CN112967750A - Model for predicting clopidogrel antiplatelet effect and application thereof - Google Patents
Model for predicting clopidogrel antiplatelet effect and application thereof Download PDFInfo
- Publication number
- CN112967750A CN112967750A CN202110305967.0A CN202110305967A CN112967750A CN 112967750 A CN112967750 A CN 112967750A CN 202110305967 A CN202110305967 A CN 202110305967A CN 112967750 A CN112967750 A CN 112967750A
- Authority
- CN
- China
- Prior art keywords
- clopidogrel
- score
- antiplatelet
- platelet
- cyp2c19
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- GKTWGGQPFAXNFI-HNNXBMFYSA-N clopidogrel Chemical compound C1([C@H](N2CC=3C=CSC=3CC2)C(=O)OC)=CC=CC=C1Cl GKTWGGQPFAXNFI-HNNXBMFYSA-N 0.000 title claims abstract description 73
- 239000005552 B01AC04 - Clopidogrel Substances 0.000 title claims abstract description 72
- 229960003009 clopidogrel Drugs 0.000 title claims abstract description 72
- 230000006502 antiplatelets effects Effects 0.000 title claims abstract description 17
- 108010026925 Cytochrome P-450 CYP2C19 Proteins 0.000 claims abstract description 53
- 230000000694 effects Effects 0.000 claims abstract description 49
- 239000003146 anticoagulant agent Substances 0.000 claims abstract description 21
- 230000000702 anti-platelet effect Effects 0.000 claims abstract description 19
- 108091006082 receptor inhibitors Proteins 0.000 claims abstract description 11
- 208000032843 Hemorrhage Diseases 0.000 claims abstract description 9
- OEKWJQXRCDYSHL-FNOIDJSQSA-N ticagrelor Chemical compound C1([C@@H]2C[C@H]2NC=2N=C(N=C3N([C@H]4[C@@H]([C@H](O)[C@@H](OCCO)C4)O)N=NC3=2)SCCC)=CC=C(F)C(F)=C1 OEKWJQXRCDYSHL-FNOIDJSQSA-N 0.000 claims abstract description 7
- 229960002528 ticagrelor Drugs 0.000 claims abstract description 7
- 229960004588 cilostazol Drugs 0.000 claims abstract description 6
- RRGUKTPIGVIEKM-UHFFFAOYSA-N cilostazol Chemical compound C=1C=C2NC(=O)CCC2=CC=1OCCCCC1=NN=NN1C1CCCCC1 RRGUKTPIGVIEKM-UHFFFAOYSA-N 0.000 claims abstract description 6
- 239000003112 inhibitor Substances 0.000 claims abstract description 6
- KGFYHTZWPPHNLQ-AWEZNQCLSA-N rivaroxaban Chemical compound S1C(Cl)=CC=C1C(=O)NC[C@@H]1OC(=O)N(C=2C=CC(=CC=2)N2C(COCC2)=O)C1 KGFYHTZWPPHNLQ-AWEZNQCLSA-N 0.000 claims abstract description 6
- 229960001148 rivaroxaban Drugs 0.000 claims abstract description 6
- 102000019057 Cytochrome P-450 CYP2C19 Human genes 0.000 claims abstract 9
- 108090000190 Thrombin Proteins 0.000 claims description 8
- 229960004072 thrombin Drugs 0.000 claims description 8
- 208000028867 ischemia Diseases 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 230000007774 longterm Effects 0.000 claims description 2
- 230000004044 response Effects 0.000 claims description 2
- 208000034158 bleeding Diseases 0.000 abstract description 5
- 230000000740 bleeding effect Effects 0.000 abstract description 5
- 238000001514 detection method Methods 0.000 abstract description 4
- 230000002785 anti-thrombosis Effects 0.000 abstract 1
- 102100029363 Cytochrome P450 2C19 Human genes 0.000 description 45
- 208000029078 coronary artery disease Diseases 0.000 description 7
- 208000007536 Thrombosis Diseases 0.000 description 5
- 229940079593 drug Drugs 0.000 description 5
- 239000003814 drug Substances 0.000 description 5
- 238000000034 method Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 230000000302 ischemic effect Effects 0.000 description 4
- 230000004060 metabolic process Effects 0.000 description 4
- 208000010110 spontaneous platelet aggregation Diseases 0.000 description 4
- 108700028369 Alleles Proteins 0.000 description 3
- 101150003340 CYP2C19 gene Proteins 0.000 description 3
- 238000013176 antiplatelet therapy Methods 0.000 description 3
- 239000008280 blood Substances 0.000 description 3
- 210000004369 blood Anatomy 0.000 description 3
- 230000005764 inhibitory process Effects 0.000 description 3
- 229940127218 antiplatelet drug Drugs 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000000106 platelet aggregation inhibitor Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000002560 therapeutic procedure Methods 0.000 description 2
- 239000005995 Aluminium silicate Substances 0.000 description 1
- 102000002004 Cytochrome P-450 Enzyme System Human genes 0.000 description 1
- 108010015742 Cytochrome P-450 Enzyme System Proteins 0.000 description 1
- 102000004190 Enzymes Human genes 0.000 description 1
- 108090000790 Enzymes Proteins 0.000 description 1
- 235000009421 Myristica fragrans Nutrition 0.000 description 1
- 108010094028 Prothrombin Proteins 0.000 description 1
