US20110065593A1 - Computer Methods and Devices for Detecting Kidney Damage - Google Patents

Computer Methods and Devices for Detecting Kidney Damage Download PDF

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US20110065593A1
US20110065593A1 US12/852,202 US85220210A US2011065593A1 US 20110065593 A1 US20110065593 A1 US 20110065593A1 US 85220210 A US85220210 A US 85220210A US 2011065593 A1 US2011065593 A1 US 2011065593A1
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analyte
sample
microglobulin
alpha
concentrations
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Samuel T. Labrie
James P. Mapes
Ralph L. McDade
Dominic Eisinger
Karri L. Ballard
Michael D. Spain
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Myriad RBM Inc
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Rules Based Medicine Inc
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Definitions

  • the invention encompasses methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal.
  • the present invention provides methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal.
  • the urinary system in particular the kidneys, perform several critical functions such as maintaining electrolyte balance and eliminating toxins from the bloodstream.
  • the pair of kidneys together process roughly 20% of the total cardiac output, amounting to about 1 L/min in a 70-kg adult male. Because compounds in circulation are concentrated in the kidney up to 1000-fold relative to the plasma concentration, the kidney is especially vulnerable to injury due to exposure to toxic compounds.
  • kidney injury is a major cause for delay during the development of candidate drugs.
  • regulatory agencies have required drug companies to provide results of blood urea nitrogen (BUN) and serum creatinine tests, two common diagnostic tests for renal function, to address concerns of potential kidney damage as part of the regulatory approval process.
  • BUN blood urea nitrogen
  • serum creatinine tests two common diagnostic tests for renal function, to address concerns of potential kidney damage as part of the regulatory approval process.
  • these diagnostic tests typically detect only late signs of kidney damage and provide little information as to the location of kidney damage.
  • kidney damage may also result from renal disorders such as kidney trauma, nephritis, kidney cancer, and kidney transplant rejection. Kidney damage may also occur as a secondary side effect of more systemic diseases such as diabetes, hypertension, and autoimmune diseases.
  • Existing diagnostic tests such as BUN and serum creatine tests typically detect only advanced stages of kidney damage.
  • Other diagnostic tests such as kidney tissue biopsies or CAT scans have the advantage of enhanced sensitivity to earlier stages of kidney damage, but these tests are also generally costly, slow, and/or invasive.
  • the detection of the early signs and locations of drug-induced kidney damage would be useful in guiding important decisions on lead compounds and dosage.
  • the early detection of kidney damage would help medical practitioners to diagnose and treat kidney damage more quickly and effectively.
  • the present invention provides computer methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal.
  • the present invention provides methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal.
  • One aspect of the present invention provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes providing a test sample that includes a sample of bodily fluid taken from the mammal, and determining the presence of a combination of three or more sample analytes in the test sample.
  • the analytes in the test sample may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • the combination of sample analytes is compared to the entries of a dataset in which each entry includes a combination of three or more diagnostic analytes reflective of a particular renal disorder.
  • the particular renal disorder of the mammal is identified as the renal disorder in the database having the combination of diagnostic analytes that essentially match the combination of sample analytes.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations for three or more sample analytes in the test sample.
  • the analytes may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • sample concentrations is compared to the entries of a dataset in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder.
  • Each minimum diagnostic concentration is the maximum concentration of a range of analyte concentrations for a healthy mammal.
  • a matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of calbindin, clusterin, CTGF, GST-alpha, KIM-1, and VEGF in the test sample.
  • the combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder.
  • a matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of beta-2 microglobulin, cystatin C, NGAL, osteopontin, and TIMP-1 in the test sample.
  • the combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder.
  • a matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of alpha-1 microglobulin, THP, and TFF-3 in the test sample.
  • the combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder.
  • a matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample comprising a sample of bodily fluid taken from the mammal and determining the concentrations of three or more sample analytes in a panel of biomarkers in the test sample.
  • the sample analytes may be selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • Diagnostic analytes are then identified in the test sample, wherein the diagnostic analytes are the sample analytes whose concentrations are statistically different from concentrations found in a control group of humans who do not suffer from a renal disorder.
  • the combination of diagnostic analytes are compared to a dataset comprising at least one entry, wherein each entry of the dataset comprises a combination of three or more diagnostic analytes reflective of a particular renal disorder.
  • the particular renal disorder in the list is identified as the renal disorder having the combination of diagnostic analytes that essentially match the combination of sample analytes.
  • An additional aspect provides a computer readable media encoded with an application that includes modules executable by a processor and configured to diagnose, monitor, or determine a renal disorder in a mammal.
  • An analyte input module receives three or more sample analyte concentrations that may include alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • a comparison module compares each sample analyte concentration to an entry of a renal disorder database, where each entry includes a list of minimum diagnostic concentrations reflective of a particular renal disorder.
  • An analysis module determines a most likely renal disorder by combining the particular renal disorders identified by the comparison module for all of the sample analyte concentrations.
  • Yet another aspect provides a system for diagnosing, monitoring, or determining a renal disorder in a mammal that includes a database to store a plurality of renal disorder database entries as well as a processing device that includes a renal disorder diagnosis application containing modules executable by the processing device.
  • the modules of the renal disorder diagnosis application include an analyte input module to receive three or more sample analyte concentrations selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • Another module compares each sample analyte concentration to an entry of the renal disorder database.
  • Each entry of the renal disorder database contains a list of minimum diagnostic concentrations reflective of a particular renal disorder.
  • An analysis module determines a most likely renal disorder by combining the particular renal disorders identified by the comparison module for all of the sample analyte concentrations.
  • An aspect provides a device for diagnosing, monitoring, or determining a renal disorder in a mammal that includes three or more antibodies and a plurality of indicators attached to each of the antibodies.
  • the antigenic determinants of the antibodies are analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • Another aspect provides a device for diagnosing, monitoring, or determining a renal disorder in a mammal that includes three or more capture antibodies, three or more capture agents, three or more detection antibodies, and three or more indicators.
  • the antigenic determinants of the capture antibodies are analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • One of the capture agents is attached to each of the capture antibodies, and includes an antigenic moiety.
  • the antigenic determinant of the detection antibodies is the antigenic moiety.
  • Each of the indicators is attached to one of the detection antibodies.
  • a final aspect provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes providing an analyte concentration measurement device that includes three or more detection antibodies, in which each detection antibody includes an antibody coupled to an indicator.
  • the antigenic determinants of the antibodies are sample analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • a test sample that contains three or more sample analytes and a bodily fluid taken from the mammal is provided and contacted with the detection antibodies.
  • the detection antibodies are allowed to bind to the sample analytes.
  • the concentrations of the sample analytes are determined by detecting the indicators of the detection antibodies bound to the sample analytes in the test sample.
  • the concentrations of each sample analyte are compared to a corresponding minimum diagnostic concentration reflective of a particular renal disorder.
  • FIG. 1 depicts four graphs comparing (A) the concentrations of alpha-1 microglobulin in the urine of normal controls, kidney cancer patients, and patients with other cancer types; (B) the concentrations of beta-2 microglobulin in the urine of normal controls, kidney cancer patients, and patients with other cancer types; (C) the concentrations of NGAL in the urine of normal controls, kidney cancer patients, and patients with other cancer types; and (D) the concentrations of THP in the urine of normal controls, kidney cancer patients, and patients with other cancer types.
  • FIG. 2 shows the four different disease groups from which samples were analyzed, and a plot of two different estimations on eGFR outlining the distribution within each group.
  • FIG. 3 is a number of scatter plots of results on selected proteins in urine and plasma. The various groups are indicated as follows—control: blue, AA: red, DN: green, GN: yellow, OU: orange.
