CN111983085A - Application of a group of serum metabolic markers in prediction of individual drug effects of warfarin and guidance of individual warfarin administration - Google Patents

Application of a group of serum metabolic markers in prediction of individual drug effects of warfarin and guidance of individual warfarin administration Download PDF

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CN111983085A
CN111983085A CN202010847495.7A CN202010847495A CN111983085A CN 111983085 A CN111983085 A CN 111983085A CN 202010847495 A CN202010847495 A CN 202010847495A CN 111983085 A CN111983085 A CN 111983085A
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warfarin
metabolic markers
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黄青
曹玲
樊夏雷
谭力
钱翰宇
罗楠
施海蔚
马跃新
张莹
刘书娟
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JIANGSU INSTITUTE FOR FOOD AND DRUG CONTROL
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Abstract

The invention discloses application of a group of serum metabolic markers in predicting individual drug effects of warfarin and guiding individual warfarin administration. The invention discovers that the prediction accuracy of different pharmacodynamic samples of warfarin is obviously improved by combining the serum metabolic markers of guanosine, uridine, fumaric acid and thromboxane B2 with the traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone. Therefore, the group of serum metabolic markers can be used for predicting individual drug effects of warfarin or guiding individual administration of warfarin in combination with traditional indexes, and can be prepared into a kit for predicting individual drug effects of warfarin or guiding individual administration of warfarin in combination with traditional indexes.

Description

Application of a group of serum metabolic markers in prediction of individual drug effects of warfarin and guidance of individual warfarin administration
Technical Field
The invention belongs to the field of biochemistry, relates to application of markers in clinical medication guidance, and particularly relates to application of a group of serum metabolism markers in prediction of individual drug effects of warfarin and guidance of individual warfarin medication.
Background
Warfarin (warfarin) is the most commonly used anticoagulant agent for the prevention and treatment of thromboembolic diseases worldwide, and cardiac surgery patients often require long-term or lifelong administration of treatment. Warfarin is also one of the drugs that causes the most serious adverse events due to its very narrow therapeutic window and high variability in pharmacodynamic response between individuals. In clinical practice, in order to achieve the therapeutic window for warfarin with the index of anticoagulant effect INR (International Normalized Ratio), the difference in dose between different patients with warfarin is more than 10-fold. If the anticoagulant dose is insufficient, the risk of thrombotic events increases; while a slightly larger dosage increases the risk of bleeding episodes, and incorrect dosage may lead to serious adverse effects.
At present, the clinical warfarin dosing regimen usually relies on experience and trial and error (trial-and-error), first giving a certain initial dose, then the clinician increases or decreases the dose until the INR reaches the therapeutic target range, which we call empirical dosing, according to the INR value measured after each patient takes the drug. Traditional empirical medication has a long dose adjustment period, and the steady dose of warfarin can be adjusted to 3-4 weeks in general. After heart valve replacement, bleeding, vascular embolism and other complications frequently occur within 2 weeks at the early stage of the operation, and the INR value is frequently fluctuated greatly in the initial anticoagulation stage of warfarin according to the traditional method, and the adverse reaction incidence rate is highest just in the initial anticoagulation stage.
The genetic pharmacological research shows that the gene polymorphism of VKORC1 and CYP2C9 of patients and some clinical factors (age, weight, concomitant medication, smoking and the like) can influence the steady-state dose of warfarin, and a multiple linear regression equation is formed on the basis of the gene polymorphism, such as a Gage model, an IWPC model, a Wadelius model and the like, which are called gene-oriented individualized medication models. However, the gene-oriented personalized medicine model can only explain about 40-60% of warfarin dosage individual differences, and at least the reason for 40% of warfarin dosage individual differences is not clear at present, so that the prediction precision needs to be further improved. While there are many disputes as to whether these algorithms outperform traditional clinical guidance dosing algorithms when applied in the real world, several central randomized controlled studies have been reported in succession without reaching a consistent conclusion. The algorithm only aims at the maintenance dose of the warfarin in the steady state, the internal environment condition of the patient in the initial treatment period (week 1) after the operation is complex, and the blood coagulation factor synthesized by vitamin K in the liver cells is not exhausted, so the requirement of the initial dose is different from that in the steady state, and the maintenance dose is not suitable when the maintenance dose is given in the initial state.
