra/s. Obtained spectra were deconvoluted using decoMS2 and our in house MS/MS analysis pipeline. The LC- MS data files were analysed using the untargeted approach described elsewhere ( B. Brand, et al, PloS one, 2015, 10, e0125044). The peak picking and raw data processing was performed with Mass Hunter software package (Agilent, USA). Targeted feature extraction (recursive workflow) and feature alignment and identification of the compounds was performed in Mass Profiler Professional. The generated data matrix was then normalized on internal standards using R software package (R, Auckland, New Zealand).
It was then checked whether the metabolites were present in the in-house developed database using spectra matching (MS and MS/MS spectra were compared by visual inspection). Identification of bio markers was performed with our in-house developed database using the spectra matching protocol (I. Agilent Technologies, 2013). If the compounds were not present in the library, de novo identification was performed using the seven golden rules (T. Kind and O. Fiehn, BMC Bioinformatics, 2007, 8, 105, 1. Agilent Technologies, Agilent, 2011). In both cases, either library-matched or de novo identification, the possible candidates were compared against urine metabolome database (http://www.urinemetabolome.ca/) and literature data in order to ensure the sustainability of the findings. 1.2.3 Prognostic model
Each collected sample was labelled as infected, healthy or blank. Levels of 1783 metabolites in positive and 993 metabolites in negative mode were measured. In order to determine which metabolites can predict the infection status of the sample relation between the metabolite level and the infection status for each metabolite separately was considered. For each metabolite multiple linear regressions were performed, with metabolite level as an outcome. The task was to separate whether an infected status of a sample has an effect on the metabolite level, while accounting for the "blank" (extraction solvent, methanol containing internal standards) status of the sample as the confounder. Therefore, the healthy status was used as the reference, Boolean infected status of a sample was used as one independent predictor and Boolean "blank" status of a sample was used as the second independent predictor (F. Eduati, et al, PloS one, 2010, 5). The effect of the infected status on the metabolite level was analysed, accounting for the blank samples as potential confounders. After performing the regression for each metabolite, the strength of association between the metabolite level and the infected status was examined, by assessing the statistical significance of the coefficient for the infected status in the regression equation for the metabolite.
Since it was tested for multiple outcomes (each metabolite representing a single outcome), it was necessary to prevent the false positive rate from accumulating with each test. Therefore, multiple testing corrections had to be performed, thus controlling the family-wise error rate. To that effect, the Holm-Bonferroni correction procedure (M. Aickin and H. Gensler, American journal of public health, 1996, 86, 726-728) h.lpxezwc was employed, setting the maximum allowed FWER to a=0.05. The metabolites and the corresponding p-values for the infected status coefficients in the ascending order were sorted and from 1 to N enumerated. The boundary metabolite k, for which pk > a /(N+l-k) was chosen, all the hypotheses from k to N were rejected and 11 the hypotheses from 1 to k-1 were accepted. Hence, the association between the infected status of the samples and the metabolites heneicosanoyi-giycero-3-phospho-(l '-glycerol), thicno- (2,3-c)pyridine and 5-(Dimethyiamino)-3,4-dimethyl-l-pentyn-3-oi was established. It was statistically significant, with FWER < 0.05. 2. Results and discussion
A robust workflow to address the needs for a quick clinical prognosis of UTI in the context of urine metabolite profiling was developed. Moreover, it was of importance to determine if there are metabolic biomarkers which are present when UTI is diagnosed, regardless of the pathogen causing the infection. All urine samples underwent untargeted UPLC-MS analysis and the acquired data was used for multivariate statistical analysis and modelling. The data matrix was constructed with samples as observations and peak intensity as response variables for further analysis, accounting for 1034 peaks in total. Thus, training set, consisting of subjects' urine samples (28 healthy and 28 infected) was used to develop a prognostic model where, with the help of significant regression