  • A1M in plasma (B) cystatin C in plasma, (C) B2M in urine, (D) cystatin C in urine.
  • FIG. 4 depicts the multivariate analysis of the disease groups and their respective matched controls using plasma results. Relative importance shown using the random forest model.
  • FIG. 5 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish disease samples vs. normal samples.
  • FIG. 6 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish disease (AA+GN+ON+DN) samples vs. normal samples from plasma (A) and urine (B and C).
  • FIG. 7 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish analgesic abuse samples vs. normal samples. Abbreviations as in FIG. 4 .
  • FIG. 8 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish analgesic abuse samples vs. normal samples from plasma (A) and urine (B and C).
  • FIG. 9 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish analgesic abuse samples vs. diabetic nephropathy samples. Abbreviations as in FIG. 4 .
  • FIG. 10 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish analgesic abuse samples vs. diabetic nephropathy samples from plasma (A) and urine (B and C).
  • FIG. 11 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish glomerulonephritis samples vs. analgesic abuse samples. Abbreviations as in FIG. 4 .
  • FIG. 12 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish glomerulonephritis samples vs. analgesic abuse samples from plasma (A) and urine (B and C).
  • FIG. 13 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish obstructive uropathy samples vs. analgesic abuse samples. Abbreviations as in FIG. 4 .
  • FIG. 14 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish obstructive uropathy samples vs. analgesic abuse samples from plasma (A) and urine (B and C).
  • FIG. 15 is a block diagram of an emplary computing environment for implementing a renal disorder diagnostic system.
  • FIG. 16 is a block diagram that depicts an exemplary renal disorder diagnostic system.
  • FIG. 17 illustrates a method for diagnosing, monitoring, or determining a renal disorder in a mammal in accordance with an aspect of the renal disorder diagnostic system.
  • a multiplexed panel of up to 16 biomarkers may be used to detect early renal damage and pinpoint the location of renal damage within the kidney.
  • the biomarkers included in the multiplexed panel are analytes known in the art that may be detected in the urine, serum, plasma and other bodily fluids of mammals.
  • the analytes of the multiplexed panel may be readily extracted from the mammal in a test sample of bodily fluid.
  • the concentrations of the analytes within the test sample may be measured using known analytical techniques such as a multiplexed antibody-based immunological assay.
  • the combination of concentrations of the analytes in the test sample may be compared to empirically determined combinations of minimum diagnostic concentrations and combinations of diagnostic concentration ranges associated with healthy kidney function or one or more particular renal disorders to determine whether a renal disorder is indicated in the mammal.
  • the potentially large number of combinations of diagnostic analyte concentrations makes possible a wide range of diagnostic criteria that may be used to identify a variety of renal disorders and pinpoint the location in the kidney of a renal injury, using a single multiplexed assay to evaluate a single test sample.
  • the term “renal disorder” includes, but is not limited to glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, analgesic nephropathy, and acute tubular necrosis.
  • the multiplexed analyte panel identifies secondary kidney damaged caused by exposure to a toxic compound including but not limited to therapeutic drugs, recreational drugs, contrast agents, medical imaging contrast agents, and toxins.
  • therapeutic drugs may include an analgesic (e.g. aspirin, acetaminophen, ibuprofen, naproxen sodium), an antibiotic (e.g.
  • a chemotherapy agent e.g.
  • Cisplatin (Platinol®), Carboplatin (Paraplatin®), Cytarabine (Cytosar-U®), Gemtuzumab ozogamicin (Mylotarg®), Gemcitabine (Gemzar®), Melphalan (Alkeran®), Ifosfamide (Ifex®), Methotrexate (Rheumatrex®), Interleukin-2 (Proleukin®), Oxaliplatin (Eloxatin®), Streptozocin (Zanosar®), Pemetrexed (Alimta®), Plicamycin (Mithracin®), and Trimetrexate (Neutrexin®).
  • the kidney damage may be due to kidney stones, ischemia, liver transplantation, heart transplantation, lung transplantation, or hypovolemia.
  • the multiplexed analyte panel identifies kidney damage caused by disease including but not limited to diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to any infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate disease.
  • One embodiment of the present invention provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes determining the presence or concentration of a combination of three or more sample analytes in a test sample containing the bodily fluid of the mammal. The measured concentrations of the combination of sample analytes is compared to the entries of a dataset in which each entry contains the minimum diagnostic concentrations of a combination of three of more analytes reflective of a particular renal disorder.
  • Other embodiments provide computer-readable media encoded with applications containing executable modules, systems that include databases and processing devices containing executable modules configured to diagnose, monitor, or determine a renal disorder in a mammal.
  • Still other embodiments provide antibody-based devices for diagnosing, monitoring, or determining a renal disorder in a mammal.
  • analytes used as biomarkers in the multiplexed assay methods of diagnosing, monitoring, or determining a renal disorder using measurements of the analytes, systems and applications used to analyze the multiplexed assay measurements, and antibody-based devices used to measure the analytes are described in detail below.
  • One embodiment of the invention measures the concentrations of at least three, six, or preferably sixteen biomarker analytes within a test sample taken from a mammal and compares the measured analyte concentrations to minimum diagnostic concentrations to diagnose, monitor, or determine kidney damage in a mammal.
  • the biomarker analytes are known in the art to occur in the urine, plasma, serum and other bodily fluids of mammals.
  • the biomarker analytes are proteins that have known and documented associations with early kidney damage in humans.
  • the biomarker analytes may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, VEGF A, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, GST-mu, EGF, and cortisol.
  • a description of some of the biomarker analytes are given below.
  • Alpha-1 microglobulin (A1M, Swiss-Prot Accession Number P02760) is a 26 kDa glycoprotein synthesized by the liver and reabsorbed in the proximal tubules. Elevated levels of A1M in human urine are indicative of glomerulotubular dysfunction. A1M is a member of the lipocalin super family and is found in all tissues. Alpha-1-microglobulin exists in blood in both a free form and complexed with immunoglobulin A (IgA) and heme. Half of plasma A1M exists in a free form, and the remainder exists in complexes with other molecules including prothrombin, albumin, immunoglobulin A and heme.
  • IgA immunoglobulin A
  • Half of plasma A1M exists in a free form, and the remainder exists in complexes with other molecules including prothrombin, albumin, immunoglobulin A and heme.
  • A1M A1M in human urine
  • proximal tubular cells Nearly all of the free A1M in human urine is reabsorbed by the megalin receptor in proximal tubular cells, where it is then catabolized. Small amounts of A1M are excreted in the urine of healthy humans. Increased A1M concentrations in human urine may be an early indicator of renal damage, primarily in the proximal tubule.
  • Beta-2 Microglobulin (b) Beta-2 Microglobulin (B2M)
  • Beta-2 microglobulin (B2M, Swiss-Prot Accession Number P61769) is a protein found on the surfaces of all nucleated cells and is shed into the blood, particularly by tumor cells and lymphocytes. Due to its small size, B2M passes through the glomerular membrane, but normally less than 1% is excreted due to reabsorption of B2M in the proximal tubules of the kidney. Therefore, high plasma levels of B2M occur as a result of renal failure, inflammation, and neoplasms, especially those associated with B-lymphocytes.
  • Calbindin (Calbindin D-28K, Swiss-Prot Accession Number P05937) is a Ca-binding protein belonging to the troponin C superfamily. It is expressed in the kidney, pancreatic islets, and brain. Calbindin is found predominantly in subpopulations of central and peripheral nervous system neurons, in certain epithelial cells involved in Ca2+ transport such as distal tubular cells and cortical collecting tubules of the kidney, and in enteric neuroendocrine cells.
