Method for evaluating influence of traditional Chinese medicine on warfarin anticoagulant efficacy based on PK / PD theory

Abstract

The invention discloses a method for evaluating influence of traditional Chinese medicine on warfarin anticoagulant efficacy based on a PK / PD theory, and belongs to the technical field of medicine evaluation. In order to clarify the material basis and the influence degree of traditional Chinese medicine on warfarin anticoagulation, the method comprises the following steps: carrying out grouped animal experiments, then respectively collecting pharmacodynamic experiment data, drug absorption experiment data, drug metabolism experiment data, drug plasma protein binding rate experiment data and drug excretion experiment data, and constructing a multi-dimensional data set; performing preprocessing, including removing abnormal values and performing data standardization processing to obtain a preprocessed data set; performing dimension reduction analysis by using a principal component analysis (PCA), extracting main components, and generating a score matrix and a load matrix; performing orthogonal partial least square discriminant analysis on the score matrix, and classifying the data based on an orthogonal partial least square discriminant analysis method to obtain a classification result; and obtaining an evaluation result of the influence of the traditional Chinese medicine on the warfarin anticoagulant efficacy based on the obtained classification result. The method is accurate in evaluation.

Description

Technical Field

[0001] The invention belongs to the technical field of drug evaluation, and specifically relates to a method for evaluating the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin based on the PK / PD theory. Background Art

[0002] Warfarin is the most commonly used anticoagulant for the prevention and treatment of vascular embolic diseases worldwide. Some patients with a high risk of thrombosis usually require long-term or lifelong treatment. Due to the very narrow therapeutic window of warfarin and the high variability of the pharmacodynamic response between individuals, it is also one of the drugs that causes the most serious adverse events. In clinical practice, in order to make the INR value (International Normalized Ratio), the anticoagulant efficacy indicator of warfarin, reach the therapeutic window, the dose of warfarin used by different patients may differ by more than 10 times. If the anticoagulant dose is insufficient, the risk of thrombotic events increases; if the dose is slightly larger, the risk of bleeding events increases, and inappropriate doses may lead to serious adverse reactions. Drug interactions caused by combined medication are an important influencing factor in changes in the anticoagulant efficacy of warfarin.

[0003] At present, the research on the effect of traditional Chinese medicine on warfarin anticoagulation at home and abroad mainly focuses on drug metabolizing enzymes. There is no research on the pharmacokinetics of the whole process of absorption, distribution, metabolism and excretion combined with pharmacodynamics. A comprehensive evaluation system for the effect of traditional Chinese medicine on warfarin anticoagulation has not been established. The research methods and evaluation models have limitations, and the research conclusions are incomplete and uncertain. The evaluation method has limitations. Summary of the invention

[0004] The problem to be solved by the present invention is to clarify the material basis and degree of influence of traditional Chinese medicine on the anticoagulant effect of warfarin, and to propose an evaluation method for the influence of traditional Chinese medicine on the anticoagulant effect of warfarin based on the PK / PD theory.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions:

[0006] A method for evaluating the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin based on PK / PD theory comprises the following steps:

[0007] S1. Conduct grouped animal experiments, and then collect pharmacodynamic experimental data, drug absorption experimental data, drug metabolism experimental data, drug plasma protein binding rate experimental data, and drug excretion experimental data to construct a multidimensional data set;

[0008] S2. Preprocessing the multidimensional data set obtained in step S1, including removing outliers and standardizing the data, to obtain a preprocessed data set;

[0009] S3. Perform dimensionality reduction analysis on the preprocessed data set obtained in step S2 using principal component analysis (PCA), extract the main components, and generate a score matrix and a loading matrix;

[0010] S4. Performing orthogonal partial least squares discriminant analysis on the score matrix obtained in step S3, classifying the data based on the orthogonal partial least squares discriminant analysis method to obtain a classification result;

[0011] S5. Based on the classification results obtained in step S4, an evaluation result of the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin is obtained.

[0012] Furthermore, the specific implementation method of step S1 includes the following steps:

[0013] S1.1. Animal experiments were conducted in groups, with eight rats in each group. Group A was a blank group, which was given normal saline by intraperitoneal injection for 14 consecutive days and normal saline by gavage on the eighth day; Group B was a warfarin group, which was given normal saline by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day; Group C was a danshensu group, which was given danshensu by intraperitoneal injection for 14 consecutive days; Group D was a lithospermic acid group, which was given lithospermic acid by intraperitoneal injection for 14 consecutive days; Group E was a warfarin + danshensu combined administration group, which was given danshensu by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day; Group F was a warfarin + lithospermic acid combined administration group, which was given lithospermic acid by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day;

[0014] S1.2. Collect pharmacodynamic experimental data. Blood was collected from animals in groups A, B, C, D, E, and F at the following time points after administration of warfarin or saline on the eighth day: 0, 4, 8, 12, 24, 36, 48, 72, 96, 120, and 144 h;

[0015] Blood was collected into EP tubes containing 0.32% sodium citrate solution (1:9), and immediately centrifuged at 3500 rpm for 10 min at room temperature. The upper plasma was taken and the prothrombin time PT and activated partial thromboplastin time APTT were measured using a semi-automatic coagulometer, and the international normalized ratio INR was calculated using the following formula:

[0016] INR = (PT test / PT normal ) ISI

[0017] Among them, PT test is the PT value of the actual plasma sample, PT normal is the PT value of plasma samples in group A, and ISI stands for the international sensitivity index;

