CA3108719A1 - Process and system for identifying individuals having a high risk of inflammatory bowel disease and a method of treatment - Google Patents
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B40/20—Supervised data analysis
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
- A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
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- 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
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/158—Expression markers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/06—Gastro-intestinal diseases
- G01N2800/065—Bowel diseases, e.g. Crohn, ulcerative colitis, IBS
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
Abstract
A process and system directed to a more effective, individual based treatment regimen which is built on clinical identified predictive target biomarkers associated with predicting the risk of an individual developing IBD and includes one or more predictive panels of prediction target biomarkers that are used to determine the risk of an individual developing IBD for determining if a therapy should be administered to reduce the risk and further determines the efficacy of treating the individual with mesalamine and effectively identifies and validates novel drug targets for new IBD therapeutics, new diagnostics and diagnostics standards for IBD therapeutic strategies.
Description
Description PROCESS AND SYSTEM FOR IDENTIFYING INDIVIDUALS HAVING A
Claims (24)
1. A process of identifying individuals that may have or have a high risk of developing inflammatory bowel disease (IBD) prior to diagnosis of IBD, the process comprises the steps of:
identifying an individual to be tested;
obtaining a first blood sample of the individual;
selecting a panel of predictive target biomarkers;
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers;
determining the total level of protein in the blood sample;
selecting a prediction logistic regression model for predicting the risk of an individual developing IBD and using the selected logistic regression model and the levels of each predictive target biomarker and the total level of protein in the blood sample to calculate a risk value; and determining if the risk value is above or below a cut-off value for the selected prediction logistic regression model;
wherein if the risk value is greater than the cut-off value, the process includes the step of administering a therapy to reduce the risk of the individual developing IBD; and wherein if the risk value is below the cut-off value the process includes the step of administering a therapy to monitor the individual for detecting an increase in the risk of developing IBD.
identifying an individual to be tested;
obtaining a first blood sample of the individual;
selecting a panel of predictive target biomarkers;
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers;
determining the total level of protein in the blood sample;
selecting a prediction logistic regression model for predicting the risk of an individual developing IBD and using the selected logistic regression model and the levels of each predictive target biomarker and the total level of protein in the blood sample to calculate a risk value; and determining if the risk value is above or below a cut-off value for the selected prediction logistic regression model;
wherein if the risk value is greater than the cut-off value, the process includes the step of administering a therapy to reduce the risk of the individual developing IBD; and wherein if the risk value is below the cut-off value the process includes the step of administering a therapy to monitor the individual for detecting an increase in the risk of developing IBD.
2. The process of Claim 1 wherein if the risk value of the individual developing IBD is greater than the cut-off value, the process includes the steps of:
selecting a panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease;
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease;
selecting a prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease and using the logistic regression model and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease and the total level of protein in the blood sample to calculate a risk value for developing Crohn's disease;
determining if the risk value for developing Crohn's disease is above or below a cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease;
wherein if the risk value for developing Crohn's disease is greater than the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease, the process includes the step of administering a therapy to reduce the risk of developing Crohn's disease;
wherein if the risk value for developing Crohn's disease is below the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease, the process includes the step of administering a therapy to monitor the individual for detecting an increase in the risk of developing Crohn's disease.
selecting a panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease;
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease;
selecting a prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease and using the logistic regression model and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease and the total level of protein in the blood sample to calculate a risk value for developing Crohn's disease;
determining if the risk value for developing Crohn's disease is above or below a cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease;
wherein if the risk value for developing Crohn's disease is greater than the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease, the process includes the step of administering a therapy to reduce the risk of developing Crohn's disease;
wherein if the risk value for developing Crohn's disease is below the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease, the process includes the step of administering a therapy to monitor the individual for detecting an increase in the risk of developing Crohn's disease.