  • Clusterin (Swiss-Prot Accession Number P10909) is a highly conserved protein that has been identified independently by many different laboratories and named SGP2, S35-S45, apolipoprotein J, SP-40, 40, ADHC-9, gp80, GPIII, and testosterone-repressed prostate message (TRPM-2).
  • SGP2 S35-S45
  • apolipoprotein J SP-40
  • 40 ADHC-9
  • gp80 gp80
  • GPIII testosterone-repressed prostate message
  • TRPM-2 testosterone-repressed prostate message
  • clusterin protein has also been implicated in physiological processes that do not involve apoptosis, including the control of complement-mediated cell lysis, transport of beta-amyloid precursor protein, shuttling of aberrant beta-amyloid across the blood-brain barrier, lipid scavenging, membrane remodeling, cell aggregation, and protection from immune detection and tumor necrosis factor induced cell death.
  • CTGF Connective Tissue Growth Factor
  • CTGF Connective tissue growth factor
  • P29279 Connective tissue growth factor
  • Creatinine is a metabolite of creatine phosphate in muscle tissue, and is typically produced at a relatively constant rate by the body. Creatinine is chiefly filtered out of the blood by the kidneys, though a small amount is actively secreted by the kidneys into the urine. Creatinine levels in blood and urine may be used to estimate the creatinine clearance, which is representative of the overall glomerular filtration rate (GFR), a standard measure of renal function. Variations in creatinine concentrations in the blood and urine, as well as variations in the ratio of urea to creatinine concentration in the blood, are common diagnostic measurements used to assess renal function.
  • GFR overall glomerular filtration rate
  • Cystatin C (Cyst C, Swiss-Prot Accession Number P01034) is a 13 kDa protein that is a potent inhibitor of the C1 family of cysteine proteases. It is the most abundant extracellular inhibitor of cysteine proteases in testis, epididymis, prostate, seminal vesicles and many other tissues. Cystatin C, which is normally expressed in vascular wall smooth muscle cells, is severely reduced in both atherosclerotic and aneurismal aortic lesions.
  • Glutathione S-transferase alpha (GST-alpha, Swiss-Prot Accession Number P08263) belongs to a family of enzymes that utilize glutathione in reactions contributing to the transformation of a wide range of compounds, including carcinogens, therapeutic drugs, and products of oxidative stress. These enzymes play a key role in the detoxification of such substances.
  • Kidney Injury Molecule-1 Kidney Injury Molecule-1 (KIM-1)
  • Kidney injury molecule-1 (KIM-1, Swiss-Prot Accession Number Q96D42) is an immunoglobulin superfamily cell-surface protein highly upregulated on the surface of injured kidney epithelial cells. It is also known as TIM-1 (T-cell immunoglobulin mucin domain-1), as it is expressed at low levels by subpopulations of activated T-cells and hepatitis A virus cellular receptor-1 (HAVCR-1). KIM-1 is increased in expression more than any other protein in the injured kidney and is localized predominantly to the apical membrane of the surviving proximal epithelial cells.
  • TIM-1 T-cell immunoglobulin mucin domain-1
  • HAVCR-1 hepatitis A virus cellular receptor-1
  • Albumin is the most abundant plasma protein in humans and other mammals. Albumin is essential for maintaining the osmotic pressure needed for proper distribution of body fluids between intravascular compartments and body tissues. Healthy, normal kidneys typically filter out albumin from the urine. The presence of albumin in the urine may indicate damage to the kidneys. Albumin in the urine may also occur in patients with long-standing diabetes, especially type 1 diabetes. The amount of albumin eliminated in the urine has been used to differentially diagnose various renal disorders. For example, nephrotic syndrome usually results in the excretion of about 3.0 to 3.5 grams of albumin in human urine every 24 hours. Microalbuminuria, in which less than 300 mg of albumin is eliminated in the urine every 24 hours, may indicate the early stages of diabetic nephropathy.
  • Neutrophil gelatinase-associated lipocalin (NGAL, Swiss-Prot Accession Number P80188) forms a disulfide bond-linked heterodimer with MMP-9. It mediates an innate immune response to bacterial infection by sequestrating iron. Lipocalins interact with many different molecules such as cell surface receptors and proteases, and play a role in a variety of processes such as the progression of cancer and allergic reactions.
  • Osteopontin (OPN, Swiss-Prot Accession Number P10451) is a cytokine involved in enhancing production of interferon-gamma and IL-12, and inhibiting the production of IL-10.
  • OPN is essential in the pathway that leads to type I immunity.
  • OPN appears to form an integral part of the mineralized matrix.
  • OPN is synthesized within the kidney and has been detected in human urine at levels that may effectively inhibit calcium oxalate crystallization. Decreased concentrations of OPN have been documented in urine from patients with renal stone disease compared with normal individuals.
  • Tamm-Horsfall protein (THP, Swiss-Prot Accession Number P07911), also known as uromodulin, is the most abundant protein present in the urine of healthy subjects and has been shown to decrease in individuals with kidney stones.
  • THP is secreted by the thick ascending limb of the loop of Henley.
  • THP is a monomeric glycoprotein of ⁇ 85 kDa with ⁇ 30% carbohydrate moiety that is heavily glycosylated.
  • THP may act as a constitutive inhibitor of calcium crystallization in renal fluids.
  • Tissue Inhibitor of Metalloproteinase-1 (n) Tissue Inhibitor of Metalloproteinase-1 (TIMP-1)
  • Tissue inhibitor of metalloproteinase-1 (TIMP-1, Swiss-Prot Accession Number P01033) is a major regulator of extracellular matrix synthesis and degradation.
  • a certain balance of MMPs and TIMPs is essential for tumor growth and health. Fibrosis results from an imbalance of fibrogenesis and fibrolysis, highlighting the importance of the role of the inhibition of matrix degradation role in renal disease.
  • Trefoil factor 3 (TFF3, Swiss-Prot Accession Number Q07654), also known as intestinal trefoil factor, belongs to a small family of mucin-associated peptides that include TFF1, TFF2, and TFF3.
  • TFF3 exists in a 60-amino acid monomeric form and a 118-amino acid dimeric form. Under normal conditions TFF3 is expressed by goblet cells of the intestine and the colon. TFF3 expression has also been observed in the human respiratory tract, in human goblet cells and in the human salivary gland. In addition, TFF3 has been detected in the human hypothalamus.
  • VEGF Vascular Endothelial Growth Factor
  • VEGF Vascular endothelial growth factor
  • the method for diagnosing, monitoring, or determining kidney damage involves determining the presence or concentrations of a combination of sample analytes in a test sample.
  • the combinations of sample analytes are any group of three or more analytes selected from the biomarker analytes, including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • the combination of analytes may be selected to provide a group of analytes associated with a wide range of potential types of kidney damage.
  • the combination of analytes may be selected to provide a group of analytes associated with a particular type of kidney damage or region of renal injury.
  • the combination of sample analytes may be any three of the biomarker analytes. In other embodiments, the combination of sample analytes may be any four, any five, any six, any seven, any eight, any nine, any ten, any eleven, any twelve, any thirteen, any fourteen, any fifteen, or all sixteen of the sixteen biomarker analytes. In some embodiments, the combination of sample analytes comprises alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1. In another embodiment, the combination of sample analytes may comprise a combination listed in Table A.
  • the combination of sample analytes may include creatinine, KIM-1, and THP.
  • the combination of sample analytes may include microalbumin, creatinine, and KIM-1.
  • the combination of sample analytes may include creatinine, TIMP-1, and THP.
  • the combination of sample analytes may include creatinine, microalbumin, and THP.
  • test sample is an amount of bodily fluid taken from a mammal.
  • bodily fluids include urine, blood, plasma, serum, saliva, semen, perspiration, tears, mucus, and tissue lysates.
  • the bodily fluid contained in the test sample is urine, plasma, or serum.