- 102100027378 Prothrombin Human genes 0.000 description 1
- 230000001154 acute effect Effects 0.000 description 1
- 235000012211 aluminium silicate Nutrition 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 229940127217 antithrombotic drug Drugs 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- HODFCFXCOMKRCG-UHFFFAOYSA-N bitolterol mesylate Chemical compound CS([O-])(=O)=O.C1=CC(C)=CC=C1C(=O)OC1=CC=C(C(O)C[NH2+]C(C)(C)C)C=C1OC(=O)C1=CC=C(C)C=C1 HODFCFXCOMKRCG-UHFFFAOYSA-N 0.000 description 1
- 230000023555 blood coagulation Effects 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 239000001913 cellulose Substances 0.000 description 1
- 229920002678 cellulose Polymers 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 230000035602 clotting Effects 0.000 description 1
- 210000004351 coronary vessel Anatomy 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013211 curve analysis Methods 0.000 description 1
- 108010012052 cytochrome P-450 CYP2C subfamily Proteins 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011977 dual antiplatelet therapy Methods 0.000 description 1
- 229940088598 enzyme Drugs 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 210000003494 hepatocyte Anatomy 0.000 description 1
- 238000000338 in vitro Methods 0.000 description 1
- 230000003834 intracellular effect Effects 0.000 description 1
- NLYAJNPCOHFWQQ-UHFFFAOYSA-N kaolin Chemical compound O.O.O=[Al]O[Si](=O)O[Si](=O)O[Al]=O NLYAJNPCOHFWQQ-UHFFFAOYSA-N 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 210000004185 liver Anatomy 0.000 description 1
- 239000001115 mace Substances 0.000 description 1
- 239000002207 metabolite Substances 0.000 description 1
- 238000011119 multifactor regression analysis Methods 0.000 description 1
- 208000010125 myocardial infarction Diseases 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000004783 oxidative metabolism Effects 0.000 description 1
- 230000002974 pharmacogenomic effect Effects 0.000 description 1
- 230000010118 platelet activation Effects 0.000 description 1
- 229940002612 prodrug Drugs 0.000 description 1
- 239000000651 prodrug Substances 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 229940039716 prothrombin Drugs 0.000 description 1
- 108020003175 receptors Proteins 0.000 description 1
- 102000005962 receptors Human genes 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 230000008320 venous blood flow Effects 0.000 description 1
Images
Classifications
-
- 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
- G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K49/00—Preparations for testing in vivo
- A61K49/0004—Screening or testing of compounds for diagnosis of disorders, assessment of conditions, e.g. renal clearance, gastric emptying, testing for diabetes, allergy, rheuma, pancreas functions
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Epidemiology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Primary Health Care (AREA)
- Biomedical Technology (AREA)
- Pathology (AREA)
- Physiology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Endocrinology (AREA)
- Veterinary Medicine (AREA)
- Diabetes (AREA)
- Animal Behavior & Ethology (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Toxicology (AREA)
- Gastroenterology & Hepatology (AREA)
- Biotechnology (AREA)
- Evolutionary Biology (AREA)
- Rheumatology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Theoretical Computer Science (AREA)
- Urology & Nephrology (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention discloses a model for predicting clopidogrel antiplatelet effect and application thereof, belonging to the technical field of biological detection. The model comprises a data input unit, an assignment unit, a curative effect prediction unit and a report output unit for three independent risk factors of age, basic platelet activity and CYP2C19 genotype of a detected object, an ABC scoring system is constructed according to the above units, the total score is 7, if the ABC score is less than or equal to 3, clopidogrel can be used as a platelet P2Y12Receptor inhibitors are antiplatelet; if the ABC score is more than or equal to 4, the combination of ticagrelor, clopidogrel and cilostazol or the combination of clopidogrel and small-dose rivaroxaban can be selected. The model may be used to guide a clinicianAccurate selection of platelets P2Y12The receptor inhibitor has accurate and reliable result for predicting the curative effect of the clopidogrel, and can balance the antithrombotic benefit and the bleeding risk.