[0018] Then record the maximum prothrombin time PT of each group max , maximum international normalized ratio INR maxand maximum activated partial thromboplastin time (APTT) max ;

[0019] S1.3. Collection of drug absorption experimental data, in situ in vivo one-way intestinal perfusion experiment (absorption): eight rats in each group, male Wistar rats were divided into warfarin administration group, tanshinone administration + warfarin combined administration group, lithospermic acid administration and warfarin combined administration group;

[0020] Only a single intestinal segment was used for each rat for a single perfusion test, and each administration group consisted of duodenum, jejunum, ileum, and colon groups;

[0021] During the experiment, the wound was kept moist; preheated 37°C saline was used to flush the intestinal contents at a flow rate of 5 mL / min; blank Krebs-Ringer buffer was used to perfuse and expel air; the pipeline and intestinal segment were then equilibrated with drug-containing perfusion fluid for 30 minutes at a flow rate of 0.2 ml / min, and then the perfusion fluid that had been accurately weighed was replaced to continue the perfusion. The outflowing fluid was collected with a weighed test tube, and the timer started when the first drop of liquid dripped from the outlet tube. The weight of the perfusion fluid for 15 minutes was collected and recorded. 200 μl of perfusion fluid was collected every 15 minutes, and HPLC detection was performed, and then the data was calculated: the effective permeability coefficient RDP of R-warfarin in the duodenum eff 、The effective permeability coefficient of R-warfarin in the jejunum RJP eff 、R-warfarin effective permeability coefficient RIP in the ileum eff 、The effective permeability coefficient RCP of R-warfarin in the colon eff 、The effective permeability coefficient SDP of S-warfarin in the duodenum eff 、The effective permeability coefficient of S-warfarin in the jejunum SJP eff 、The effective permeability coefficient of S-warfarin in the ileum SIP eff 、The effective permeability coefficient of S-warfarin in the colon SCP eff ;

[0022] P eff Calculated according to the following formula:

[0023]

[0024] Among them, C out(cor) is the corrected concentration of the effluent, Q in is the inflow into the intestine, C in is the concentration of the infusion solution, C outis the measured concentration of the effluent; L is the length of the intestinal segment; r is the cross-sectional radius of the perfused intestinal segment, Q out The outflow volume into the intestine is

[0025] S1.4. Collect drug metabolism experimental data. After the animals in groups B, E, and F were given warfarin or saline on the eighth day, blood was collected at the following time points: 0.5, 1, 1.5, 2, 4, 8, 12, 24, 48, 72, 96, 120, and 144 h. The blood was collected in EP tubes rinsed with sodium heparin, and then the collected blood was centrifuged at 12,000 rpm for 15 minutes at 4°C. After sample pretreatment, the blood concentrations of R and S-warfarin in each sample were detected by UPLC-MS / MS. The pharmacokinetic software was established using the mean and standard deviation of the eight samples at each time point, and the pharmacokinetic parameter R-warfain peak concentration RC of each group was calculated using DAS2.0 software. max , R-warfain concentration peak time RT max 、R-warfain elimination half-life Rt 1 / 2 , R-warfain 0-t time under the curve area R-AUC 0-t 、R-warfain total area under the drug-time curve R-AUC 0-∞ , S-warfain concentration reaches peak concentration SC max 、S-warfain concentration peak time ST max 、S-warfain elimination half-life St 1 / 2 、S-warfain 0-t time area under the curve S-AUC 0-t 、S-warfain total area under the concentration-time curve S-AUC 0-∞ ;

[0026] S1.5. Collect experimental data on drug plasma protein binding rate. Use animals in groups B, E, and F to collect blood 1.5 hours after warfarin or saline administration on the eighth day and place it in a centrifuge tube with heparin anticoagulant and centrifuge it at 9000r / min for 10 minutes. Divide the rat plasma into two parts. Take the supernatant of one part and place it in a 0.5ml centrifuge tube. Place the other centrifuged plasma in an ultrafiltration centrifuge tube and centrifuge it at 9000r / min for 15 minutes. Place the plasma at the bottom of the ultrafiltration centrifuge tube in a 0.5ml centrifuge tube. After sample pretreatment, detect it by UPLC-MS / MS to obtain the concentrations of the supernatant, C, and C, respectively. 总 , concentration C of ultrafiltration centrifugation 游离 Then the plasma protein binding rate BRPP of each group was calculated using the following formula:

[0027] BRPP=(C 总 -C 游离)×100% / C 总 ;

[0028] S1.6. Collect drug excretion experimental data. After the animals in groups B, E, and F were given warfarin or saline on the eighth day, urine was collected at 0-6h, 6-12h, 12-24h, 24-36h, 36-48h, 48-60h, 60-72h, 72-84h, 84-96h, 96-108h, 108-120h, 120-132h, and 132-144h. After sample pretreatment, UPLC-MS / MS was used to detect the drug time curve in urine, and the drug half-life in urine was calculated. The half-life of R-warafrin in urine was 2.17kcal, 1.2kcal, and 1.3kcal, respectively. 1 / 2 , half-life of S-warafrin in urine, S-Nt 1 / 2 ;

[0029] S1.7. Construct a multidimensional dataset using the data obtained in steps S1.1 to S1.6.