  • a mammal as defined herein, is any organism that is a member of the class Mammalia.
  • mammals appropriate for the various embodiments include humans, apes, monkeys, rats, mice, dogs, cats, pigs, and livestock including cattle and oxen.
  • the mammal is a human.
  • the bodily fluids of the test sample may be taken from the mammal using any known device or method so long as the analytes to be measured by the multiplexed assay are not rendered undetectable by the multiplexed assay.
  • devices or methods suitable for taking bodily fluid from a mammal include urine sample cups, urethral catheters, swabs, hypodermic needles, thin needle biopsies, hollow needle biopsies, punch biopsies, metabolic cages, and aspiration.
  • the test sample may be diluted to reduce the concentration of the sample analytes prior to analysis.
  • the degree of dilution may depend on a variety of factors including but not limited to the type of multiplexed assay used to measure the analytes, the reagents utilized in the multiplexed assay, and the type of bodily fluid contained in the test sample.
  • the test sample is diluted by adding a volume of diluent ranging from about 1 ⁇ 2 of the original test sample volume to about 50,000 times the original test sample volume.
  • test sample is human urine and the multiplexed assay is an antibody-based capture-sandwich assay
  • the test sample is diluted by adding a volume of diluent that is about 100 times the original test sample volume prior to analysis.
  • test sample is human serum and the multiplexed assay is an antibody-based capture-sandwich assay
  • the test sample is diluted by adding a volume of diluent that is about 5 times the original test sample volume prior to analysis.
  • test sample is human plasma and the multiplexed assay is an antibody-based capture-sandwich assay
  • the test sample is diluted by adding a volume of diluent that is about 2,000 times the original test sample volume prior to analysis.
  • the diluent may be any fluid that does not interfere with the function of the multiplexed assay used to measure the concentration of the analytes in the test sample.
  • suitable diluents include deionized water, distilled water, saline solution, Ringer's solution, phosphate buffered saline solution, TRIS-buffered saline solution, standard saline citrate, and HEPES-buffered saline.
  • the concentration of a combination of sample analytes is measured using a multiplexed assay device capable of measuring the concentrations of up to sixteen of the biomarker analytes.
  • a multiplexed assay device as defined herein, is an assay capable of simultaneously determining the concentration of three or more different sample analytes using a single device and/or method. Any known method of measuring the concentration of the biomarker analytes may be used for the multiplexed assay device.
  • Non-limiting examples of measurement methods suitable for the multiplexed assay device include electrophoresis, mass spectrometry, protein microarrays, and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, and particle-based capture-sandwich immunoassays.
  • electrophoresis mass spectrometry
  • protein microarrays protein microarrays
  • immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, and particle-based capture-sandwich immunoassays.
  • the concentration of a combination of sample analytes is measured using a multiplexed assay device capable of measuring the concentrations of up to 189 of the biomarker analytes.
  • a multiplexed assay device as defined herein, is an assay capable of simultaneously determining the concentration of three or more different sample analytes using a single device and/or method. Any known method of measuring the concentration of the biomarker analytes may be used for the multiplexed assay device.
  • Non-limiting examples of measurement methods suitable for the multiplexed assay device include electrophoresis, mass spectrometry, protein microarrays, and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, vibrational detection using MicroElectroMagnetic Devices (MEMS), and particle-based capture-sandwich immunoassays.
  • electrophoresis mass spectrometry
  • protein microarrays and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, vibrational detection using MicroElectroMagnetic Devices (MEMS), and particle-based capture-sandwich immunoassays.
  • ELISA enzyme-linked immunosorbent assay
  • MEMS MicroElectroMagnetic Devices
  • the multiplexed immunoassay device includes three or more capture antibodies.
  • Capture antibodies as defined herein, are antibodies in which the antigenic determinant is one of the biomarker analytes.
  • Each of the at least three capture antibodies has a unique antigenic determinant that is one of the biomarker analytes.
  • the capture antibodies form antigen-antibody complexes in which the analytes serve as antigens.
  • the capture antibodies may be attached to a substrate in order to immobilize any analytes captured by the capture antibodies.
  • suitable substrates include paper or cellulose strips, polystyrene or latex microspheres, and the inner surface of the well of a microtitration tray.
  • an indicator is attached to each of the three or more capture antibodies.
  • the indicator as defined herein, is any compound that registers a measurable change to indicate the presence of one of the sample analytes when bound to one of the capture antibodies.
  • Non-limiting examples of indicators include visual indicators and electrochemical indicators.
  • Visual indicators are compounds that register a change by reflecting a limited subset of the wavelengths of light illuminating the indicator, by fluorescing light after being illuminated, or by emitting light via chemiluminescence.
  • the change registered by visual indicators may be in the visible light spectrum, in the infrared spectrum, or in the ultraviolet spectrum.
  • Non-limiting examples of visual indicators suitable for the multiplexed immunoassay device include nanoparticulate gold, organic particles such as polyurethane or latex microspheres loaded with dye compounds, carbon black, fluorophores, phycoerythrin, radioactive isotopes, nanoparticles, quantum dots, and enzymes such as horseradish peroxidase or alkaline phosphatase that react with a chemical substrate to form a colored or chemiluminescent product.
  • Electrochemical indicators are compounds that register a change by altering an electrical property.
  • the changes registered by electrochemical indicators may be an alteration in conductivity, resistance, capacitance, current conducted in response to an applied voltage, or voltage required to achieve a desired current.
  • Non-limiting examples of electrochemical indicators include redox species such as ascorbate (vitamin C), vitamin E, glutathione, polyphenols, catechols, quercetin, phytoestrogens, penicillin, carbazole, murranes, phenols, carbonyls, benzoates, and trace metal ions such as nickel, copper, cadmium, iron and mercury.
  • test sample containing a combination of three or more sample analytes is contacted with the capture antibodies and allowed to form antigen-antibody complexes in which the sample analytes serve as the antigens.
  • concentrations of the three or more analytes are determined by measuring the change registered by the indicators attached to the capture antibodies.
  • the indicators are polyurethane or latex microspheres loaded with dye compounds and phycoerythrin.
  • the multiplexed immunoassay device has a sandwich assay format.
  • the multiplexed sandwich immunoassay device includes three or more capture antibodies as previously described. However, in this embodiment, each of the capture antibodies is attached to a capture agent that includes an antigenic moiety. The antigenic moiety serves as the antigenic determinant of a detection antibody, also included in the multiplexed immunoassay device of this embodiment. In addition, an indicator is attached to the detection antibody.
  • the test sample is contacted with the capture antibodies and allowed to form antigen-antibody complexes in which the sample analytes serve as antigens.
  • the detection antibodies are then contacted with the test sample and allowed to form antigen-antibody complexes in which the capture agent serves as the antigen for the detection antibody. After removing any uncomplexed detection antibodies the concentration of the analytes are determined by measuring the changes registered by the indicators attached to the detection antibodies.
  • the concentrations of each of the sample analytes may be determined using any approach known in the art.
  • a single indicator compound is attached to each of the three or more antibodies.
  • each of the capture antibodies having one of the sample analytes as an antigenic determinant is physically separated into a distinct region so that the concentration of each of the sample analytes may be determined by measuring the changes registered by the indicators in each physically separate region corresponding to each of the sample analytes.
  • each antibody having one of the sample analytes as an antigenic determinant is marked with a unique indicator.
  • a unique indicator is attached to each antibody having a single sample analyte as its antigenic determinant.
  • all antibodies may occupy the same physical space. The concentration of each sample analyte is determined by measuring the change registered by the unique indicator attached to the antibody having the sample analyte as an antigenic determinant.
  • the multiplexed immunoassay device is a microsphere-based capture-sandwich immunoassay device.