Description
Technical Field
The invention belongs to the technical field of biological detection, and particularly relates to a model for predicting clopidogrel antiplatelet efficacy and application thereof, which can be used for guiding a clinician to accurately select platelet P2Y12Receptor inhibitors for assessing ischemia andthe risk of bleeding.
Background
Platelet activation is a key link in the development and progression of coronary heart disease, playing an important role in thrombotic events, and antiplatelet therapy is the cornerstone of coronary heart disease therapy. Coronary heart disease patients in China increase year by year, nearly 100 million patients are treated with coronary artery intervention (PCI) every year, and most of the patients need to jointly use the platelet P2Y on the basis of aspirin12Receptor inhibitors, as dual anti-platelet therapy can effectively reduce the occurrence of acute ischemic events. Compared with ticagrelor, clopidogrel has the advantages of low price, high use compliance, low bleeding risk and the like, and is the most commonly used platelet P2Y at present12A receptor inhibitor.
It is worth noting that significant individual differences exist in clopidogrel therapeutic effect, and the antiplatelet effect of the same dose of the drug on different patients is different. After more than 20% of patients take conventional dose of clopidogrel, the platelet aggregation capability is not effectively inhibited, and the residual high platelet activity (HRPR) causes the patients to be still in high-risk states such as thrombus in a stent, myocardial infarction and the like. Obviously, the unpredictability of the anti-platelet effect of clopidogrel brings interference and challenge to clinical work, if the curative effect of clopidogrel can be estimated in advance, high-risk patients can be identified at early stage, and individualized anti-platelet strategies can be adopted for the patients.
Clopidogrel pharmacogenomics offers the possibility of individualized antiplatelet therapy, but there are still some points to be improved. Clopidogrel is a prodrug, which needs to be biologically converted into active metabolites in the liver through hepatocyte cytochrome P450(CYP450) to exert an antiplatelet effect, wherein CYP2C19 participates in two steps of clopidogrel oxidative metabolism. Therefore, if a patient carries a loss-of-function (LOF) site of CYP2C19, the activity of the enzyme may be decreased, so that active metabolites of clopidogrel are decreased, thereby the antiplatelet effect is weakened. The research finds that nearly 60% of patients with coronary heart disease carry CYP2C19LOF sites, and the CYP2C19 x 2 and x 3LOF sites influence the curative effect of clopidogrel through four aspects of intracellular molecular level, platelet function, perioperative events and long-term prognosis. In addition, although the east asian population carries much more frequent CYP2C19LOF sites than the european and american population, the same clopidogrel treatment does not increase the event rate, even lower than that of the european and american population, perhaps because of the inherent platelet activity. CYP2C19 gene polymorphism affects clopidogrel metabolism, but does not directly affect platelets; the curative effect of antiplatelet drugs is related to the activity of platelets in addition to drugs. For patients with coronary heart disease in China, if the fact that treatment is guided only according to the CYP2C19 genotype means that more than half of patients need to replace clopidogrel, the method is excessive and inaccurate. The TAILOR-PCI study published by the ACC conference in 2020, which is the most extensive assessment to date using CYP2C19 gene testing to guide the selection of anti-platelet strategies, also showed that simple reliance on CYP2C19 genotype was insufficient to guide individualized platelet therapy, and that the study population included east asian patients and the results did not reach positive endpoints.
Disclosure of Invention
The invention aims to provide a model for predicting clopidogrel antiplatelet effect, and the ABC scoring model for accurately predicting clopidogrel antiplatelet effect is constructed by combining the age of a patient and the activity of basic platelets with the existing CYP2C19 genotype factors on the basis of earlier research, so that the prediction effect is good.
The second purpose of the invention is to provide the model for predicting the anti-platelet efficacy of clopidogrel for guiding clinicians to accurately select the platelet P2Y12Use of a receptor inhibitor to balance the risk of ischemia and hemorrhage in a patient.