3. The process of Claim 1 wherein if the risk value for the individual developing IBD is greater than the cut-off value, the process includes the steps of:
selecting a panel of predictive target biomarkers for use in determining the risk of the individual developing ulcerative colitis (UC);
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing UC;
selecting a prediction logistic regression model for use in determining the risk of the individual developing UC and using the logistic regression model and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing UC and the total level of protein in the blood sample to calculate a risk value for developing UC;
determining if the risk value for developing UC is above or below a cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC;
wherein if the risk value for developing UC is greater than the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC disease, the process includes the step of administering a therapy to reduce the risk of developing UC;
wherein if the risk value for developing UC is below the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC, the process includes the step of administering a therapy to monitor the individual for detecting an increase in the risk of developing UC.
selecting a panel of predictive target biomarkers for use in determining the risk of the individual developing ulcerative colitis (UC);
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing UC;
selecting a prediction logistic regression model for use in determining the risk of the individual developing UC and using the logistic regression model and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing UC and the total level of protein in the blood sample to calculate a risk value for developing UC;
determining if the risk value for developing UC is above or below a cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC;
wherein if the risk value for developing UC is greater than the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC disease, the process includes the step of administering a therapy to reduce the risk of developing UC;
wherein if the risk value for developing UC is below the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC, the process includes the step of administering a therapy to monitor the individual for detecting an increase in the risk of developing UC.
4. The process of Claim 1 wherein if the risk value for the individual developing IBD is greater than the cut-off value, the process includes the step of predicting the effectiveness of mesalamine treatment for the individual.
5. The process of Claim 1 wherein if the risk value for the individual developing IBD is greater than the cut-off value, the process further includes the step of using a blood sample from the individual to determine if mesalamine therapy will be effective or if an alternate therapy should be administered to the individual.
6. The process of Claim 1 wherein the prediction target biomarkers panel for use in predicting the risk of the individual developing IBD, without environmental change and/or impactful stress being considered, comprises protein predictive target biomarkers of HP, GCSF, RETN, CRP, sICAM and antibody target biomarkers antibody to tetanus toxoid and identifies the relationships of sICAM x HP, GCSF x CRP and GCSF x RETN.
7. The process of Claim 1 wherein the prediction logistic regression model for determining the risk value for developing IBD without considering environmental change and/or impactful stress is: Log (p/lip) = -641.8833706 +
71.65755693 x Haptoglobin ¨ 41.87442414 x GCSF ¨ 45.27490174 x RETN ¨
16.22723673 x CRP ¨ 1.029456032 x Antibody TT + 14.476981343 x sICAM +
5.667294456 x (sICAM x Haptoglobin) ¨ 0.80715758 x (GCSF x CRP) ¨
2.288843531 x (GCSF x RETN).
71.65755693 x Haptoglobin ¨ 41.87442414 x GCSF ¨ 45.27490174 x RETN ¨
16.22723673 x CRP ¨ 1.029456032 x Antibody TT + 14.476981343 x sICAM +
5.667294456 x (sICAM x Haptoglobin) ¨ 0.80715758 x (GCSF x CRP) ¨
2.288843531 x (GCSF x RETN).
8. The process of Claim 1 for identifying individuals that may have or have a high risk of developing inflammatory bowel disease (IBD), wherein the prediction target biomarkers panel for use in predicting the risk of the individual developing IBD with environmental change and/or impact stress being considered comprises protein predictive target biomarkers of sICAM, GCSF, HP, CRP, RETN and antibody target biomarkers antibody to tetanus toxoid.
9. The process of Claim 1 wherein the prediction logistic regression model for determining the risk value for developing IBD with considering environmental change and/or impacfful stress is: Log (p/1 ip) = 1101.571616 ¨
0.813305575 x deployment ¨ 104.1257102 x sICAM ¨ 62.63858365 x GCSF ¨
5.604142451 x sICAM x GCSF + 65.611507602 x HP + 5.107130532 x sICAM
x HP -36.81637743 x TT ¨ 1.711888269 x GCSF x TT + 0.767135503 x CRP
+ 1.770741857 x RETN.
0.813305575 x deployment ¨ 104.1257102 x sICAM ¨ 62.63858365 x GCSF ¨
5.604142451 x sICAM x GCSF + 65.611507602 x HP + 5.107130532 x sICAM
x HP -36.81637743 x TT ¨ 1.711888269 x GCSF x TT + 0.767135503 x CRP
+ 1.770741857 x RETN.