  • the device includes a mixture of three or more capture-antibody microspheres, in which each capture-antibody microsphere corresponds to one of the biomarker analytes.
  • Each capture-antibody microsphere includes a plurality of capture antibodies attached to the outer surface of the microsphere.
  • the antigenic determinant of all of the capture antibodies attached to one microsphere is the same biomarker analyte.
  • the microsphere is a small polystyrene or latex sphere that is loaded with an indicator that is a dye compound.
  • the microsphere may be between about 3 ⁇ m and about 5 ⁇ m in diameter.
  • Each capture-antibody microsphere corresponding to one of the biomarker analytes is loaded with the same indicator. In this manner, each capture-antibody microsphere corresponding to a biomarker analyte is uniquely color-coded.
  • the multiplexed immunoassay device further includes three or more biotinylated detection antibodies in which the antigenic determinant of each biotinylated detection antibody is one of the biomarker analytes.
  • the device further includes a plurality of streptaviden proteins complexed with a reporter compound.
  • a reporter compound as defined herein, is an indicator selected to register a change that is distinguishable from the indicators used to mark the capture-antibody microspheres.
  • the concentrations of the sample analytes may be determined by contacting the test sample with a mixture of capture-antigen microspheres corresponding to each sample analyte to be measured.
  • the sample analytes are allowed to form antigen-antibody complexes in which a sample analyte serves as an antigen and a capture antibody attached to the microsphere serves as an antibody. In this manner, the sample analytes are immobilized onto the capture-antigen microspheres.
  • the biotinylated detection antibodies are then added to the test sample and allowed to form antigen-antibody complexes in which the analyte serves as the antigen and the biotinylated detection antibody serves as the antibody.
  • the streptaviden-reporter complex is then added to the test sample and allowed to bind to the biotin moieties of the biotinylated detection antibodies.
  • the antigen-capture microspheres may then be rinsed and filtered.
  • the concentration of each analyte is determined by first measuring the change registered by the indicator compound embedded in the capture-antigen microsphere in order to identify the particular analyte. For each microsphere corresponding to one of the biomarker analytes, the quantity of analyte immobilized on the microsphere is determined by measuring the change registered by the reporter compound attached to the microsphere.
  • the indicator embedded in the microspheres associated with one sample analyte may register an emission of orange light
  • the reporter may register an emission of green light
  • a detector device may measure the intensity of orange light and green light separately. The measured intensity of the green light would determine the concentration of the analyte captured on the microsphere, and the intensity of the orange light would determine the specific analyte captured on the microsphere.
  • Any sensor device may be used to detect the changes registered by the indicators embedded in the microspheres and the changes registered by the reporter compound, so long as the sensor device is sufficiently sensitive to the changes registered by both indicator and reporter compound.
  • suitable sensor devices include spectrophotometers, photosensors, colorimeters, cyclic coulometry devices, and flow cytometers.
  • the sensor device is a flow cytometer.
  • the multiplexed immunoassay device has a vibrational detection format using a MEMS.
  • the immunoassay device uses capture antibodies as previously described.
  • the capture antibodies are attached to a microscopic silicon microcantilever beam structure.
  • the microcantilevers are micromechanical beams that are anchored at one end, such as diving spring boards that can be readily fabricated on silicon wafers and other materials.
  • the microcantilever sensors are physical sensors that respond to surface stress changes due to chemical or biological processes. When fabricated with very small force constants, they can measure forces and stresses with extremely high sensitivity. The very small force constant of a cantilever allows detection not surface stress variation due to the binding of an analyte to the capture antibody on the microcantilever.
  • Binding of the analyte results in a differential surface stress due to adsorption-induced forces, which manifests as a deflection which can be measured.
  • the vibrational detection may be multiplexed.
  • a method for diagnosing, monitoring, or determining a renal disorder includes providing a test sample, determining the concentration of a combination of three or more a sample analytes, comparing the measured concentrations of the combination of sample analytes to the entries of a dataset, and identifying a particular renal disorder based on the comparison between the concentrations of the sample analytes and the minimum diagnostic concentrations contained within each entry of the dataset.
  • the concentrations of the sample analytes are compared to the entries of a dataset.
  • each entry of the dataset includes a combination of three or more minimum diagnostic concentrations indicative of a particular renal disorder.
  • a minimum diagnostic concentration is the concentration of an analyte that defines the limit between the concentration range corresponding to normal, healthy renal function and the concentration reflective of a particular renal disorder.
  • each minimum diagnostic concentration is the maximum concentration of the range of analyte concentrations for a healthy, normal individual.
  • the minimum diagnostic concentration of an analyte depends on a number of factors including but not limited to the particular analyte and the type of bodily fluid contained in the test sample. As an illustrative example, Table 1 lists the expected normal ranges of the biomarker analytes in human plasma, serum, and urine.
  • the high values shown for each of the biomarker analytes in Table 1 for the analytic concentrations in human plasma, sera and urine are the minimum diagnostics values for the analytes in human plasma, sera, and urine, respectively.
  • the minimum diagnostic concentration in human plasma of alpha-1 microglobulin is about 16 ⁇ g/ml
  • beta-2 microglobulin is about 2.2 ⁇ g/ml
  • calbindin is greater than about 5 ng/ml
  • clusterin is about 134 ⁇ g/ml
  • CTGF is about 16 ng/ml
  • cystatin C is about 1170 ng/ml
  • GST-alpha is about 62 ng/ml
  • KIM-1 is about 0.57 ng/ml
  • NGAL is about 375 ng/ml
  • osteopontin is about 25 ng/ml
  • THP is about 0.052 ⁇ g/ml
  • TIMP-1 is about 131 ng/ml
  • TFF-3 is about 0.49 ⁇ g
  • the minimum diagnostic concentration in human sera of alpha-1 microglobulin is about 17 ⁇ g/ml
  • beta-2 microglobulin is about 2.6 ⁇ g/ml
  • calbindin is greater than about 2.6 ng/ml
  • clusterin is about 152 ⁇ g/ml
  • CTGF is greater than about 8.2 ng/ml
  • cystatin C is about 1250 ng/ml
  • GST-alpha is about 52 ng/ml
  • KIM-1 is greater than about 0.35 ng/ml
  • NGAL is about 822 ng/ml
  • osteopontin is about 12 ng/ml
  • THP is about 0.053 ⁇ g/ml
  • TIMP-1 is about 246 ng/ml
  • TFF-3 is about 0.17 ⁇ g/ml
  • VEGF is about 1630 ⁇ g/ml.
  • the minimum diagnostic concentration in human urine of alpha-1 microglobulin is about 233 ⁇ g/ml
  • beta-2 microglobulin is greater than about 0.17 ⁇ g/ml
  • calbindin is about 233 ng/ml
  • clusterin is greater than about 0.089 ⁇ g/ml
  • CTGF is greater than about 0.90 ng/ml
  • cystatin C is about 1170 ng/ml
  • GST-alpha is greater than about 26 ng/ml
  • KIM-1 is about 0.67 ng/ml
  • NGAL is about 81 ng/ml
  • osteopontin is about 6130 ng/ml
  • THP is about 2.6 ⁇ g/ml
  • TIMP-1 is greater than about 3.9 ng/ml
  • TFF-3 is greater than about 21 ⁇ g/ml
  • VEGF is about 517 ⁇ g/ml.
  • the minimum diagnostic concentrations represent the maximum level of analyte concentrations falling within an expected normal range.
  • a renal disorder may be indicated if the concentration of an analyte is higher than the minimum diagnostic concentration for the analyte.
  • the minimum diagnostic concentration may not be an appropriate diagnostic criterion for identifying the particular renal disorder indicated by the sample analyte concentrations.