In order to achieve the purpose, the invention adopts the technical scheme that:
a model for predicting clopidogrel antiplatelet effect comprises a data input unit, an assignment unit, an effect prediction unit and a report output unit for three independent risk factors of age (age, A), basal platelet activity (B) and CYP2C19 genotype (CYP2C19 genotype, C) of a detected object, and an ABC scoring system is constructed according to the units; wherein,
the data input module collects and records three independent risk factor data of the age, the basal platelet activity and the CYP2C19 genotype of a detected object and stores the data; wherein the basal platelet activity is represented by thrombin-induced MA values measured by thromboelastography, and the CYP2C19 genotype is selected from one of a no CYP2C19LOF site, a CYP2C19LOF site, and two CYP2C19LOF sites;
the assignment module comprises an age assignment subunit, a basal platelet activity subunit and a CYP2C19 genotype subunit in parallel, and assigns a value by:
in the age assignment subunit, if the age is less than or equal to 60 years, the score is 0; a score of 1 if the age is 61-75 years; score 2 if age >75 years;
in the basal platelet activity subunit, if the thrombin-induced MA value is greater than 65.7mm, the score is 3, otherwise the score is 0;
in the CYP2C19 genotype subunit, if the CYP2C19 genotype is without CYP2C19LOF locus, the score is 0; scoring 1 if the CYP2C19 genotype is one CYP2C19LOF locus; a score of 2 if the CYP2C19 genotype is two CYP2C19LOF loci;
the curative effect prediction unit receives the assignment of the assignment module, performs clopidogrel antiplatelet effect prediction according to the total score of the assignment, and is counted according to the total score of 7:
if the ABC score is less than or equal to 3 points, clopidogrel alone can be selected as the platelet P2Y12The receptor inhibitor is antiplatelet, has low price and low bleeding risk;
if the ABC score is more than or equal to 4, the combination of ticagrelor, clopidogrel and cilostazol or the combination of clopidogrel and small-dose rivaroxaban can be selected.
According to some embodiments of the invention, the subject is a patient chronically taking clopidogrel antiplatelet.
The invention also provides the prediction of clopidogrel antiplatelet effectIn guiding the clinician in the precise selection of platelets P2Y12Use of a receptor inhibitor to balance the risk of ischemia and hemorrhage in a patient.
According to an embodiment of the invention, if the ABC score is less than or equal to 3 points, the response of the patient to clopidogrel is estimated to be acceptable, and clopidogrel can be used as a platelet P2Y12Receptor inhibitors are antiplatelet; if the ABC score is not less than 4 points, estimating the clopidogrel reaction difference, and selecting combination of ticagrelor, clopidogrel and cilostazol or combination of clopidogrel and small-dose rivaroxaban.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the invention, on the basis of earlier research, the age and the basic platelet activity (the value of MA (thrombin-induced value) measured by a thromboelastogram) of a patient are independent risk factors for predicting the curative effect of clopidogrel besides the CYP2C19 genotype, and the three independent risk factors have an overlapping effect on the curative effect of clopidogrel, so that the prediction result is more accurate and reliable compared with the existing method for predicting the curative effect of clopidogrel by only depending on the CYP2C19 genotype. In addition, the three factors can be detected quickly in clinic, and are simple, convenient and quick.
(2) The model of the invention is an upstream prediction rather than a post-evaluation, used to guide the clinician in the precise selection of platelets P2Y12The receptor inhibitor balances the ischemia and hemorrhage risks of patients, and can effectively avoid the phenomenon of insufficient curative effect of the antithrombotic drug in the perioperative period.
The above-described and other features, aspects, and advantages of the present application will become more apparent with reference to the following detailed description.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of the structure of a model for predicting clopidogrel antiplatelet efficacy in accordance with the present invention;
FIG. 2 is an evaluation system of a model for predicting clopidogrel antiplatelet efficacy in accordance with the present invention;
FIG. 3 is a graph of basal maximal platelet activity versus residual platelet activity in the metabolic process of drug-to-platelet activity inhibition;
FIG. 4 shows that CYP2C19LOF of Chinese population has dose-effect relationship with clopidogrel's antiplatelet effect;
fig. 5 is a correlation of residual high platelet activity (HRPR) and one year ischemic event (MACE) after clopidogrel treatment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The use of "first," "second," and similar terms in the description and claims of the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. Also, the use of the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one.
In the prior art, CYP2C19 genotype only influences the metabolism of clopidogrel, so that the active drug concentration of the clopidogrel is different, and the clopidogrel curative effect cannot be predicted sufficiently. On the basis of earlier research, the invention discovers that the antiplatelet effect of the drug is related to the drug, patients and platelets, integrates clinical factors of the patients such as age, platelet activity (the MA value (prothrombin) induced by thrombin detected by a thromboelastogram can be used as the index of the baseline platelet activity in patients with coronary heart disease) and CYP2C19 genotype, better predicts the curative effect of the patients on using the clopidogrel for antiplatelet, and accurately guides the platelets P2Y12Selection of receptor inhibitors, as shown in FIG. 1 and2, wherein:
if ABC score is less than or equal to 3 points, platelet P2Y12The receptor inhibitor can be clopidogrel, and has low price and low bleeding risk;
if the ABC score is more than or equal to 4 points, the platelet P2Y12The receptor inhibitor can be selected from ticagrelor, or a combination of clopidogrel and cilostazol, or a combination of clopidogrel and small dose of rivaroxaban.