10. The process of Claim 2 wherein the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease comprises protein predictive target biomarkers of SCL70, AcE, RETN, CRP, GCSF and an antibody target biomarker of antibody to tetanus toxoid (TT) and identifies the relationships of sICAM x RETN, GCSF x TT,
11. The process of Claim 2 wherein the prediction logistic regression model for determining the risk value for developing Crohn's disease is Log (p/1ip) =
-174.4 + 171.2 x SLC70 ¨ 4.0 x AcE ¨ 32.2 x RETN + 1.1 x CRP + 15.9 x GCSF
¨ 57.4 x TT + 11.6 x (sICAM x RETN) ¨ 2.7 x (GCSF x TT).
-174.4 + 171.2 x SLC70 ¨ 4.0 x AcE ¨ 32.2 x RETN + 1.1 x CRP + 15.9 x GCSF
¨ 57.4 x TT + 11.6 x (sICAM x RETN) ¨ 2.7 x (GCSF x TT).
12. The process of Claim 3 wherein the panel of predictive target biomarkers for use in determining the risk of the individual developing UC comprises protein predictive target biomarkers of HP, SICAM1 and RETN, and identifies the relationship of sICAM1 x HP.
13. The process of Claim 3 wherein the prediction logistic regression model for determining the risk value for developing UC is Log (p/1-p) = 221.7 + 2.2 x Resistin + 15.1 x sICAM + 61.5 x Haptoglobin + 4.9 x (sICAM x Haptoglobin).
14. A process of identifying individuals that may have or have a high risk of developing inflammatory bowel disease (IBD) prior to diagnosis of IBD, the process comprises the steps of:
identifying an individual to be tested;
obtaining a first blood sample of the individual;
selecting a panel of predictive target biomarkers for IBD;
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for IBD;
determining the total level of protein in the blood sample;
selecting a prediction logistic regression model for predicting the risk of an individual developing IBD and using the selected logistic regression model for predicting the risk of an individual developing IBD and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for IBD and the total level of protein in the blood sample to calculate a risk value for the individual developing IBD; and determining if the risk value for the individual developing IBD is above or below a cut-off value for the selected prediction logistic regression model for predicting the risk of an individual developing IBD;
wherein if the risk value for the individual developing IBD is below the cut-off value the process includes the step of determining if a therapy to monitor the individual for detecting an increase in the risk of developing 1130 should be administered;
wherein if the risk value for the individual developing IBD is greater than the cut-off value, the process includes the steps of:
selecting a panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease;
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease;
selecting a prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease and using the logistic regression model and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease and the total level of protein in the blood sample to calculate a risk value for developing Crohn's disease;
determining if the risk value for developing Crohn's disease is above or below a cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease;
wherein if the risk value for developing Crohn's disease is greater than the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease, the process includes the step of administering a therapy to reduce the risk of developing Crohn's disease; and wherein if the risk value for developing Crohn's disease is below the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease, the process includes the steps of selecting a panel of predictive target biomarkers for use in determining the risk of the individual developing ulcerative colitis (UC);
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing UC;
selecting a prediction logistic regression model for use in determining the risk of the individual developing UC and using the logistic regression model and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing UC and the total level of protein in the blood sample to calculate a risk value for developing UC;
determining if the risk value for developing UC is above or below a cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC;
wherein if the risk value for developing UC is greater than the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC disease, the process includes the step of administering a therapy to reduce the risk of developing UC; and wherein if the risk value for developing UC is below the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC, the process includes the step of determining if a therapy to monitor the individual for detecting an increase in the risk of developing UC should be adminstered.