  • a maximum diagnostic concentration may define the limit between the expected normal concentration range for the analyte and a sample concentration reflective of a renal disorder. In those cases in which a maximum diagnostic concentration is the appropriate diagnostic criterion, sample concentrations that fall below a maximum diagnostic concentration may indicate a particular renal disorder.
  • a critical feature of the method of the multiplexed analyte panel is that a combination of sample analyte concentrations may be used to diagnose a renal disorder.
  • the analytes may be algebraically combined and compared to corresponding diagnostic criteria.
  • two or more sample analyte concentrations may be added and/or subtracted to determine a combined analyte concentration.
  • two or more sample analyte concentrations may be multiplied and/or divided to determine a combined analyte concentration.
  • the combined analyte concentration may be compared to a diagnostic criterion in which the corresponding minimum or maximum diagnostic concentrations are combined using the same algebraic operations used to determine the combined analyte concentration.
  • the analyte concentration measured from a test sample containing one type of body fluid may be algebraically combined with an analyte concentration measured from a second test sample containing a second type of body fluid to determine a combined analyte concentration.
  • the ratio of urine calbindin to plasma calbindin may be determined and compared to a corresponding minimum diagnostic urine:plasma calbindin ratio to identify a particular renal disorder.
  • any sample concentration falling outside the expected normal range indicates a renal disorder.
  • the multiplexed analyte panel may be used to evaluate the analyte concentrations in test samples taken from a population of patients having a particular renal disorder and compared to the normal expected analyte concentration ranges.
  • any sample analyte concentrations that are significantly higher or lower than the expected normal concentration range may be used to define a minimum or maximum diagnostic concentration, respectively.
  • sample analyte concentrations of a population of patients exposed to varying dosages of a potentially drug may be compared to each other and to the expected normal analyte concentrations. Any sample analyte concentrations falling significantly outside the expected normal analyte concentration range may be used to define diagnostic criteria.
  • sample analyte concentrations may be correlated to the dosage of the potentially toxic drug in order to define a diagnostic criteria used to determine the severity of a particular renal disorder based on the sample analyte concentration.
  • kidney damage identified by the multiplexed analyte panel include, but are not limited to glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, and acute tubular necrosis.
  • the multiplexed analyte panel identifies secondary kidney damaged caused by exposure to agents including but not limited to therapeutic drugs, recreational drugs, medical imaging contrast agents, toxins, kidney stones, ischemia, liver transplantation, heart transplantation, lung transplantation, and hypovolemia.
  • the multiplexed analyte panel identifies kidney damage caused by disease including but not limited to diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to any infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate disease.
  • diseases including lupus, Wegener's granulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to any infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate disease.
  • LDD least detectable doses
  • LLOQ lower limits of quantitation
  • the concentrations of the analytes were measured using a capture-sandwich assay using antigen-specific antibodies. For each analyte, a range of standard sample dilutions ranging over about four orders of magnitude of analyte concentration were measured using the assay in order to obtain data used to construct a standard dose response curve.
  • the dynamic range for each of the analytes defined herein as the range of analyte concentrations measured to determine its dose response curve, is presented below.
  • a filter-membrane microtiter plate was pre-wetted by adding 100 ⁇ L wash buffer, and then aspirated using a vacuum manifold device. The contents of the wells of the hard-bottom plate were then transferred to the corresponding wells of the filter-membrane plate. All wells of the hard-bottom plate were vacuum-aspirated and the contents were washed twice with 100 ⁇ L of wash buffer. After the second wash, 100 ⁇ L of wash buffer was added to each well, and then the washed microspheres were resuspended with thorough mixing. The plate was then analyzed using a Luminex 100 Analyzer (Luminex Corporation, Austin, Tex., USA). Dose response curves were constructed for each analyte by curve-fitting the median fluorescence intensity (MFI) measured from the assays of diluted standard samples containing a range of analyte concentrations.
  • MFI median fluorescence intensity
  • the least detectable dose was determined by adding three standard deviations to the average of the MFI signal measured for 20 replicate samples of blank standard solution (i.e. standard solution containing no analyte).
  • the MFI signal was converted to an LDD concentration using the dose response curve and multiplied by a dilution factor of 2.
  • the lower limit of quantification defined herein as the point at which the coefficient of variation (CV) for the analyte measured in the standard samples was 30%, was determined by the analysis of the measurements of increasingly diluted standard samples.
  • the standard solution was diluted by 2 fold for 8 dilutions.
  • samples were assayed in triplicate, and the CV of the analyte concentration at each dilution was calculated and plotted as a function of analyte concentration.
  • the LLOQ was interpolated from this plot and multiplied by a dilution factor of 2.
  • the results of this experiment characterized the least detectible dose and the lower limit of quantification for fourteen analytes associated with various renal disorders using a capture-sandwich assay.
  • the analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • A1M alpha-1 microglobulin
  • B2M beta-2 microglobulin
  • calbindin clusterin
  • CTGF cystatin C
  • GST-alpha cystatin C
  • KIM-1 NGAL
  • osteopontin OPN
  • THP TIMP-1
  • TFF-3 vascular endothelialpha
  • the results of this experiment characterized the precision of a capture-sandwich assay for fourteen analytes associated with various renal disorders over a wide range of analyte concentrations.
  • the precision of the assay varied between about 1% and about 15% error within a given run, and between about 5% and about 15% error between different runs.
  • the percent errors summarized in Table 2 provide information concerning random error to be expected in an assay measurement caused by variations in technicians, measuring instruments, and times of measurement.
  • analytes spiked into urine, serum, and plasma samples were assessed by an assay used to measure the concentration of analytes associated with renal disorders.
  • the analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • the concentrations of the analytes in the samples were measured using the methods described in Example 1.
  • the average % recovery was calculated as the proportion of the measurement of analyte spiked into the urine, serum, or plasma sample (observed) to the measurement of analyte spiked into the standard solution (expected).
  • the results of the spike recovery analysis are summarized in Table 5.
  • the sandwich-type assay is reasonably sensitive to the presence of all analytes measured, whether the analytes were measured in standard samples, urine samples, plasma samples, or serum samples.
  • the analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • A1M alpha-1 microglobulin
  • B2M beta-2 microglobulin
  • calbindin clusterin
  • CTGF cystatin C
  • cystatin C GST-alpha
  • KIM-1 NGAL
  • osteopontin OPN
  • THP TIMP-1
  • TFF-3 VEGF
  • Matrix interference was assessed by spiking hemoglobin, bilirubin, and triglyceride into standard analyte samples and measuring analyte concentrations using the methods described in Example 1. A % recovery was determined by calculating the ratio of the analyte concentration measured from the spiked sample (observed) divided by the analyte concentration measured form the standard sample (expected). The results of the matrix interference analysis are summarized in Table 6.
  • analytes spiked into urine, serum, and plasma samples were assessed to assess the ability of analytes spiked into urine, serum, and plasma samples to tolerate freeze-thaw cycles.
  • the analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • A1M alpha-1 microglobulin
  • B2M beta-2 microglobulin
  • calbindin clusterin
  • CTGF cystatin C
  • GST-alpha cystatin C
  • KIM-1 NGAL
  • osteopontin osteopontin
  • THP TIMP-1
  • TFF-3 VEGF
  • the concentrations of the analytes in the samples were measured using the methods described in Example 1 after the initial addition of the analyte, and after one, two and three cycles of freezing and thawing.
  • analyte concentrations in urine, serum and plasma samples were measured immediately after the addition of the analyte to the samples as well as after storage at room temperature for two hours and four hours, and after storage at 4° C. for 2 hours, four hours, and 24 hours.
  • KIM-1 Control 1.5 100 0.23 100 0.24 100 (ng/mL) 2 hr @ 1.2 78 0.2 86 0.22 90 room temp 2 hr. @ 1.6 106 0.23 98 0.21 85 4° C. 4 hr @ 1.3 84 0.19 82 0.2 81 room temp 4 hr. @ 1.4 90 0.22 93 0.19 80 4° C. 24 hr. @ 1.1 76 0.18 76 0.23 94 4° C.
  • VEGF Control 851 100 1215 100 670 100 (pg/mL) 2 hr @ 793 93 1055 87 622 93 room temp 2 hr. @ 700 82 1065 88 629 94 4° C.
  • Cys- Control 52 100 819 100 476 100 tatin 2 hr @ 50 96 837 102 466 98 C room temp (ng/mL) 2 hr. @ 44 84 884 108 547 115 4° C. 4 hr @ 49 93 829 101 498 105 room temp 4 hr. @ 46 88 883 108 513 108 4° C. 24 hr. @ 51 97 767 94 471 99 4° C.
  • NGAL Control 857 100 302 100 93 100 (ng/mL) 2 hr @ 888 104 287 95 96 104 room temp 2 hr. @ 923 108 275 91 92 100 4° C.
  • TIMP-1 Control 17 100 349 100 72 100 (ng/mL) 2 hr @ 17 98 311 89 70 98 room temp 2 hr. @ 16 94 311 89 68 95 4° C. 4 hr @ 17 97 306 88 68 95 room temp 4 hr. @ 16 93 329 94 74 103 4° C. 24 hr. @ 18 105 349 100 72 100 4° C.
  • Urine concentrations of analytes included in a human kidney toxicity panel were measured by the assay, including alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • FIG. 1 summarizes the urine concentrations of those analytes that differed significantly from control urine concentrations.
  • the urine concentrations of A1M, NGAL, and THP were slightly elevated for the renal cancer patient group and more significantly elevated for the “other” cancer patient group.
  • Urine B2M concentrations appeared to be elevated for both the renal cancer and “other” cancer patient groups, although the BRM concentrations exhibited more variability than the other analyte concentrations shown in FIG. 1 .
  • a screen for potential protein biomarkers in relation to kidney toxicity/damage was performed using a panel of biomarkers, in a set of urine and plasma samples from patients with documented renal damage.
  • the investigated patient groups included diabetic nephropathy (DN), obstructive uropathy (OU), analgesic abuse (AA) and glomerulonephritis (GN) along with age, gender and BMI matched control groups.
  • DN diabetic nephropathy
  • OU obstructive uropathy
  • AA analgesic abuse
  • GN glomerulonephritis
  • Multiplexed immunoassays were applied in order to quantify the following protein analytes: Alpha-1 Microglobulin ( ⁇ 1M), KIM-1, Microalbumin, Beta-2-Microglobulin ( ⁇ 2M), Calbindin, Clusterin, CystatinC, TreFoilFactor-3 (TFF-3), CTGF, GST-alpha, VEGF, Calbindin, Osteopontin, Tamm-HorsfallProtein (THP), TIMP-1 and NGAL.
  • Li-Heparin plasma and mid-stream spot urine samples were collected from four different patient groups. Samples were also collected from age, gender and BMI matched control subjects. 20 subjects were included in each group resulting in a total number of 160 urine and plasma samples. All samples were stored at ⁇ 80° C. before use. Glomerular filtration rate for all samples was estimated using two different estimations (Modification of Diet in Renal Disease or MDRD, and the Chronic Kidney Disease Epidemiology Collaboration or CKD-EPI) to outline the eGFR (estimated glomerular filtration rate) distribution within each patient group ( FIG. 2 ). Protein analytes were quantified in human plasma and urine using multiplexed immunoassays in the Luminex xMAPTM platform.
  • microsphere-based multiplex immunoassays consist of antigen-specific antibodies and optimized reagents in a capture-sandwich format. Output data was given as g/ml calculated from internal standard curves. Because urine creatinine (uCr) correlates with renal filtration rate, data analysis was performed without correction for uCr. Univariate and multivariate data analysis was performed comparing all case vs. control samples as well as cases vs. control samples for the various disease groups.
  • Urine and plasma samples were taken from 80 normal control group subjects and 20 subjects from each of four disorders: analgesic abuse, diabetic nephropathy, glomerulonephritis, and obstructive uropathy.
  • the samples were analyzed for the quantity and presence of 16 different proteins (alpha-1 microglobulin ( ⁇ 1M), beta-2 microglobulin ( ⁇ 2M), calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF) as described in Example 1 above.
  • the goal was to determine the analytes that distinguish between a normal sample and a diseased sample, a normal sample and an obstructive uropathy (OU) sample, and finally, an glomerulonephritis sample from the other disease samples (diabetic nephropathy (DN), analgesic abuse (AA), and glomerulonephritis (GN)).
  • DN diabetic nephropathy
  • AA analgesic abuse
  • GN glomerulonephritis
  • the mean error rates and AUROC were calculated from urine and AUROC was calculated from plasma for 100 runs of the above method for each of the following comparisons: disease (AA+GN+OU+DN) vs. normal ( FIG. 5 , Table 11), AA vs. normal ( FIG. 7 , Table 13), DN vs. AA ( FIG. 9 , Table 15, AA vs. GN ( FIG. 11 , Table 17), and AA vs. OU ( FIG. 13 , Table 19).
  • FIG. 15 is a block diagram of an exemplary computing environment 1500 for diagnosing, monitoring, and/or determining a renal disorder in a mammal.
  • the computing environment 1500 includes sample input device 1502 , a renal disorder diagnostics system (RDSS) 1504 , and a data source 1506 .
  • RDSS renal disorder diagnostics system
  • sample input device 1502 is a computer or processing device 1508 , such as a personal computer, a server computer, or a mobile processing device.
  • the computer 1508 may include a display such as a computer monitor, for viewing data, and an input device, such as a keyboard or a pointing device (e.g., a mouse, trackball, pen, touch pad, or other device), for entering data.
  • the computer 1508 is used by a user to enter analyte concentrations of a test sample for processing by the RDSS 1504 .
  • the user uses the keyboard to interact with an analyte concentration entry form (not shown) on the display to enter test sample analyte data that includes, for example, three or more analyte concentrations.
  • test sample analyte concentrations are collected and then transmitted to the RDSS 1504 via an analyte measurement/sensor device 1510 (e.g., multiplexed immunoassay device) that measures the sample analyte concentration.
  • the analyte measurement/sensor device 1510 communicates the measured sample analyte concentrations data to the RDSS 1504 via a data cable, infrared signal, wireless connection, or other methods of data transmission known in the art.
  • the RDDS 1504 executes a renal disorder determining application 1512 in response to test sample analyte concentration data received from the received from the sample input device 102 .
  • the renal disorder determining application (RDDA) 1512 analyzes the analyte concentration data for the test sample and determines whether the received analyte concentration data is indicative of renal disorder and, if so, a type of renal disorder.
  • the renal disorder determining application 1512 displays whether the result of the analysis is positive or negative for a renal disorder and, if applicable, the type of renal disorder.
  • the RDDS 1904 retrieves concentration threshold data and/or disorder threshold data from the data source 1506 to determine whether the received analyte concentration data is indicative of one or more renal disorders.
  • the data source 1506 is, for example, a computer system, a database, or another data system that stores data, electronic documents, records, other documents, and/or other data.
  • the data source 1506 may include memory and one or more processors or processing systems to receive, process, and transmit communications and store and retrieve data.
  • the data source 1506 includes a diagnostic analytic concentrations database 1514 that stores normal ranges of biomarker analytes for human plasma, serum, and urine, such described above in connection with Table 1.
  • the entries of the diagnostic analytic concentrations database 1514 may also include additional minimum diagnostic concentrations to further define diagnostic criteria including but not limited to minimum diagnostic concentrations for additional types of bodily fluids, additional types of mammals, and severities of a particular disorder. As described above, if the measured concentration for a particular analyte of a sample of plasma exceeds the high value in Table 1, then the measured concentration of that particular may be indicative of a renal disorder or disease in the subject from with the test sample was collected.
  • the disorder database 1516 includes various data tables index by disorder or disease type.
  • Each data table corresponds to a specific disorder/disease type and identifies a list of minimum diagnostic concentrations that are indicative of that particular disease.
  • diabetic nephropathy data table indicates by sample type (i.e., plasma, urine, serum) the minimum concentration required, if any, for each of sixteen analyte biomarkers described above in connection with Table 1.
  • the data source is illustrated in FIG. 15 as being integrated with the RDDS 1504 , it is contemplated that in other aspects the data source 1506 may be separate and/or remote from the RDDS 1504 .
  • the RDDS 1504 communicates with the data source 1506 over a communication network, such as the Internet, an intranet, an Ethernet network, a wireline network, a wireless network, and/or another communication network, to identify relevant images, electronic documents, records, other documents, and/or other data to retrieve from the data source 1506 .
  • the sample input device 1502 communicates with the RDDS 1904 through the communication network.
  • the RDDS 1504 communicates with the data source 1506 through a direct connection.
  • FIG. 16 is a block diagram that depicts an exemplary RDDS 1504 .
  • the RDDS 1504 includes a processing system 1602 that executes the RDDA 1512 to determine whether the received analyte concentration data is indicative of renal disorder and, if so, the type of renal disorder.
  • the processing system 1602 includes memory and one or more processors, and the processing system 1602 can reside on a computer or other processing system.
  • the data source 1506 is not shown and is, for example, located remotely from the RDDS 1504 .
  • the RDDA 1512 includes instructions or modules that are executable by the processing system 1602 to manage the retrieval of renal disorder diagnostic data, including a record, from the data source 1506 .
  • the RDDS 1504 includes computer readable media 1604 configured with the RDDA 1512 .
  • Computer readable medium (CRM) 1604 may include volatile media, nonvolatile media, removable media, non-removable media, and/or another available medium that can be accessed by the RDDS 1504 .
  • computer readable medium 1604 comprises computer storage media and communication media.
  • Computer storage media includes memory, volatile media, nonvolatile media, removable media, and/or non-removable media implemented in a method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.
  • Communication media may embody computer readable instructions, data structures, program modules, or other data and include an information delivery media or system.
  • An analyte input module 1606 receives three or more sample analyte concentrations that include the biomarker analytes.
  • the sample analyte concentrations are entered as input by a user of the computer 1508 .
  • the sample analyte concentrations are received directly from analyte measure/sensor device 1510 , such as a multiplexed immunoassay device.
  • the analyte input module 1606 receives sample analyte concentrations for at least six biomarker analytes.
  • the at least six biomarker analytes include alpha 1 microglobulin, cystatin C, KIM-1, Tamm-Horsfall, Beta 2-microglobulin, and TIMP-1.
  • the analyte input module 1606 receives sample analyte concentrations for sixteen biomarker analytes.
  • the sixteen biomarker analytes include the analyte types shown in Table 1.
  • a comparison module 1608 compares each analyte concentration of a sample received from the analyte input module 1606 to a corresponding analyte entry in the diagnostic analyte database to determine if one or more concentrations for a particular analyte of the sample are exceed the minimum diagnostic value for that particular analyte. For example, referring briefly to Table 1, if the sample concentrations are obtained from plasma and the particular analyte is calbindin, the comparison module compares the measured calbindin analyte concentration to the sample to the corresponding high concentration value for plasma to determine if it is greater than about 5 ng/ml.
  • a measured calbindin analyte concentration less than about 5 ng/ml indicates is not indicative of renal disorder.
  • a measured calbindin analyte concentration that is greater than about 5 ng/ml is indicative of a renal disorder.
  • An analysis module 1610 determines a most likely renal disorder as a function of the particular measured analyte concentrations identified as indicative of a renal disorder by the comparison module. For example, the analysis module 1610 compares the particular measured analyte concentrations to entries in the disorder tables stored in the renal disorder database 1516 to identify the most likely type renal disorder.
  • Each disorder table includes, for example, the minimum concentrations or threshold concentrations for each of the sixteen analytes types shown in Table 1 that are associated with the diagnosis of a particular renal disorder or disease. It is also contemplated that the analyte types listed in a disorder table for particular renal disorder or disease may be different from the analyte types listed in another disorder table for a different renal disorder or disease.
  • the most likely renal disorder is the particular renal disorder type in the disorder database 1516 having the most minimum diagnostic concentrations that are less than the corresponding sample analyte concentrations.
  • the most likely disorder is identified from the disorder table that includes the most threshold concentrations that are exceeded by the sample analyte concentrations. For example, consider that five of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in the disorder table for a first renal disorder, such as analgesic abuse. Also, consider that four of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a second renal disorder, such as obstructive uropathy. In this example, the most likely renal disorder is analgesic abuse.
  • the most likely renal disorder is the particular renal disorder type in the disorder database 1516 having the most minimum diagnostic concentrations that are less than the corresponding sample analyte concentrations.
  • the most likely disorder is identified from the disorder table that includes the most threshold concentrations that are exceeded by the sample analyte concentrations. For example, consider that five of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a first renal disorder, such as analgesic abuse. Also, consider that four of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a second renal disorder, such as obstructive uropathy. In this example, the most likely renal disorder is analgesic abuse.
  • the most likely renal disorder is the particular renal disorder from the database entry having minimum diagnostic concentrations that are all less than the corresponding sample analyte concentrations.
  • the analysis module 1610 combines the sample analyte concentrations algebraically to calculate a combined sample analyte concentration that is compared to a combined minimum diagnostic concentration calculated from the corresponding minimum diagnostic criteria using the same algebraic operations. See Table A for example combinations. Other combinations of sample analyte concentrations from within the same test sample, or combinations of sample analyte concentrations from two or more different test samples containing two or more different bodily fluids may be used to determine a particular renal disorder in still other embodiments.
  • An output module 1612 generates a display of analyte types and corresponding concentrations for each of the measured analytes identified as indicative of a renal disorder by the comparison module.
  • the output module 1612 also generates a display of the most likely renal disorder determined by the analysis module 1610 .
  • FIG. 17 illustrates a method for diagnosing, monitoring, or determining a renal disorder in a mammal in accordance with an aspect of the RDDS 1504 .
  • analyte concentrations read by an assay device or defined via user input at a computer are communicated to the renal disorder determining application 1512 .
  • the sample analyte concentrations are transferred to the RDSS 1504 for processing.
  • the concentration of each analyte type in the sample is compared to a corresponding threshold analyte concentration in a diagnostic analyte database at 1706 .
  • the threshold analyte concentrations in the diagnostic analyte database correspond to analyte concentration for various sample types that have been previous determined to be indicative of one or more renal disorders or diseases. If none of the analyte concentrations for the sample are determined to be greater than the corresponding threshold analyte concentrations at 1708 . The one or more of the analyte concentrations and/or a message indicating the concentrations are within normal range is generated for display via the computer at 1710 .
  • the one or more analyte concentrations for the sample are then compared to disorder threshold analyte concentrations in a disorder database at 1712 .
  • the disorder threshold analyte concentrations correspond to minimum analyte concentrations associated with a particular renal disorder or disease.
  • the particular disorder that corresponds to the disorder table that has the most disorder threshold analyte concentrations exceeded by the sample analyte concentrations is identified as the most likely renal disorder.
  • the one or more of the analyte concentrations for the sample and the most likely renal disorder type is generated for display via the computer at 1716 .
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US20160231332A1 (en) 2016-08-11
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