Meanwhile, due to the realistic problems of hospital bed turnover, hospitalization cost and the like, if serious complications do not exist, a patient is generally left after the operation and is continuously subjected to anticoagulation treatment outside the hospital. The outpatient follow-up rate after discharge and whether the patient can readjust the dose administered with the follow-up INR in the actual practice are all great risks. Therefore, how to adjust warfarin doses of different patients to a safe and effective range as soon as possible and enter a treatment window in a hospital period is an urgent and troublesome problem and is particularly important for subsequent treatment of patients.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide application of a group of serum metabolic markers in predicting individual drug effects of warfarin and guiding individual administration of warfarin.
The above purpose of the invention is realized by the following technical scheme:
use of a set of serum metabolic markers consisting of guanosine, uridine, fumaric acid and thromboxane B2 for the preparation of a kit for predicting the efficacy of warfarin individuals after administration of different initial doses of warfarin by combining traditional indices consisting of VKORC1 genotype, body weight, whether serum creatinine and amiodarone are co-administered.
Further, the pharmacological effect strength is represented by INR value.
Use of a panel of serum metabolic markers consisting of guanosine, uridine, fumaric acid and thromboxane B2 for the manufacture of a kit for instructing the individualized administration of warfarin by combining traditional criteria consisting of VKORC1 genotype, body weight, whether blood creatinine and amiodarone are co-administered, wherein said instructing the individualized administration of warfarin means determining the initial dose of warfarin that an individual needs to administer to achieve different pharmacological effect strengths.
Further, the pharmacological effect strength is represented by INR value.
A kit for predicting the efficacy of warfarin in an individual in combination with conventional measures, said prediction of the efficacy of warfarin in an individual in predicting the intensity of pharmacological effects of warfarin at different initial doses in the individual after administration, said conventional measures consisting of the VKORC1 genotype, body weight, serum creatinine and amiodarone, whether or not co-administration is indicated, said kit comprising reagents for quantifying the serum metabolic markers guanosine, uridine, fumaric acid and thromboxane B2.
Further, the pharmacological effect strength is represented by INR value.
A kit for use in instructing individualized administration of warfarin in combination with conventional guidelines for determining the initial dose of warfarin required to achieve different strengths of pharmacological effect in an individual, the conventional guidelines consisting of whether VKORC1 genotype, body weight, blood creatinine and amiodarone are to be co-administered, the kit comprising reagents for quantifying the serum metabolic markers guanosine, uridine, fumaric acid and thromboxane B2.
Further, the pharmacological effect strength is represented by INR value.
Has the advantages that:
the invention finds that the prediction accuracy of different pharmacodynamic samples of warfarin is remarkably improved by combining four serum metabolic markers of guanosine, uridine, fumaric acid and thromboxane B2 on the basis of the most representative four traditional indexes (whether VKORC1 genotype, body weight, blood creatinine and amiodarone are combined for medication). Therefore, the four serum metabolic markers can be used for predicting the drug effect of individual warfarin by combining traditional indexes (whether VKORC1 genotype, body weight, blood creatinine and amiodarone are combined or not), predicting the pharmacological effect intensity of warfarin with different initial doses on the individual after the warfarin is taken, and preparing a kit for predicting the drug effect of the individual warfarin by combining the traditional indexes, wherein the kit contains reagents for quantifying serum metabolic markers guanosine, uridine, fumaric acid and thromboxane B2. So far, those skilled in the art know that, since a mathematical model of four traditional indexes + four serum metabolic markers and warfarin individual drug efficacy INR has been established and the reliability of the model is verified to be high, those skilled in the art can obtain a suitable daily average dosage in the initial treatment period by reverse-deducing from the model under the condition of obtaining values of the four traditional indexes + four serum metabolic markers of an individual and setting the target/ideal INR thereof. Therefore, the four serum metabolic markers can be used for combining traditional indexes (VKORC1 genotype, body weight, whether blood creatinine and amiodarone are used together or not) to guide individualized administration of warfarin, and a kit for combining traditional indexes to guide individualized administration of warfarin can be prepared, wherein the kit contains reagents for quantifying guanosine, uridine, fumaric acid and thromboxane B2 as serum metabolic markers.
