Methods for evaluating the pharmacodynamics of cinacalcet in the treatment of secondary hyperparathyroidism

Abstract

The present invention belongs to the field of medical technology. The present invention discloses a method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism, the method comprising the following steps: collecting and processing secondary parathyroid data to construct a literature data set, performing quality evaluation on the constructed literature data set, and obtaining a quality evaluation result; constructing a pharmacodynamic model, evaluating the pharmacodynamic model, and obtaining a pharmacodynamic model evaluation result; quantitatively analyzing and processing the time effect distribution of cinacalcet in the clinical application of treating patients with secondary hyperparathyroidism and the influencing factors of predetermined indicators, and obtaining the result data of the effect parameters and the degree of influence of covariates to assist in determining the best clinical medication treatment strategy for cinacalcet. The present invention has the advantage of being able to quantitatively analyze and evaluate the drug time effect distribution of cinacalcet and the influencing factors of predetermined indicators to assist in determining the best clinical medication treatment strategy for cinacalcet.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and more specifically, to a method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism. Background Art

[0002] Cinacalcet is the first drug in a new class of compounds called calcimimetics. It can activate calcium receptors in the parathyroid glands, thereby reducing the secretion of parathyroid hormone (PTH). It is widely used in the treatment of patients with secondary hyperparathyroidism (SHPT). At present, although Cinacalcet has been widely used in the treatment of secondary hyperparathyroidism (SHPT) in hospitals, there is still some controversy over its treatment strategy during clinical application.

[0003] In clinical practice, traditional meta-analysis statistical methods are commonly used to evaluate the pharmacodynamics of cinacalcet in the treatment of secondary hyperparathyroidism, that is, to evaluate the PTH level of SHPT patients and the corresponding biochemical indexes (serum calcium, serum phosphorus, etc.) achieved by cinacalcet through meta-analysis statistical methods. Although this method can meet certain needs, it is often ignored in the evaluation process due to the technical limitations of traditional meta-analysis statistical methods in the process of use. The time course of treatment and influencing factors are usually ignored in the evaluation process. In addition, cinacalcet is usually used for the treatment of secondary hyperparathyroidism (SHPT) by dose titration, while the statistical evaluation method of the time course of treatment and influencing factors is ignored. Due to the lack of analysis and evaluation of factors such as baseline serum calcium (Ca) and serum phosphorus (P), dialysis duration, etc., it is difficult to obtain a more accurate drug effect evaluation result. Therefore, it is very important to thoroughly understand the time-effect relationship and influencing factors of cinacalcet for the clinical treatment of secondary hyperparathyroidism.

[0004] How to solve the above problems has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for evaluating the drug effect of cinacalcet in the treatment of secondary hyperparathyroidism, which can quantitatively analyze and evaluate the drug time effect distribution of cinacalcet and the influencing factors of predetermined indicators to assist in determining the optimal clinical drug treatment strategy of cinacalcet.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] The present invention provides a method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism, the method comprising the following steps:

[0008] S1. According to a predetermined standard, corresponding secondary parathyroid data are collected and processed to construct a literature data set, and then a quality evaluation is performed on the constructed literature data set according to a predetermined standard through a predetermined program to obtain a quality evaluation result;

[0009] S2. Based on the quality evaluation result of step S1, a pharmacodynamic model is constructed according to a predetermined standard based on a predetermined efficacy index in a literature data set corresponding to the quality evaluation result, and the pharmacodynamic model is evaluated according to the predetermined standard to obtain a pharmacodynamic model evaluation result to determine the accuracy of the pharmacodynamic model simulation evaluation;

[0010] S3. The pharmacodynamic model constructed in step S2 is used to quantitatively analyze the time effect distribution and the influencing factors of the predetermined indicators in the clinical application of Cinacalcet in the treatment of patients with secondary hyperparathyroidism, and then simulate and predict the result data of the pharmacodynamic parameters and the degree of influence of covariates of Cinacalcet to assist in determining the optimal clinical medication treatment strategy of Cinacalcet.

[0011] Preferably, in step S1, the collection and processing of the corresponding secondary parathyroid data includes:

[0012] 2.1. Use the predetermined GetData Graph Digitizer software to extract the secondary parathyroid data of clinical patients or corresponding literature tests pre-stored in the hospital according to the predetermined standards, and enter the data into the predetermined module of the predetermined Microsoft Excel software to form a database, thereby constructing a literature data set;

[0013] 2.2. Use the predetermined Review Manager software to conduct a literature quality assessment on the constructed literature data set according to the predetermined standards to obtain the quality evaluation results.

[0014] Preferably, in step 2.1, the inclusion and exclusion criteria for extracting the secondary parathyroid data of patients pre-stored in the hospital clinic are:

[0015] 3.1. The inclusion criteria for secondary parathyroid data are:

[0016] (1) Data from pre-planned randomized controlled trials or quasi-randomized controlled trials involving patients at all stages of chronic kidney disease with clinical manifestations of secondary hyperparathyroidism;

[0017] (2) Predetermined standard treatment data involving cinacalcet as monotherapy or in combination with vitamin D compounds;

[0018] (3) Corresponding data for patients in the control group of randomized controlled trials who received placebo or predetermined alternative treatment;

[0019] (4) The data include at least one of the following: parathyroid hormone level data, serum phosphorus level data, and serum calcium level data;

[0020] (5) The corresponding data are published in papers with full texts available for review;

[0021] 3.2. The exclusion criteria for secondary parathyroid data are:

[0022] (1) Data from conference or paper abstracts and reviews, meta-analysis, post hoc analysis, biomarker studies, or other non-clinical studies;

[0023] (2) data on patients diagnosed with primary hyperparathyroidism;

[0024] (3) clinical trial data for non-adult populations;

[0025] (4) Research data not published in English.

[0026] Preferably, the data of secondary parathyroid glands extracted according to predetermined standards using the predetermined GetData Graph Digitizer graph reading software is:

[0027] (1) When the data is literature test data, it includes the author, publication year, and the corresponding data of the literature DOI content object;

[0028] (2) When the data are clinical patient data, the trial characteristic data include trial type, sample size, trial drug, dosage, treatment duration, trial location, and funding source; the subject characteristic data include age, proportion of male subjects, dialysis type, dialysis duration, proportion of patients using vitamin D compounds or phosphate binders, serum parathyroid hormone level, and clinical outcome indicator data including changes in serum parathyroid hormone and calcium and phosphorus concentrations at each follow-up point relative to the predetermined baseline values.

[0029] Preferably, in the process of extracting secondary parathyroid data according to predetermined standards using predetermined GetData Graph Digitizer reading software and entering the data into a predetermined module of predetermined Microsoft Excel software to form a database, when the data is presented in the form of a chart in the predetermined module of predetermined Microsoft Excel software, the following processing steps are also included:

[0030] 5.1. At least two assessors shall independently use the corresponding GetData Graph Digitizer software to extract the corresponding secondary parathyroid data according to the predetermined standards to obtain the corresponding secondary parathyroid data;

[0031] 5.2. Enter the data extracted by each evaluator into the corresponding Microsoft Excel software into a predetermined module to form a database, thereby constructing the corresponding literature data set;

[0032] 5.3. Use the predetermined Review Manager software to conduct a literature quality assessment on the literature data sets constructed by each party according to the predetermined standards to obtain the corresponding quality evaluation results;

[0033] 5.4. Compare the quality evaluation results with each other according to the predetermined standards to obtain the comparison results. If the difference tolerance value of the comparison results exceeds the preset 2% difference tolerance value, return to step 5.1 to re-extract and analyze the data until the difference tolerance value of the comparison results is lower than the preset 2% difference tolerance value.

[0034] Preferably, in step 5.3, the evaluation method for the constructed literature dataset is:

[0035] The quality evaluation of the corresponding test indicator data in the literature data set was performed according to the predetermined standards using the predetermined Cochrane bias risk assessment tool preset in the Review Manager software, thereby obtaining the quality evaluation results of the literature data set; wherein, the quality evaluation strategy for the corresponding test indicator data in the literature data set includes:

[0036] (1) Random sequence generation based on test indicator data;

[0037] (2) allocation concealment based on trial indicator data;

[0038] (3) blinding of participants and personnel;

[0039] (4) Blinding of quality outcome assessment;

[0040] (5) Evaluation of incomplete outcome data;

[0041] (6) Evaluation of selective reporting;

[0042] (7) Evaluation of other non-comparable deviation data.

[0043] Preferably, in step S2, a pharmacodynamic model is constructed according to a predetermined standard based on the corresponding efficacy indicators of serum parathyroid hormone, blood calcium and blood phosphorus concentrations of patients with secondary hyperparathyroidism after cinacalcet intervention in the literature data corresponding to the quality evaluation result. At the same time, a predetermined R software is used to perform a meta-analysis on the pharmacodynamic model according to a predetermined standard and simulate the change process of serum parathyroid hormone, serum calcium and serum phosphorus concentrations relative to a predetermined standard baseline over time, thereby simulating and evaluating the accuracy of the pharmacodynamic model simulation evaluation; wherein, the evaluation model of the pharmacodynamic model is composed of the following functions:

[0044] Emax Model:

[0045] Exponential model: E=E max ×(1-e -k*time )--------Formula 2

[0046] In the formula, E max is the maximum effect value, which reflects the potential maximum drug efficacy; time is the observation time, and its measurement value is in months; ET 50 For drugs to reach E max The time required for 50% of the drug to be effective is the onset rate, k is the drug's onset efficiency parameter, and k is related to ET 50 The relationship can be converted into ET 50 =0.693 / k.

[0047] Preferably, the constructed pharmacodynamic model includes a structural model and a covariate model that can describe the differences in the efficacy of cinacalcet in different trials; wherein,

[0048] The structural model is represented by the following function:

[0049] P i =P typical +η i ----------------------------Formula 3

[0050]

[0051]

[0052] Where P typical is the typical value of the efficacy parameter; η i is the inter-experimental variation of the efficacy parameter, assuming that it has a mean of 0 and a variance of ω 2 Normal distribution, Y obs,i,j Y is the drug effect of the i-th study at the j-th observation time point; pre,i,j is the drug effect predicted for the i-th study at the j-th observation time point;i,j is the residual of the ith study at the jth observation time point, N i,j is the sample size of the i-th study at the j-th observation time point, ε i,j It needs to be corrected by the square root of the reciprocal of the sample size, that is, the larger the sample size, the smaller the residual value; εi,j is centered at 0 and has a variance of σ 2 Normal distribution of

[0053] The covariate model is represented by the following function:

[0054] P i =P Typical +(COV-COV median )×θ cov +η i --------Formula 6

[0055] P i =P Typical ×(COV / COV median ) θ cov +η i ----------Formula 7

[0056] P i =P Typical +COV×θ cov +η i --------------------Formula 8

[0057] Where P i is the individual value of the pharmacodynamic parameter of the ith trial, P Typical is the population value of the pharmacodynamic parameter, COV is the covariate value of the ith trial, and COV median is the median value of the covariate, θ cov is the correction factor for the pharmacodynamic parameter covariate, η i It is the inter-group variability of pharmacodynamic parameters. In the application process, the influence of each covariate on the model parameters is examined one by one. If the predetermined objective function value of the model decreases by more than 3.84, that is, the boundary value of the chi-square distribution with its degree of freedom as 1 and P<0.05, the covariate is considered to have a significant effect on the parameter. All covariates that are found to have significant influencing factors through screening one by one are screened again by the predetermined forward method and backward method to confirm the covariates that finally enter the model. Among them, the boundary value of the objective function value of the forward method is set at 3.84, that is, P<0.05; the boundary value of the objective function value of the backward method is set at 6.63, that is, P<0.01.

[0058] Preferably, in step S2, the evaluation of the pharmacodynamic model further comprises the following steps:

[0059] 9.1. By coordinating the predetermined observation and individual prediction model diagnostic graph, observation and group prediction model diagnostic graph, conditional weighted residual and group prediction model diagnostic graph, and conditional weighted residual and time model diagnostic graph, the goodness of fit of the pharmacodynamic model is evaluated according to the predetermined standards to obtain the change process of the drug effect of cinacalcet in the treatment of secondary hyperparathyroidism;

[0060] 9.2. Use the predetermined discard-one-out cross-validation method to conduct sensitivity analysis on the change process of drug effect according to the predetermined standards, that is, remove one group of test data from the original data set each time, and estimate the pharmacodynamic model parameters for the remaining data to examine the impact of each group of test data on the pharmacodynamic model parameters, thereby obtaining an optimized pharmacodynamic model;

[0061] 9.3. Use a predetermined visual prediction test method combined with a Monte Carlo simulation method to further verify the optimized pharmacodynamic model according to predetermined standards to obtain a verification result, and then compare the verification result with the predetermined measured value to simulate and evaluate the accuracy of the pharmacodynamic model;

[0062] 9.4. Then, the predetermined Bootstrap method is used to perform a predetermined number of repeated samplings with replacement on the predetermined efficacy indicators in the literature data set, thereby obtaining the median threshold of the distribution of pharmacodynamic model parameters, and the median threshold is compared with the predetermined parameters of the original data set to obtain the comparison results to evaluate the robustness of the pharmacodynamic model.

[0063] Preferably, in step S3, the following steps are also included:

[0064] 10.1. Using a predetermined Bayesian estimation method to obtain the pharmacodynamic parameters of a predetermined individual, and then correcting the pharmacodynamic parameters of the predetermined individual through a covariate model; obtaining pharmacodynamic parameter correction result data;

[0065] 10.2. The pharmacodynamic parameter correction result data of the predetermined individuals are summarized by using the predetermined single-group rate Meta-analysis method to obtain the data summary results, and then the data summary results are simulated and analyzed for a predetermined number of times according to the predetermined standards by using the predetermined drug effect simulation method and Monte Carlo simulation method to obtain the simulation analysis results, and then the simulation analysis results are compared with the predetermined experimental drug effect measured values ​​to obtain the comparison results, thereby obtaining the difference between the efficacy of cinacalcet and the drug effect simulated by the pharmacodynamic model, that is, at the same baseline level, if the experimental drug effect measured value is outside the 95% confidence interval of the drug effect value simulated by the pharmacodynamic model, then the dosage efficacy of the medication is significantly different from the drug effect simulated by the pharmacodynamic model, otherwise it is equivalent to the drug effect;

[0066] 10.3. Based on the comparison results of step 10.2, the result data of the pharmacodynamic parameters and the degree of influence of covariates after the administration of cinacalcet are obtained through the preset standards, that is, the time effect process of the pharmacodynamic indicators of PTH, serum calcium, and serum phosphorus corresponding to the pharmacodynamic parameters and the degree of influence of covariates are obtained, thereby providing data support for adjusting the dosage and administration time of cinacalcet to adjust the treatment plan and assist in determining the best medication strategy to achieve better treatment effects.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] During the use of the present invention, the corresponding secondary parathyroid data is collected and processed according to a predetermined standard to construct a literature data set, and then the quality of the constructed literature data set is evaluated according to a predetermined standard by a predetermined program to obtain a quality evaluation result; then a pharmacodynamic model is constructed according to a predetermined standard based on a predetermined efficacy index in the literature data set corresponding to the quality evaluation result, and the pharmacodynamic model is evaluated according to a predetermined standard to obtain a pharmacodynamic model evaluation result to determine the accuracy of the pharmacodynamic model simulation evaluation; the time effect distribution of Cinacalcet in the clinical application of patients with secondary hyperparathyroidism and the influencing factors of the predetermined index are quantitatively analyzed and processed by the constructed pharmacodynamic model, and then the result data of the pharmacodynamic parameters and the degree of influence of the covariates of Cinacalcet drugs are simulated and predicted, which can assist in determining the best clinical medication treatment strategy of Cinacalcet and provide a supporting basis for the development decision of Cinacalcet drugs. Therefore, the present invention has the advantage of being able to quantitatively analyze and evaluate the drug time effect distribution of Cinacalcet and the influencing factors of the predetermined indexes to assist in determining the best clinical medication treatment strategy of Cinacalcet. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0070] Figure 1 It is a flow chart of the method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism according to the present invention;

[0071] Figure 2 It is a schematic diagram of the characteristic changes of PTH value over time in SHPT patients after taking Cinacalcet in the pharmacodynamic evaluation method of Cinacalcet for treating secondary hyperparathyroidism of the present invention;

[0072] Figure 3 Schematic diagram of the covariate screening process of the pharmacodynamic evaluation method of cinacalcet for treating secondary hyperparathyroidism according to the present invention;

[0073] Figure 4It is a schematic diagram of the goodness of fit results of the pharmacodynamic evaluation method of cinacalcet for treating secondary hyperparathyroidism of the present invention to the measured values;

[0074] Figure 5 It is a schematic diagram of the results of sensitivity analysis using the discard-one-out cross-validation method in the pharmacodynamic evaluation method of cinacalcet for treating secondary hyperparathyroidism described in the present invention. DETAILED DESCRIPTION

[0075] See also Figures 1 to 5 As shown, a method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism is characterized in that the method comprises the following steps:

[0076] S1. Using a predetermined program to collect and process corresponding secondary parathyroid data according to a predetermined standard to construct a literature data set, and then using a predetermined program to perform quality evaluation on the constructed literature data set according to a predetermined standard to obtain a quality evaluation result;

[0077] S2. Based on the quality evaluation result of step S1, a pharmacodynamic model is constructed according to a predetermined standard based on a predetermined efficacy index in a literature data set corresponding to the quality evaluation result, and the pharmacodynamic model is evaluated according to the predetermined standard to obtain a pharmacodynamic model evaluation result to determine the accuracy of the pharmacodynamic model simulation evaluation;

[0078] S3. The pharmacodynamic model constructed in step S2 is used to quantitatively analyze the time effect distribution and the influencing factors of the predetermined indicators in the clinical application of Cinacalcet in the treatment of patients with secondary hyperparathyroidism, and then simulate and predict the result data of the pharmacodynamic parameters and the degree of influence of covariates of Cinacalcet to assist in determining the optimal clinical medication treatment strategy of Cinacalcet.

[0079] The implementation case of this embodiment is as follows: This embodiment simulates the change characteristics of PTH values ​​over time in SHPT patients with four PTH baseline levels (300, 600, 800 and 1200 pg / ml) after taking cinacalcet (see Figure 2). Taking the baseline PTH value of 600pg / ml as an example, its Emax value is 290pg / ml. The PTH values ​​at 3, 6, 12 and 24 weeks after medication dropped to 420.6, 378.3, 348.5 and 330.7pg / ml, respectively, which were reduced by 179.4, 221.7, 251.5 and 269.3pg / ml, accounting for 61.9%, 76.4%, 86.7% and 92.9% of its Emax value. The results suggest that the PTH-lowering effect of cinacalcet can approach the drug efficacy platform at about 12 weeks. When the subjects' PTH baseline concentration was 300, 600, 800, and 1200 pg / ml, PTH decreased by 86.1, 179.4, 242.1, and 366.9 pg / ml at week 3, respectively, and decreased by 28.7%, 29.9%, 30.3%, and 30.6% from the baseline period, respectively, approaching or reaching the expected target of 30%. When the subjects' PTH baseline concentration was 300, 600, 800, and 1200 pg / ml, the PTH values ​​dropped to 179.4, 348.8, 461.3, and 687.1 pg / ml after 12 weeks of cinacalcet use. Taking the PTH target value of 130-600 pg / ml recommended by the KDIGO guidelines as the target value, patients with a PTH baseline value of 1184 pg / ml or above cannot meet the target.

[0080] Among them, in this embodiment, the predetermined index influencing factors are serum parathyroid hormone, calcium and phosphorus.

[0081] In addition, the factors affecting the predetermined indicators can also be adjusted as needed.

[0082] In this embodiment, in step S1, the collection and processing of the corresponding secondary parathyroid data includes:

[0083] 2.1. Use the predetermined GetData Graph Digitizer software to extract the secondary parathyroid data of clinical patients or corresponding literature tests pre-stored in the hospital according to the predetermined standards, and enter the data into the predetermined module of the predetermined Microsoft Excel software to form a database, thereby constructing a literature data set;

[0084] 2.2. Use the predetermined Review Manager software to conduct a literature quality assessment on the constructed literature data set according to the predetermined standards to obtain the quality evaluation results.

[0085] In this embodiment, in step 2.1, the inclusion and exclusion criteria for extracting the secondary parathyroid data of patients pre-stored in the hospital clinic are:

[0086] 3.1. The inclusion criteria for secondary parathyroid data are:

[0087] (1) Data from pre-planned randomized controlled trials or quasi-randomized controlled trials involving patients at all stages of chronic kidney disease with clinical manifestations of secondary hyperparathyroidism;

[0088] (2) Predetermined standard treatment data involving cinacalcet as monotherapy or in combination with vitamin D compounds;

[0089] (3) Corresponding data for patients in the control group of randomized controlled trials who received placebo or predetermined alternative treatment;

[0090] (4) The data include at least one of the following: parathyroid hormone level data, serum phosphorus level data, and serum calcium level data;

[0091] (5) The corresponding data are published in papers with full texts available for review;

[0092] 3.2. The exclusion criteria for secondary parathyroid data are:

[0093] (1) Data from conference or paper abstracts and reviews, meta-analysis, post hoc analysis, biomarker studies, or other non-clinical studies;

[0094] (2) data on patients diagnosed with primary hyperparathyroidism;

[0095] (3) clinical trial data for non-adult populations;

[0096] (4) Research data not published in English.

[0097] In this embodiment, the data of secondary parathyroid glands extracted according to predetermined standards using the predetermined GetData Graph Digitizer graph reading software are:

[0098] (1) When the data is literature test data, it includes the author, publication year, and the corresponding data of the literature DOI content object;

[0099] (2) When the data are clinical patient data, the trial characteristic data include trial type, sample size, trial drug, dosage, treatment duration, trial location, and funding source; the subject characteristic data include age, proportion of male subjects, dialysis type, dialysis duration, proportion of patients using vitamin D compounds or phosphate binders, serum parathyroid hormone level, and clinical outcome indicator data including changes in serum parathyroid hormone and calcium and phosphorus concentrations at each follow-up point relative to the predetermined baseline values.

[0100] In this embodiment, in the process of extracting secondary parathyroid data according to predetermined standards using the predetermined GetData Graph Digitizer graph reading software and entering the data into a predetermined module of the predetermined Microsoft Excel software to form a database, when the data is presented in the form of a chart in the predetermined module of the predetermined Microsoft Excel software, the following processing steps are also included:

[0101] 5.1. At least two assessors shall independently use the corresponding GetData Graph Digitizer software to extract the corresponding secondary parathyroid data according to the predetermined standards to obtain the corresponding secondary parathyroid data;

[0102] 5.2. Enter the data extracted by each evaluator into the corresponding Microsoft Excel software into a predetermined module to form a database, thereby constructing the corresponding literature data set;

[0103] 5.3. Use the predetermined Review Manager software to conduct a literature quality assessment on the literature data sets constructed by each party according to the predetermined standards to obtain the corresponding quality evaluation results;

[0104] 5.4. Compare the quality evaluation results with each other according to the predetermined standards to obtain the comparison results. If the difference tolerance value of the comparison results exceeds the preset 2% difference tolerance value, return to step 5.1 to re-extract and analyze the data until the difference tolerance value of the comparison results is lower than the preset 2% difference tolerance value.

[0105] In this embodiment, in step 5.3, the evaluation method for the constructed literature dataset is:

[0106] The quality evaluation of the corresponding test indicator data in the literature data set was performed according to the predetermined standards using the predetermined Cochrane bias risk assessment tool preset in the Review Manager software, thereby obtaining the quality evaluation results of the literature data set; wherein, the quality evaluation strategy for the corresponding test indicator data in the literature data set includes:

[0107] (1) Random sequence generation based on test indicator data;

[0108] (2) allocation concealment based on trial indicator data;

[0109] (3) blinding of participants and personnel;

[0110] (4) Blinding of quality outcome assessment;

[0111] (5) Evaluation of incomplete outcome data;

[0112] (6) Evaluation of selective reporting;

[0113] (7) Evaluation of other non-comparable deviation data.

[0114] In this embodiment, in step S2, a pharmacodynamic model is constructed according to a predetermined standard based on the corresponding efficacy indicators of serum parathyroid hormone, blood calcium and blood phosphorus concentrations of patients with secondary hyperparathyroidism after cinacalcet intervention in the literature data corresponding to the quality evaluation result. At the same time, a predetermined R software is used to perform a meta-analysis on the pharmacodynamic model according to a predetermined standard and simulate the change process of serum parathyroid hormone, serum calcium and serum phosphorus concentrations relative to a predetermined standard baseline over time, thereby simulating and evaluating the accuracy of the pharmacodynamic model simulation evaluation; wherein, the evaluation model of the pharmacodynamic model is composed of the following functions:

[0115] Emax Model:

[0116] Exponential model: E=E max ×(1-e -k*time )--------Formula 2

[0117] In the formula, E max is the maximum effect value, which reflects the potential maximum drug efficacy; time is the observation time, and its measurement value is in months; ET 50 For drugs to reach E max The time required for 50% of the drug to be effective is the onset rate, k is the drug's onset efficiency parameter, and k is related to ET 50 The relationship can be converted into ET 50 =0.693 / k.

[0118] The constructed pharmacodynamic model includes a structural model and a covariate model that can describe the differences in the efficacy of cinacalcet in different trials;

[0119] The structural model is represented by the following function:

[0120] P i =P typical +η i ----------------------------Formula 3

[0121]

[0122]

[0123] Where P typical is the typical value of the efficacy parameter; η i is the inter-experimental variation of the efficacy parameter, assuming that it has a mean of 0 and a variance of ω2 Normal distribution, Y obs,i,j Y is the drug effect of the i-th study at the j-th observation time point; pre,i,j is the drug effect predicted for the i-th study at the j-th observation time point; i,j is the residual of the ith study at the jth observation time point, N i,j is the sample size of the i-th study at the j-th observation time point, ε i,j It needs to be corrected by the square root of the reciprocal of the sample size, that is, the larger the sample size, the smaller the residual value; εi,j is centered at 0 and has a variance of σ 2 Normal distribution of

[0124] The covariate model is represented by the following function:

[0125] P i =P Typical +(COV-COV median )×θ cov +η i --------Formula 6

[0126] P i =P Typical ×(COV / COV median ) θ cov +η i ----------Formula 7

[0127] P i =P Typical +COV×θ cov +η i --------------------Formula 8

[0128] Where P i is the individual value of the pharmacodynamic parameter of the ith trial, P Typical is the population value of the pharmacodynamic parameter, COV is the covariate value of the ith trial, and COV median is the median value of the covariate, θ cov is the correction factor for the pharmacodynamic parameter covariate, η iIt is the inter-group variability of pharmacodynamic parameters. In the application process, the influence of each covariate on the model parameters is examined one by one. If the predetermined objective function value of the model decreases by more than 3.84, that is, the boundary value of the chi-square distribution with its degree of freedom as 1 and P<0.05, the covariate is considered to have a significant effect on the parameter. All covariates that are found to have significant influencing factors through screening one by one are screened again by the predetermined forward method and backward method to confirm the covariates that finally enter the model. Among them, the boundary value of the objective function value of the forward method is set at 3.84, that is, P<0.05; the boundary value of the objective function value of the backward method is set at 6.63, that is, P<0.01.

[0129] In this embodiment, in step S2, the evaluation of the pharmacodynamic model further includes the following steps:

[0130] 9.1. By coordinating the predetermined observation and individual prediction model diagnostic graph, observation and group prediction model diagnostic graph, conditional weighted residual and group prediction model diagnostic graph, and conditional weighted residual and time model diagnostic graph, the goodness of fit of the pharmacodynamic model is evaluated according to the predetermined standards to obtain the change process of the drug effect of cinacalcet in the treatment of secondary hyperparathyroidism;

[0131] 9.2. Use the predetermined discard-one-out cross-validation method to conduct sensitivity analysis on the change process of drug effect according to the predetermined standards, that is, remove one group of test data from the original data set each time, and estimate the pharmacodynamic model parameters for the remaining data to examine the impact of each group of test data on the pharmacodynamic model parameters, thereby obtaining an optimized pharmacodynamic model;

[0132] 9.3. Use a predetermined visual prediction test method combined with a Monte Carlo simulation method to further verify the optimized pharmacodynamic model according to predetermined standards to obtain a verification result, and then compare the verification result with the predetermined measured value to simulate and evaluate the accuracy of the pharmacodynamic model;

[0133] 9.4. Then, the predetermined Bootstrap method is used to perform a predetermined number of repeated samplings with replacement on the predetermined efficacy indicators in the literature data set, thereby obtaining the median threshold of the distribution of pharmacodynamic model parameters, and the median threshold is compared with the predetermined parameters of the original data set to obtain the comparison results to evaluate the robustness of the pharmacodynamic model.

[0134] In this embodiment, step S3 further includes the following steps:

[0135] 10.1. Using a predetermined Bayesian estimation method to obtain the pharmacodynamic parameters of a predetermined individual, and then correcting the pharmacodynamic parameters of the predetermined individual through a covariate model; obtaining pharmacodynamic parameter correction result data;

[0136] 10.2. The pharmacodynamic parameter correction result data of the predetermined individuals are summarized by using the predetermined single-group rate Meta-analysis method to obtain the data summary results, and then the data summary results are simulated and analyzed for a predetermined number of times according to the predetermined standards by using the predetermined drug effect simulation method and Monte Carlo simulation method to obtain the simulation analysis results, and then the simulation analysis results are compared with the predetermined experimental drug effect measured values ​​to obtain the comparison results, thereby obtaining the difference between the efficacy of cinacalcet and the drug effect simulated by the pharmacodynamic model, that is, at the same baseline level, if the experimental drug effect measured value is outside the 95% confidence interval of the drug effect value simulated by the pharmacodynamic model, then the dosage efficacy of the medication is significantly different from the drug effect simulated by the pharmacodynamic model, otherwise it is equivalent to the drug effect;

[0137] 10.3. Based on the comparison results of step 10.2, the result data of the pharmacodynamic parameters and the degree of influence of covariates after the administration of cinacalcet are obtained through the preset standards, that is, the time effect process of the pharmacodynamic indicators of PTH, serum calcium, and serum phosphorus corresponding to the pharmacodynamic parameters and the degree of influence of covariates are obtained, thereby providing data support for adjusting the dosage and administration time of cinacalcet to adjust the treatment plan and assist in determining the best medication strategy to achieve better treatment effects.

[0138] In the clinical trials of this embodiment, a total of 727 research trials were preliminarily identified, of which 26 (3.6%) were included in the analysis, involving 4242 subjects (mean age, 55.7 years; 39.6% female; 60.4% male). Another 20 reported parathyroid hormone, 20 reported serum calcium, and 17 reported serum phosphorus. The baseline value of parathyroid hormone was 174.2pg / ml-1281.4pg / ml (median, 636pg / ml), the baseline value of serum calcium was 8.94mg / dl-11.28mg / dl (median, 9.73mg / dl), and the baseline value of serum phosphorus was 4.4mg / dl-7.1mg / dl (median, 5.75mg / dl). The screening results of this embodiment show that the time-effect relationship of PTH conforms to the Emax model, while the time-effect relationship of Ca and P conforms to the exponential model. Covariate analysis (for details of the covariate screening process, see Figure 3 ) found that baseline parathyroid hormone values ​​and the proportion of patients using vitamin D compounds had significant effects on the maximum drug effect values ​​(Emax) of PTH and CA, respectively, as follows:

[0139] E max_PTH =308+(Baseline PTH -636)×0.503

[0140] E max_CA =0.811-(VD-66)×0.00588

[0141] The formula shows that when the baseline value of PTH is 636pg / ml (median), the maximum drug effect value of PTH (Emax_PTH) is 308pg / ml, and the Emax_PTH value will increase by 5.03pg / ml for every 10pg / ml increase in the baseline value of PTH. When the proportion of patients using vitamin D compounds is 66% (median), the Emax_CA value is 0.811mg / dl, and the Emax_CA value will decrease by 0.0588mg / dl for every 10% increase in the proportion of patients using vitamin D compounds.

[0142] This example did not find any covariates that had a significant effect on the time-effect model parameters of serum phosphorus. In addition, this example calculated the time (ET) for serum parathyroid hormone, calcium, and phosphorus to reach 50% of the maximum drug effect. 50 ). Serum PTH, Serum Calcium, and Serum Phosphorus Model for ET 50 The typical values ​​are 0.46, 0.15 and 0.29 months respectively. The final diagnostic graph shows that the goodness of fit of this embodiment to the measured values ​​is good (see Figure 4 The distribution of model parameters obtained by the bootstrap method is close to the original estimated values, indicating that the model parameter estimation is relatively robust. The visual prediction test (VPC) shows that most of the measured values ​​are distributed within the 95% CI credible interval predicted by the model, reflecting the good predictive ability of the model (see Figure 1 ), the sensitivity analysis of the leave-one-out cross-validation method showed that the model parameters were less affected by individual studies (see Figure 5 ).

[0143] When this embodiment is used specifically, first, the predetermined GetData Graph Digitizer graph reading software is used to extract the secondary parathyroid data of clinical patients or corresponding literature tests pre-stored in the hospital according to the predetermined standards, and the data is entered into the predetermined module of the predetermined Microsoft Excel software to form a database, thereby constructing a literature data set; secondly, the quality of the corresponding test index data in the literature data set is evaluated according to the predetermined standards by the predetermined Cochrane bias risk assessment tool preset in the Review manager software, thereby obtaining the quality evaluation result of the literature data set; then, based on the quality evaluation result, a pharmacodynamic model is constructed according to the predetermined standards based on the predetermined efficacy index in the literature data set corresponding to the quality evaluation result, and the pharmacodynamic model is evaluated according to the predetermined standards to obtain the pharmacodynamic model evaluation result to determine the accuracy of the pharmacodynamic model simulation evaluation; finally, the time effect distribution of cinacalcet in the clinical application of the drug in the treatment of secondary hyperparathyroidism patients and the influencing factors of the predetermined indicators are quantitatively analyzed and processed by the constructed pharmacodynamic model, and then the result data of the pharmacodynamic parameters and the degree of influence of covariates of cinacalcet are simulated and predicted to assist in determining the optimal clinical medication treatment strategy of cinacalcet.

[0144] In summary, the present invention adopts the above method, which has the advantage of being able to quantitatively analyze and evaluate the drug time effect distribution of Cinacalcet and the influencing factors of predetermined indicators to assist in determining the optimal clinical medication treatment strategy for Cinacalcet.

[0145] For those skilled in the art, various other corresponding changes and deformations can be made according to the technical solutions and concepts described above, and all of these changes and deformations should fall within the protection scope of the claims of the present invention.

Claims

1. A method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism, characterized in that: The method comprises the following steps: S1. Using the predetermined GetData Graph Digitizer software to collect and process the corresponding secondary parathyroid data according to the predetermined standard to construct a literature data set, and then using the predetermined Reviewmanager software through a predetermined program to perform quality evaluation on the constructed literature data set according to the predetermined standard to obtain a quality evaluation result; S2. Based on the quality evaluation result of step S1, a pharmacodynamic model is constructed according to a predetermined standard based on a predetermined efficacy index in a literature data set corresponding to the quality evaluation result, and the pharmacodynamic model is evaluated according to the predetermined standard to obtain a pharmacodynamic model evaluation result to determine the accuracy of the pharmacodynamic model simulation evaluation; S3, using the pharmacodynamic model constructed in step S2, quantitatively analyzing the time effect distribution of cinacalcet in the clinical application of treating patients with secondary hyperparathyroidism and the influencing factors of the predetermined indicators, and then simulating and predicting the result data of the pharmacodynamic parameters and the degree of influence of covariates of cinacalcet to assist in determining the optimal clinical medication treatment strategy of cinacalcet; In step S2, a pharmacodynamic model is constructed according to a predetermined standard based on the corresponding efficacy indicators of serum parathyroid hormone, serum calcium and serum phosphorus concentrations of patients with secondary hyperparathyroidism after cinacalcet intervention in the literature data corresponding to the quality evaluation results. At the same time, a meta-analysis of the pharmacodynamic model is performed according to a predetermined standard using a predetermined R software, and the change process of serum parathyroid hormone, serum calcium and serum phosphorus concentrations relative to a predetermined standard baseline over time is simulated, thereby simulating and evaluating the accuracy of the pharmacodynamic model simulation evaluation; wherein, the evaluation model of the pharmacodynamic model is composed of the following functions: Emax Model: Exponential Model: In the formula, E max is the maximum effect value, which reflects the potential maximum drug efficacy; time is the observation time, and its measurement value is in months; ET 50 For drugs to reach E max The time required for 50% of the drug to be effective is the onset rate, k is the drug's onset efficiency parameter, and k is related to ET 50 The relationship can be converted into ET 50 =0.693 / k; The constructed pharmacodynamic model includes a structural model and a covariate model that can describe the differences in the efficacy of cinacalcet in different trials; The structural model is represented by the following function: P i = P typical + η i ---------------------------Formula 3 Where P typical is the typical value of the efficacy parameter; η i is the inter-experimental variation of the efficacy parameter, assuming that it has a mean of 0 and a variance of ω 2 Normal distribution, Y obs,i,j Y is the drug effect of the i-th study at the j-th observation time point; pre,i,j is the drug effect predicted for the i-th study at the j-th observation time point; i,j is the residual of the ith study at the jth observation time point, N i,j is the sample size of the i-th study at the j-th observation time point, ε i,j It needs to be corrected by the square root of the reciprocal of the sample size, that is, the larger the sample size, the smaller the residual value; εi,j is centered at 0 and has a variance of σ 2 Normal distribution of The covariate model is represented by the following function: P i =P Typical +(COV-COV median )×θ cov +η i -------- formula 6 P i =P Typical ×(COV / COV median ) θ cov +η i --------- formula 7 P i = P Typical + COV × θ cov + η i -------------------- Equation 8 Where P i is the individual value of the pharmacodynamic parameter of the ith trial, P Typical is the population value of the pharmacodynamic parameter, COV is the covariate value of the ith trial, and COV median is the median value of the covariate, θ cov is the correction factor for the pharmacodynamic parameter covariate, η i It is the inter-group variability of pharmacodynamic parameters. In the application process, the influence of each covariate on the model parameters is examined one by one. If the predetermined objective function value of the model decreases by more than 3.84, that is, the boundary value of the chi-square distribution with a degree of freedom of 1 and P<0.05, the covariate is considered to have a significant effect on the parameter. All covariates that are found to have significant influencing factors through screening one by one are screened again by the predetermined forward method and backward method to confirm the covariates that finally enter the model. Among them, the boundary value of the objective function value of the forward method is set at 3.84, that is, P<0.05; the boundary value of the objective function value of the backward method is set at 6.63, that is, P<0.

01. In step S3, the following steps are also included: 10.

1. Using a predetermined Bayesian estimation method to obtain the pharmacodynamic parameters of a predetermined individual, and then correcting the pharmacodynamic parameters of the predetermined individual through a covariate model; obtaining pharmacodynamic parameter correction result data; 10.

2. The pharmacodynamic parameter correction result data of the predetermined individuals are summarized by using the predetermined single-group rate Meta-analysis method to obtain the data summary results, and then the data summary results are simulated and analyzed for a predetermined number of times according to the predetermined standards by using the predetermined drug effect simulation method and Monte Carlo simulation method to obtain the simulation analysis results, and then the simulation analysis results are compared with the predetermined experimental drug effect measured values ​​to obtain the comparison results, thereby obtaining the difference between the efficacy of cinacalcet and the drug effect simulated by the pharmacodynamic model, that is, at the same baseline level, if the experimental drug effect measured value is outside the 95% confidence interval of the drug effect value simulated by the pharmacodynamic model, then the dosage efficacy of the medication is significantly different from the drug effect simulated by the pharmacodynamic model, otherwise it is equivalent to the drug effect; 10.

3. Based on the comparison results of step 10.2, the result data of the pharmacodynamic parameters and the degree of influence of covariates after the administration of cinacalcet are obtained through the preset standards, that is, the time effect process of the pharmacodynamic indicators of PTH, serum calcium, and serum phosphorus corresponding to the pharmacodynamic parameters and the degree of influence of covariates are obtained, thereby providing data support for adjusting the dosage and administration time of cinacalcet to adjust the treatment plan and assist in determining the best medication strategy to achieve better treatment effects.

2. The method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism according to claim 1, characterized in that: In step S1, the corresponding secondary parathyroid data is collected and processed The following steps are involved: 2.

1. Use the predetermined GetData Graph Digitizer software to extract the secondary parathyroid data of clinical patients or corresponding literature tests pre-stored in the hospital according to the predetermined standards, and enter the data into the predetermined module of the predetermined Microsoft Excel software to form a database, thereby constructing a literature data set; 2.

2. Use the predetermined Review Manager software to conduct a literature quality assessment on the constructed literature data set according to the predetermined standards to obtain the quality evaluation results.

3. The method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism according to claim 2, characterized in that: In step 2.1, the inclusion and exclusion criteria for extracting the secondary parathyroid data of patients pre-stored in the hospital clinic are: 3.

1. The inclusion criteria for secondary parathyroid data are: (1) Data from pre-planned randomized controlled trials or quasi-randomized controlled trials involving patients at all stages of chronic kidney disease with clinical manifestations of secondary hyperparathyroidism; (2) Predetermined standard treatment data involving cinacalcet as monotherapy or in combination with vitamin D compounds; (3) Corresponding data for patients in the control group of randomized controlled trials who received placebo or predetermined alternative treatment; (4) The data include at least one of the following: parathyroid hormone level data, serum phosphorus level data, and serum calcium level data; (5) The corresponding data are published in papers with full texts available for review; 3.

2. The exclusion criteria for secondary parathyroid data are: (1) Data from conference or paper abstracts and reviews, meta-analysis, post hoc analysis, biomarker studies, or other non-clinical studies; (2) data on patients diagnosed with primary hyperparathyroidism; (3) clinical trial data for non-adult populations; (4) Research data not published in English.

4. The method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism according to claim 2, characterized in that: The data of secondary parathyroid glands extracted according to the predetermined standards using the predetermined GetData Graph Digitizer graph reading software are as follows: (1) When the data is literature test data, it includes the author, publication year, and the corresponding data of the literature DOI content object; (2) When the data are clinical patient data, the trial characteristics data include trial type, sample size, trial drug, dose, treatment duration, trial location, and funding source; The subject characteristic data included age, proportion of male subjects, dialysis type, dialysis duration, proportion of patients using vitamin D compounds or phosphate binders, serum parathyroid hormone levels, and clinical outcome indicator data including changes in serum parathyroid hormone and calcium and phosphorus concentrations at each follow-up point relative to the predetermined baseline values.

5. The method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism according to claim 4, characterized in that, in the process of extracting secondary parathyroid data according to predetermined standards using a predetermined GetData Graph Digitizer graph reading software and entering the data into a predetermined module of a predetermined Microsoft Excel software to form a database, when the data is presented in the form of a chart in a predetermined module of the predetermined Microsoft Excel software, the following processing steps are also included: 5.

1. At least two assessors shall independently use the corresponding GetData Graph Digitizer software to extract the corresponding secondary parathyroid data according to the predetermined standards to obtain the corresponding secondary parathyroid data; 5.

2. Enter the data extracted by each evaluator into the corresponding Microsoft Excel software into a predetermined module to form a database, thereby constructing the corresponding literature data set; 5.

3. Use the predetermined Review Manager software to conduct a literature quality assessment on the literature data sets constructed by each party according to the predetermined standards to obtain the corresponding quality evaluation results; 5.

4. Compare the quality evaluation results with each other according to the predetermined standards to obtain the comparison results. If the difference tolerance value of the comparison results exceeds the preset 2% difference tolerance value, return to step 5.1 to re-extract and analyze the data until the difference tolerance value of the comparison results is lower than the preset 2% difference tolerance value.

6. The method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism according to claim 5, characterized in that: In step 5.3, the evaluation method for the constructed literature data set is: the quality of the corresponding test indicator data in the literature data set is evaluated according to the predetermined standards by using the predetermined Cochrane bias risk assessment tool preset in the Reviewmanager software, thereby obtaining the quality evaluation results of the literature data set; wherein, the quality evaluation strategy for the corresponding test indicator data in the literature data set includes: (1) Random sequence generation based on test indicator data; (2) allocation concealment based on trial indicator data; (3) blinding of participants and personnel; (4) Blinding of quality outcome assessment; (5) Evaluation of incomplete outcome data; (6) Evaluation of selective reporting; (7) Evaluation of other non-comparable deviation data.

7. The method for evaluating the pharmacodynamic effect of cinacalcet in treating secondary hyperparathyroidism according to claim 1, characterized in that: In step S2, the evaluation of the pharmacodynamic model further includes the following steps: 9.

1. By coordinating the predetermined observation and individual prediction model diagnostic graph, observation and group prediction model diagnostic graph, conditional weighted residual and group prediction model diagnostic graph, and conditional weighted residual and time model diagnostic graph, the goodness of fit of the pharmacodynamic model is evaluated according to the predetermined standards to obtain the change process of the drug effect of cinacalcet in the treatment of secondary hyperparathyroidism; 9.

2. Use the predetermined discard-one-out cross-validation method to conduct sensitivity analysis on the change process of drug effect according to the predetermined standards, that is, remove one group of test data from the original data set each time, and estimate the pharmacodynamic model parameters for the remaining data to examine the impact of each group of test data on the pharmacodynamic model parameters, thereby obtaining an optimized pharmacodynamic model; 9.

3. Use a predetermined visual prediction test method combined with a Monte Carlo simulation method to further verify the optimized pharmacodynamic model according to predetermined standards to obtain a verification result, and then compare the verification result with the predetermined measured value to simulate and evaluate the accuracy of the pharmacodynamic model; 9.

4. Then, the predetermined Bootstrap method is used to perform a predetermined number of repeated samplings with replacement on the predetermined efficacy indicators in the literature data set, thereby obtaining the median threshold of the distribution of pharmacodynamic model parameters, and the median threshold is compared with the predetermined parameters of the original data set to obtain the comparison results to evaluate the robustness of the pharmacodynamic model.

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