identifying an individual to be tested;
obtaining a first blood sample of the individual;
selecting a panel of predictive target biomarkers for IBD;
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for IBD;
determining the total level of protein in the blood sample;
selecting a prediction logistic regression model for predicting the risk of an individual developing IBD and using the selected logistic regression model for predicting the risk of an individual developing IBD and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for IBD and the total level of protein in the blood sample to calculate a risk value for the individual developing IBD; and determining if the risk value for the individual developing IBD is above or below a cut-off value for the selected prediction logistic regression model for predicting the risk of an individual developing IBD;
wherein if the risk value for the individual developing IBD is below the cut-off value the process includes the step of determining if a therapy to monitor the individual for detecting an increase in the risk of developing 1130 should be administered;
wherein if the risk value for the individual developing IBD is greater than the cut-off value, the process includes the steps of:
selecting a panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease;
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease;
selecting a prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease and using the logistic regression model and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease and the total level of protein in the blood sample to calculate a risk value for developing Crohn's disease;
determining if the risk value for developing Crohn's disease is above or below a cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease;
wherein if the risk value for developing Crohn's disease is greater than the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease, the process includes the step of administering a therapy to reduce the risk of developing Crohn's disease; and wherein if the risk value for developing Crohn's disease is below the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing Crohn's disease, the process includes the steps of selecting a panel of predictive target biomarkers for use in determining the risk of the individual developing ulcerative colitis (UC);
examining the blood sample to obtain a level of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing UC;
selecting a prediction logistic regression model for use in determining the risk of the individual developing UC and using the logistic regression model and the levels of each predictive target biomarker listed on the panel of predictive target biomarkers for use in determining the risk of the individual developing UC and the total level of protein in the blood sample to calculate a risk value for developing UC;
determining if the risk value for developing UC is above or below a cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC;
wherein if the risk value for developing UC is greater than the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC disease, the process includes the step of administering a therapy to reduce the risk of developing UC; and wherein if the risk value for developing UC is below the cut-off value for the prediction logistic regression model for use in determining the risk of the individual developing UC, the process includes the step of determining if a therapy to monitor the individual for detecting an increase in the risk of developing UC should be adminstered.
15. The process of Claim 14 wherein if the risk value for the individual developing IBD is greater than the cut-off value, the process includes the step of predicting the effectiveness of mesalamine treatment for the individual.
16. The process of Claim 14 wherein if the risk value for the individual developing IBD is greater than the cut-off value, the process further includes the step of using a blood sample from the individual to determine if mesalamine therapy will be effective or if an alternate therapy should be administered to the individual.
17. The process of Claim 14 wherein the prediction target biomarkers panel for use in predicting the risk of the individual developing IBD, without environmental change and/or impactful stress being considered, comprises protein predictive target biomarkers of HP, GCSF, RETN, CRP, sICAM and antibody target biomarker of antibody to tetanus toxoid and identifies the relationships of sICAM x HP, GCSF x CRP and GCSF x RETN.
18. The process of Claim 14 wherein the prediction logistic regression model for determining the risk value for developing IBD without considering environmental change and/or impactful stress is: Log (p/1 ip) = -641.8833706 +
71.65755693 x Haptoglobin ¨ 41.87442414 x GCSF ¨ 45.27490174 x RETN ¨
16.22723673 x CRP ¨ 1.029456032 x Antibody TT + 14.476981343 x sICAM +
5.667294456 x (sICAM x Haptoglobin) ¨ 0.80715758 x (GCSF x CRP) ¨
2.288843531 x (GCSF x RETN).
71.65755693 x Haptoglobin ¨ 41.87442414 x GCSF ¨ 45.27490174 x RETN ¨
16.22723673 x CRP ¨ 1.029456032 x Antibody TT + 14.476981343 x sICAM +
5.667294456 x (sICAM x Haptoglobin) ¨ 0.80715758 x (GCSF x CRP) ¨
2.288843531 x (GCSF x RETN).
19. The process of Claim 14 for identifying individuals that may have or have a high risk of developing inflammatory bowel disease (IBD), wherein the prediction target biomarkers panel for use in predicting the risk of the individual developing IBD with environmental change and/or impacfful stress being considered comprises protein predictive target biomarkers of sICAM, GCSF, HP, CRP, RETN and antibody target biomarkers antibody to tetanus toxoid.
20 The process of Claim 14 wherein the prediction logistic regression model for determining the risk value for developing IBD with considering of environmental change and/or impactful stress is: Log (p/1 ip) = 1101.571616 ¨
0.813305575 x deployment ¨ 104.1257102 x sICAM ¨ 62.63858365 x GCSF ¨
5.604142451 x sICAM x GCSF + 65.611507602 x HP + 5.107130532 x sICAM
x HP -36.81637743 x TT ¨ 1.711888269 x GCSF x TT + 0.767135503 x CRP
+ 1.770741857 x RETN.
0.813305575 x deployment ¨ 104.1257102 x sICAM ¨ 62.63858365 x GCSF ¨
5.604142451 x sICAM x GCSF + 65.611507602 x HP + 5.107130532 x sICAM
x HP -36.81637743 x TT ¨ 1.711888269 x GCSF x TT + 0.767135503 x CRP
+ 1.770741857 x RETN.
