Genetic polymorphism-based oxcarbazepine quantitative pharmacological model and construction method thereof

Abstract

The invention discloses an oxcarbazepine quantitative pharmacological model based on genetic polymorphism and a construction method thereof, and belongs to the field of pharmacokinetics. The model construction method comprises the following steps: collecting steady-state blood drug valley concentration data and clinical data of an epileptic child patient, establishing a population pharmacokinetic model of oxcarbazepine active metabolites by adopting a nonlinear mixed effect model method, and quantitatively investigating the influence of physiological factors, combined medication and genetic factors on pharmacokinetic characteristic parameters of drugs; and finally, performing internal verification and evaluation on the model by using a goodness-of-fit diagram, a bootstrap method and visual prediction inspection. The quantitative pharmacological model constructed by the method has stable and accurate prediction performance; according to the present invention, the weight and the gene polymorphism ABCC2rs2273697 are determined as the effective covariants affecting MHD CL / F, and the oxcarbazepine dosage optimization scheme is established based on the weight and the genotype of the patient so as to improve the treatment effect and avoid the adverse reaction;

Description

Technical Field

[0001] The invention belongs to the field of pharmacokinetics, and in particular relates to an oxcarbazepine quantitative pharmacological model based on genetic polymorphism and a construction method thereof. Background Art

[0002] Epilepsy is one of the common chronic neurological diseases, characterized by abnormal discharges of brain neurons causing recurrent epileptic seizures. Children are at high risk of epilepsy, with 4 to 9 children suffering from the disease in every 1,000, which seriously affects the quality of life of countless children's families.

[0003] Oxcarbazepine is currently recommended by the International League Against Epilepsy (ILAE) as a first-line anti-epileptic drug for children, and has a wide range of effects on various epileptic seizures. After oral administration, oxcarbazepine is rapidly absorbed in the body and rapidly and almost completely degraded into a pharmacologically active metabolite, 10-hydroxycarbamazepine (MHD), with an effective blood concentration range of 3-35 mg / L. Because children are in the growth and development period, their physiological functions are not yet mature, and their metabolic enzyme systems and organ functions are not developed; and MHD has large individual metabolic differences and nonlinear protein binding, which leads to large individual differences in blood drug concentration at the same dose, which can further cause treatment failure or serious adverse reactions.

[0004] Compared with traditional pharmacokinetic studies, population pharmacokinetic (PPK) studies can use sparse drug concentration data to accurately estimate the errors caused by inter- and intra-individual variations. At the same time, a small number of blood sampling points is also conducive to studying children, which is an effective method for designing individualized treatment plans. Compared with empirical medication, model-guided precision medication (MIPD) is a new method for formulating medication plans based on the patient's physiological, pathological, genetic, and disease characteristics, which can improve the safety and effectiveness of drug treatment. Summary of the invention

[0005] The present invention provides a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism and a method for constructing the same. The purpose is to solve the problem that after taking oxcarbazepine, the large difference in blood drug concentration between different individuals in pediatric epilepsy patients, which leads to treatment failure or serious adverse reactions, is caused by the incomplete development of the metabolic enzyme system and organ function in the body. The present invention adopts a nonlinear mixed effect model method, combines classical pharmacokinetic theory and statistical principles to establish a population pharmacokinetic model of the active metabolite MHD of oxcarbazepine, and quantitatively examines the effects of physiological factors, combined medication and genetic factors on pharmacokinetic characteristic parameters. The final model is internally verified and evaluated using a goodness of fit plot, bootstrap method and visual prediction test. When the model is applied, the MHD trough concentration under different dosing regimens is predicted in combination with the Monte Carlo simulation method, and dosage recommendations are made for different subgroups of patients, thereby achieving safe, effective and individualized medication.

[0006] The technical solution of the present invention is achieved in this way:

[0007] A method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism comprises the following steps:

[0008] (1) Design of research plan: population pharmacokinetic analysis was used to establish inclusion criteria and select gene polymorphism sites related to drug transporters, drug metabolism, and oxcarbazepine efficacy;

[0009] (2) Data collection: data including demographic data, biochemical indices, medication status, concomitant medications, blood drug concentrations of the active metabolite of oxcarbazepine MHD, and genotyping of selected gene polymorphism sites;

[0010] (3) Basic model: A one-compartment model of first-order absorption and elimination (ADVAN2 TRANS2) was used for modeling; the inter-individual variation model was fitted by an exponential model, and the residual variation model was fitted by a mixed model;

[0011] (4) Fixed-effect model: A covariate model was established using the forward inclusion / backward elimination method; fixed-effect parameters that significantly affect the pharmacokinetics of the active metabolite of oxcarbazepine, MHD, were screened from various influencing factors, and the covariates to be investigated were plotted against the individual clearance estimated by the empirical Bayesian method; the final model was obtained; body weight and the gene polymorphism ABCC2rs2273697 were retained in the final model;

[0012] (5) Model evaluation: Graphical and statistical methods were used to evaluate the stability and predictive effectiveness of the model. After comprehensive evaluation, the final model was confirmed to be stable and reliable with high predictive performance.

[0013] (6) Dose optimization: The virtual patients were divided into different subgroups according to the included covariates. The Monte Carlo simulation method was used based on the final model to predict the distribution of steady-state blood drug concentrations under different dosing regimens. The model was constructed to provide a basis for individualized rational drug use in children with epilepsy.

[0014] Preferably, in step (1):

[0015] The inclusion and exclusion criteria include inclusion criteria and exclusion criteria; the inclusion criteria are: 1) children under 12 years old who meet the epilepsy seizure types of the International League Against Epilepsy (ILAE); 2) those who take oxcarbazepine in strict accordance with the doctor's instructions according to the diagnosis; 3) those who did not receive special treatments such as dialysis or diuretics that significantly affect drug elimination during the study; the exclusion criteria are: 1) those with missing key research data, including unknown dosage, no recent weight records, and abnormal weight changes; 2) those with unclear blood drug concentration monitoring information or the sampling time does not meet the requirements; 3) those with obvious abnormal liver and kidney function during medication.

[0016] The gene polymorphism sites include ABCB1 rs1128503, ABCB1 rs3789243, ABCB1rs1045642, ABCC2 rs2273697, ABCC2 rs717620, ABCC2 rs3740066, ADORA2Ars2298383, AS3MT rs7085104, IL1B rs16944, MTHFR rs1801131, MTHFRrs1801133, SCN1A rs6730344, SCN1A rs2298771, SCN1A rs10167228, SCN1Ars3812718, SCN1A rs6732655, SCN2A rs2304016, SCN2A rs17183814, UGT2B7rs7668258, UGT2B7rs7668282, UGT2B7 rs12233719, UGT2B7 rs28365063, UGT2B7 rs7439366, UGT1A9rs2741049, UGT1A6 rs6759892.

[0017] Preferably, in step (2):

[0018] The blood drug concentration of the active metabolite of oxcarbazepine, MHD, is determined by homogeneous enzyme immunoassay;

[0019] The genotyping of the gene polymorphism sites was performed using the iPLEX GoldAssay method of the MassARRAY platform of Agena Company to perform genotyping experiments and select genotypes.

[0020] The selected genotypes were subjected to Hardy-Weinberg equilibrium (HWE) test. If the genotype frequency did not conform to the HWE law, that is, when P<0.05 in the chi-square test, it indicated that the population genes were genetically unbalanced, and the stepwise method was no longer used to examine whether it had a significant effect on the pharmacokinetic parameters.

[0021] Preferably, in step (3):

[0022] The one-compartment model of first-order absorption and elimination had an absorption rate constant of 0.83 / h.

[0023] Preferably, in step (4): the individual clearance rates estimated by the Bayesian method are plotted as scatter plots and box plots against the included covariates, which helps to identify covariates with significant effects.

[0024] The covariates include continuous covariates and categorical covariates; the continuous covariates include age, weight, albumin, alanine aminotransferase, hemoglobin, hematocrit, creatinine, and daily dose of oxcarbazepine; the categorical covariates include gender, concomitant medication, and genetic polymorphisms; the concomitant medications include valproic acid, levetiracetam, and clonazepam; the genetic polymorphisms include ABCB1 rs1128503, ABCB1rs3789243, ABCB1 rs1045642, ABCC2 rs2273697, ABCC2rs717620, ABCC2rs3740066, ADORA2A rs2298383, AS3MT rs7085104, IL1B rs16944, MTHFRrs1801131, MTHFR rs1801133, SCN1A rs6730344, SCN1A rs2298771, SCN1Ars10167228, SCN1A rs3812718, SCN1A rs6732655, SCN2A rs2304016, SCN2Ars17183814, UGT2B7rs7668258, UGT2B7 rs7668282, UGT2B7 rs12233719, UGT2B7 rs28365063, UGT2B7rs7439366, UGT1A9 rs2741049, UGT1A6rs6759892.

[0025] The final model is specifically:

[0026]

[0027]

[0028] V / F(L)=17.8;

[0029] Among them, CL / F is clearance, V / F is apparent volume of distribution, Age is age (years), BW is body weight (kg), ABCC2 is ABCC2 rs2273697 polymorphism, ABCC2 GG=1, ABCC2AG=0 represents wild-type patients, ABCC2 AG=1, ABCC2GG=0 represents heterozygous AG genotype patients, and ABCC2GG=0, ABCC2AG=0 represents homozygous AA genotype patients.

[0030] Preferably, in step (5):

[0031] The graphical methods include goodness-of-fit plots and visual prediction tests;

[0032] The statistical method is the bootstrap method.

[0033] The goodness of fit diagram (GOF diagram) is a goodness of fit diagram of the basic model and the final model, which mainly includes: scatter diagrams of observed concentration value-group predicted value, observed concentration value-individual predicted value, conditional weight residual-time and conditional weight residual-group predicted value. The GOF diagram can intuitively evaluate whether the predicted value can well describe the central tendency and dispersion degree of the data, and evaluate the degree of fit of the model through the correlation between the predicted value and the measured value, the distribution range of the residual, and whether the residual is evenly distributed on both sides of the x-axis.

[0034] The visual prediction test (VPC diagram) is as follows: a simulated data set is generated according to the parameter estimation values ​​of the final model, and the percentiles (5%, 50% and 95%) of the observed data set and the simulated data set at each time point are calculated respectively, and the degree of proximity between the two and whether the distribution characteristics are the same are compared. If the observed value falls within the 95% confidence interval (CI) of the simulated data, it indicates that the model prediction performance is good; the VPC diagram can intuitively compare the degree of overlap between the model fitting value and the measured value, which serves as a reference for assisting in evaluating the accuracy and predictive ability of the model.

[0035] The bootstrap method: sampling with replacement is performed from the original modeling data, the sampling number is 1000 times, and 1000 data sets are generated; the 1000 data sets are fitted based on the final model, the number of successful fittings is counted, the robustness of the model is calculated, the median value and 95% CI of each parameter estimate are summarized and compared with the final model fitting result, and then the credibility and stability of the model parameter estimation value are evaluated; the higher the robustness of the model, and the parameters of the final model fall within the 95% CI of the corresponding parameters of the bootstrap method, the better the internal stability and predictive reliability of the model.

[0036] Preferably, in step (6), the number of virtual patients is ≥1000.

[0037] The drug is oxcarbazepine.

[0038] A quantitative pharmacological model of oxcarbazepine based on genetic polymorphism is obtained by the above construction method.

[0039] The quantitative pharmacological model of oxcarbazepine based on genetic polymorphism is specifically:

[0040]

[0041]

[0042] V / F(L)=17.8;

[0043] Among them, CL / F is clearance, V / F is apparent volume of distribution, Age is age (years), BW is body weight (kg), ABCC2 is ABCC2 rs2273697 polymorphism, ABCC2 GG=1, ABCC2AG=0 represents wild-type patients, ABCC2 AG=1, ABCC2GG=0 represents heterozygous AG genotype patients, and ABCC2GG=0, ABCC2AG=0 represents homozygous AA genotype patients.

[0044] The parameter estimates of the quantitative pharmacological model of oxcarbazepine based on genetic polymorphism are shown in Table 1:

[0045] Table 1 Parameter estimation table of the final model

[0046]

[0047]

[0048] Note: RSE is relative standard error.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The invention provides a method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism. The constructed quantitative pharmacological model of oxcarbazepine based on genetic polymorphism is a population pharmacokinetic (PPK) model of the active metabolite MHD of oxcarbazepine in children with epilepsy constructed by using quantitative pharmacological technology. The model has stable and accurate prediction performance. Body weight and gene polymorphism ABCC2 rs2273697 are determined as effective covariates affecting MHD CL / F, so as to better understand the inter-individual variation in the pharmacokinetic parameters of MHD. Furthermore, an oxcarbazepine dosage optimization scheme is established based on the patient's body weight and genotype, so as to improve the therapeutic effect thereof and avoid the occurrence of adverse reactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a technical roadmap for the population pharmacokinetic analysis of oxcarbazepine in Example 1 of the present invention.

[0052] Figure 2 It is a box plot of the MHD individual clearance rate estimated by the Bayesian method for different genotypes of the gene polymorphism ABCC2 rs2273697 in Example 1 of the present invention and the final model.

[0053] Figure 3 It is a goodness of fit diagram of the basic model and the final model in Example 1 of the present invention, wherein (A) is the observed value concentration vs. the group predicted value; (B) is the observed value concentration vs. the individual predicted value; (C) is the conditional weighted residual vs. the group predicted value; (D) is the conditional weighted residual vs. time.

[0054] Figure 4 It is a visual prediction test diagram of the final model in Example 1 of the present invention, where the black dots represent the observed concentrations; the black lines from top to bottom represent the 5th, 50th, and 95th percentiles of the observed values, and the shaded area represents the 95% confidence interval of each line based on the simulation.

[0055] Figure 5 This is a diagram of the oxcarbazepine dosage simulation results of the final model in Example 1 of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0058] Currently, PPK uses mathematical modeling and simulation technology to conduct group analysis using scattered blood drug concentration data from routine clinical monitoring, integrating relevant information such as patients, drugs, and diseases. It can quantitatively analyze the impact of individual differences (such as gender, age, weight, concomitant medication, related genotypes, etc.) on the pharmacokinetics of oxcarbazepine, and formulate the best individualized dosing plan based on the individual characteristics and treatment goals of the patient.

[0059] The polymorphism of drug-metabolizing enzymes and other related genes (including UGTs, ABC transporters, etc.) is an important factor in the individual differences in the pharmacokinetics of the active metabolite of oxcarbazepine MHD. Therefore, the present invention establishes a PPK model of MHD by adopting a nonlinear mixed effects model method, quantitatively examines the effects of influencing factors such as genetic polymorphism on the pharmacokinetic parameters of MHD, and provides an important reference for guiding the individualized medication of oxcarbazepine.

[0060] At present, children with epilepsy have underdeveloped metabolic enzyme systems and organ functions, and there is a disadvantage that after oral administration of the same dose of oxcarbazepine, the individual differences in blood drug concentrations cause treatment failure or serious adverse reactions. In order to solve the above technical problems, the present invention proposes a method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism. The present invention adopts a nonlinear mixed effect model method, combines classical pharmacokinetic theory and statistical principles to establish a population pharmacokinetic model of the active metabolite MHD of oxcarbazepine, and quantitatively examines the effects of physiological factors, combined medications and genetic factors on pharmacokinetic characteristic parameters. The final model is internally verified and evaluated using a goodness of fit plot, bootstrap method and visual prediction test. When the model is applied, the Monte Carlo simulation method is combined to predict the MHD trough concentration under different dosing regimens, and dosage recommendations are made for different subgroups of patients, thereby achieving safe, effective and individualized medication.

[0061] Although the population pharmacokinetic model of oxcarbazepine in pediatric population has been reported in domestic and foreign studies, they mainly involve the relationship between demographic factors and the variability of oxcarbazepine pharmacokinetic parameters, and few people pay attention to the influence of gene polymorphism, and no recommended dose based on genetic factors has been proposed. It is worth noting that the polymorphism of genes such as drug metabolizing enzymes is an important factor in the individual differences in pharmacokinetics. Therefore, the present invention uses a nonlinear mixed effect model method to construct a population pharmacokinetic model of oxcarbazepine in children with epilepsy, and quantitatively examines the influence of various influencing factors including gene polymorphism on the pharmacokinetic characteristic parameters of oxcarbazepine; and comprehensively evaluates the stability and predictive performance of the model, and combines the Monte Carlo simulation method to formulate a reasonable dosing regimen for children, guiding individualized medication.

[0062] Example 1

[0063] like Figure 1 As shown, a method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism adopts a population pharmacokinetic model method. This embodiment specifically includes the following steps:

[0064] 1. Design a research plan

[0065] Population pharmacokinetic analysis was used to formulate strict exclusion criteria, select gene polymorphism sites related to drug transporters, drug metabolism, and oxcarbazepine efficacy, and include patient data that meet the conditions in the analysis; the specific methods are as follows:

[0066] Population pharmacokinetic analysis was performed using NONMEM software (Version 7.5, ICON Development Solution, MD, USA). Homogeneous enzyme immunoassay was used to determine the plasma concentration of oxcarbazepine. Genotyping experiments were performed using the iPLEX Gold Assay method of the MassARRAY platform of Agena.

[0067] Inclusion criteria were: 1) children under 12 years old who met the latest epileptic seizure types of the International League Against Epilepsy (ILAE); 2) those who strictly followed the doctor's orders or took oxcarbazepine according to the diagnosis or the requirements of this study; 3) those who did not receive special treatments such as dialysis or diuretics that significantly affected drug elimination during the study. Exclusion criteria were: 1) those with missing key research data: such as unknown dosage, no recent weight records, and abnormal weight changes; 2) those with unclear blood drug concentration monitoring information or those with sampling time that did not meet the requirements; 3) those with obvious abnormal liver and kidney function during medication. This study protocol was approved by the Medical Ethics Committee of Shenzhen Bao'an District Maternal and Child Health Hospital, and the patients and their guardians signed the informed consent.

[0068] 2. Collecting Data

[0069] Basic information of children with epilepsy treated with oxcarbazepine was collected retrospectively: (1) general information: name, registration number, date of birth, etc. (2) demographic information: gender, age, height and weight, etc.; (3) biochemical indicators: albumin, alanine aminotransferase, hemoglobin, hematocrit, creatinine, etc.; (4) medication status: oxcarbazepine dosage form, dosing regimen, daily dose of oxcarbazepine, date of starting oxcarbazepine and date of change of oxcarbazepine dose, administration time and blood collection time; (5) concomitant medication: other antiepileptic drugs such as valproic acid, levetiracetam, clonazepam, etc.; (6) oxcarbazepine blood concentration detection data; (7) Genetic polymorphism of the genotype selected in this study.

[0070] According to the format requirements of NONMEM software, the data were organized into a comma-delimited plain text format ".csv" file, and a unified format table (Table 2) was made, that is, two-dimensional data containing columns and rows, and the data accuracy was checked. Missing values, outliers, and abnormal values ​​were processed, and the data were entered and checked by two people to ensure the reliability and accuracy of the data. Then, data exploratory analysis was performed: a scatter plot of blood drug concentration and time was drawn to intuitively understand the distribution of blood drug concentration in patients and preliminarily estimate the pharmacokinetic characteristics of oxcarbazepine; the distribution of the data was observed, and the median, minimum, maximum, mean, and standard deviation of each variable were calculated.

[0071] Table 2 Example of information collection of NONMEM data file for oxcarbazepine

[0072]

[0073] This study included 91 pediatric patients taking oxcarbazepine for epilepsy, and a total of 320 blood drug concentrations were collected. Table 3 lists the main demographic characteristics and concomitant medications of the patients.

[0074] Table 3 Demographic information of children with epilepsy

[0075]

[0076]

[0077] The selected genotypes were subjected to Hardy-Weinberg equilibrium (HWE) test. If the genotype frequency conformed to the HWE law, that is, when the chi-square test P>0.05, it indicated that the population genes were in genetic balance and the data came from the same Mendelian population. For the gene polymorphism sites that did not conform to the HWE balance, the stepwise method was no longer used to examine whether they had a significant effect on the pharmacokinetic parameters (Table 4).

[0078] Table 4 Selected genotype frequency distribution and HWE balance judgment

[0079]

[0080]

[0081] SCN1A rs6732655 did not conform to the Harwin equilibrium and was not included in the covariate screening, while the other 24 SNPs all conformed to it.

[0082] 3. Basic Model

[0083] The basic model is a model that characterizes the overall characteristics of the data, which includes two parts: the structural model and the random effects model. The structural model is a classic pharmacokinetic model. Further analysis is performed on the basis of the selected structural model. The inter-individual variation model is fitted with an exponential model, and the residual variation model examines the additive, proportional and mixed types respectively to select the best one. The estimation method of the model pharmacokinetic parameters uses the first-order rate conditional estimation method (FOCE-I) with inter-individual-residual variation interaction.

[0084] In this study, since the samples collected were from routine blood drug concentration monitoring results, most patients had their blood drawn before taking the drug (steady-state trough concentration), so the first-order absorption and elimination one-compartment model (ADVAN2TRANS2) was used for modeling, and the absorption rate constant (K a ) is set to 0.83 / h; the lack of information on the absorption phase and distribution phase will lead to large inter-individual variability in the estimated V / F, so V / F is fixed. The inter-individual variability model is fitted by the exponential model, and the residual variability model is fitted by the mixed model.

[0085] 4. Fixed Effects Model

[0086] After the base model was constructed, the covariate model was established using the forward inclusion / backward elimination method.

[0087] The purpose of screening covariates is to describe and explain the sources of variation in pharmacokinetic parameters. First, the Bayesian principle method is used to calculate individual parameters, and then a scatter plot is made between the individual parameters and the covariates to be examined, and covariates with trend distribution are included in further statistical tests. From various influencing factors, several fixed effect parameters that significantly affect the pharmacokinetics of oxcarbazepine are found. After the full model is established, the reverse elimination method is used to examine each influencing factor, and the final model is obtained after eliminating the fixed effect parameters with no significant significance.

[0088] Forward inclusion method: add covariates to the basic model one by one to examine the change in the objective function value (OFV); if the addition of a covariate causes the model OFV to decrease by more than 3.84 (χ 2 , P<0.05), indicating that the factor can significantly improve the goodness of fit of the model, the covariate is retained, otherwise it is eliminated; when all covariates are added to the basic model, the model with the largest decrease in OFV value is used as the basic model for the next round; and so on, until all covariates are screened and the full regression model is finally obtained; the selection of the model should also consider the degree of reduction of inter-individual variation of the parameters.

[0089] Reverse elimination method: The model after forward inclusion may include some unnecessary covariates, resulting in model instability or over-parameterization. Therefore, based on the full regression model, only one covariate is eliminated in each round, and the changes in model parameters and OFV values ​​are examined to examine the necessity of the factor in the model. After the reverse elimination process, the retained OFV increase value is greater than 6.63 (χ 2 , P<0.01) were added to obtain the final model; in the process of obtaining the final model, if the condition number exceeded 1000, it indicated that the model was overfitting, and the model structure should be simplified or the model parameter values ​​should be fixed to improve the accuracy of the pharmacokinetic parameter estimation of the model.

[0090] After constructing the basic model, the forward inclusion / reverse elimination method was used to establish the covariate model. The full regression model was established one by one using statistically significant continuous covariates (age, weight, albumin, hemoglobin, creatinine, daily dose of oxcarbazepine, etc.) and categorical covariates (gender, concomitant medications, and genotype). The specific screening process is shown in Table 5. Combination medications with a ratio of more than 5% were evaluated. Due to the strong correlation between age and weight, their effects on the clearance of oxcarbazepine should be examined separately and repeated inclusion in the model should be avoided. Appendix Figure 2This is the box plot of the individual clearance rate calculated based on the Bayesian algorithm for different genotypes of ABCC2rs2273697 in this study. It can be seen that the effect of its polymorphism on CL / F has a relatively obvious distribution trend.

[0091] Table 5 Analysis of the effects of covariates on CL / F of MHD

[0092]

[0126]

[0127] After a series of model fittings and reverse elimination of covariates, the weight segmentation model with age 2 as the cutoff point had a better fitting effect on the data. Weight and gene polymorphism ABCC2rs2273697 were retained in the final model, and the final model of MHD was obtained:

[0128]

[0129]

[0130] V / F(L)=17.8.

[0131] Among them, CL / F is clearance, V / F is apparent volume of distribution, Age is age (years), BW is body weight (kg), ABCC2 is ABCC2 rs2273697 polymorphism, ABCC2 GG = 1, ABCC2 AG = 0 is wild-type patients, ABCC2 AG = 1, ABCC2GG = 0 is heterozygous AG genotype patients, ABCC2GG = 0, ABCC2 AG = 0 is homozygous AA genotype patients. The condition number is 391, and the model shows no signs of over-parameterization.

[0132] Table 6 Estimated values ​​of parameters for the base model, final model and bootstrap validation

[0133]

[0134] a There were 967 successes out of 1000 bootstrap runs, which were used to calculate point estimates and 95% CIs.

[0135] b Relative error % = (bootstrap median - final model parameter estimate) / final model parameter estimate × 100%.

[0136] 5. Model Evaluation

[0137] Graphical methods (goodness-of-fit plots, visual prediction tests) and statistical methods (bootstrap method) were used to evaluate the stability and predictive effectiveness of the model.

[0138] Goodness of fit diagram (GOF diagram): Goodness of fit diagrams of the basic model and the final model are drawn respectively, mainly including: scatter plots of observed concentration value-group predicted value, observed concentration value-individual predicted value, conditional weight residual-time and conditional weight residual-group predicted value. The GOF diagram can intuitively evaluate whether the predicted value can well describe the central trend and dispersion of the data, and evaluate the degree of fit of the model through the correlation between the predicted value and the measured value, the distribution range of the residual, and whether the residual is evenly distributed on both sides of the x-axis.

[0139] Visual prediction test (VPC diagram): Generate a simulated data set based on the parameter estimation value of the final model, calculate the percentiles (5%, 50% and 95%) of the observed data set and the simulated data set at each time point, and compare the closeness of the two and whether the distribution characteristics are the same. If the observed value falls within the 95% confidence interval (CI) of the simulated data, it indicates that the model prediction performance is good; the VPC diagram can intuitively compare the degree of overlap between the model fitting value and the measured value, which serves as a reference for assisting in evaluating the accuracy and prediction ability of the model.

[0140] Bootstrap method: Sampling with replacement is performed from the original modeling data for 1000 times to generate 1000 data sets; the 1000 data sets are fitted based on the final model, the number of successful fittings is counted, the robustness of the model is calculated, the median value and 95% CI of each parameter estimate are summarized and compared with the final model fitting results, and the credibility and stability of the model parameter estimates are evaluated; the higher the robustness of the model, and the more all the parameters of the final model fall within the 95% CI of the corresponding parameters of the bootstrap method, the better the internal stability and predictive reliability of the model.

[0141] Compare the goodness-of-fit plots based on the base model and the final model ( Figure 3 ), the trend lines of the final model's population prediction value and individual prediction value are highly coincident with the reference line, the goodness of fit is significantly improved, and the central trend of the data is well described; the vast majority of conditional weighted residuals are symmetrically distributed on both sides of the reference line (y=0), and are distributed between -2 and +2, with no obvious deviation or significant trend; the above shows that the model has a better prediction effect after incorporating the two covariates of weight and genotype ABCC2 rs2273697. The results of the visual prediction test of the final model are shown in Figure 4The 5th, 50th, and 95th percentiles of the observed results were roughly distributed within the 95% CI of the simulated concentration in each interval, indicating that the model has good predictive performance. The robustness of the bootstrap analysis was 96.7%, and the parameter estimates of the final model were included in the 95% CI range of the model parameters obtained by the bootstrap method, and the relative error with the corresponding median was less than 4.4%, which showed the accuracy and robustness of the final model parameter estimation (Table 6).

[0142] 6. Dose Optimization

[0143] Selecting an appropriate dosing regimen to maintain the steady-state blood concentration of oxcarbazine within the therapeutic window is a necessary condition to ensure its safety and effectiveness. In order to make the final model more directly serve the clinic, this study adopted The $SIMULATION module in the ABCC2rs2273697 gene polymorphism was used to perform Monte Carlo simulations on patients with different body weights and ABCC2rs2273697 gene polymorphisms, and the steady-state trough concentration simulation results of 1,000 virtual patients were investigated under the dosing regimen of oxcarbazepine 10-70 mg / kg / day ( Figure 5 ), which has important reference value for optimizing drug administration regimen.

[0144] Virtual patients (n=1,000) were divided into different subgroups according to the included covariates, and the Monte Carlo simulation method was used based on the final model to calculate the steady-state trough concentrations of MHD under different dosage regimens.

[0145] The quantitative pharmacological model of oxcarbazepine based on genetic polymorphism of the present invention, namely, the pharmacokinetic model of MHD children population, is specifically:

[0146]

[0147]

[0148] V / F(L)=17.8.

[0149] Among them, CL / F is clearance, V / F is apparent volume of distribution, Age is age (years), BW is body weight (kg), ABCC2 is ABCC2 rs2273697 polymorphism, ABCC2 GG=1, AG=0 refers to wild-type patients, ABCC2 AG=1, GG=0 refers to heterozygous AG genotype patients, and ABCC2 GG=0, AG=0 refers to homozygous AA genotype patients.

[0150] The final model parameters for population pharmacokinetics are shown in Table 1.

[0151] The results showed that the MHD trough concentration gradually increased with increasing doses, and children over 12 kg (2 years old) were more sensitive to changes in drug doses, with a risk of high exposure at doses up to 40-60 mg / kg / day. In patients with homozygous (AA genotype) and heterozygous (AG genotype) variants of ABCC2rs2273697, CL / F was reduced by 25% and 14%, respectively, compared with wild homozygous (GG genotype). Therefore, for patients carrying variant alleles, a lower maintenance dose is required at the same body weight. Taking a typical patient of 10.5 years old and 35 kg as an example, patients with the GG genotype are more likely to reach the target blood drug concentration range at a dose of 40 mg / kg / day, while patients carrying variant alleles are recommended to be given a smaller dose of 30 mg / kg / day to prevent the concentration from exceeding the toxic threshold.

[0152] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism, and the constructed quantitative pharmacological model of oxcarbazepine based on genetic polymorphism is a population pharmacokinetic (PPK) model of the active metabolite MHD of oxcarbazepine in children with epilepsy constructed by using quantitative pharmacological technology, and the model has stable and accurate prediction performance; body weight and genetic polymorphism ABCC2 rs2273697 are determined as effective covariates affecting MHDCL / F, so as to better understand the inter-individual variation in the pharmacokinetic parameters of MHD; and an oxcarbazepine dosage optimization scheme is further established based on the patient's body weight and genotype to improve its therapeutic effect and avoid the occurrence of adverse reactions.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism, characterized in that: The following steps are involved: (1) Design of research plan: population pharmacokinetic analysis was used to establish inclusion criteria and select gene polymorphism sites related to drug transporters, drug metabolism, and oxcarbazepine efficacy; (2) Data collection: data including demographic data, biochemical indices, medication status, concomitant medications, blood drug concentrations of the active metabolite of oxcarbazepine MHD, and genotyping of selected gene polymorphism sites; (3) Basic model: Modeling is performed using a one-compartment model of first-order absorption and elimination; The inter-individual variation model was fitted by an exponential model, and the residual variation model was fitted by a mixed model; (4) Fixed-effect model: A covariate model was established using the forward inclusion / backward elimination method; fixed-effect parameters that significantly affect the pharmacokinetics of the active metabolite of oxcarbazepine, MHD, were screened from various influencing factors, and the covariates to be investigated were plotted against the individual clearance estimated by the empirical Bayesian method; the final model was obtained; body weight and the gene polymorphism ABCC2rs2273697 were retained in the final model; (5) Model evaluation: graphical and statistical methods are used to evaluate the stability and predictive effectiveness of the model; (6) Dose optimization: The virtual patients were divided into different subgroups according to the included covariates. The Monte Carlo simulation method was used based on the final model to predict the distribution of steady-state blood drug concentrations under different dosing regimens to complete the model construction.

2. The method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism according to claim 1, characterized in that: In step (1): the inclusion and exclusion criteria include inclusion criteria and exclusion criteria; the inclusion criteria are: 1) children under 12 years old who meet the epilepsy seizure type of the International League Against Epilepsy; 2) those who strictly follow the doctor's instructions to take oxcarbazepine according to the diagnosis; 3) those who did not receive special treatments such as dialysis or diuretics that significantly affect drug elimination during the study; the exclusion criteria are: 1) those with missing key research data, including unknown dosage, no recent weight records, and abnormal weight changes; 2) Those whose blood drug concentration monitoring information is unclear or whose sampling time does not meet the requirements; 3) Those whose liver and kidney function are obviously abnormal during medication.

3. The method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism according to claim 1, characterized in that: In step (1): the gene polymorphism sites include ABCB1 rs1128503, ABCB1 rs3789243, ABCB1 rs1045642, ABCC2 rs2273697, ABCC2 rs717620, ABCC2 rs3740066, ADORA2Ars2298383, AS3MT rs7085104, IL1B rs16944, MTHFR rs1801131, MTHFR rs1801133, SCN1Ars6730344, SCN1A rs2298771, SCN1A rs10167228, SCN1A rs3812718, SCN1A rs6732655, SCN2A rs2304016, SCN2A rs17183814, UGT2B7 rs7668258, UGT2B7 rs7668282, UGT2B7rs12233719, UGT2B7 rs28365063, UGT2B7 rs7439366, UGT1A9 rs2741049, UGT1A6rs6759892.

4. The method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism according to claim 1, characterized in that: In step (2): the blood drug concentration of the active metabolite of oxcarbazepine, MHD, is measured by homogeneous enzyme immunoassay; The genotyping of the gene polymorphism sites was performed using the iPLEX Gold Assay method of the MassARRAY platform of Agena Company to perform genotyping experiments and select genotypes; The selected genotypes were subjected to Hardy-Weinberg equilibrium test. If the genotype frequency did not conform to the HWE law, that is, when P<0.05 in the chi-square test, it indicated that the population genes were genetically unbalanced, and the stepwise method was no longer used to examine whether it had a significant effect on the pharmacokinetic parameters.

5. The method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism according to claim 1, characterized in that: In step (3): the absorption rate constant of the one-compartment model of first-order absorption and elimination is 0.83 / h.

6. The method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism according to claim 1, characterized in that: In step (4): the individual clearance rate estimated by the Bayesian method is plotted as a scatter plot and a box plot for the included covariates; The covariates include continuous covariates and categorical covariates; the continuous covariates include age, weight, albumin, alanine aminotransferase, hemoglobin, hematocrit, creatinine, and daily dose of oxcarbazepine; the categorical covariates include gender, concomitant medication, and genetic polymorphisms; the concomitant medications include valproic acid, levetiracetam, and clonazepam; the genetic polymorphisms include ABCB1 rs1128503, ABCB1rs3789243, ABCB1 rs1045642, ABCC2 rs2273697, ABCC2rs717620, ABCC2rs3740066, ADORA2A rs2298383, AS3MT rs7085104, IL1B rs16944, MTHFRrs1801131, MTHFR rs1801133, SCN1A rs6730344, SCN1A rs2298771, SCN1Ars10167228, SCN1A rs3812718, SCN1A rs6732655, SCN2A rs2304016, SCN2Ars17183814, UGT2B7rs7668258, UGT2B7 rs7668282, UGT2B7 rs12233719, UGT2B7 rs28365063, UGT2B7rs7439366, UGT1A9 rs2741049, UGT1A6rs6759892.

7. The method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism according to claim 1, characterized in that: In step (4): the final model is specifically: V / F(L)=17.8; Among them, CL / F is clearance, V / F is apparent volume of distribution, Age is age (years), BW is body weight (kg), ABCC2 is ABCC2 rs2273697 polymorphism, ABCC2 GG=1, ABCC2AG=0 represents wild-type patients, ABCC2 AG=1, ABCC2GG=0 represents heterozygous AG genotype patients, and ABCC2GG=0, ABCC2AG=0 represents homozygous AA genotype patients.

8. The method for constructing a quantitative pharmacological model of oxcarbazepine based on genetic polymorphism according to claim 1, characterized in that: In step (5): the graphical method includes a goodness-of-fit diagram and a visual prediction test; and the statistical method is a bootstrap method. In step (6), the number of virtual patients is ≥ 1000; The drug is oxcarbazepine.

9. A quantitative pharmacological model of oxcarbazepine based on genetic polymorphism is obtained by the construction method according to any one of claims 1 to 9.

10. The quantitative pharmacological model of oxcarbazepine based on genetic polymorphism according to claim 9, characterized in that: Specifically: V / F(L)=17.8; Among them, CL / F is clearance, V / F is apparent volume of distribution, Age is age (years), BW is body weight (kg), ABCC2 is ABCC2 rs2273697 polymorphism, ABCC2 GG=1, ABCC2AG=0 represents wild-type patients, ABCC2 AG=1, ABCC2GG=0 represents heterozygous AG genotype patients, and ABCC2GG=0, ABCC2AG=0 represents homozygous AA genotype patients.

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