Applicable objects are as follows: patients who need to use clopidogrel for a long time.
And (3) detection required:
detecting CYP2C19 genotype, wherein an x 2 locus and an x 3 locus are required to be detected simultaneously;
if the genotype is CYP2C19 x 1/x 1, the genotype is wild type and does not carry CYP2C19 LOF;
if the genotype is CYP2C19 x 1/x 2 or x 1/x 3, then it is carried 1 CYP2C19 LOF;
if the genotype is CYP2C19 x 2/' 2 or x 2/' 3 or x 3/' 3, then 2 CYP2C19LOF are carried.
Detecting thrombin-induced MA value by Thromboelastography (TEG), wherein the TEG analyzer mainly comprises two parts: an oscillatable rotating heating cup and a needle freely suspended from the tension wire. The fresh blood is placed in a small cup, the suspended needle is soaked in the blood, and when the blood sample is in a liquid state, the movement of the small cup does not affect the needle. And when the clot begins to form, the cellulose and platelets in the clot form a clot, coupling the movement of the cuvette with the needle, the magnitude of the needle movement being related to the strength of the clot that has formed, and decoupling the needle from the clot as it retracts or dissolves. The above changes and the elasticity of the blood clot are transmitted by the effect information generated by the connection with the needle, and then transmitted to a TEG recorder through amplification, and the thrombus elasticity pattern is formed on a thermal paper by recording at the speed of 2 mm/min. In the detection process, slow venous blood flow is simulated in vitro, blood clot formation is induced by adding a reagent containing kaolin, the time and the quantity of thrombus formation are measured by a receptor, and a computer is used for drawing blood clotting speed and intensity curves to obtain an MA value.
The process of constructing the model for predicting the anti-platelet efficacy of clopidogrel is as follows:
studies show that the final factor in determining the efficacy of antiplatelet drugs is residual platelet activity, not just the rate of drug-to-platelet activity inhibition, and that individualized antiplatelet therapy cannot place ocular light on pharmacokinetics alone (fig. 3).
② CYP2C19 gene polymorphism affecting clopidogrel metabolism, and individual CYP2C19 genotype is used alone to guide individualized P2Y12The choice of receptor inhibitors is not accurate enough.
③ different people have different effects of CYP2C19LOF on the effect of clopidogrel, and east Asia people have lower effect of CYP2C19LOF and dose-effect relationship. Research shows that CYP2C19 x 2 and CYP2C19 x 3 are obviously related to platelet aggregation rate induced by ADP and inhibition rate of clopidogrel to platelets, the frequency of carrying CYP2C19 x 2 allele by Chinese coronary heart disease patients reaches 53.7%, the frequency of carrying CYP2C19 x 3 allele also has 10.5%, and the CYP2C19 x 2 and CYP2C19 x 3 allele are independent risk factors for predicting the antiplatelet effect of clopidogrel and have no obvious difference in influence; the study also found that 2 LOF carriers (CYP2C19 × 2/, CYP2C19 × 2/, CYP2C 3 and CYP2C19 × 3 /) had a higher platelet aggregation rate than 1 LOF carrier (CYP2C19 × 1/, 2 and CYP2C19 × 1/, 3) (57.6% ± 22.9% vs. 48.2% ± 25.2%, p ═ 0.008), while the one that did not carry LOF (CYP2C19 ±) had the lowest platelet aggregation rate (38.6 ± 22.7% vs. 48.2% ± 25.2%, p <0.001) (fig. 4).
And fourthly, performing multi-factor regression analysis on clinical factors, biochemical factors and CYP2C19 genotype which may influence the antiplatelet effect of clopidogrel (Table 1), wherein the results show that age, the MA value induced by thrombin and the CYP2C19 genotype are independent risk factors for predicting residual high platelet activity (HRPR) of clopidogrel after antiplatelet treatment, so that an ABC scoring model is established for predicting the curative effect of clopidogrel, and corresponding assignment is given according to the OR value.
TABLE 1
Fifthly, comparing the ABC scoring model with the method for predicting the clopidogrel curative effect by singly depending on the CYP2C19 genotype, taking HRPR and one-year ischemic events as endpoints, and displaying that the AUC of the ABC scoring model is obviously superior to the CYP2C19 genotype through ROC curve analysis (Table 2 and figure 5).