21. The process of Claim 14 wherein the panel of predictive target biomarkers for use in determining the risk of the individual developing Crohn's disease comprises protein predictive target biomarkers of SCL70, AcE, RETN, CRP, GCSF and antibody target biomarker of antibody to tetanus toxoid (TT) and identifies the relationships of sICAM x RETN, GCSF x TT.
22. The process of Claim 14 wherein the prediction logistic regression model for determining the risk value for developing Crohn's disease is Log (p/1ip) =
-174.4 + 171.2 x SLC70 ¨ 4.0 x AcE ¨ 32.2 x RETN + 1.1 x CRP + 15.9 x GCSF
¨ 57.4 x TT + 11.6 x (sICAM x RETN) ¨ 2.7 x (GCSF x TT).
-174.4 + 171.2 x SLC70 ¨ 4.0 x AcE ¨ 32.2 x RETN + 1.1 x CRP + 15.9 x GCSF
¨ 57.4 x TT + 11.6 x (sICAM x RETN) ¨ 2.7 x (GCSF x TT).
23. The process of Claim 14 wherein the panel of predictive target biomarkers for use in determining the risk of the individual developing UC
comprises protein predictive target biomarkers of HP, SICAM1 and RETN, and identifies the relationship of sICAM1 x HP.
comprises protein predictive target biomarkers of HP, SICAM1 and RETN, and identifies the relationship of sICAM1 x HP.
24. The process of Claim 14 wherein the prediction logistic regression model for determining the risk value for developing UC is Log (p/1-p) = 221.7 + 2.2 x Resistin + 15.1 x sICAM + 61.5 x Haptoglobin + 4.9 x (sICAM x Haptoglobin).
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201862720468P | 2018-08-21 | 2018-08-21 | |
| US62/720,468 | 2018-08-21 | ||
| PCT/US2019/047231 WO2020041287A1 (en) | 2018-08-21 | 2019-08-20 | Process and system for identifying individuals having a high risk of inflammatory bowel disease and a method of treatment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA3108719A1 true CA3108719A1 (en) | 2020-02-27 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA3108719A Pending CA3108719A1 (en) | 2018-08-21 | 2019-08-20 | Process and system for identifying individuals having a high risk of inflammatory bowel disease and a method of treatment |
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| Country | Link |
|---|---|
| EP (1) | EP3840831A4 (en) |
| AU (1) | AU2019325238A1 (en) |
| CA (1) | CA3108719A1 (en) |
| WO (1) | WO2020041287A1 (en) |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009114756A2 (en) * | 2008-03-14 | 2009-09-17 | Exagen Diagnostics, Inc. | Biomarkers for inflammatory bowel disease and irritable bowel syndrome |
| US20120171672A1 (en) * | 2009-04-14 | 2012-07-05 | Prometheus Laboratories Inc. | Inflammatory bowel disease prognostics |
| EP2419529B1 (en) | 2009-04-14 | 2015-05-20 | Nestec S.A. | Inflammatory bowel disease prognostics |
| WO2012037199A2 (en) * | 2010-09-14 | 2012-03-22 | The Johns Hopkins University | Methods for diagnosis and prognosis of inflammatory bowel disease using cytokine profiles |
| CA2852954A1 (en) * | 2011-10-21 | 2013-04-25 | Nestec S.A. | Methods for improving inflammatory bowel disease diagnosis |
| WO2014182689A1 (en) * | 2013-05-06 | 2014-11-13 | Yacyshyn Bruce R | Method of using biomarkers in predicting inflammatory bowel disease |
| US10295527B2 (en) * | 2016-03-14 | 2019-05-21 | Bruce Yacyshyn | Process and system for predicting responders and non-responders to mesalamine treatment of ulcerative colitis |
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2019
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- 2019-08-20 AU AU2019325238A patent/AU2019325238A1/en active Pending
- 2019-08-20 WO PCT/US2019/047231 patent/WO2020041287A1/en active Search and Examination
- 2019-08-20 CA CA3108719A patent/CA3108719A1/en active Pending
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| AU2019325238A1 (en) | 2021-03-18 |
| EP3840831A1 (en) | 2021-06-30 |
| EP3840831A4 (en) | 2022-05-11 |
| WO2020041287A1 (en) | 2020-02-27 |
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