Drawings
FIG. 1 is a comparison of the serum levels of guanosine, uridine, fumaric acid and thromboxane B2 in different warfarin pharmacodynamic samples.
FIG. 2 is a comparison of the four conventional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone in different warfarin drug effect samples.
In fig. 3, a is the prediction effectiveness of the four traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone on different pharmacodynamic samples of warfarin, and B is the prediction effectiveness of the four serum metabolic markers of guanosine, uridine, fumaric acid and thromboxane B2 on the training set in combination with the four traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone on different pharmacodynamic samples of warfarin.
FIG. 4 is a graph A showing the results of verifying the efficacy of four conventional indicators VKORC1 genotype, body weight, blood creatinine and amiodarone on different pharmacodynamic samples of warfarin, and B showing the results of verifying the efficacy of four serum metabolic markers, namely, guanosine, uridine, fumaric acid and thromboxane B2, on different pharmacodynamic samples of warfarin, combined with the four conventional indicators VKORC1 genotype, body weight, blood creatinine and amiodarone.
Detailed Description
The following detailed description of the present invention is provided in connection with the accompanying drawings and examples, but not intended to limit the scope of the invention.
Test sample, instrument and reagent
1. Test specimen
Training set samples: 84 patients in the eastern war zone of China's liberated military were collected, 15 of which were INRday7<1.3 and 57 cases 1.3. ltoreq. INRday72.4 or less and 12 INR casesday7>2.4；
Verifying the set sample: 21 patients in the general Hospital of the east war zone of the Chinese people's liberation army were collectedExample 3 INRday7<1.3, 15 cases 1.3. ltoreq. INRday7Less than or equal to 2.4 and 3 INR casesday7>2.4；
Sample grouping criteria:
the study subjects were from 105 patients who received warfarin for the first time after heart valve replacement surgery. Based on the Chinese expert consensus on warfarin anticoagulation treatment (the initial dose of Chinese is 1-3 mg, and INR can reach the target range within 2-4 weeks), the initial dose of all patients is set by a clinician according to clinical experience, and the actual dose range is 2.14 +/-0.60 mg.
Exclusion criteria:
cases included in the model were excluded according to the following criteria:
(1) non-chinese han nationality patients;
(2) patients who have had atrial fibrillation therapy with warfarin prior surgery;
(3) patients who underwent valve replacement and warfarin anticoagulation, and withheld for less than three months;
(4) patients who use clopidogrel, ticagrelor, aspirin and other medicines which affect the blood coagulation function in three months before admission and during the hospital stay;
(5) patients with liver, kidney, gastrointestinal dysfunction;
(6) patients with other diseases.
2. Test instrument and reagent
The instrument comprises the following steps:
UHPLC-Q-active Orbitrap-MS ultra-high performance liquid chromatography-orbital trap high-resolution mass spectrometry combined detection system (Q-active, U.S. Thermo corporation); the liquid chromatography column is ACQUITY UPLC BEH Amide (1.7 μm × 2.1 μm × 100mm, Waters, USA); a200PCR amplification instrument (Hangzhou Langzhou scientific instruments, Inc.); PyroMark Q24 pyrosequencer (QIAGEN, germany); legand Micro 21R refrigerated centrifuge (Thermo corporation, USA); BG-200 constant temperature mixer (Hangzhou Langzhou scientific instruments Co., Ltd.); BSA124S-CW analytical balance (Sartorious, Germany) OneDropTMOD-1000 uv-vis spectrophotometer (mikyo wushu technologies ltd); TNG-T98 vacuum drying instrument (Taicang Huameisheng)Chemical instrumentation factory, china); forma 900 series ultra-low temperature refrigerator (Thermo Fisher Scientific, USA); BHC-1300IIA/B2 type biological cleaning safety cabinet (Suzhou decontamination Equipment Co., Ltd.).