TABLE 2
| Predicting HRPR | AUC(95％CI) | P value | Sensitivity | Specificity |
| CYP2C19 genotype | 0.597(0.550-0.643) | <0.001 | 72.82 | 43.02 |
| ABC scoring model | 0.738(0.695-0.778) | <0.001 | 59.22 | 74.41 |
| |
AUC(95％CI) | P value | Sensitivity | Specificity |
| CYP2C19 genotype | 0.604(0.550-0.643) | 0.0312 | 31.58 | 86.80 |
| ABC scoring model | 0.676(0.630-0.719) | <0.001 | 57.89 | 69.19 |
While there have been shown and described what are at present considered the fundamental principles and essential features of the invention and its advantages, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and the description is given here only for clarity, and those skilled in the art should integrate the description, and the embodiments may be combined appropriately to form other embodiments understood by those skilled in the art.
Claims (4)
1. A model for predicting clopidogrel antiplatelet effect is characterized by comprising a data input unit, an assignment unit, an effect prediction unit and a report output unit for three independent risk factors of age, basic platelet activity and CYP2C19 genotype of a detected object, wherein an ABC scoring system is constructed according to the units; wherein,
the data input module collects and records three independent risk factor data of the age, the basic platelet activity and the CYP2C19 genotype of a detected object, the basic platelet activity is expressed by an MA value induced by thrombin measured by a thromboelastogram, and the CYP2C19 genotype is selected from one of a non-CYP 2C19LOF locus, a CYP2C19LOF locus and two CYP2C19LOF loci;
the assignment module comprises an age assignment subunit, a basal platelet activity subunit and a CYP2C19 genotype subunit in parallel, and assigns a value by:
in the age assignment subunit, if the age is less than or equal to 60 years, the score is 0; a score of 1 if the age is 61-75 years; score 2 if age >75 years;
in the basal platelet activity subunit, if the thrombin-induced MA value is greater than 65.7mm, the score is 2, otherwise the score is 0;
in the CYP2C19 genotype subunit, if the CYP2C19 genotype is without CYP2C19LOF locus, the score is 0; scoring 1 if the CYP2C19 genotype is one CYP2C19LOF locus; a score of 2 if the CYP2C19 genotype is two CYP2C19LOF loci;
the curative effect prediction unit receives the assignment of the assignment module, performs clopidogrel antiplatelet effect prediction according to the total score of the assignment, and counts according to the total score of 7:
if the ABC score is less than or equal to 3 points, the response of the patient to clopidogrel is estimated to be acceptable, and the clopidogrel can be used as a platelet P2Y12Receptor inhibitors are antiplatelet;
if the ABC score is more than or equal to 4 points, estimating the clopidogrel reaction difference, and selecting ticagrelor, clopidogrel and cilostazol for combination or clopidogrel and small-dose rivaroxaban for combination to resist platelet.
2. The model for predicting clopidogrel antiplatelet efficacy according to claim 1, wherein the subject is a patient who has long-term use of clopidogrel antiplatelet.
3. The model for predicting clopidogrel antiplatelet efficacy according to claim 1 or 2 for guiding a clinician to precisely select platelets P2Y12Use of a receptor inhibitor to balance the risk of ischemia and hemorrhage in a patient.
4. Use according to claim 3, characterized in that clopidogrel is platelet P2Y if the ABC score is less than or equal to 312Receptor inhibitors are antiplatelet; and if the ABC score is more than or equal to 4, selecting combination of ticagrelor, clopidogrel and cilostazol or combination of clopidogrel and small-dose rivaroxaban for resisting platelet.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110305967.0A CN112967750B (en) | 2021-03-23 | 2021-03-23 | Model for predicting antiplatelet efficacy of clopidogrel and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110305967.0A CN112967750B (en) | 2021-03-23 | 2021-03-23 | Model for predicting antiplatelet efficacy of clopidogrel and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112967750A true CN112967750A (en) | 2021-06-15 |
| CN112967750B CN112967750B (en) | 2024-03-29 |
Family
ID=76278035