coefficients, statistically significant compounds were selected regardless of the pathogenic source. Out of 1034 metabolites, only 3 of them had p < 0.05 after the correction, which was the criterion for metabolites to be considered as potential bio markers. The next step was principal component analysis (PCA) on the training data set in order to decipher whether an unsupervised method of multivariate statistical analysis could discriminate groups. Therefore, we performed PCA built on the training set with 3 metabolites, shown in Figure 1. This PCA model yields a graphical display and offers an interpretation tool that enables investigation of the key metabolites of UTI. The scores plot of the first two components PCA model showed that UTI group could be separated from the UTI-free group.
Data for healthy group tended to cluster to the left part of the figure, whereas that for UTI group clustered to the right. This model provided good group separation, with first principal component accounts for 52,57 % of overall variability while the second principal component explains 27,76 % of overall variability.
In the next step, another PCA model with the results from the training set was built (Figure 2). Validation set consisted of 16 healthy and 20 infected urine samples. Trend separating infected samples from the non- infected samples is present in first two principal components, indicating that combination of 3 biomarkers is driving the dataset and contribute to the clustering. This model confirmed the results from the training set and the visual trend driving the data set into two clusters is clearly noticeable. According to the results given by the PCA scores plot of the validation set, this statistical model predicted 83,93 % which is explained with the first two principal components.
One of the biomarkers of this research is metabolite heneicosanoyl-glycero-3-phospho-(l '- glycerol) that is significantly increased in the infected samples as shown in Figure 3A. Another interesting compound is thieno-(2,3-c)pyridine. Figure 3B shows our findings that relative concentration of this compound is increased by more than 2 fold in the urine of the infected group. h.3o7alnk Another interesting compound responsible for clustering infected samples from the healthy ones is 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol. Our findings show that its concentration is significantly elevated in the infected group as shown in Figure 3C.
Although prev iously reported studies have focused on the discovery and/or v al idation of metabolomic biomarkers, they rarely pay attention to application of these biomarkers based on effective measures ( X. Liu, et a!., J. Proteome Res, 2015, 14, 2322-2330). Receiver operating characteristic ( ROC) curve analysis is a standard method for assessing performance, and it is widely accepted as a statistically valid and object ive means for determining the clinical utility of a bio marker in metabolomic studies where area under the curve (AUC ) of the ROC curve facilitates the identificat ion of sensit ive and specific biological markers (J. Peng, et ai., Anal Chem, 2014, 86, 6540-6547). For each statist ically significant metabolite, the binary logistic regression model was establ ished to obtain the receiver operating characterist ic ( ROC ) curve of the combinatorial bio markers. The AUC for thieno-(2,3-c)pyridine was 0.8882 in the validat ion set, as shown in Figure 4 A. The AUC for heneicosanoyl-glycero-3-phospho-( 1 '-glycerol ) was 0.9259 in the validation set, as shown in Figure 4B. The AUC for 5-(Dimethylamino)-3,4-dimethyl-l-pentyn- 3-ol AUC was 0.8017 in the training set, as shown in Figure 4C.
The AUC for the combination of the metabolites thieno-(2,3-c)pyridine, heneicosanoyl- glycero-3-phospho-(l '-glycerol), and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol was also determined. The AUC for thieno-(2,3-c)pyridine, heneicosanoyl-glycero-3-phospho-(l '-glycerol), and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol was 0.931 1 in the training set, as shown in Figure 5A, and the AUC for thieno-(2,3-c)pyridine, heneicosanoyl-glycero-3-phospho-(l '- glycerol), and 5-(Dimethylamino)-3,4-dimethyl-l-pentyn-3-ol was 0.9656 in the validation set, as shown in Figure 5B.
These findings reveal that biomarkers obtained with our UPLC-MS platform are biologically meaningful and suitable as non-invasive clinical biomarkers that can readily dist inguish UTI subjects from the healthy ones. The results of the prediction confirm that the built model has good sensitivity for all measured samples, regardless of the pathogen origin.