Reagent:
guanosine, uridine, fumaric acid and thromboxane B2 standards were purchased from Sigma (> 98%); methanol and acetonitrile were purchased from Thermo corporation, usa, and chromatographically pure; ammonium acetate and ammonia were purchased from CNW Technologies, germany, chromatographically pure; 500bp DNA Ladder marker, rTaq DNA polymerase (Takara, Japan); sepharose Beads (GE Healthcare, USA); whole blood DNA extraction kit, PyroMark Gold Q96 kit and binding and annealing buffer (QIAGEN, germany); other reagents are analytically pure; the experimental water was ultrapure water. All primers were synthesized by Shanghai Invitrogen corporation, and the specific primer names and sequences are shown in the following table:
Figure BDA0002643581050000041
Figure BDA0002643581050000051
wherein F, R is an upstream primer and a downstream primer for PCR amplification; seq is a sequencing primer; biotin is a 5' end Biotin modified primer.
Second, test method
1. Blood sample collection and preservation
Collecting venous blood samples of patients in a fasting state from morning before operation.
Blood samples for INR value determination were taken and measured immediately after collection. Plasma samples for genomics testing using EDTA-2K+After collection of the anticoagulation tube, whole blood is frozen. And (3) centrifuging the serum sample subjected to metabonomics detection at a low speed of 2000rpm for 10min, carefully sucking the supernatant into a clean EP tube by using a pipette after centrifugation, centrifuging the supernatant at a high speed of 8000rpm for 5min again, taking the supernatant, subpackaging the supernatant into a marked cryopreservation tube, and cryopreserving the sample at-80 ℃ to avoid repeated freeze thawing.
2. Determination of the content of blood metabolism markers (guanosine, uridine, fumaric acid, and thromboxane B2)
Standard curves were constructed using standard addition methods. The blank mixed serum containing powdered activated carbon was shaken at 200rpm for 24h to remove the target endogenous substance analyte. Serum after centrifugation at 20000rpm for 20min and filtration through a 0.22 μm filter membrane is subpackaged in Eppendorf tubes as a blank matrix, frozen at-80 ℃ and taken out newly. When preparing the standard curve, the standard solution with the continuously diluted concentration is added into the blank matrix and evenly mixed, and all the standard curves consist of more than 5 concentration points. Sample preparation 100. mu.L of patient serum sample (blank matrix and standard sample are taken for standard curve, the same method is adopted for preparation), 400. mu.L of methanol acetonitrile extracting solution (1:1, v/v) is added, and the mixture is shaken and mixed evenly; centrifuging at 12000rpm at 4 deg.C for 15 min; taking the supernatant in a clean EP tube, and volatilizing the solvent in a vacuum concentrator; and re-dissolving by using 50% acetonitrile extracting solution to be detected. The QC quality control sample is prepared by mixing 10 mu L of each sample.
The determination is carried out by adopting a Thermo UHPLC-Q-active Orbitrap-MS ultra-high performance liquid chromatography-orbital trap high resolution mass spectrometer. The column was a Waters UPLC BEH Amide column (1.7 μm. times.2.1 μm. times.100 mm), the injection volume was 2 μ L, the mobile phase: a (25mM ammonium acetate and 25mM ammonia) -B (acetonitrile) at a flow rate of 0.5mL/min, and the gradient elution program was set up as follows: 0-5min, 5% A; 7min, 35% A; 8-9min, 60% A; 9.1-12min, 5% A. The mass spectrum conditions are as follows: ESI positive ionization mode, sheath gas flow rate 45Units, auxiliary gas flow rate 12Units, spray voltage 3.5kV, capillary temperature 350 ℃. Full MS/dd-MS2Mode collection, wherein a FullMS mode adopts a mass scanning range of 60-750 m/z, Orbitrap resolution of 75000, maximum delay time of 100ms and automatic gain control target of 3 multiplied by 106；dd-ms2The mode adopts Orbitrap resolution 17500, the maximum delay time is 50ms, and the automatic gain control target is 1 multiplied by 105 Cycle number 5, high energy collision dissociation energies are 20, 40 and 60. Data analysis was performed using Xcalibur software. Calculating the concentration of each biomarker by using a standard curve linear equation, wherein the correlation coefficients of the linear equations measured by the biomarkers are all>0.99。
3. Detection of traditional indexes and screening of most representative traditional indexes
The screening range for the conventional index includes sex, age, height, weight, blood creatinine, urea nitrogen, whether smoking is performed, whether amiodarone is used in combination, whether digoxin is used in combination, preoperative basal INR, CYP2C9 x 2(rs1788853) genotype, CYP2C9 x 3(rs1057910) genotype, VKORC1(rs1788853) genotype.
3.1 conventional indicator-Gene polymorphism SNP site detection
The gene polymorphism of warfarin individual difference related gene mutation sites reported in the literature, including rs1788853 site and rs1057910 site of CYP2C9 and rs9923231 site of VKORC1, is detected by a pyrosequencing method. The eluate containing the sample DNA was obtained following the Qiagen DNA extraction kit protocol. After the concentration and the purity are measured by an ultraviolet spectrophotometry, the mixture is diluted by using 1 xTE buffer solution until the final concentration is 20-100 mg/L.
PCR amplification System: 10 XBuffer 2.5 uL, MgCl22mmol/L, dNTP 200. mu. mol/L, rTaq DNA polymerase 1.25U, upstream and downstream primers 400nmol/L, DNA template 1. mu.L, and water to 25. mu.L.
And (3) PCR reaction conditions: pre-denaturation at 94 ℃ for 5 min; (94 ℃, 30 s; 55 ℃, 30 s; 72 ℃, 30s)35 cycles of amplification; extension at 72 ℃ for 7 min.
The preparation of the single-stranded template required for the pyrosequencing reaction and the sequencing reaction were performed by analysis with a pyrosequencing instrument model Pyromark Q24 from QIAGEN.
(1) Adding 40 mu L Binding Buffer and 5 mu L Sepharose Beads after vortex mixing into an eight-link tube, adding water to supplement the volume to 60 mu L, and fully vortex mixing again;
(2) adding 20 mu L of PCR product into the system, and sealing the eight-connected tubes by using a pore plate strip to ensure that no leakage exists between pore grooves;
(3) fixing the eight-connection pipe on a pore plate mixer, and oscillating for 7min at 14000rpm at room temperature;
(4) adding 24 mu L of 1 × Annealing Buffer and 1 mu L of sequencing primer into a Q24 pore plate in the oscillation process;
(5) work station preparation, buffer solution in each hole is supplemented;
(6) cleaning a probe and a membrane, washing the probe with 70% ethanol for 5s after absorbing Beads, then soaking the probe into a degrading Buffer for 5s, and finally washing the probe with 1 multiplied by Wash Buffer for 10 s;
(7) adding Beads into the corresponding 24-hole plate, placing in a warm bath at 80 ℃ for 2min, taking out, and standing at room temperature for 10 min;
(8) starting pyroMark Q24 software, and adding enzyme, substrate and dNTP into corresponding holes of the reagent cabin according to the Pre Run Information display data;
(9) and (4) placing the 24-hole plate and the reagent bin to the corresponding positions of the instrument, setting corresponding parameters, and automatically sequencing.
3.2 Ten conventional clinical parameters except genotype
Ten conventional clinical parameters other than genotype were gender, age, height, weight, blood creatinine, urea nitrogen, whether to smoke, whether to use amiodarone in combination, whether to use digoxin in combination, preoperative basal INR. These parameters are available from patient admission clinical history queries, derived from physical and query (gender, age, height, weight, smoking or not), respectively; blood biochemical tests (blood creatinine, urea nitrogen, preoperative basal INR); medication prescription inquiry (whether amiodarone medication is prescribed, whether digoxin is prescribed, yes/no record 1/0).
4. Data processing method
In the most representative traditional index screening, a traditional linear regression model (stepwise regression stepwise model) is adopted in combination with partial correlation analysis. The daily dose and thirteen traditional indexes are used as independent variables X in the model during the initial treatment period (7 days) to screen factors (X) which independently influence the inter-individual variability of the drug effect INR (Y) during the initial treatment period after operation. The screening principle is as follows: only variables with significant regression (p <0.05) and partial correlation p <0.1 (here the partial correlation sets the daily average dose as the control variable, thus eliminating the effect of daily average dose on Y) were retained in the model. The most representative traditional indexes are screened from a plurality of traditional indexes disclosed in the prior art by the screening method and principle.
In the metabolic marker screening, multivariate partial least squares regression analysis (PLS) is combined with partial correlation analysis. Daily average dose and all detected metabolic small molecules in the blood sample (6800 total characteristic ions, including 3819 characteristic ions analyzed by GC-QTOF/MS platform detection, wherein 228 compounds can be identified, and 2981 characteristic ions analyzed by LC-QTOF/MS platform detection, wherein 483 compounds can be identified) are used as independent variables X to enter a model and are subjected to partial least squares regression analysis and partial correlation analysis with efficacy index INR (Y) in the initial treatment period. In partial least squares regression analysis, the predictive ability of the marker on the drug effect individual variability is characterized by the magnitude of the variable projection importance value (VIP value) to screen factors (X) independently influencing the inter-INR individual variability. In the partial correlation analysis, the daily average dose is used as a control variable to eliminate the influence on the efficacy index INR (Y). The final metabolic marker selection principle is as follows: (1) VIP >1.5 and | pcorr | 0.3 in PLS model; (2) significant off-correlation with INR (p <0.05), where the daily average dose was used as a control variable to eliminate the effect of dose on Y; (3) the metabolic marker can be quantitatively analyzed, and the sensitivity, the repeatability, the stability and the like all meet the quantitative requirements of an analysis method; (4) only one metabolite marker is selected in the same metabolic pathway.
And establishing an optimized prediction model based on the metabolic markers by combining the four novel metabolic markers with the screened most representative traditional indexes, and verifying the prediction efficiency of the four novel metabolic markers combined with the traditional indexes. The Partial Least Squares Regression (PLS-Regression) was used to directly predict the warfarin efficacy index INR (INR) value 7 days after administrationday7) And compared with the performance of the conventional index prediction INR value. The model is subjected to internal 7-fold cross validation and 200-time iterative permutation validation to ensure that the model has no overfitting, and the individual in the validation set is subjected to external validation.
Third, test results
1. Screening results of metabolic markers in training set and differences of target serum metabolic markers in training set in different pharmacodynamic samples of warfarin
The following four metabolic markers were screened from serum according to the screening methods and principles described previously:
Figure BDA0002643581050000081
the contents of guanosine, uridine, fumaric acid and thromboxane B2 in serum and warfarin efficacy index INRday7Partial least squares regression significant (VIP)>1.5) and is in extremely significant partial correlation (p)<0.01); the content of warfarin in different pharmacodynamic samples is obviously different, as shown in figure 1. Wherein, L, M, H in FIG. 1 represents the samples of different warfarin effects, respectively corresponding to INRday7<1.3、1.3≤INRday7≤2.4、INRday7>2.4。
The results indicate that serum guanosine, uridine, fumaric acid, and thromboxane B2 are all associated with warfarin potency.
2. Screening results of the most representative traditional indexes in the training set and differences of the most representative traditional indexes in the training set in different pharmacodynamic samples of warfarin
The four most representative traditional indexes are screened out from the traditional indexes disclosed in the prior art according to the screening method and the principle: VKORC1 genotype, body weight, blood creatinine and amiodarone. Among them, the bias correlation between blood creatinine and VKORC1 genotypes was 0.05< p <0.1, relaxing the statistical significance and entering the classical model.
The four traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone are different from warfarin in different pharmacodynamic samples, as shown in FIG. 2. Wherein, in FIG. 2, L, M, H represents the samples of different warfarin drug effects, corresponding to INR respectivelyday7<1.3、1.3≤INRday7≤2.4、INRday7>2.4。
The results show that the traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone combined administration are also related to warfarin potency.
3. Constructing mathematical models of four traditional indexes, four traditional indexes and four serum metabolic markers for predicting different pharmacodynamic samples of warfarin in training set samples
Four traditional indexes: firstly, detecting results of four traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone combination of each sample in a training set, initial daily average dose and actually measured INRday7Value input software, a PLS partial least squares regression model is used for constructing mathematical models of four traditional indexes for predicting different pharmacodynamic samples of warfarin, and then the INR of each sample in a training set is subjected to the mathematical modelsday7Predicting the value to obtain a prediction INRday7Value, final comparison prediction INRday7Value and measured INRday7Correlation of values. The results are shown in FIG. 3A, where the measured value VS predicted value R2The predicted value of 22.6% (19/84) is not within the range of 75 to 125% of the actual value 0.4879.
Four traditional indices + four serum metabolic markers: firstly, detecting whether four traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone of each sample in a training set are combined to use medicine, detecting results of four serum metabolic markers of guanosine, uridine, fumaric acid and thromboxane B2, initial daily average dose and actually measured INRday7The value input software uses a PLS partial least squares regression model to construct a mathematical model of four traditional indexes and four serum metabolic markers for predicting different pharmacodynamic samples of warfarin, and then uses the mathematical model to carry out INR on each sample in a training setday7Predicting the value to obtain a prediction INRday7Value, final comparison prediction INRday7Value and measured INRday7Correlation of values. The results are shown in FIG. 3B, where the measured value VS predicted value R20.6914, the predicted value of only 8.3% (7/84) is not within the range of 75 to 125% of the actual value.
Correlation between predicted values and measured values of four traditional indexes and four serum metabolic markers (R)20.6914) are remarkably superior to the correlation R of the predicted value and the measured value of four traditional indexes20.4879, the accuracy of the prediction of different pharmacodynamic samples of warfarin is higher by the four traditional indexes and the four serum metabolic markers. The comparison is as follows:
training is concentrated with traditional index and four serum metabolism markersObject-associated conventional index prediction INRday7Comparison of Performance
Figure BDA0002643581050000091
4. Verification set verification
Four traditional indexes: substituting the detection results of four traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone combined medication and the initial daily average dose of each sample in the verification set into the mathematical model obtained by training the training set to obtain the predicted INR of each sample in the verification setday7Value, comparative prediction INRday7Value and measured INRday7Correlation of values. The results are shown in FIG. 4A, where the measured value VS predicted value R2When 0.2067 is found, 42.8% (9/21) of the predicted values are not within the range of 75 to 125% of the actual values. That is, the prediction accuracy is only 57.2% within 75 to 125% of the actual measurement value in consideration of the allowable error.
Four traditional indices + four serum metabolic markers: substituting the detection results of the four traditional indexes of VKORC1 genotype, body weight, blood creatinine and amiodarone combined medication, the initial daily average dose and the detection results of four serum metabolic markers of guanosine, uridine, fumaric acid and thromboxane B2 of each sample in the verification set into the mathematical model obtained by training the training set to obtain the predicted INR of each sample in the verification setday7Value, comparative prediction INRday7Value and measured INRday7Correlation of values. The results are shown in FIG. 4B, where the measured value VS predicted value R20.6677, the predicted value of only 9.5% (2/21) is not within the range of 75 to 125% of the actual value. That is, the prediction accuracy is up to 90.5% within 75-125% of the measured value in consideration of the allowable error.
The comparison is as follows:
prediction of INR by combining traditional indexes in prediction set and four serum metabolic markers with traditional indexesday7Comparison of Performance
Figure BDA0002643581050000101
The results show that the prediction accuracy of different pharmacodynamic samples of warfarin is remarkably improved by combining four serum metabolic markers, namely guanosine, uridine, fumaric acid and thromboxane B2, on the basis of the four most representative traditional indexes (whether VKORC1 genotype, body weight, blood creatinine and amiodarone are combined or not). Therefore, the four serum metabolic markers can be used for predicting the drug effect of individual warfarin by combining traditional indexes (whether VKORC1 genotype, body weight, blood creatinine and amiodarone are combined or not), predicting the pharmacological effect intensity of warfarin with different initial doses on the individual after the warfarin is taken, and preparing a kit for predicting the drug effect of the individual warfarin by combining the traditional indexes, wherein the kit contains reagents for quantifying serum metabolic markers guanosine, uridine, fumaric acid and thromboxane B2. So far, those skilled in the art know that, since a mathematical model of four traditional indexes + four serum metabolic markers and warfarin individual drug efficacy INR has been established and the reliability of the model is verified to be high, those skilled in the art can obtain a suitable daily average dosage in the initial treatment period by reverse-deducing from the model under the condition of obtaining values of the four traditional indexes + four serum metabolic markers of an individual and setting the target/ideal INR thereof. Therefore, the four serum metabolic markers can be used for combining traditional indexes (VKORC1 genotype, body weight, whether blood creatinine and amiodarone are used together or not) to guide individualized administration of warfarin, and a kit for combining traditional indexes to guide individualized administration of warfarin can be prepared, wherein the kit contains reagents for quantifying guanosine, uridine, fumaric acid and thromboxane B2 as serum metabolic markers.
The above-described embodiments are intended to be illustrative of the nature of the invention, but those skilled in the art will recognize that the scope of the invention is not limited to the specific embodiments.

Claims (8)

1. Use of a set of serum metabolic markers consisting of guanosine, uridine, fumaric acid and thromboxane B2 for the preparation of a kit for predicting the efficacy of warfarin individuals after administration of different initial doses of warfarin by combining traditional indices consisting of VKORC1 genotype, body weight, whether serum creatinine and amiodarone are co-administered.
2. Use according to claim 1, characterized in that: the pharmacological effect strength is represented by an INR value.
3. Use of a panel of serum metabolic markers consisting of guanosine, uridine, fumaric acid and thromboxane B2 for the manufacture of a kit for instructing the individualized administration of warfarin by combining traditional criteria consisting of VKORC1 genotype, body weight, whether blood creatinine and amiodarone are co-administered, wherein said instructing the individualized administration of warfarin means determining the initial dose of warfarin that an individual needs to administer to achieve different pharmacological effect strengths.
4. Use according to claim 3, characterized in that: the pharmacological effect strength is represented by an INR value.
5. A kit for predicting the efficacy of warfarin individuals in combination with conventional guidelines for predicting the intensity of pharmacological effects of warfarin at different initial doses in individuals after administration, the conventional guidelines consisting of whether VKORC1 genotype, body weight, serum creatinine, and amiodarone are administered in combination, wherein: the kit contains reagents for quantifying guanosine, uridine, fumaric acid and thromboxane B2 which are serum metabolic markers.
6. The kit of claim 5, wherein: the pharmacological effect strength is represented by an INR value.
7. A kit for use in instructing individualized administration of warfarin in combination with conventional guidelines for determining the initial dose of warfarin required to achieve different strengths of pharmacological effect in an individual, the conventional guidelines consisting of whether VKORC1 genotype, body weight, blood creatinine and amiodarone are to be administered in combination, characterized by: the kit contains reagents for quantifying guanosine, uridine, fumaric acid and thromboxane B2 which are serum metabolic markers.
8. The kit of claim 7, wherein: the pharmacological effect strength is represented by an INR value.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040121305A1 (en) * 2002-12-18 2004-06-24 Wiegand Roger Charles Generation of efficacy, toxicity and disease signatures and methods of use thereof
US20100322869A1 (en) * 2008-11-11 2010-12-23 Boehringer Ingelheim International Gmbh Method for treating or preventing thrombosis using dabigatran etexilate or a salt thereof with improved safety profile over conventional warfarin therapy
CN102534001A (en) * 2012-01-06 2012-07-04 尹彤 Warfarin individual anticoagulant pharmacogenomics detection kit suitable for Chinese population
CN110564844A (en) * 2019-10-18 2019-12-13 中国人民解放军总医院 Kit for predicting warfarin bleeding risk based on SNP marker CYP4F2rs3093168

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040121305A1 (en) * 2002-12-18 2004-06-24 Wiegand Roger Charles Generation of efficacy, toxicity and disease signatures and methods of use thereof
US20100322869A1 (en) * 2008-11-11 2010-12-23 Boehringer Ingelheim International Gmbh Method for treating or preventing thrombosis using dabigatran etexilate or a salt thereof with improved safety profile over conventional warfarin therapy
CN102534001A (en) * 2012-01-06 2012-07-04 尹彤 Warfarin individual anticoagulant pharmacogenomics detection kit suitable for Chinese population
CN110564844A (en) * 2019-10-18 2019-12-13 中国人民解放军总医院 Kit for predicting warfarin bleeding risk based on SNP marker CYP4F2rs3093168

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
钱正月等: "华法林个体化基因检测临床应用分析", 《心血管病学进展》, vol. 40, no. 03, 31 May 2019 (2019-05-31), pages 433 - 436 *

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