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110305967.0A Active CN112967750B (en) | 2021-03-23 | 2021-03-23 | Model for predicting antiplatelet efficacy of clopidogrel and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112967750B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111323570A (en) * | 2020-03-25 | 2020-06-23 | 天津市宝坻区人民医院 | Aspirin drug resistance detection method based on thromboelastogram |
| CN113593718A (en) * | 2021-07-29 | 2021-11-02 | 复旦大学附属中山医院 | Model for predicting clopidogrel antiplatelet effect and application thereof |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100113506A1 (en) * | 2007-01-17 | 2010-05-06 | Hiroyuki Kawano | Composition for preventing or treating thrombus- or embolus- associated disease |
| WO2013014069A1 (en) * | 2011-07-22 | 2013-01-31 | Universite Pierre Et Marie Curie (Paris 6) | Method for predicting the risk of early stent thrombosis in a subject with a clopidogrel treatment |
| CN105671165A (en) * | 2016-03-05 | 2016-06-15 | 复旦大学附属华山医院 | Kit for detecting efficient typing of gene loci related to clopidogrel drug resistance reaction |
| RU2623051C1 (en) * | 2016-04-05 | 2017-06-21 | Федеральное государственное бюджетное научное учреждение "Научно-исследовательский институт кардиологии" | Method for prediction of antiplatelet therapy efficiency reduction risk |
| CN108715805A (en) * | 2018-06-11 | 2018-10-30 | 首都医科大学附属北京潞河医院 | The automatic presentment system of clopidogrel medication related gene examining report |
| CN110951870A (en) * | 2020-01-03 | 2020-04-03 | 中国人民解放军总医院 | Application of miRNA expression quantity in predicting therapeutic effect of clopidogrel |
| US20200191738A1 (en) * | 2018-12-12 | 2020-06-18 | Nanjing First Hospital | Use of c4bp-alpha protein in human serum to predict clopidogrel resistance and its clinical application as a novel biomarker |
| CN111323571A (en) * | 2020-03-25 | 2020-06-23 | 天津市宝坻区人民医院 | Thromboelastogram-based platelet inhibition rate determination method |
| CN111710425A (en) * | 2020-06-19 | 2020-09-25 | 复旦大学附属中山医院 | Method, system and device for evaluating cardiotoxicity of immune checkpoint inhibitor |
| CN111733222A (en) * | 2019-03-25 | 2020-10-02 | 北京大学第一医院 | ITGB1 gene SNP marker for assisting accurate medication of clopidogrel |
| CN111948393A (en) * | 2020-08-06 | 2020-11-17 | 复旦大学附属中山医院 | Model for evaluating prognosis of malignant tumor patient based on sST2 and application thereof |
| CN112011608A (en) * | 2020-09-23 | 2020-12-01 | 常州市武进人民医院 | Platelet treatment guidance method based on drug gene polymorphism and thromboelastogram |
-
2021
- 2021-03-23 CN CN202110305967.0A patent/CN112967750B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100113506A1 (en) * | 2007-01-17 | 2010-05-06 | Hiroyuki Kawano | Composition for preventing or treating thrombus- or embolus- associated disease |
| WO2013014069A1 (en) * | 2011-07-22 | 2013-01-31 | Universite Pierre Et Marie Curie (Paris 6) | Method for predicting the risk of early stent thrombosis in a subject with a clopidogrel treatment |
| CN105671165A (en) * | 2016-03-05 | 2016-06-15 | 复旦大学附属华山医院 | Kit for detecting efficient typing of gene loci related to clopidogrel drug resistance reaction |
| RU2623051C1 (en) * | 2016-04-05 | 2017-06-21 | Федеральное государственное бюджетное научное учреждение "Научно-исследовательский институт кардиологии" | Method for prediction of antiplatelet therapy efficiency reduction risk |
| CN108715805A (en) * | 2018-06-11 | 2018-10-30 | 首都医科大学附属北京潞河医院 | The automatic presentment system of clopidogrel medication related gene examining report |
| US20200191738A1 (en) * | 2018-12-12 | 2020-06-18 | Nanjing First Hospital | Use of c4bp-alpha protein in human serum to predict clopidogrel resistance and its clinical application as a novel biomarker |
| CN111733222A (en) * | 2019-03-25 | 2020-10-02 | 北京大学第一医院 | ITGB1 gene SNP marker for assisting accurate medication of clopidogrel |
| CN110951870A (en) * | 2020-01-03 | 2020-04-03 | 中国人民解放军总医院 | Application of miRNA expression quantity in predicting therapeutic effect of clopidogrel |
| CN111323571A (en) * | 2020-03-25 | 2020-06-23 | 天津市宝坻区人民医院 | Thromboelastogram-based platelet inhibition rate determination method |
| CN111710425A (en) * | 2020-06-19 | 2020-09-25 | 复旦大学附属中山医院 | Method, system and device for evaluating cardiotoxicity of immune checkpoint inhibitor |
| CN111948393A (en) * | 2020-08-06 | 2020-11-17 | 复旦大学附属中山医院 | Model for evaluating prognosis of malignant tumor patient based on sST2 and application thereof |
| CN112011608A (en) * | 2020-09-23 | 2020-12-01 | 常州市武进人民医院 | Platelet treatment guidance method based on drug gene polymorphism and thromboelastogram |