[0030] Furthermore, the specific implementation method of step S3 includes the following steps:

[0031] S3.1. Calculate the covariance matrix Cov(X) using the standardized data obtained in step S2. The calculation expression of the covariance matrix is:

[0032]

[0033] Where X is a data matrix, the rows in the data matrix represent observations, and the columns in the data matrix represent different variables; is the mean vector of each variable, is the inner product after data centering, which is used to measure the covariance between variables, and n is the number of observed samples;

[0034] S3.2. Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue and eigenvector matrix W, where the eigenvalue corresponds to the importance of the principal component and the eigenvector in the eigenvector matrix represents the direction of each principal component;

[0035] S3.3. Project the original data to the principal component space defined by the eigenvector and calculate the score matrix T of the principal component, which is expressed as:

[0036] T=XW

[0037] Where W is the eigenvector matrix of the principal components;

[0038] S3.4. The generation expression of the load matrix P is:

[0039] P=(v1,v2,…v m )

[0040] Among them, v1, v2, v m are the eigenvectors of the covariance matrix.

[0041] Furthermore, the specific implementation method of step S4 includes the following steps:

[0042] S4.1. Construct an orthogonal partial least squares discriminant analysis model, the expression is:

[0043] Y=X p C+F

[0044] Among them, Y is the categorical response variable, X p is the prediction component, C is the classification coefficient matrix, and F is the residual matrix;

[0045] S4.2. Perform orthogonal partial least squares discriminant analysis on the score matrix obtained in step S3 using the model constructed in step S4.1, classify the data based on the orthogonal partial least squares discriminant analysis method, and obtain the classification results.

[0046] Further, step S5 obtains the evaluation result of the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin as follows:

[0047] The classification model was used to analyze and interpret the sample data of different categories. The PCA analysis results showed that the Danshensu and Lithospermic Acid combined administration group was completely separated from the warfarin group. Both groups had a significant impact on the anticoagulant effect of warfarin, and the Danshensu combined administration group was the farthest from the horizontal axis of the warfarin group, with the greatest impact.

[0048] In the OPLS-DA analysis, the three groups of data were completely separated and independent of each other, which shows that the danshensu combined administration group has the greatest effect on the anticoagulant effect of warfarin, and is greater than the lithospermic acid combined administration group. Taking different PD / PK parameters as dependent variables, the warfarin control group, danshensu-warfarin combined administration group and lithospermic acid-warfarin combined administration group as independent variables, OPLS-DA can effectively distinguish the PD / PK parameters of the three groups of warfarin control group, danshensu combined administration group and lithospermic acid combined administration group. The independent variable fitting index and dependent variable fitting index (R2x, R2y) in this analysis were 0.830 and 0.847, respectively, and the model prediction index was 0.583. The cumulative explanatory parameter R2 and the predictive ability parameter Q2 were both over 0.5, indicating that the model fitting results were acceptable;

[0049] To exclude the possibility of overfitting of the model, 200 permutation tests were further performed. The intersection of the Q2 regression line and the vertical axis was less than 0, indicating that the model was not overfitted and the model validation was effective. It is believed that this result can be used to explore the clustering of different dosing groups based on PD / PK parameters, and to further explore the contribution of PD / PK parameters to classification in this experiment;

[0050] VIP map can see Rt 1 / 2 The VIP value of Rt is the largest, indicating that 1 / 2 The greatest contribution;

[0051] Danshensu and lithospermic acid groups mainly affected the Rt of warfarin 1 / 2 Affect the anticoagulant effect of warfarin;

[0052] RC max , RDP eff , RIP eff , APTT max 、INR max , SC max The VIP values ​​of all parameters are greater than 1. The change of the half-life of R-warfarin has the greatest impact on warfarin anticoagulation. The other parameters with VIP values ​​greater than 1 also affect the anticoagulation effect of warfarin to a certain extent.

[0053] Beneficial effects of the present invention:

[0054] The present invention discloses a method for evaluating the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin based on the PK / PD theory, establishes PCA and OPLSD-DA technologies to perform multivariate analysis on the PK-ADME / PD of traditional Chinese medicine combined with warfarin, reveals the key factors affecting the efficacy of warfarin by traditional Chinese medicine ingredients, and can deeply and systematically interpret the material basis, degree of influence and related laws of the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin.

[0055] The method for evaluating the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin based on the PK / PD theory described in the present invention is suitable for comprehensive, systematic and standardized evaluation and prediction of the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin to prevent medication risks; it can also be used for large-scale data set analysis in the fields of metabolomics, chemometrics or bioinformatics. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of a method for evaluating the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin based on the PK / PD theory of the present invention;

[0057] Figure 2 It is the principal component analysis score diagram of the present invention;

[0058] Figure 3 It is the score diagram of OPLS-DA orthogonal partial least squares discriminant analysis of the present invention;

[0059] Figure 4 This is a cross-check result diagram of the OPLS-DA orthogonal partial least squares discriminant analysis model of the present invention;

[0060] Figure 5It is the variable importance projection diagram of the OPLS-DA orthogonal partial least squares discriminant analysis of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0062] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0063] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 5 The detailed instructions are as follows: Specific implementation method one:

[0065] A method for evaluating the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin based on PK / PD theory comprises the following steps:

[0066] S1. Conduct grouped animal experiments, and then collect pharmacodynamic experimental data, drug absorption experimental data, drug metabolism experimental data, drug plasma protein binding rate experimental data, and drug excretion experimental data to construct a multidimensional data set;

[0067] Furthermore, the specific implementation method of step S1 includes the following steps:

[0068] S1.1. Animal experiments were conducted in groups, with eight rats in each group. Group A was a blank group, which was given normal saline by intraperitoneal injection for 14 consecutive days and normal saline by gavage on the eighth day; Group B was a warfarin group, which was given normal saline by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day; Group C was a danshensu group, which was given danshensu by intraperitoneal injection for 14 consecutive days; Group D was a lithospermic acid group, which was given lithospermic acid by intraperitoneal injection for 14 consecutive days; Group E was a warfarin + danshensu combined administration group, which was given danshensu by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day; Group F was a warfarin + lithospermic acid combined administration group, which was given lithospermic acid by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day;

[0069] S1.2. Collect pharmacodynamic experimental data. Blood was collected from animals in groups A, B, C, D, E, and F at the following time points after administration of warfarin or saline on the eighth day: 0, 4, 8, 12, 24, 36, 48, 72, 96, 120, and 144 h;

[0070] Blood was collected into EP tubes containing 0.32% sodium citrate solution and immediately centrifuged at 3500 rpm for 10 min at room temperature. The upper plasma was taken and the prothrombin time PT and activated partial thromboplastin time APTT were measured using a semi-automatic coagulometer, and the international normalized ratio INR was calculated using the following formula:

[0071] INR = (PT test / PT normal ) ISI

[0072] Among them, PT test is the PT value of the actual plasma sample, PT normal is the PT value of plasma samples in group A, and ISI stands for the international sensitivity index;

[0073] Then record the maximum prothrombin time PT of each group max , maximum international normalized ratio INR max and maximum activated partial thromboplastin time (APTT) max ;

[0074] S1.3. Collection of drug absorption experimental data, in situ in vivo one-way intestinal perfusion experiment (absorption): eight rats in each group, male Wistar rats were divided into warfarin administration group, tanshinone administration + warfarin combined administration group, lithospermic acid administration and warfarin combined administration group;

[0075] Only a single intestinal segment was used for each rat for a single perfusion test, and each administration group consisted of duodenum, jejunum, ileum, and colon, with 8 rats in each group;

[0076] Before the experiment, physiological saline, Krebs-Ringer buffer, and drug-containing perfusion fluid were preheated in a constant temperature water bath. Rats were fasted for 18 hours, during which they were free to drink water. They were weighed before the experiment and anesthetized with 1.5g / kg of 25% urethane solution injected intraperitoneally. Rats were fixed on the operating table and kept at body temperature. The middle line of the abdomen was opened 3 to 4 cm, the common bile duct was ligated to avoid enterohepatic circulation, and the experimental intestinal segments were carefully separated. The intestinal segments to be investigated were about 10 cm. The first segment was the duodenum, 1 cm distal to the pyloric sphincter, the second segment was the jejunum, 20 cm from the pyloric sphincter, the third segment was the ileum, 20 cm proximal to the cecum, and the fourth segment was the colon, 2 cm distal to the cecum. Incisions were made at both ends of the selected intestinal segments, and silicone tubes were inserted into the upper and lower ends of the intestinal segments, all tied with clean sterile surgical thread. After intubation, the small intestine was carefully returned to the abdominal cavity, and the wound was covered with gauze soaked in physiological saline to keep it moisturized.

[0077] During the experiment, the wound was kept moist. The intestinal contents were flushed with preheated 37°C saline at a flow rate of 5 mL / min; the air was expelled by perfusion with blank Krebs-Ringer buffer; the pipeline and the intestinal segment were then balanced with drug-containing perfusion fluid for 30 minutes at a flow rate of 0.2 ml / min, and then the perfusion fluid that had been accurately weighed was replaced to continue the perfusion. The outflow was collected with a weighed test tube, and the timer was started from the first drop of liquid dripping from the outlet tube. The weight of the perfusion fluid for 15 minutes was collected and recorded. 200 μl of perfusion fluid was collected every 15 minutes, and HPLC was used for detection, and then the data was calculated: the effective permeability coefficient RDP of R-warfarin in the duodenum eff 、The effective permeability coefficient of R-warfarin in the jejunum RJP eff 、R-warfarin effective permeability coefficient RIP in the ileum eff 、The effective permeability coefficient RCP of R-warfarin in the colon eff 、The effective permeability coefficient SDP of S-warfarin in the duodenum eff 、The effective permeability coefficient of S-warfarin in the jejunum SJP eff 、The effective permeability coefficient of S-warfarin in the ileum SIP eff 、The effective permeability coefficient of S-warfarin in the colon SCP eff ;

[0078] P eff Calculated according to the following formula:

[0079]

[0080] Among them, C out(cor) is the corrected concentration of the effluent, Q inis the inflow into the intestine, C in is the concentration of the infusion solution, C out is the measured concentration of the effluent; L is the length of the intestinal segment; r is the cross-sectional radius of the perfused intestinal segment, Q out The outflow volume into the intestine is

[0081] S1.4. Collect drug metabolism experimental data. After the animals in groups B, E, and F were given warfarin or saline on the eighth day, blood was collected at the following time points: 0.5, 1, 1.5, 2, 4, 8, 12, 24, 48, 72, 96, 120, and 144 h. The blood was collected in EP tubes rinsed with sodium heparin, and then the collected blood was centrifuged at 12,000 rpm for 15 minutes at 4°C. After sample pretreatment, the blood concentrations of R and S-warfarin in each sample were detected by UPLC-MS / MS. The pharmacokinetic software was established using the mean and standard deviation of the eight samples at each time point, and the pharmacokinetic parameters of each group, such as the peak concentration of R-warfarin, were calculated using DAS2.0 software. max , R-warfain concentration peak time RT max 、R-warfain elimination half-life Rt 1 / 2 , R-warfain 0-t time under the curve area R-AUC 0-t 、R-warfain total area under the drug-time curve R-AUC 0-∞ , S-warfain concentration reaches peak concentration SC max 、S-warfain concentration peak time ST max 、S-warfain elimination half-life St 1 / 2 、S-warfain 0-t time area under the curve S-AUC 0-t 、S-warfain total area under the concentration-time curve S-AUC 0-∞ ;

[0082] S1.5. Collect experimental data on drug plasma protein binding rate. Use animals in groups B, E, and F to collect blood 1.5 hours after warfarin or saline administration on the eighth day and place it in a centrifuge tube with heparin anticoagulant and centrifuge it at 9000r / min for 10 minutes. Divide the rat plasma into two parts. Take the supernatant of one part and place it in a 0.5ml centrifuge tube. Place the other centrifuged plasma in an ultrafiltration centrifuge tube and centrifuge it at 9000r / min for 15 minutes. Place the plasma at the bottom of the ultrafiltration centrifuge tube in a 0.5ml centrifuge tube. After sample pretreatment, detect it by UPLC-MS / MS to obtain the concentrations of the supernatant, C, and C, respectively. 总 , concentration C of ultrafiltration centrifugation 游离 Then the plasma protein binding rate BRPP of each group was calculated using the following formula:

[0083] BRPP=(C 总 -C 游离 )×100% / C 总 ;

[0084] S1.6. Collect drug excretion experimental data. After the animals in groups B, E, and F were given warfarin or saline on the eighth day, urine was collected at 0-6h, 6-12h, 12-24h, 24-36h, 36-48h, 48-60h, 60-72h, 72-84h, 84-96h, 96-108h, 108-120h, 120-132h, and 132-144h. After sample pretreatment, UPLC-MS / MS was used to detect the drug time curve in urine, and the drug half-life in urine was calculated. The half-life of R-warafrin in urine was 2.17kcal, 1.2kcal, and 1.3kcal, respectively. 1 / 2 , half-life of S-warafrin in urine, S-Nt 1 / 2 .

[0085] S1.7. Construct a multidimensional dataset using the data obtained in steps S1.1 to S1.6.

[0086] S2. Preprocessing the multidimensional data set obtained in step S1, including removing outliers and standardizing the data, to obtain a preprocessed data set;

[0087] S3. Perform dimensionality reduction analysis on the preprocessed data set obtained in step S2 using principal component analysis (PCA), extract the main components, and generate a score matrix and a loading matrix;

[0088] Furthermore, the specific implementation method of step S3 includes the following steps:

[0089] S3.1. Calculate the covariance matrix Cov(X) using the standardized data obtained in step S2. The calculation expression of the covariance matrix is:

[0090]

[0091] Where X is a data matrix, the rows in the data matrix represent observations, and the columns in the data matrix represent different variables; is the mean vector of each variable, is the inner product after data centering, which is used to measure the covariance between variables, and n is the number of observed samples;

[0092] S3.2. Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue and eigenvector matrix W, where the eigenvalue corresponds to the importance of the principal component and the eigenvector in the eigenvector matrix represents the direction of each principal component;

[0093] Furthermore, the direction of the eigenvector represents the direction of the principal component. For example, in a two-dimensional data space (assuming there are two variables x1 and x2), if the data is projected onto a line determined by the eigenvector, the projection direction is the direction of the first principal component. Each principal component has a corresponding eigenvector to determine its direction in the data space.

[0094] S3.3. Project the original data to the principal component space defined by the eigenvector and calculate the score matrix T of the principal component, which is expressed as:

[0095] T=XW

[0096] Where W is the eigenvector matrix of the principal components;

[0097] S3.4. The generation expression of the load matrix P is:

[0098] P=(v1,v2,…v m )

[0099] Among them, v1, v2, v m are the eigenvectors of the covariance matrix.

[0100] Furthermore, the loading matrix is ​​a p×m matrix. The element p in the loading matrix is jk That is v jk Indicates the strength of the correlation between the jth original variable and the kth principal component. The relationship between each principal component and the original variable can be understood by analyzing the loading matrix, thereby explaining the actual meaning of the principal component. m is the eigenvector of the covariance matrix (or correlation matrix);

[0101] Furthermore, the principal component analysis (PCA) in step S3 is used in the process of dimensionality reduction to extract at least 95% of the data variance information;

[0102] S4. Performing orthogonal partial least squares discriminant analysis on the score matrix obtained in step S3, classifying the data based on the orthogonal partial least squares discriminant analysis method to obtain a classification result;

[0103] Furthermore, the specific implementation method of step S4 includes the following steps:

[0104] S4.1. Construct an orthogonal partial least squares discriminant analysis model, the expression is:

[0105] Y=X p C+F

[0106] Among them, Y is the categorical response variable, X p is the prediction component, C is the classification coefficient matrix, and F is the residual matrix;

[0107] S4.2. Perform orthogonal partial least squares discriminant analysis on the score matrix obtained in step S3 using the model constructed in step S4.1, classify the data based on the orthogonal partial least squares discriminant analysis method, and obtain the classification results.

[0108] Furthermore, the OPLS-DA model in step S4 is used to distinguish data samples of two or more categories and reduce interference information between categories through orthogonal components;

[0109] S5. Based on the classification results obtained in step S4, an evaluation result of the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin is obtained.

[0110] Further, step S5 obtains the evaluation result of the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin as follows:

[0111] The classification model was used to analyze and interpret the sample data of different categories. From the PCA analysis results, the combined administration group of danshensu and lithospermic acid was completely separated from the warfarin group. Both groups had a significant impact on the anticoagulant effect of warfarin, and the combined administration group of danshensu was the farthest from the horizontal axis of the warfarin group, with the greatest impact.

[0112] In the OPLS-DA analysis, the three groups of data were completely separated and independent of each other, which showed that the tanshinone combined administration group had the greatest effect on the anticoagulant effect of warfarin, and was greater than the lithospermic acid combined administration group;

[0113] VIP map can see Rt 1 / 2 The VIP value of Rt is the largest, indicating that 1 / 2 The greatest contribution;

[0114] Danshensu and lithospermic acid groups mainly affected the Rt of warfarin 1 / 2 Affect the anticoagulant effect of warfarin;

[0115] RC max , RDP eff , RIP eff , APTT max 、INR max , SC max The VIP values ​​of all parameters are greater than 1. The change of the half-life of R-warfarin has the greatest impact on warfarin anticoagulation. The other parameters with VIP values ​​greater than 1 also affect the anticoagulation effect of warfarin to a certain extent.

[0116] Figure 2This is the principal component analysis score diagram of this embodiment. There are two coordinate axes in the figure, marked as t[1] and t[2] respectively. t[1] and t[2] are principal component score vectors, which represent the position of the sample in the principal component space. The first principal component (PC1) corresponds to the t[1] axis, and the second principal component (PC2) corresponds to the t[2] axis. These principal components are linear combinations of the original variables, obtained by reducing the dimensionality of the original data. The points in the figure represent different samples. These sample points are marked with different colors and symbols to distinguish different groups. For example, there are blue (warfarin group), red (lithospermic acid + warfarin group) and green (tanshinone + warfarin group) points in the figure, each of which represents a sample, and the distribution of the points reflects the positional relationship of samples of different groups in the principal component space. There are ellipses around the sample points of different groups in the figure, and these ellipses are usually Hotelling's T2 ellipses (with the mark "Hotelling's T2 (95%)" in the lower right corner of the figure). Hotelling's T2 ellipse is used to define the 95% confidence interval, that is, most of the sample points in the same group should fall within this ellipse. If a sample point falls outside the ellipse, it means that the sample is an outlier in the group.

[0117] Figure 3 This is the OPLS-DA orthogonal partial least squares discriminant analysis score diagram of this embodiment. There are two coordinate axes in the figure, marked as R2X[2] and t[2]. R2X[2] is usually related to the explanatory power of the model, reflecting the degree of explanation of the model on the variance of the X variable; t[2] is a score vector, which represents the position of the sample in the OPLS-DA model space. The points in the figure represent different samples. These sample points are marked with different colors and symbols to distinguish different groups. For example, there are blue (warfarin group), red (lithospermic acid + warfarin group) and green (tanshinone + warfarin group) points in the figure, each of which represents a sample, and the distribution of points reflects the positional relationship of samples from different groups in the OPLS-DA model space. There are ellipses around the sample points of different groups in the figure, which are usually Hotelling's T2 ellipses (marked "Hotelling's T2 (95%)" in the lower right corner of the figure). Hotelling's T2 ellipse is used to define the 95% confidence interval, that is, most of the sample points in the same group should fall within this ellipse. If a sample point falls outside the ellipse, it may mean that the sample is an outlier in the group.

[0118] Figure 4This is the cross-validation result of the OPLS-DA orthogonal partial least squares discriminant analysis model. There are two indicators on the vertical axis, namely R2 and Q2. R2: measures the degree of fit of the model to the data. The range of R2 values ​​is between 0 and 1. The higher the value, the better the model fits the data. It is represented by green dots and lines in the figure. Q2: obtained through cross-validation and used to evaluate the predictive ability of the model. The range of Q2 values ​​is also between 0 and 1. The higher the value, the stronger the predictive ability of the model. It is represented by blue squares and lines in the figure. There are two unit standards on the horizontal axis. The unit standard on the left represents the number of permutations, which is used for permutation test. Permutation test is a method to evaluate whether the model produces good results due to random factors. By randomly permuting the category labels of the samples multiple times, recalculating the R2 and Q2 values ​​of the model, and observing their changes, the intersection point of the Q2 regression line and the vertical axis is less than 0, indicating that the model does not have overfitting and the model verification is valid. The unit standard on the right represents the number of components in the model, that is, the number of principal components used in the OPLS-DA model. The performance of the OPLS-DA model can be evaluated by observing the changes in R2 and Q2 values.

[0119] Figure 5 This is the variable importance projection diagram of the OPLS-DA orthogonal partial least squares discriminant analysis in this embodiment. The vertical axis is labeled VIP[1]2+2+0j, which represents the value of the variable importance projection (Variable Importance in the Projection). The VIP value is used to measure the importance of each variable in the OPLS-DA model. The higher the VIP value, the greater the contribution of the variable to the model in distinguishing different groups. The names of the variables (VarID) are marked on the horizontal axis. These variables are the characteristic variables in the original data. For example, in the figure, you can see that the variable name such as Rt 1 / 2 ,RC max , RDP eff These variables may come from a measurement indicator in an experiment or analysis. The green bar in the figure represents the VIP value of each variable. The length of the bar corresponds to the size of the VIP value, which intuitively shows the relative importance of each variable in the model.

[0120] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0121] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for evaluating the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin based on PK / PD theory, characterized in that: The steps include: S1. Conduct grouped animal experiments, and then collect pharmacodynamic experimental data, drug absorption experimental data, drug metabolism experimental data, drug plasma protein binding rate experimental data, and drug excretion experimental data to construct a multidimensional data set; S2. Preprocessing the multidimensional data set obtained in step S1, including removing outliers and standardizing the data, to obtain a preprocessed data set; S3. Perform dimensionality reduction analysis on the preprocessed data set obtained in step S2 using principal component analysis (PCA), extract the main components, and generate a score matrix and a loading matrix; S4. Performing orthogonal partial least squares discriminant analysis on the score matrix obtained in step S3, classifying the data based on the orthogonal partial least squares discriminant analysis method to obtain a classification result; S5. Based on the classification results obtained in step S4, an evaluation result of the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin is obtained.

2. The method for evaluating the effect of Chinese medicine on the anticoagulant efficacy of warfarin based on the PK / PD theory according to claim 1, characterized in that: The specific implementation method of step S1 includes the following steps: S1.

1. Animal experiments were conducted in groups, with eight rats in each group. Group A was a blank group, which was given normal saline by intraperitoneal injection for 14 consecutive days and normal saline by gavage on the eighth day; Group B was a warfarin group, which was given normal saline by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day; Group C was a danshensu group, which was given danshensu by intraperitoneal injection for 14 consecutive days; Group D was a lithospermic acid group, which was given lithospermic acid by intraperitoneal injection for 14 consecutive days; Group E was a warfarin + danshensu combined administration group, which was given danshensu by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day; Group F was a warfarin + lithospermic acid combined administration group, which was given lithospermic acid by intraperitoneal injection for 14 consecutive days and warfarin by gavage on the eighth day; S1.

2. Collect pharmacodynamic experimental data. Blood was collected from animals in groups A, B, C, D, E, and F at the following time points after administration of warfarin or saline on the eighth day: 0, 4, 8, 12, 24, 36, 48, 72, 96, 120, and 144 h; Blood was collected into EP tubes containing 0.32% sodium citrate solution and immediately centrifuged at 3500 rpm for 10 min at room temperature. The upper plasma was taken and the prothrombin time PT and activated partial thromboplastin time APTT were measured using a semi-automatic coagulometer, and the international normalized ratio INR was calculated using the following formula: INR=(PT test / PT normal ) ISI Among them, PT test is the PT value of the actual plasma sample, PT normal is the PT value of plasma samples in group A, and ISI stands for the international sensitivity index; Then record the maximum prothrombin time PT of each group max , maximum international normalized ratio INR max and maximum activated partial thromboplastin time (APTT) max ; S1.

3. Collection of drug absorption experimental data, in situ in vivo one-way intestinal perfusion experiment: eight male Wistar rats in each group were divided into warfarin administration group, danshensu administration + warfarin combined administration group, lithospermic acid administration and warfarin combined administration group; Only a single intestinal segment was used for each rat for a single perfusion test, and each administration group consisted of duodenum, jejunum, ileum, and colon groups; During the experiment, the wound was kept moist, and the intestinal contents were flushed with preheated 37°C saline at a flow rate of 5 mL / min; blank Krebs-Ringer buffer was used to perfuse and expel air; the pipeline and intestinal segment were then equilibrated with drug-containing perfusion fluid for 30 minutes at a flow rate of 0.2 ml / min, and then the perfusion fluid that had been accurately weighed was replaced to continue the perfusion. The outflowing fluid was collected with a weighed test tube, and the timing began when the first drop of liquid dripped from the outlet tube. The weight of the perfusion fluid for 15 minutes was collected and recorded. 200 μl of perfusion fluid was collected every 15 minutes, and HPLC detection was performed, and then the data were calculated: the effective permeability coefficient RDP of R-warfarin in the duodenum eff 、The effective permeability coefficient of R-warfarin in the jejunum RJP eff 、R-warfarin effective permeability coefficient RIP in the ileum eff 、The effective permeability coefficient RCP of R-warfarin in the colon eff 、The effective permeability coefficient SDP of S-warfarin in the duodenum eff 、The effective permeability coefficient of S-warfarin in the jejunum SJP eff 、The effective permeability coefficient of S-warfarin in the ileum SIP eff 、The effective permeability coefficient of S-warfarin in the colon SCP eff ; P eff Calculated according to the following formula: Among them, C out(cor) is the corrected concentration of the effluent, Q in is the inflow into the intestine, C in is the concentration of the infusion solution, C out is the measured concentration of the effluent; L is the length of the intestinal segment; r is the cross-sectional radius of the perfused intestinal segment, Q out The outflow volume into the intestine is S1.

4. Collect drug metabolism experimental data. After the animals in groups B, E, and F were given warfarin or saline on the eighth day, blood was collected at the following time points: 0.5, 1, 1.5, 2, 4, 8, 12, 24, 48, 72, 96, 120, and 144 h. The blood was collected in EP tubes rinsed with sodium heparin, and then the collected blood was centrifuged at 12,000 rpm for 15 minutes at 4°C. After sample pretreatment, the blood concentrations of R and S-warfarin in each sample were detected by UPLC-MS / MS. The pharmacokinetic software was established using the mean and standard deviation of the eight samples at each time point, and the pharmacokinetic parameter R-warfain peak concentration RC of each group was calculated using DAS2.0 software. max , R-warfain concentration peak time RT max 、R-warfain elimination half-life Rt 1 / 2 , R-warfain 0-t time under the curve area R-AUC 0-t 、R-warfain total area under the drug-time curve R-AUC 0-∞ , S-warfain concentration reaches peak concentration SC max 、S-warfain concentration peak time ST max 、S-warfain elimination half-life St 1 / 2 、S-warfain 0-t time area under the curve S-AUC 0-t 、S-warfain total area under the concentration-time curve S-AUC 0-∞ ; S1.

5. Collect experimental data on drug plasma protein binding rate. Use animals in groups B, E, and F to collect blood 1.5 hours after warfarin or saline administration on the eighth day and place it in a centrifuge tube with heparin anticoagulant and centrifuge it at 9000r / min for 10 minutes. Divide the rat plasma into two parts. Take the supernatant of one part and place it in a 0.5ml centrifuge tube. Place the other centrifuged plasma in an ultrafiltration centrifuge tube and centrifuge it at 9000r / min for 15 minutes. Place the plasma at the bottom of the ultrafiltration centrifuge tube in a 0.5ml centrifuge tube. After sample pretreatment, detect it by UPLC-MS / MS to obtain the concentrations of the supernatant, C, and C, respectively. 总 , concentration C of ultrafiltration centrifugation 游离 Then the plasma protein binding rate BRPP of each group was calculated using the following formula: BRPP=(C 总 -C 游离 )×100% / C 总 ; S1.

6. Collect drug excretion experimental data. After the animals in groups B, E, and F were given warfarin or saline on the eighth day, urine was collected at 0-6h, 6-12h, 12-24h, 24-36h, 36-48h, 48-60h, 60-72h, 72-84h, 84-96h, 96-108h, 108-120h, 120-132h, and 132-144h. After sample pretreatment, UPLC-MS / MS was used to detect the drug time curve in urine, and the drug half-life in urine was calculated. The half-life of R-warafrin in urine was 2.17kcal, 1.2kcal, and 1.3kcal, respectively. 1 / 2 , half-life of S-warafrin in urine, S-Nt 1 / 2 ; S1.

7. Construct a multidimensional dataset using the data obtained in steps S1.1 to S1.

6.

3. The method for evaluating the effect of Chinese medicine on the anticoagulant efficacy of warfarin based on PK / PD theory according to claim 2, characterized in that: The specific implementation method of step S3 includes the following steps: S3.

1. Calculate the covariance matrix Cov(X) using the standardized data obtained in step S2. The calculation expression of the covariance matrix is: Where X is a data matrix, the rows in the data matrix represent observations, and the columns in the data matrix represent different variables; is the mean vector of each variable, is the inner product after data centering, which is used to measure the covariance between variables, and n is the number of observed samples; S3.

2. Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue and eigenvector matrix W, where the eigenvalue corresponds to the importance of the principal component and the eigenvector in the eigenvector matrix represents the direction of each principal component; S3.

3. Project the original data to the principal component space defined by the eigenvector and calculate the score matrix T of the principal component, which is expressed as: T=XW Where W is the eigenvector matrix of the principal components; S3.

4. The generation expression of the load matrix P is: P=(v1,v2,…v m ) Among them, v1, v2, v m are the eigenvectors of the covariance matrix.

4. The method for evaluating the effect of Chinese medicine on the anticoagulant efficacy of warfarin based on PK / PD theory according to claim 3, characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. Construct an orthogonal partial least squares discriminant analysis model, the expression is: Y=X p C+F Among them, Y is the categorical response variable, X p is the prediction component, C is the classification coefficient matrix, and F is the residual matrix; S4.

2. Perform orthogonal partial least squares discriminant analysis on the score matrix obtained in step S3 using the model constructed in step S4.1, classify the data based on the orthogonal partial least squares discriminant analysis method, and obtain the classification results.

5. The method for evaluating the effect of Chinese medicine on the anticoagulant efficacy of warfarin based on the PK / PD theory according to claim 4, characterized in that: Step S5 obtains the evaluation result of the effect of traditional Chinese medicine on the anticoagulant efficacy of warfarin: The classification model was used to analyze and interpret the sample data of different categories. The PCA analysis results showed that the Danshensu and Lithospermic Acid combined administration group was completely separated from the warfarin group. Both groups had a significant impact on the anticoagulant effect of warfarin, and the Danshensu combined administration group was the farthest from the horizontal axis of the warfarin group, with the greatest impact. In the OPLS-DA analysis, the three groups of data were completely separated and independent of each other, which showed that the tanshinone combined administration group had the greatest effect on the anticoagulant effect of warfarin, and was greater than the lithospermic acid combined administration group; VIP map shows Rt 1 / 2 The VIP value of Rt is the largest, indicating that 1 / 2 The greatest contribution; Danshensu and lithospermic acid groups mainly affected the Rt of warfarin 1 / 2 Affect the anticoagulant effect of warfarin; RC max , RDP eff , RIP eff , APTT max 、INR max , SC max The VIP values ​​of all parameters are greater than 1. The change of the half-life of R-warfarin has the greatest impact on warfarin anticoagulation. The other parameters with VIP values ​​greater than 1 also affect the anticoagulation effect of warfarin to a certain extent.

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