Claims

4U CLAIMS
A method of diagnosing a urinary tract infection (UTI) in a patient comprising the step of: determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyi-glycero-phospho-(glyceroi),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
The method of claim 1, wherein the level of one or more metabolites is compared to a reference level of one or more metabolites.
The method of claims 1 or 2, wherein an algorithm or mathematical function is applied to the level of one or more metabolites.
A method of determining whether a patient responds to a therapeutic treatment of a urinary tract infection comprising the step of:
determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-glycero-phospho-(glyceroi),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
The method of claim 4, wherein the patient is a patient to whom at least once a drug to be used in said therapeutic treatment is administered or has been administered.
The method of claim 5, wherein the biological sample is isolated from the patient after at least the first administration of said drug.
The method of any one of claims 4 to 6, wherein the level of one or more metabolites is compared to a reference level of one or more metabolites.
8. The method of any one or claims 4 to 7, wherein an algorithm or mathematical function is applied to the level of one or more metabolites.
9. The method of any one of claims 1 to 8, wherein the one or more metabolites are selected from the group consisting of: heneicosanoyl-giycero-3-phospho-(l '-glycerol), thieno-(2,3- c)pyridine, and 5-(dimethyiamino)-3,4-dimethyl-l-pentyn-3-ol.
10. The method of claim 9, wherein
the level of the metabolite
(i) heneicosanoyl-glycero-3-phospho-(l "-glycerol),
(ii) thieno-(2,3-c)pyridine, or
(iii) 5 -( d i m ethy I am i no )-3 ,4-dimethyl- 1 -pentyn-3 -o 1
is determined, or
the level of the metabolites
(i) heneicosanoyl-giycero-3-phospho-(l '-glycerol) and thieno-(2,3-c)pyridine,
(ii) heneicosanoyi-giycero-3-phospho-(l '-glycerol) and 5-(dimethyiamino)-3,4- dimethyl- 1 -pentyn-3-ol,
(iii) thieno-(2,3-c)pyridine and 5-(dimethyiamino)-3,4-dimethyl-l-pentyn-3-ol, or
(iv) heneieosanoyl-glyccro-3-phospho-( 1 "-glycerol), thieno-(2,3-c)pyridine, and 5- (dimethylamino)-3,4-dimethyl-l-pentyn-3-ol
is determined.
11. The method of any one of claims 1 to 10, wherein the biological sample is a body fluid sample, preferably selected from the group consisting of a blood sample, a urine sample, and a mixture thereof.
12. Use of one or more metabolites for diagnosing a urinary tract infection in a patient or for determining whether a patient responds to a therapeutic treatment of a urinary tract infection, wherein said one or more metabolites are selected from the group consisting of: heneicosanoyl-glycero-phospho-(glycerol),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
13. The use of claim 12, wherein the one or more metabolites are selected from the group consisting of: heneicosanoyi-glycero-3-phospho-(l '-glycerol), thicno-(2.3-c (pyridine, and 5-(dimethylamino)-3 ,4-dimcthyl- 1 -pentyn-3-o 1.
14. A kit comprising means for determining the level of one o more metabolites in a biological sample from a pat ient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-glycero-phospho-(glyceroi),
thienopyridine, and
a metabolite with the sum formula C9H17NO, wherein the O is present in a hydroxy group, the N is present in a tertiary amine group, and the metabolite comprises a -C≡C-H group.
15. The kit of claim 14, wherein the one or more metabolites are selected from the group consisting of: hencicosanoyl-glycero-3-phospho-( 1 "-glycerol), thieno-(2,3-c)pyridine, and 5 -( d i met hy I am i no )-3 ,4-dimethyl- 1 -pentyn-3-o 1.
1 6. A method of treating urinary tract infect ion (UTI) in a patient comprising the step of: diagnosing UTI in said patient or determining whether said patient responds to a therapeutic treatment of UTI comprising the steps of:
determining the level of one or more metabolites in a biological sample from a patient, wherein said one or more metabolites are selected from the group consisting of:
heneicosanoyl-glycero-phospho-(glycerol),
th ienopyridine, and
a metabolite with the sum formula Coll i -NO, wherein the O is present in a hydroxy group, the N is present in a te tiary amine group, and the metabolite comprises a -C≡C-H group; diagnosing UTI when the level of one or more of said metabolites is above the reference level or determining that the patient does not respond to a therapeutic treatment of UTI when the level of said one or more metabolites is abov e the reference lev el ;
treating said patient with a drug approv ed for the treatment of UTI or changing the treatment regimen in view of non-responsiveness to the therapeutic treatment by changing the drug approved for UTI.
1 7. The method according to claim 16 wherein the treatment is by ant ibiotics.
18. The treatment of claim 1 6 or 1 7 wherein treatment is by nitrofurantoin or trimethoprim suifamethoxazo ie .
19. The method according to any one of claims 16 to 18 wherein the drug for treatment is selected from trimethoprim/sulfamethoxazole, fosfomycin, nitrofurantoin, ciprofloxacin, levofloxacin, cephalexin, ceftriaxone, azithromycin, doxycycline.
20. The method according to any one of claims 16 to 19 wherein the dosage of drug, in particular ant ibiot ics, is increased when non-responding to the therapeutic treatment.
PCT/EP2017/055918 2016-03-14 2017-03-14 Diagnosis of urogenital tract infections WO2017157893A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP16160116 2016-03-14
EP16160116.6 2016-03-14

Publications (1)

Publication Number Publication Date
WO2017157893A1 true WO2017157893A1 (en) 2017-09-21

Family

ID=55587069

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2017/055918 WO2017157893A1 (en) 2016-03-14 2017-03-14 Diagnosis of urogenital tract infections

Country Status (1)

Country Link
WO (1) WO2017157893A1 (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050101023A1 (en) * 2003-03-28 2005-05-12 Rogers James A. Methods for diagnosing urinary tract and prostatic disorders

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050101023A1 (en) * 2003-03-28 2005-05-12 Rogers James A. Methods for diagnosing urinary tract and prostatic disorders

Non-Patent Citations (19)

* Cited by examiner, † Cited by third party
Title
"A multilingual glossary of biotechnological terms: (IUPAC Recommendations", 1995, HELVETICA CHIMICA ACTA
B. BRAND ET AL., PLOS ONE, vol. 10, 2015, pages E0125044
B. MISHRA ET AL., INTERNATIONAL JOURNAL OF CLINICAL PRACTICE, vol. 66, 2012, pages 493 - 498
F. EDUATI ET AL., PLOS ONE, vol. 5, 2010
FUKUI Y ET AL: "A metabonomic approach identifies human urinary phenylacetylglutamine as a novel marker of interstitial cystitis", JOURNAL OF CHROMATOGRAPHY B: BIOMEDICAL SCIENCES & APPLICATIONS, ELSEVIER, AMSTERDAM, NL, vol. 877, no. 30, 15 November 2009 (2009-11-15), pages 3806 - 3812, XP026708808, ISSN: 1570-0232, [retrieved on 20090926], DOI: 10.1016/J.JCHROMB.2009.09.025 *
I. AGILENT TECHNOLOGIES, 2011
I. AGILENT TECHNOLOGIES, 2013
J. L. HYBITTLE ET AL., BRITISH JOURNAL OF CANCER, vol. 45, 1982, pages 361 - 366
J. PENG ET AL., ANAL CHEM, vol. 86, 2014, pages 6540 - 6547
L. HOLMBERG; A. VICKERS, PLOS MEDICINE, vol. 10, 2013, pages E1001491
LAM CHING-WAN ET AL: "NMR-based metabolomic urinalysis: A rapid screening test for urinary tract infection", CLINICA CHIMICA ACTA, ELSEVIER BV, AMSTERDAM, NL, vol. 436, 6 June 2014 (2014-06-06), pages 217 - 223, XP029016157, ISSN: 0009-8981, DOI: 10.1016/J.CCA.2014.05.014 *
M. AICKIN; H. GENSLER, AMERICAN JOURNAL OF PUBLIC HEALTH, vol. 86, 1996, pages 726 - 728
M. FRANZ; W. H. HORL, NEPHROLOGY, DIALYSIS, TRANSPLANTATION : OFFICIAL PUBLICATION OF THE EUROPEAN DIALYSIS AND TRANSPLANT ASSOCIATION - EUROPEAN RENAL ASSOCIATION, vol. 14, 1999, pages 2746 - 2753
NANDA NEHA ET AL: "Novel biomarkers for the diagnosis of urinary tract infection-a systematic review", BIOMARKER INSIGHTS, LIBERTAS ACADEMICA LTD, NEW ZEALAND, vol. 4, 1 January 2009 (2009-01-01), pages 111 - 121, XP002598543, ISSN: 1177-2719 *
NISHIOKA M ET AL: "Determination and mutagenic activity of nitrogen-containing thiophenic compounds in coal-derived products", FUEL, IPC SCIENCE AND TECHNOLOGY PRESS, GUILDFORD, GB, vol. 65, no. 5, 1 May 1986 (1986-05-01), pages 711 - 714, XP025455262, ISSN: 0016-2361, [retrieved on 19860501], DOI: 10.1016/0016-2361(86)90369-8 *
O. MELANDER ET AL., JAMA, vol. 302, 2009, pages 49 - 57
T. KIND; O. FIEHN, BMC BIOINFORMATICS, vol. 8, 2007, pages 105
TIZIANA PACCHIAROTTA ET AL: "Exploratory analysis of urinary tract infection using a GC-APCI-MS platform", THE ANALYST, vol. 140, no. 8, 1 January 2015 (2015-01-01), GB, pages 2834 - 2841, XP055364931, ISSN: 0003-2654, DOI: 10.1039/C5AN00033E *
X. LIU ET AL., J. PROTEOME RES, vol. 14, 2015, pages 2322 - 2330

Similar Documents

Publication Publication Date Title
Cho et al. Identification of serum biomarkers for active pulmonary tuberculosis using a targeted metabolomics approach
Koomen et al. Plasma protein profiling for diagnosis of pancreatic cancer reveals the presence of host response proteins
Schwamborn et al. Serum proteomic profiling in patients with bladder cancer
JP2008545960A (en) Tuberculosis diagnosis
JP2011501133A (en) Method for detecting major cardiovascular or cerebrovascular adverse events
US11275093B2 (en) Biomarker for the diagnosis of pulmonary hypertension (PH)
JP2011501133A5 (en)
SG173310A1 (en) Apolipoprotein fingerprinting technique
JP2018504609A (en) Biomarkers for pancreatic cancer
CN113192552B (en) Active tuberculosis marker, kit, detection method and model construction method
Liang et al. Metabolomics of alcoholic liver disease: a clinical discovery study
US20220268776A1 (en) Biomarker panel for diagnosing pancreatic cancer and use thereof
US20130280733A1 (en) Biomarkers for the Diagnosis of ALS
Basso et al. Peptidomic and proteomic analysis of stool for diagnosing IBD and deciphering disease pathogenesis
EP3631453A1 (en) Novel stool-based protein biomarkers for colorectal cancer screening
JP2016526167A (en) Substances and methods related to Alzheimer&#39;s disease
Wu et al. Identification of serum biomarkers for ovarian cancer using MALDI–TOF-MS combined with magnetic beads
Niu et al. Specific serum protein biomarkers of rheumatoid arthritis detected by MALDI-TOF-MS combined with magnetic beads
CN112599239B (en) Metabolite marker and application thereof in cerebral infarction diagnosis
JP6389184B2 (en) Means and methods for determining clearance normalized amounts of metabolite disease biomarkers in a sample
WO2009156747A2 (en) Assay
WO2017157893A1 (en) Diagnosis of urogenital tract infections
US20180252706A1 (en) Novel biomarkers for diagnosis and progression of primary progressive multiple sclerosis (ppms)
WO2021015619A1 (en) Progression markers for colorectal adenomas
CN115372628B (en) Metabolic marker related to transthyretin amyloidosis and application thereof

Legal Events

Date Code Title Description
NENP Non-entry into the national phase

Ref country code: DE

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17709713

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 17709713

Country of ref document: EP

Kind code of ref document: A1