Non-Patent Citations (7)
| Title |
|---|
| 吴鸿谊;吕海辰;钱菊英;王齐兵;孙爱军;葛均波;: "血栓弹力图评价吸烟对双联抗血小板药物作用的影响", 上海医学, no. 01 * |
| 张婷;于敏;: "探讨氯吡格雷联合氟伐他汀在脑梗死治疗中的临床疗效研究", 中国医药指南, no. 33 * |
| 徐化洁;赵昕;吴鸿谊;樊冰;: "CYP2C19基因多态性影响稳定型冠心病患者氯吡格雷治疗后血小板P2Y_(12)受体信号通路的分子机制", 复旦学报(医学版), no. 04 * |
| 晶晶;陈逸伦;马永东;高洁;耿晓雯;任艺虹;: "替格瑞洛与氯吡格雷对急性冠脉综合征患者经皮冠脉动脉介入术后血小板抑制效果的比较", 现代生物医学进展, no. 18 * |
| 海松;贾永平;: "替格瑞洛在急性冠脉综合征介入治疗中的应用", 中西医结合心脑血管病杂志, no. 17 * |
| 王欣荣;: "氯吡格雷联合阿司匹林治疗急性心肌梗死的临床效果探讨", 中国冶金工业医学杂志, no. 06 * |
| 轩继中;程兆云;赵子牛;王圣;夏东升;胡俊龙;: "抗血小板药物基因检测技术在冠状动脉旁路移植术后个体化用药中的临床应用", 中国动脉硬化杂志, no. 09 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111323570A (en) * | 2020-03-25 | 2020-06-23 | 天津市宝坻区人民医院 | Aspirin drug resistance detection method based on thromboelastogram |
| CN113593718A (en) * | 2021-07-29 | 2021-11-02 | 复旦大学附属中山医院 | Model for predicting clopidogrel antiplatelet effect and application thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112967750B (en) | 2024-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Field et al. | Recent developments in prognostic and predictive testing in uveal melanoma | |
| Kittur et al. | Clinical correlates of JAK2V617F allele burden in essential thrombocythemia | |
| Goncalves et al. | Usefulness of mean platelet volume as a biomarker for long-term outcomes after percutaneous coronary intervention | |
| Cui et al. | Cell-free circulating DNA: a new biomarker for the acute coronary syndrome | |
| Prentis et al. | Submaximal cardiopulmonary exercise testing predicts 90‐day survival after liver transplantation | |
| Dale et al. | Comparison of the effects of apixaban and rivaroxaban on prothrombin and activated partial thromboplastin times using various reagents | |
| CN112967750A (en) | Model for predicting clopidogrel antiplatelet effect and application thereof | |
| Martin et al. | Direct oral anticoagulant drug level testing in clinical practice: a single institution experience | |
| Toorop et al. | Inter‐and intra‐individual concentrations of direct oral anticoagulants: the KIDOAC study | |
| Collett et al. | Evaluation of coagulation status using viscoelastic testing in intensive care patients with coronavirus disease 2019 (COVID-19): an observational point prevalence cohort study | |
| Toma et al. | Impact of anemia on platelet response to clopidogrel in patients undergoing percutaneous coronary stenting | |
| Hann et al. | Predictive value of alpha-fetoprotein in the long-term risk of developing hepatocellular carcinoma in patients with hepatitis B virus infection–results from a clinic-based longitudinal cohort | |
| Wells et al. | Predicting major bleeding during extended anticoagulation for unprovoked or weakly provoked venous thromboembolism | |
| Danese et al. | Relationship between pharmacokinetics and pharmacodynamics of clopidogrel in patients undergoing percutaneous coronary intervention: comparison between vasodilator‐stimulated phosphoprotein phosphorylation assay and multiple electrode aggregometry | |
| Tefferi et al. | Development of a prognostically relevant cachexia index in primary myelofibrosis using serum albumin and cholesterol levels | |
| Freynhofer et al. | Variability of on-treatment platelet reactivity in patients on clopidogrel | |
| Chiu et al. | Elucidation of a causal relationship between platelet count and hypertension: a Bi-directional mendelian randomization study | |
| Reeves et al. | Immature platelet dynamics in immune-mediated thrombocytopenic states | |
| Findlay et al. | A comparison of cardiovascular risk scores in native and migrant South Asian populations | |
| Carmeli et al. | Gene regulation contributes to explain the impact of early life socioeconomic disadvantage on adult inflammatory levels in two cohort studies | |
| Ichihara et al. | Effect of impaired renal function on the maintenance dose of warfarin in Japanese patients | |
| Zhou et al. | miR-124 is downregulated in serum of acute cerebral infarct patients and shows diagnostic and prognostic value | |
| Chen et al. | Exosomal miR-152-5p and miR-3681-5p function as potential biomarkers for ST-segment elevation myocardial infarction | |
| Su et al. | Circulating microRNA-1 in the diagnosis and predicting prognosis of patients with chest pain: a prospective cohort study | |
| Liu et al. | Potential predictors for mental stress-induced myocardial ischemia in patients with coronary artery disease |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |