CN111972353A - Method for constructing group pharmacokinetic model of compound salvia miltiorrhiza dropping pill multi-component in rat body - Google Patents
Method for constructing group pharmacokinetic model of compound salvia miltiorrhiza dropping pill multi-component in rat body Download PDFInfo
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- CN111972353A CN111972353A CN201910426582.2A CN201910426582A CN111972353A CN 111972353 A CN111972353 A CN 111972353A CN 201910426582 A CN201910426582 A CN 201910426582A CN 111972353 A CN111972353 A CN 111972353A
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Abstract
The invention relates to a method for constructing a pharmacokinetic model of a compound salvia miltiorrhiza dripping pill multi-component in a rat body group, which comprises the following steps: 1) and (3) drawing up sampling point time: on the basis of holographic sampling, sampling points are randomly grouped, and each group of sampling points is 3-4; 2) determining covariates to be investigated (covariates, i.e. influencing factors): taking physiological and pathological information of the rat as covariates to be investigated, and investigating the influence of the factors on drug metabolism; 3) a certain number of test samples were included: aiming at the covariate inclusion sample, the included sample is confirmed to be uniformly distributed on the covariate, the testing range is wider, and the later prediction range is met; 4) administration, sample collection and information collection, sample determination: determining the administration scheme of the compound red sage root dripping pill aqueous solution, collecting blood samples in a preset sampling time range, recording corresponding collecting time, recording individual related information aiming at a preset covariate, and completing sample determination work by applying a detection method; 5) and (5) constructing a model.
Description
Technical Field
The invention belongs to the field of pharmacokinetics, and particularly relates to a method for constructing a pharmacokinetic model of a compound salvia miltiorrhiza dripping pill multi-component in a rat body group.
Background
The compound red sage dripping pill has the functions of promoting blood circulation to disperse blood clots, regulating vital energy and relieving pain. Can be used for treating thoracic obstruction due to qi stagnation and blood stasis, with symptoms of chest distress and precordial pain; coronary heart disease and angina pectoris is prepared from Saviae Miltiorrhizae radix, Notoginseng radix, and Borneolum, and its preparation method can refer to pharmacopoeia method. The pharmacokinetic research reports of the compound salvia miltiorrhiza dropping pill mainly focus on the determination of pharmacokinetic parameters of active ingredients such as danshensu, ginsenoside Rb1 and ginsenoside Rg1 in human or animal bodies, and related pharmacokinetic parameters are determined by establishing different determination methods and pharmacokinetic models so as to further research the metabolic conditions of the active ingredients in the human or animal bodies. The research focuses mainly on the construction and optimization of a determination method, so that the sensitivity of separation and detection of the determination method is improved, and the in-vivo metabolism condition of the determination method is researched from multiple angles. For example:
hongxiang, etc. (pharmacokinetics research of tanshinol in compound Danshen dripping pills, new Chinese medicine and clinical pharmacology, 2000,11(5),286-288) uses p-hydroxybenzoic acid as internal standard, and uses HPLC-UV method to measure the concentration of tanshinol in rat serum, the result shows that the pharmacokinetics process of tanshinol in rat body after rat is drenched with compound Danshen dripping pills can be described by open two-chamber model, and tanshinol can be quickly absorbed in gastrointestinal tract (K alpha is 4.9563 h) -1) The blood concentration of the danshensu reaches the peak value after 0.5h, T1/2(α)0.199h, T1/2(Ab)The dose is 0.140h, which indicates that the danshensu is absorbed quickly and distributed quickly in the rat body (alpha-3.4867 h)-1) The medicine has quick effect, and the elimination rate constant beta of the tanshinol in the serum is 0.211h-1，T1/2(α)Is 3.28h, T1/2βMuch greater than T1/2αIt is indicated that danshensu mainly takes the elimination process in vivo, and belongs to the slow elimination process;
the group of subjects of Tianshili (Study of the determination and pharmacologic kinetics of Compound Danshen drippings wells in human serum by column switching method Chromatography in mass spectrometry. journal of Chromatography B,809(2004) 237-242) established a method of liquid Chromatography-mass spectrometry (LC-MS/MS) to simultaneously measure the concentration of the six components of danshensu, protocatechuic acid, notoginsenoside R1, ginsenoside Rb1 and ginsenoside Rg1 in the plasma of rats after oral administration of Danshen Dripping Pills, and studied the concentration change in the plasma, and the results (as shown in Table 1) showed the pharmacokinetic processes of the six different components:
table 1: pharmacokinetic profiles of six different components
After the subject group (Simultaneous determination and pharmacologics of danshensu, protocathechanic aldehyde, 4-hydroxy-3-methyxyphenyl lactic acid and protocathechenic acid in human plasma by LC-MS/MS after oral administration of Compound danshenn dropping balls. journal of Pharmaceutical and Biomedical Analysis,2017,145:860-864), a liquid chromatography-mass spectrometry (LC-MS/MS) method is established to simultaneously determine the concentration of danshensu, protocatechualdehyde, 4-hydroxy-3-methyloxyphenyllactic acid and protocatechuic acid in human plasma after oral administration of the Dripping pills, and the pharmacokinetic parameters of the four components are determined, the method shows good results, and the danshensu components are good markers for researching the pharmacokinetics of the Compound danshensu components in human body;
Zhaboli topic group (Plasma AND Urriny Tanshinol from Salvia militari, Danshenn) Can Be Used as pharmacological Markers for Cardiotonic wells, a Carboviral lipid medicine, DRUG METABABISM AND DISPOSITION,2008,36(8):1578-1586) separately measured the content of danshensu in dog, rat, human Plasma AND urine by ultra performance liquid chromatography AND mass spectrometry, AND separately obtained the Pharmacokinetic parameters thereofIn the pharmacokinetic behavior experiment after taking the compound red sage root dripping pill, the result shows that the compound red sage root dripping pill is administrated to treat female blood plasma CmaxValue and AUC0-6hAre significantly higher than in males, and when the dose is adjusted to be administered by body weight, the difference is no longer significant, regardless of the dose, such as t1/2，MRT，CLtot,p/F，VSSThere was no significant gender difference for the/F, and urine sample assay data showed that women were significantly higher for fe and CumRThere was no significant gender difference.
At present, the existing traditional Chinese medicine pharmacokinetics research mainly describes the change rule of effective components, effective parts, single medicines or compound medicines in the traditional Chinese medicine in vivo after entering the organism in different dosage forms or through various administration routes in a quantitative way, and the existing traditional Chinese medicine pharmacokinetics research mainly has 3 defects; firstly, clinical pharmacokinetics research of traditional Chinese medicines requires that all subjects need to draw blood and collect specimens for a plurality of times (at least 9 times) within a certain time (mostly 3-10h) according to requirements after taking the traditional Chinese medicines, and frequent blood collection process makes many people, especially patients, to be forbidden; secondly, the traditional pharmacokinetic research is completed under the condition of eliminating the interference of other medicines in the body, preferably under the condition of singly using a certain traditional Chinese medicine, so that the traditional pharmacokinetic research has great difference with the actual condition of clinical medication; it is well known that the difference of physiological, pathological and other factors among individuals causes the difference of the treatment mode of the drug in vivo, while the study object of the classical pharmacokinetics/pharmacodynamics (PK/PD) is usually a healthy volunteer or a strictly selected patient, and is a homogeneous population, and the study only pays attention to the average condition, and the difference among individuals is usually eliminated by adopting a complex experimental design or strict selection standard, but the study is always carried out under artificial conditions, so that the clinical application of the drug in the future is difficult to be satisfactorily guided. Most of traditional Chinese medicines are compound medicines, the components are complex and have multiple target points, the traditional clinical pharmacokinetics research is limited, and in order to better represent the dispersion degree and distribution of clinical PK/PD parameters, determine the population value and the variation degree of the parameters and simultaneously investigate the influence of different fixed effects on PK/PD, the population pharmacokinetics/pharmacodynamics (PPK/PPD) research must be carried out. For example, in the development process of a compound new Chinese medicine, the PK behavior of the compound changes when different formulations and different components are combined, and special medicine interaction may be generated when Chinese and western medicines are combined. In addition, the group pharmacokinetics (PPK) is also applied to the traditional Chinese medicine safety research and the individual medication, and some researches include common pathological and physiological factors such as age, sex, liver and kidney functions and the like of patients, and also include some factors which are relevant to the clinical application of traditional Chinese medicines such as traditional Chinese medicine symptoms, traditional Chinese medicine physique typing, traditional Chinese medicine compatibility and the like, so that the clinical administration is quantitatively optimized. Compared with classical pharmacokinetics, the group pharmacokinetics have the following characteristics: sparse data can be analyzed, and 1-4 times of blood samples are taken from a subject or a tested animal; secondly, data obtained by different tests can be combined for analysis, and differences among the tests can be distinguished; ③ combining with the Bayesian method, adopting 1 or 2 blood drug concentrations of the patient as feedback to obtain more ideal individual pharmacokinetic parameters so as to design an optimized drug administration scheme. The research idea of group pharmacokinetics is to obtain PPK parameters by combining a classical pharmacokinetic model with statistics and a multivariate nonlinear optimization method, wherein the PPK parameters comprise group typical values, fixed effect parameters, inter-individual variation and intra-individual variation, and determinants of drug concentration in a patient group can be quantitatively examined and used as the basis of clinical individualized dosing. The group determination variation comprises deterministic variation and stochastic variation, wherein the deterministic variation refers to the influence of relatively fixed and definite factors such as age, weight, height and the like on drug treatment, and the stochastic variation is also called stochastic effect and comprises inter-individual variation and individual self variation. Inter-individual variability refers to random error between different patients, except for deterministic variability. The variation of an individual is also called residual error, which refers to variation caused by different experimental researchers, different experimental methods, and patients over time, and model setting error, and is also called random effect.
The most widely used estimation method for population pharmacokinetic parameters is currently the non-linear mixed effect model (NONMEM) method. The NONMEM method adopts Taylor expansion approximation to control statistical nonlinearity, can explain a more complex error model by adopting an extended least square method, and quantitatively describes the influence of each covariate on the pharmacokinetic process, becomes a PPK method and software with the most comprehensive feasibility evaluation of the international model, algorithm and statistical analysis at present, and is also a method which has the most extensive application, the most mature function development and is approved by the FDA. The main steps of the NONMEM include experimental design, data acquisition, model establishment analysis and model verification: 1) experimental design is to define the study objective before performing the group pharmacokinetic study, to understand the qualitative aspects of the model and the preliminary pharmacokinetic information, such as the major clearance pathways of the drug. The research should establish a sensitive and specific blood concentration detection method. 2) When data is collected, medication related data and related information of each patient are collected as much as possible, and the medication related data comprises medication schemes, dosages, administration routes, sampling time, combined medication information and the like, which are the basis of modeling. The patient-related information is mainly demographic information, including gender, age, weight, height, etc.; and pathophysiological data including clinical examination results, important organ functions, and the like. 3) The model building and analysis are the processing of collected data, mainly including data collection and editing, processing of missing data and abnormal values, and data management by computer software. The nonlinear mixed effect model consists of a pharmacokinetic model, a fixed effect model and a random effect model: A) an initial pharmacokinetic model is established, which is basically consistent with the traditional pharmacokinetics and can be selected according to actual conditions. Common pharmacokinetic models are compartmental (one-, two-, or three-compartment, etc.) and Michaelis-Menten models. The reported pharmacokinetic model can also be selected by looking up historical literature; or carrying out dense sampling, and fitting the model by using a traditional pharmacokinetic method; the common model can also be verified, and the best model is selected according to the size of the Objective Function Value (OFV). B) The fixed effect model examines the effect of the fixed effect on pharmacokinetic parameters. Common methods are POSTHOC and ordered addition and forward inclusion/backward subtraction (backward interpolation). C) The random effect model includes inter-individual variation (also called residual variation) and intra-individual variation (also called inter-individual variation). Inter-individual variation of pharmacokinetic parameters can be assessed using exponential stochastic effects, additive stochastic effects of logical proportions, or Box-Cox switching stochastic effects, as the case may be. Inter-individual variation and residual variation can be described by selecting addition, scale, exponential, and mixture models. 4) The purpose of model validation is to evaluate whether the established population model can well describe the validation data set. Methods of model verification can be divided into external verification and internal verification. There is no consensus on the current validation methods that best fit the pharmacokinetic model of the population, and the choice of validation method is generally dependent on the goals of the analysis. External validation refers to validating a model using a data set that is not used for modeling, which is the most rigorous approach. Internal validation includes data segmentation and resampling (cross-validation, knife-cutting and bootstrap).
In view of the fact that the pharmacokinetics research of the compound salvia miltiorrhiza drop pills in the prior art is the traditional pharmacokinetics research which does not incorporate the group measurement variation and does not see the group pharmacokinetics research, the non-linear mixed effect method (NONMEM method) is the most widely applied estimation method of group pharmacokinetic parameters at present, and is used in the group pharmacokinetic research of Chinese herbal compound multi-component (group pharmacokinetics/pharmacodynamics research-new field of Chinese herbal pharmacokinetics research, Shizhen Chinese medicines, 2008,19(6), 1391-1393; Chinese herbal compound multi-component system group pharmacokinetics: total amount statistical moment mathematical model and parameter calculation research, Chinese herbal medicine J.2011, 36(20), 2866-2870; single-response and multi-response group pharmacokinetic sampling optimizes application progress in Chinese herbal medicine, 2018,49(18), 4446-4452).
The invention aims to establish a method for constructing a population pharmacokinetic model of compound salvia miltiorrhiza dropping pills in a rat body by adopting a nonlinear mixed effect method (NONMEM method). The invention is characterized in that: (1) sampling at sparse points to obtain blood concentration data; (2) the final model constructed included the quantitative effect of rat week age, sex, body weight on PK parameters.
Disclosure of Invention
The group pharmacokinetics is a group for researching the in vivo process of the medicine by combining a classical pharmacokinetic model with a group statistical model
Rules, statistical distribution of pharmacokinetic parameters and their influencing factors.
The invention improves the established and frequently used group pharmacokinetic method on the basis of the prior art, and therefore, the invention improves the established and frequently used group pharmacokinetic method
Provides a method for constructing a group pharmacokinetic model of multiple components of compound salvia miltiorrhiza dripping pills in rats. The invention provides a method for constructing a pharmacokinetic model of a compound salvia miltiorrhiza dripping pill multi-component in a rat body group, which comprises the following steps: a method for determining tanshinol and ginsenoside Rb1 and Rg1 in rat plasma is established by using a UPLC-MS/MS system, sampling points are randomly grouped, rats with different ages in the week, body weights and sexes randomly enter a sampling group, samples are taken at corresponding time, blood concentration data are determined, PPK modeling is performed by using a nonlinear mixed effect method (NONMEM method), and related influence factors and quantitative degrees of metabolism of different components in the rat are analyzed.
Specifically, the invention provides a rat in-vivo population pharmacokinetic model construction method, which comprises the following steps:
1) And (3) drawing up sampling point time: on the basis of holographic sampling, sampling points are randomly grouped, and each group of sampling points is 3-4;
2) determining the covariates to be investigated: taking physiological and pathological information of the rat as covariates to be investigated, and investigating the influence of the factors on drug metabolism;
3) a certain number of test samples were included: aiming at the covariate inclusion sample, the included sample is confirmed to be uniformly distributed on the covariate, the testing range is wider, and the later prediction range is met;
4) administration, sample collection and information collection, sample determination: determining the administration scheme of the compound red sage root dripping pill aqueous solution, collecting blood samples in a preset sampling time range, recording corresponding collecting time, recording individual related information aiming at a preset covariate, and completing sample determination work by applying a detection method;
5) constructing a model:
a, data inspection is carried out, and the basic characteristics of acquisition time and blood concentration are determined;
b, covariate analysis: using the SPSS as a matrix diagram and calculating correlation coefficients among all factors, and performing frequency distribution and correlation investigation on the covariates to be investigated;
c, selecting a structural model: respectively adopting different atrioventricular extravascular administration models for fitting, adopting an extended least square method (FOCE-ELS) for calculation, comparing the objective function values of the models and determining a structural model;
d, selecting a statistical model: selecting a suitable inter-individual/intra-individual random variation model;
e, screening a fixed effect and finally modeling;
f, internal verification of the model: and internally verifying the final model by using a Bootstrap method and a VPC verification method.
The model constructed by the method of the invention is as follows:
salvianic acid was fitted with a linear abolished one-compartment extravascular dosing model with PK parameters as follows:
Ka＝tvKa*(week/14)^dKadweek*exp(nKa)
V＝tvV*(weight/260)^dVdweight*exp(nV)
Cl＝tvCl*(weight/260)^dCldweight*exp(dCldsex0*(sex＝＝0))*exp(nCl)
CObs＝C*(1+CEps)
ginsenoside Rb1 was fitted in a linear abrogation one-compartment extravascular dosing model with PK parameters as follows:
Ka＝tvKa*exp(nKa)
V＝tvV*(weight/260)^dVdweight*exp(nV)
Cl＝tvCl*(weight/260)^dCldweight*exp(nCl)
CObs＝C*(1+CEps)
ginsenoside Rg1 was fitted with a linear-elimination biventricular extravascular dosing model with PK parameters as follows:
Ka＝tvKa*exp(nKa)
V＝tvV*exp(nV)
V2＝tvV2*exp(nV2)
Cl＝tvCl*(weight/260)^dCldweight*exp(nCl)
Cl2＝tvCl2*exp(nCl2)
CObs＝C+CEps。
the following are explanations and descriptions of the nomenclature of the present invention:
holographic sampling: in order to obtain a complete blood concentration-time curve after administration, the design of a traditional pharmacokinetic research sampling time point gives consideration to the sampling scheme of an absorption phase, a balance phase (near peak concentration) and an elimination phase of a medicament, which is individual holographic sampling and generally has 8-13 sampling points distributed. For a fast-absorbing drug administered extravascularly, the first point should be avoided as much as possible as the peak concentration (Cmax); 3 time points are needed around Cmax to guarantee the validity of Cmax as much as possible. The whole sampling time is continued to 3-5 half-lives, or to 1/10-1/20 of the blood concentration Cmax.
Sampling points: the individual animal to be tested is subjected to biological sample collection at different time points after administration, and the corresponding collection time is called a sampling point.
Physiological and pathological information of rats: including physiological data (e.g., sex, week's age, body weight, body surface area, source, etc.), laboratory test results (e.g., creatinine, albumin, genotype, etc.), disease characteristics (e.g., hepatic insufficiency, renal insufficiency, etc.). In the patent, the metabolic influences of the sex, the week age and the body weight of the rat on the compound salvia miltiorrhiza dropping pill are observed.
Covariates: covariates refer to factors that affect pharmacokinetics and pharmacodynamics, and are one type of interpretable sources of variation in the study of drug population laws using nonlinear mixed effect models, including physiological factors (e.g., age, sex, race, genotype, body weight, body surface area, obesity, etc.), pathological factors (e.g., disease state, complications, liver and kidney functional status, etc.), drug-drug interactions and drug-food interactions, and other factors (circadian rhythm, etc.).
Inclusion test specimens for covariates: after defining the covariates to be studied (sex, week age, body weight according to the invention), subjects of different sex, week age, and body weight were included.
Individual related information: after the covariates to be researched are defined, the covariate information of the corresponding individual is collected in the research development process.
The detection method comprises the following steps: the pharmaceutical composition under investigation is subjected to sample detection using a suitable apparatus and method, referred to herein as the detection method of example 2.
Defining the basic characteristics of the collection time and the blood concentration: and taking the acquisition time as an abscissa and the blood concentration at the corresponding time as an ordinate to make a scatter diagram. The ordinate is expressed in a logarithmic mode, and the distribution characteristics of the blood concentration are observed, so that the fitting of the atrioventricular model is suitable for preliminary judgment.
SPSS matrix chart: a graph of the correlation matrix between different covariates was made using IBM SPSS Statistics 22 software.
Fixing effect: relevant covariates were identified that had significant effects on drug metabolism and were included in the pharmacokinetic model of the population.
Fitting is carried out on different atrioventricular extravascular administration models: and respectively carrying out data fitting by using a one-compartment model, a two-compartment model, a three-compartment model and the like for extravascular administration, and comparing which compartment model is more suitable.
The least square method, namely the FOCE-ELS method, is a parameter fitting method adopted in software modeling.
Random variation model: the random variation model is a statistical distribution of unexplained variations in a population, which generally includes inter-individual random variations, intra-individual random variations, week-to-week variations, and the like, and is related to random factors. Such factors are certainly present, but unknown and cannot be measured. Inter-individual variability refers to the difference between different individuals, and covariates are the main sources of variation among individuals. Week-to-week variation refers to the difference in the same individual from test cycle to test cycle, and is related to the test design. Random variation within an individual, also known as residual variation, refers to the difference that still exists in the same individual at different times or in repeated experiments, and is related to measurement error, model bias, or dose error.
Bootstrap method: the Bootstrap method is to repeatedly sample (usually more than or equal to 1000 times) the original data, to perform data statistics on the pharmacokinetic parameter results obtained by fitting the sampled data each time, to obtain the average value and 95% confidence interval of the results, to compare with the model fitting value, to confirm the quality of the model construction.
VPC verification method: according to the final model and the parameter estimation value of the PPK, a plurality of sets of new simulation data are generated by adopting a Monte Carlo method, the prediction values (namely median value and 90% interval) of 50%, 5% and 95% of each time phase point of a simulation data set are calculated, the simulation prediction values are overlapped with an original observation data set by drawing, the proximity degree of the median of the two sets of data and the confidence interval whether the 90% interval of the new simulation data set can cover the original data set are inspected, and therefore the model prediction performance is evaluated and whether model error exists or not is judged.
Internal verification: and judging and evaluating the accuracy, applicability and stability of the model by using the modeling data by using a proper method.
Preferably, the method for constructing the pharmacokinetic model of the rat in-vivo population comprises the following steps:
1) and (3) drawing up sampling point time: designing sampling points according to the half-life time of ginsenoside Rb1, and dividing the sampling points into 4 groups (group A: 5min, 1.5h, 6h) (group B: 15min, 2h, 8h, 24h) (group C: 30min, 3h, 10h, 48h) (group D: 1h, 4h, 12h, 72 h);
2) Determining the covariates to be investigated: taking physiological and pathological information of the rat as covariates to be investigated, investigating the influence of the factors on drug metabolism, and finally determining the sex, the week age and the weight of the rat as the covariates to be investigated;
3) a certain number of test samples were included: rats of 6-21 weeks of age are imported, half each male and female, wherein rats of the same week of age and the same sex are randomly distributed to 4 sampling groups, and the requirement that the three components have 2-3 effective data in each tested individual is met;
4) administration, sample collection and information collection, sample determination: preparing 0.3g/mL of compound salvia miltiorrhiza dropping pill aqueous solution, weighing a rat, performing single intragastric administration according to 1.5g/kg of dose, collecting a blood sample in a preset sampling time range, recording corresponding sampling time, recording individual related information, namely sex, week age and weight of the rat, and completing sample determination work;
5) construction of models
a, data inspection is carried out, basic characteristics of acquisition time and blood concentration are determined, a scatter diagram of the relationship between the acquisition time and the blood concentration of active ingredients of the compound salvia miltiorrhiza dripping pill, namely danshensu, ginsenoside Rb1 and ginsenoside Rg1 is drawn, a Y axis is logarithmic form concentration, an X axis is corresponding time after administration, and model structures of the danshensu, the ginsenoside Rb1 and the ginsenoside Rg1 are determined;
b, covariate analysis: using the SPSS as a matrix diagram and calculating correlation coefficients among all factors, and performing frequency distribution and correlation investigation on the covariates to be investigated; proper covariate inclusion requires that the corresponding factors are wide in distribution, have no obvious deviation phenomenon, are close to normal distribution and have enough representativeness;
c, selecting a structural model: fitting is carried out by respectively adopting a linearly eliminated one-atrioventricular extravascular administration model and a linearly eliminated two-atrioventricular extravascular administration model, calculation is carried out by adopting an extended least square method (FOCE-ELS), and the better fitting of the linearly eliminated one-atrioventricular extravascular administration model of the danshensu and the ginsenoside Rb1 and the better fitting of the linearly eliminated two-atrioventricular extravascular administration model of the ginsenoside Rg1 are determined;
d, selecting a statistical model: random variation among individuals assumes that the individual parameters conform to the lognormal distribution with the group parameters as typical values, so that the index model is selected for random variation among individuals of effective components, specifically Pij＝tvPij×exp(ηij) Represents;
wherein P isijIs the jth pharmacokinetic parameter of the ith individual, tvPijIs a population-typical value for the jth pharmacokinetic parameter modified by covariates. EtaijRepresentative of individual parameters PijFor population parameter tvPijHas a value of 0 as a center and a variance of ω 2Normal distribution of (2);
selecting a proportion model from random variation models in individuals of danshensu and ginsenoside Rb1, wherein CObs represents observed value, C represents fitting value, CEps represents mean 0, and variance is sigma2Random error of (2). Ginsenoside Rg1 due to low in vivo exposure, an addition model was selected by random variation in individuals, expressed as CObs ═ C + CEps, where CObs represents observed values,c represents the fitting value, CEps represents the mean of 0, and the variance is σ2Random error of (2);
e fixed effect screening and final model: introducing covariates to be considered into the model one by one, screening by adopting a forward inclusion method (P <0.01) and a backward elimination method (P <0.001) to obtain a fixed effect which has a large influence on pharmacokinetic parameters, and establishing a final model;
f, internal verification of the model: and internally verifying the final model by using a Bootstrap method and a VPC verification method.
The invention mainly adopts sparse point sampling to obtain blood concentration data and then constructs a group pharmacokinetic model. In the finally obtained evaluation model, the pharmacokinetic characteristic description of each component comprises the quantitative influence of the information of the week age, sex, weight and the like of the rat on the PK parameters, and the individual PK parameters of the rat can be predicted according to the sparse blood concentration and the corresponding blood sampling time of the known rat after the rat takes the compound red-rooted salvia dropping pill by combining the administration information and the physiological parameters of the rat, so that the individual drug time curve is obtained. The prediction capability of the model established by the method of the invention meets MPE < + > 20%, and the prediction effect is good.
The application method of the model obtained by the invention in actual work and the obtained effect are as follows:
the sampling strategy is simplified, the evaluation of the pharmacokinetic process in the compound salvia miltiorrhiza dropping pill rat body in the research after the sparse sampling is finished can be carried out according to the blood concentration of the coefficient sample.
Provides a new pharmacokinetic research scheme for other compound traditional Chinese medicines, and avoids the problems of data scientificity and statistical errors caused by poor animal individual states brought by intensive sampling in the traditional research.
Lays a theoretical research foundation for the pharmacokinetics research of clinical compound traditional Chinese medicine, can optimize a clinical sampling scheme, and has great significance for reducing the clinical test period and the expenditure, improving the compliance of tested individuals, developing the feasibility of research and avoiding ethical and moral problems.
The method and the model provided by the invention have the following advantages:
1. compared with the prior scheme, the model construction method provided by the invention has the following characteristics:
1) the object-specific differences are: compared with single-component danshensu, ginsenoside Rb1 or ginsenoside Rg1 kinetic models, the invention aims at the compound danshen dripping pill with multiple components, and the inventor finds out in the experimental process that:
the absorption rate of tanshinol is increased along with the increase of week age, the distribution volume is increased along with the increase of body weight, the clearance rate is related to both sex and body weight, male is larger than female, and the body weight is increased and the clearance rate is increased;
Ginsenoside Rb1 increases in volume distribution and clearance with increasing body weight;
the clearance rate of Rg1 is related to body weight, and the clearance rate of the medicine is increased when the body weight is increased.
The research of PPK of Chinese medicine mostly continues to use the research mode of the single component of the chemical medicament, carry on the intensive blood sampling to the characteristic of the single component, does not reflect the research characteristics such as Chinese medicine multicomponent multiple target point, etc., the promotional value is low, the invention considers that regards to regard danshensu, ginsenoside Rb1, ginsenoside Rg1 as the study target (compound radix Salviae Miltiorrhizae drops in rat and human PK marker), explore multicomponent PK study and prediction based on sampling of sparse point of compound Chinese medicine with the study method of group pharmacokinetics, quantify rat some physiological parameter influence to metabolism in vivo of medicament quantitatively, different from single ingredient study, the ingredient is complicated in the study of compound Chinese medicine, the exposure level and metabolic characteristic of the relevant ingredient in vivo are different greatly. The method takes three effective components in the compound salvia miltiorrhiza dropping pill as research objects to develop group pharmacokinetics research, and the difficulty is reflected in that:
a. the saponin components and the phenolic acid components in the compound salvia miltiorrhiza dropping pill are difficult to be detected by biological samples at the same time. The suitable pretreatment methods of the two components are not consistent, the concentration of part of target components in the biological sample is lower, and the difficulty of detecting the biological sample is increased;
b. The metabolism characteristics of related pharmacodynamic components are different greatly, and the half-life of the diol ginsenoside (>20 hours) and the triol ginsenoside (<1 hour) are different by more than 20 times, so that the pharmacokinetic research of the whole compound is more prominent in time span and sample collection density. The ginsenoside Rb1 is sampled for 72 hours, and the ginsenoside Rg1 and the danshensu need to be densely sampled within 12 hours, which causes great difficulty for the sampling of the whole compound. In non-clinical experiments, intensive sampling is characterized by large blood sampling amount, serious animal injury, shock or death of animal individuals in the experimental process caused by serious patients, and the ethical requirements of experimental animals are violated. In addition, the scientific aspect of experimental data can also be influenced. Research has shown that factors such as intensive sampling, large blood collection amount, drug action and the like cause the change of the physiological function of the organism, and generally have certain influence on the metabolism of the drug, so that the absorption amount, the elimination rate and the like of the drug are increased or reduced and deviate from the normal state. If a simple data combination method is applied, namely, a mode of respectively collecting samples of different time groups by a plurality of groups of animals and finally combining data to investigate drug metabolism parameters is adopted, the problem of error transmission exists, and the final result can only determine the average condition and cannot analyze individual difference. In clinical research, if one subject collects 8-13 blood samples, particularly for special populations (patients, old people, children and the like), the physical state and ethical level of the subject are difficult to accept, the working difficulty of workers is high, the cost of clinical research is high, and the demand of applying sparse sampling to research is more urgent.
2) Random sampling method:
many sampling methods for PPK research include a random sampling method, a Fisher Information Matrix (FIM) method, an information Block Randomized design (IBR) method, and the like. Because the invention is the initial PPK research of three effective components of the compound salvia miltiorrhiza dripping pill, the random sampling method is most effective and convenient to design experiments, therefore, the inventor designs a plurality of sampling points according to the half-life periods of the three effective components, brings rats of different ages of weeks into each half of a sex, wherein the rats of the same age of week and the same sex are randomly distributed to 4 sampling groups. The sampling scheme is to uniformly and randomly group the time points and the tested individuals, the covariates are reasonably distributed, in addition, the sampling scheme meets the requirement that the three components have 2-3 effective data in each tested individual, the whole pharmacokinetic process is covered, and the robustness of the subsequent model construction is ensured.
3) Consideration of covariates
The inventor researches and discovers that the danshensu and covariates have no obvious influence except that sex and weight influence the clearance rate in vivo of the danshensu, the absorption rate in week age and the distribution volume in weight; the covariate of the ginsenoside Rb1 or the ginsenoside Rg1 is the body weight, and has no obvious influence on the clearance rate and the distribution volume in vivo;
4) PPK modeling is carried out by adopting a nonlinear mixed effect method (NONMEM method), and related influence factors and quantitative degrees of metabolism of different components in rats are analyzed.
2. The invention applies the established PPK model, administration information, newly acquired rat information (week age, sex and weight) and coefficient blood concentration (including corresponding blood sampling time) to predict the metabolism of the three components in the rat body, and supplements a new method and a new idea for PK research of the compound salvia miltiorrhiza dripping pill.
Description of the drawings:
FIG. 1 is a scattergram of the blood sampling time and blood concentration observed value of tanshinol after the compound Danshen dripping pill is administrated;
FIG. 2 is a scattergram of blood sampling time and blood concentration observation value of ginsenoside Rb1 after administration of compound Saviae Miltiorrhizae radix dripping pill;
FIG. 3 is a scatter diagram of the blood sampling time and blood concentration observation value of ginsenoside Rg1 after the compound Danshen dripping pill is administered;
FIG. 4 is a correlation scatter plot of covariates to be investigated;
FIG. 5 is a model diagnostic plot of the basic model (left) and the final model (right) of tanshinol, wherein: sequentially from top to bottom: the individual observation value-group prediction value (DV-PRED), the individual observation value-individual prediction value (DV-IPRED), the condition weight residual-group prediction value (CWRES-PRED) and the condition weight residual-time (CWRES-TAD) are four model diagnosis graphs;
FIG. 6 is a model diagnostic plot of the basic model (left) and the final model (right) of ginsenoside Rb1, the plot being the same as in FIG. 5;
FIG. 7 is a model diagnostic graph of the basis model (left) and the final model (right) of ginsenoside Rg1, the graph is the same as that in FIG. 5;
FIGS. 8-a, 8-b, and 8-c are internal verifications of model VPC methods of danshensu, ginsenoside Rb1, and Rg1, respectively;
FIG. 9 shows the individual information to be simulated, which is imported by simulation prediction using PhoenixNLME software, wherein 1 and 2 in the figure are the numbers of two mice respectively;
FIG. 10 shows the simulation and prediction by PhoenixNLME software, and mapping after importing the corresponding information;
FIG. 11 shows simulation predictions, tab iteration number modifications using PhoenixNLME software;
FIG. 12 is a simulation of using PhoenixNLME software to perform additional form information filling;
FIG. 13 shows the results of group typical value calculations using PhoenixNLME software for simulation prediction;
FIG. 14 is a simulation of individual PK parameter prediction using PhoenixNLME software;
FIG. 15 shows the predicted plasma concentrations at different time points using PhoenixNLME software for simulation prediction;
fig. 16 is a blood concentration curve predicted by simulation prediction using PhoenixNLME software, wherein 1 and 2 are the numbers of two corresponding mice in fig. 9;
Fig. 17 is a scatter diagram of the predicted correlation of the external verification of three components of the compound salvia miltiorrhiza dropping pill.
The specific implementation mode is as follows:
the present invention will be further described with reference to the following examples.
Example 1: method for constructing pharmacokinetic model of compound salvia miltiorrhiza dropping pill multi-component in rat body group
1. And (3) drawing up sampling point time: and (3) drawing up sampling point time: considering the long half-life of ginsenoside Rb1, the sampling points were designed as follows: 5min, 15min, 30min, 1h, 1.5h, 2h, 3h, 4h, 6h, 8h, 10h, 12h, 24h, 48h and 72 h. The sampling points were divided into 4 groups, (group A: 5min, 1.5h, 6h) (group B: 15min, 2h, 8h, 24h) (group C: 30min, 3h, 10h, 48h) (group D: 1h, 4h, 12h, 72 h).
2. Determining influence factors to be investigated (the influence factors are called covariates): the physiological and pathological information of the rat is used as covariates to be investigated, the influence of the factors on the drug metabolism is investigated, and finally, the sex, the week age and the weight of the rat are determined to be used as covariates to be investigated.
3. A certain number of test samples were included: rats 6-21 weeks old were enrolled in 136 females/males. Wherein rats of the same week age and the same sex are randomly distributed to 4 sampling groups, and the requirement that the three components have 2-3 effective data in each tested individual is met.
4. Administration, sample collection and information collection, sample determination: preparing a compound salvia miltiorrhiza dripping pill aqueous solution with the concentration of 0.3g/mL, weighing rats and then performing single intragastric administration according to the dose of 1.5 g/kg. Blood samples are taken at predetermined sampling time ranges and the corresponding sampling times are recorded. Individual-related information (rat sex, week age, body weight) was recorded and sample determination was completed.
5. And (5) constructing a model.
5.1 data inspection, and defining the basic characteristics of acquisition time and blood concentration. And drawing a scatter diagram of relation between the acquisition time and the blood concentration of the three main components, wherein the Y axis is logarithmic concentration, and the X axis is corresponding time after administration. Observing the distribution of all data, as shown in figures 1-3 (wherein figure 1 is a blood sampling time and blood concentration observed value scattergram of tanshinol after the compound Danshen dripping pill is administered, figure 2 is a blood sampling time and blood concentration observed value scattergram of ginsenoside Rb1 after the compound Danshen dripping pill is administered, figure 3 is a blood sampling time and blood concentration observed value scattergram of ginsenoside Rg1 after the compound Danshen dripping pill is administered), danshensu and ginsenoside Rb1 may accord with a one-chamber model or a two-chamber model, and ginsenoside Rg1 may accord with a two-chamber model.
5.2 covariate analysis: and (4) using the SPSS as a matrix diagram and calculating correlation coefficients among all factors, and performing frequency distribution and correlation investigation on the covariates to be investigated. Proper covariate inclusion requires that the distribution of corresponding factors is wide, no obvious deviation phenomenon exists, the distribution is close to normal distribution, and the representativeness is enough. As shown in fig. 4 (correlation scattergram of covariates to be investigated), correlation analysis showed that the correlation between the week age and the body weight was the highest and was noted when jointly incorporated.
5.3 structural model selection: fitting is respectively carried out by adopting a linearly eliminated one-atrioventricular extravascular administration model and a two-atrioventricular extravascular administration model, and calculation is carried out by adopting an extended least square method (FOCE-ELS).
Finally according to the experimental results: the danshensu and ginsenoside Rb1 are selected from linearly eliminated one-atrioventricular extravascular administration model, and the ginsenoside Rg1 are selected from linearly eliminated two-atrioventricular extravascular administration model.
5.4 statistical model selection: random variation among individuals assumes that the individual parameters conform to the lognormal distribution taking the group parameters as typical values, so that the index model is selected for random variation among individuals of three components, specifically Pij＝tvPij×exp(ηij) And (4) showing.
Wherein P isijIs the jth pharmacokinetic parameter of the ith individual, tvPijIs a population-typical value for the jth pharmacokinetic parameter modified by covariates. EtaijRepresentative of individual parameters PijFor population parameter tvPijHas a value of 0 as a center and a variance of ω2Normal distribution of (2);
selecting a proportion model from random variation models in individuals of danshensu and ginsenoside Rb1, wherein CObs represents observed value, C represents fitting value, CEps represents mean 0, and variance is sigma 2Random error of (2). Ginsenoside Rg1 due to low in vivo exposure, random variation in individuals selected addition model, expressed as CObs ═ C + CEps, wherein CObs represents observed value, C represents fitting value, CEps represents mean 0, and variance is σ2Random error of (2);
5.5 fixed Effect screening and Final model: covariates to be considered are introduced into the model one by one, a forward inclusion method (P <0.01) and a backward elimination method (P <0.001) are adopted to screen and obtain a fixed effect which has a large influence on pharmacokinetic parameters, and finally, the PPK model and parameter information are shown in a table 2.
TABLE 2 Final PPK model and parametric information for three components
A comparison of model diagnostic plots for the initial model and the final model is shown in fig. 5-7 (fig. 5-7 are model diagnostic plots for the base model (left) and the final model (right) of tanshinol, ginsenoside Rb1 and ginsenoside Rg1, respectively), wherein:
figure 5 results show that: the population fitting (DV-PRED) of the final model is improved compared with that of the basic model, and particularly the overall precision of the final fitting is good at high concentration. Individual fitting (DV-IPRED) data points are further randomly and evenly distributed on two sides of the unit line, and variation of data in individuals is small. The condition weight residual error-group prediction value (CWRES-PRED) prompts the incorporation of a fixed effect, the deviation of the residual error at a high concentration position is corrected, and finally, the model shows that the CWRES is uniformly distributed within plus or minus 4 above and below a zero line along with the group concentration, and no tested abnormal data are found. In addition, the CWERES-TAD graphs are uniformly distributed above and below the zero bit line, the trend line is relatively horizontal, and the fact that the change of the weighted residual error along with time is not large is shown;
FIG. 6 results: population fit (DV-PRED) of the final model was improved over the base model, but poor at high concentrations, considered to be associated with greater individual variation in Ka (absorption Rate). Individual fitting (DV-IPRED) data points are further randomly and evenly distributed on two sides of the unit line, and variation of data in individuals is small. CRES-PRED and CRES-TAD graphs indicate that residual errors are uniformly distributed within plus or minus 4 above and below a zero bit line along with the concentration or time of a group, a trend line is relatively horizontal, and tested abnormal data are not found;
FIG. 7 results: population fit (DV-PRED) of the final model was improved over the base model, but poor at high concentrations, considered to be associated with greater inter-individual variability of V1 (one-compartment volume distribution). Individual fitting (DV-IPRED) data points are close to the unit line and fit well. CRES-PRED and CRES-TAD graphs suggest that the residuals are uniformly distributed within + -4 above and below the zero bit line with the population concentration or time, the trend line is relatively horizontal, and no tested anomaly data is seen.
The final model for the final determination of the three components is as follows:
salvianic acid was fitted with a linear abolished one-compartment extravascular dosing model with PK parameters as follows:
absorption rate Ka ═ tvKa ^ dweak (week/14) ^ d Kadweak ^ exp (nKa)
Distribution volume V tvV ^ dVdweight ^ exp (nV)
Clearance rate Cl ═ tvCl ═ weight/260 ^ dCldweight ^ exp (dCldsex0 ^ sex ═ 0)). exp (nCl)
Residual error CObs ═ C ═ 1+ CEps)
Ginsenoside Rb1 was fitted in a linear abrogation one-compartment extravascular dosing model with PK parameters as follows:
absorption rate Ka ═ tvKa × exp (nKa)
Distribution volume V tvV ^ dVdweight ^ exp (nV)
Clearance rate Cl ═ tvCl ^ weight (weight/260) ^ dCldweight ^ exp (nCl)
Residual error CObs ═ C ═ 1+ CEps)
Ginsenoside Rg1 was fitted with a linear-elimination biventricular extravascular dosing model with PK parameters as follows:
absorption rate Ka ═ tvKa × exp (nKa)
One-chamber distribution volume V tvV × exp (nV)
Two-chamber distribution volume V2 ═ tvV2 × exp (nV2)
One-chamber clearance rate Cl ═ tvCl ^ weight (weight/260) ^ dCldweight ^ exp (nCl)
Two-chamber clearance Cl2 ═ tvCl2 × exp (nCl2)
Residual error CObs ═ C + CEps
6. And (3) verifying the interior of the model: table 3 and fig. 8 show the results of the boottrap method and the VPC verification method for performing internal verification on the final model, respectively.
TABLE 3 Bootstrap (1000) validation results for three component PPK model
Note: tv represents the population-typical value of the parameter, e.g. tvKa is the population-typical value of the absorption rate. dCldsex0 represents the degree of influence of sex factors on clearance, dVweight represents the degree of influence of body weight on volume of distribution, and dCldweight represents the degree of influence of body weight on clearance.
7. And (3) performing external verification on the model: in addition, the blood concentration of rats in multiple cases is simulated and predicted to be compared with the measured value to obtain an external verification result, the predicted condition is measured by calculating MPE and MAE indexes, the range specifies MPE to be less than +/-20%, and the MAE is less than 30%.
8. And (3) prediction of a model: simulation prediction was performed using PhoenixNLME software.
1) And (3) receiving the final estimation value of the PPK model of the multiple components of the compound salvia miltiorrhiza dripping pill as the initial value of the prediction model. A simulated individual information table is prepared, including administration information, sex, week age, body weight, sparse point blood sampling time and corresponding blood concentration, as shown in FIG. 9.
2) After the corresponding information is imported, mapping is performed, see fig. 10.
3) Navigate to Run Options (Run Options) "tab, modify the value in the check box after" iteration number (N Iter) "to 0, see fig. 11.
4) To derive the blood concentration data predicted by the individual at different time points, the "Add Table" button is clicked, and "seq (0, 10, 0.1)" is input in the "time (Times)" input window, which corresponds to outputting one point every 0.1 hour from 0 to 10 hours. The value of "C" is input at the "Variables" input window, i.e., the output point, is the concentration of the central compartment, see FIG. 12.
5) The operation result comprises: the group typical value (theta), the individual PK parameter predicted value (posthoc), the predicted blood concentration (table01) at different time points and the like are shown in figures 13-15 (figure 13 shows the group typical value operation result by using PhoenixNLME software to carry out simulation prediction; figure 14 shows the individual PK parameter predicted value by using PhoenixNLME software to carry out simulation prediction; and figure 15 shows the predicted blood concentration at different time points by using PhoenixNLME software to carry out simulation prediction).
6) The output table01 is plotted as a graph, i.e., predicted plasma concentration curve, see fig. 16.
Example 2 analysis method of danshensu, ginsenoside Rb1 and ginsenoside Rg1 in rat plasma
1. Chromatographic and mass spectrum condition liquid phase method: the Shimadzu UFLC liquid phase system is obtained from Waters
HSS T3column (1.8 μm,2.1 mm. times.100 mm) chromatography column, provided with precolumn VanGuardTMHSS T3pre-column (1.8 μm,2.1 mm. times.5 mm), flow rate 0.4mL/min, column temperature 40 ℃, mobile phase: a: 0.1% formic acid-water, B: and (3) acetonitrile. Gradient elution is adopted, phase B is used as a reference, and the elution procedure is as follows: 0 → 0.8min, 5%; 0.8 → 3.1min, 5% → 90%; 3.1 → 3.3min, 90% → 5%, 3.3 → 4.5min, 5%.
Mass spectrum conditions: the mass spectrum detection is carried out synchronously according to a positive ion mode and a negative ion mode.
Positive ion mode: air Curtain pressure (Curtain gas, abbreviated as GUR): 20psi, ion source voltage: 5500V, ion source temperature: 550 ℃, ion source gas 1: 55psi, ion source gas 2: 55 psi; internal standard: estazolam (Estazolam). Negative ion mode: air curtain pressure: 20psi, ion source voltage: 4500V, ion source temperature: 450 ℃, ion source gas 1: 45psi, ion source gas 2: 45 psi; internal standard: chloramphenicol (Chloramphenicol).
The analyte and internal standard mass spectrum parameters are shown in table 4:
table 4: analyte and internal standard mass spectrum parameters
Note: the English notes referred to in the table of the present invention are as follows, Precursor ion: precursor ion, Product ion: product ion, DP: declustering voltage, EP: entry voltage, CE: collision energy, CXP: the collision cell exits the voltage.
2. Solution formulation and sample handling
Preparing a reference substance solution: precisely weighing a proper amount of danshensu sodium standard substance, and dissolving with 0.1mol/L hydrochloric acid (containing 0.2% sodium bisulfite) water solution to obtain 1.0mg/mL danshensu mother liquor. Accurately weighing appropriate amount of ginsenoside Rb1 and ginsenoside Rg1 standard substances, and dissolving with methanol respectively to obtain 1.0mg/mL mother liquor. All analyte stocks were stored in a 4 ℃ freezer in the dark. Preparing a standard curve working solution: mixing appropriate amount of salvianic acid A, ginsenoside Rb1 and ginsenoside Rg1 mother liquor, diluting with methanol-water (1:1/v: v) as solvent, and preparing standard curve stock solution containing TSL 1600ng/mL, GRb 1500 ng/mL and GRg 1100 ng/mL. The stock solution is diluted step by taking methanol-water (1:1/v: v) as a solvent to prepare standard curve working solutions SW 1-SW 8, wherein the corresponding concentration of danshensu is 1.6ng/mL, 3.2ng/mL, 8ng/mL, 24ng/mL, 80ng/mL, 240ng/mL, 800ng/mL and 1600ng/mL, the corresponding concentration of ginsenoside Rb1 is 0.5ng/mL, 1ng/mL, 2.5ng/mL, 7.5ng/mL, 25ng/mL, 75ng/mL, 250ng/mL and 500ng/mL, the corresponding concentration of ginsenoside Rb1 is 0.1ng/mL, 0.2ng/mL, 0.5ng/mL, 1.5ng/mL, 5ng/mL, 15ng/mL, 50ng/mL and 100ng/mL, and the prepared standard curve working solution is stored in a refrigerator at 4 ℃.
Preparing an internal standard stock solution: accurately weighing a proper amount of chloramphenicol and an estazolam standard substance, dissolving the chloramphenicol with 0.1mol/L hydrochloric acid (containing 0.2% sodium bisulfite) aqueous solution, and dissolving the estazolam with methanol to obtain 1.0mg/mL mother liquor. A methanol-water (1:1/v: v) solution is used as a diluting solvent to prepare a mixed internal standard working solution with two internal standards of 20ng/mL, and the mixed internal standard working solution is placed in a refrigerator at 4 ℃ for storage.
Plasma sample treatment: adding 100 mu L of methanol-water (1:1/v: v) solution into an EP tube with the volume of 100 mu L after heparin anticoagulation administration, adding 50 mu L of mixed internal standard working solution, adding 100 mu L of 1mol/L hydrochloric acid, mixing uniformly, adding 2mL of n-butyl alcohol-ethyl acetate (1:4/v: v) solution for extraction, vortexing for 3min, and centrifuging at 4500rpm and 4 ℃ for 10 min. The supernatant liquid was transferred to a clean EP tube and dried by nitrogen blowing at 25 ℃. Add 100. mu.L of freshly prepared acetonitrile-water (1:1/v: v) re-solution and vortex for 3min, then transfer the sample to a 1.5mL EP tube and centrifuge at 13300rpm for 3 min. Transferring the supernatant into a sample injection bottle, injecting 2 mu L of sample, determining according to the conditions of '1-chromatogram and mass spectrum', and carrying out quantitative analysis by using the ratio of the peak area of the sample to the peak area of the internal standard.
Example 3: rat experiment
1. Dosing and sampling regimen
The compound salvia miltiorrhiza dripping pill is administrated by gavage, and the administration time is arranged at 8 am: 00-9: 00, the dosage of the compound red sage root dripping pill is 1.5g/kg, the compound red sage root dripping pill is dissolved to be 0.3g/mL by adding normal saline before administration, the administration volume is 1mL/200g, and the specific medicine volume is adjusted according to the weight of a rat. Rats were fasted for 12h before dosing, had free access to water, and had a uniform diet 3h after dosing.
Considering the long half-life of ginsenoside Rb1, the sampling points were designed as follows: 5min, 15min, 30min, 1h, 1.5h, 2h, 3h, 4h, 6h, 8h, 10h, 12h, 24h, 48h and 72 h. Sampling points are divided into 4 groups, (A group is 5min, 1.5h and 6h) (B group is 15min, 2h, 8h and 24h) (C group is 30min, 3h, 10h and 48h) (D group is 1h, 4h, 12h and 72h), then rats with different sexes and different weeks are randomly divided into each group, blood samples are collected, and the actual blood collecting time and other covariate information are recorded.
2. Sample collection and processing
After administration, 0.5ml of blood is taken from retrobulbar venous plexus at the corresponding time of each group, the blood is placed in a heparin sodium anticoagulation tube, the heparin sodium anticoagulation tube is gently shaken to fully and uniformly mix the heparin sodium anticoagulation tube with the blood, and centrifugation is completed within 0.5 h. Plasma centrifugation: the blood sample was centrifuged at 4500rpm for 10min in a high speed centrifuge, and 0.25mL of the supernatant was collected and stored frozen at-40 ℃. The analysis and determination work was carried out according to the "method for analyzing danshensu and ginsenoside Rb1 and Rg1 in rat plasma" protocol 2.
Example 4: validation of structural models
1. Structural model selection
Fitting is carried out by respectively adopting a linear eliminated one-atrioventricular extravascular administration model and a two-atrioventricular extravascular administration model, and an Objective Function Value (OFV) of the models is obtained by adopting an extended least square method (FOCE-ELS) for calculation. OFV is-2 times the log-likelihood-maximum (LL), i.e., -2LL, and the one with the smallest OFV value is generally selected as the pharmacokinetic basic model. Besides using numbers as judgment standard, certain selection can be made from the graph layer, and the selection is determined by screening objective function values and a goodness-of-fit graph (model diagnosis graph) of each fitting model.
(1) And drawing by taking the group prediction value (PRED) as an abscissa and the individual observation value (DV) as an ordinate, so as to obtain the overall contour with good and bad curve fitting. When the fitting quality is good, the data points should be uniformly distributed on both sides of a unit line with zero intercept and 1 slope, and the closer the data points and the unit line, the higher the fitting precision.
(2) The individual concentration prediction results can be observed by plotting the individual prediction values (IPRED) as the abscissa and the individual observation values (DV) as the ordinate. When the fitting quality is good, the data points should be uniformly distributed on both sides of a unit line with zero intercept and 1 slope, and the closer the data points and the unit line, the higher the fitting precision.
(3) The population prediction value (PRED) is used as an abscissa, and the Condition Weighted Residual (CWRES) is used as an ordinate to perform mapping, so that the fitting quality of the model under different concentration conditions can be evaluated. The data points for the better model should be evenly distributed across the zero line, and are generally considered normal within a range of ± 4.
(4) By plotting Time (TAD) on the abscissa and the condition-weighted residual (CWRES) on the ordinate, the time-dependent changes in the residual can be understood. The data points for the better model should be evenly distributed across the zero line, and are generally considered normal within a range of ± 4.
2. Selecting a structural model of tanshinol:
in the literature, a common atrioventricular model of danshensu is a biventricular model, considering that all rats are sparsely sampled and danshensu data in 8h are generally 2-3, if a multi-compartmental model is used, parameters may have large deviation, and the model is unstable, so that fitting of the first atrioventricular model and the second atrioventricular model is respectively carried out on the data, and the preliminary result of the objective function value of the model is obtained as shown in table 5
Table 5: screening results of basic pharmacokinetic model of tanshinol
| Pharmacokinetics model of tanshinol | -2LL | AIC | Results |
| One-room model + additive error | 3640.6 | 3654.6 | Whether or not |
| One-room model + proportional error | 3582.7 | 3596.7 | Is that |
| Two-chamber model + additive error | 3628.0 | 3650.0 | Whether or not |
| Two-chamber model + proportional error | 3501.1 | 3523.1 | Whether or not |
Table 5 the results show: although the two-chamber model has a small objective function value, the parameter estimation is unreasonable, and a relatively conservative one-chamber model is selected after the balance is achieved.
3. Selection of structural model of ginsenoside Rb 1:
the common atrioventricular model of ginsenoside Rb1 in the literature is a one-compartment or two-compartment model, and the preliminary results of the objective function values obtained by fitting the data in the one-compartment and two-compartment modes are shown in Table 6.
TABLE 6 screening results of basic pharmacokinetic model of ginsenoside Rb1
| Pharmacokinetic model of ginsenoside Rb1 | -2LL | AIC | Results |
| One-room model + additive error | 3696.3 | 3710.3 | Whether or not |
| One-room model + proportional error | 3598.5 | 3610.6 | Is that |
| Two-chamber model + additive error | 3695.2 | 3717.2 | Whether or not |
| Two-chamber model + proportional error | 3593.8 | 3615.8 | Whether or not |
From the results in Table 6, the AIC value for a compartment model + proportional type error is the smallest and selected as the structural model.
4. Selecting a structural model of ginsenoside Rg 1:
in the literature, a common atrioventricular model of the ginsenoside Rg1 is a biventricular model, and Wade J R and the like report that if the concentration of the drug at the first time point is the highest during oral administration, the model can be simplified into an intravenous injection model. The study performed oral one-compartment and oral two-compartment and intravenous injection fitting on the data, respectively, and preliminary results of objective function values of the obtained models are shown in table 7.
TABLE 7 screening results of basic pharmacokinetic model of ginsenoside Rg1
| Pharmacokinetic model of ginsenoside Rg1 | -2LL | AIC | Results |
| One-room model + additive error | 950.0 | 964.0 | Whether or not |
| One-room model + proportional error | 914.1 | 928.1 | Whether or not |
| Two-chamber model + additive error | 859.7 | 881.7 | Is that |
| Two-chamber model + proportional error | 804.5 | 826.5 | Whether or not |
| Biventricular intravenous injection model + additive error | 915.9 | 933.9 | Whether or not |
From the results in table 7, it can be seen that the biventricular model has significantly smaller objective function values than the first and second compartments for intravenous injection, and the error model selection is additive due to the small in vivo concentration.
Example 5: model internal verification
1. And internally verifying the final model by adopting a Bootstrap method and a VPC verification method (Visual predictive checks) to verify the stability and accuracy of the model.
1.1Bootstrap method
The Bootstrap method analysis process comprises the following steps:
(1) generating a series of subdata sets from the original data file by means of the put-back samples;
(2) establishing a subdata set model through NONMEM, and storing model parameters;
(3) repeating the step 1 and the step 2 1000 times;
(4) and analyzing the distribution of the model parameters to obtain Bootstrap parameter estimation values, standard errors and confidence intervals.
And comparing the parameter estimation value during PPK modeling with the parameter average value obtained by the Bootstrap method, checking the standard error and the confidence interval, and judging whether the model is stable. If the parameter value estimated by the Bootstrap is close to the parameter value estimated by the original data, the deviation is not more than 30%, and the parameter value estimated by the original data is within the percentile range of 2.5-97.5% of the parameter value estimated by the Bootstrap, so that the model established by the original data has stability and internal effectiveness.
1.2 VPC verification method
The VPC adopts a Monte Carlo method to generate a plurality of sets of new simulation data according to a final model and a parameter estimation value, calculates the 50%, 5% and 95% prediction values (namely median value and 90% interval) of each time phase point of a simulation data set, maps to overlap the simulation prediction values with an original observed data set, and inspects the proximity degree of the median of the two sets of data and the confidence interval of whether the 90% interval of the new simulation data set can cover the original data set, thereby evaluating the model prediction performance and judging whether a model is wrong.
2. Salvianic acid PPK model verification
2.1 Bootstrap method verification of tanshinol
Bootstrap is carried out 1000 times by using Phoenix NLME software, the model is converged successfully, and the correlation analysis result is shown in a table 3.
Table 3 the results show: the mean value and the median obtained by Bootstrap calculation are close to the parameter values estimated by the original data, the deviation (CV%) is not more than 30%, and the parameter values estimated by the original data all fall within the percentile range of 2.5-97.5% of the parameter values estimated by Bootstrap, so that the model established by the original data has stability and internal effectiveness.
2.2 Salvianic acid A VPC method
VPC verification is carried out on the final model by using a Predictive check method of Phoenix NLME software, and the result shows that the established model is more accurate in prediction. The results are shown in FIG. 8-a.
The lines in the figure represent, from top to bottom, the 95, 50, 5% quantiles of the observed values, and the shaded portions represent the 95% confidence intervals for each corresponding quantile of the predicted values generated by the simulation. As can be seen from the figure, the predicted data generated by the simulation has a similar trend with the observed values, and the observed values mostly fall within the 95% confidence interval of the predicted values, which indicates that the performance of the final model is better.
3. Ginsenoside Rb1PPK model verification
3.1 ginsenoside Rb1Bootstrap method verification
Bootstrap is carried out 1000 times by using Phoenix NLME software, the model is converged successfully, and the correlation analysis result is shown in a table 3.
Table 3 the results show: the mean value and the median obtained by Bootstrap calculation are close to the parameter values estimated by the original data, the deviation (CV%) is not more than 30%, and the parameter values estimated by the original data all fall within the percentile range of 2.5-97.5% of the parameter values estimated by Bootstrap, so that the model established by the original data has stability and internal effectiveness.
3.2 ginsenoside Rb1VPC method verification
VPC verification is carried out on the final model by using a Predictive check method of Phoenix NLME software, and the result shows that the established model is more accurate in prediction. The results are shown in FIG. 8-b.
The results show that: the predicted data generated by simulation has a similar trend with the observed value, and the observed value mostly falls within a 95% confidence interval of the predicted value, which shows that the efficiency of the final model is better.
4. Model verification of ginsenoside Rg1
4.1 ginsenoside Rg1Bootstrap method verification
Bootstrap is carried out 1000 times by using Phoenix NLME software, the model is converged successfully, and the correlation analysis result is shown in a table 3.
Table 3 the results show: the mean value and the median obtained by Bootstrap calculation are close to the parameter values estimated by the original data, the deviation (CV%) is not more than 30%, and the parameter values estimated by the original data all fall within the percentile range of 2.5-97.5% of the parameter values estimated by Bootstrap, so that the model established by the original data has stability and internal effectiveness.
4.2 verification of ginsenoside Rg1VPC method
VPC verification was performed on the final model using Predictive check method of Phoenix NLME software, and the result is shown in FIG. 8-c.
The result shows that the predicted data generated by simulation has a similar trend with the observed value, and the observed value mostly falls within the 95% confidence interval of the predicted value, which indicates that the efficiency of the final model is better.
Example 6: model external validation
And (3) carrying out external verification by using 30 rats, predicting the blood concentration at the corresponding time according to the obtained final group pharmacokinetic model and a Bayesian feedback method, and comparing the simulated predicted blood concentration with an actually measured value.
The three drug prediction correlation results are shown in fig. 17 (three-component externally validated prediction correlation scattergram) and table 8:
table 8: external verification methods MPE and MAE results
The result shows that the prediction error value is in the specified range, and the prediction effect is good. (the internal established criteria for MPE and MAE are: MPE < + > 20%, MAE < 30%).
Claims (8)
1. A method of constructing a pharmacokinetic model of an in vivo population of rats, the method comprising the steps of:
1) and (3) drawing up sampling point time: on the basis of holographic sampling, sampling points are randomly grouped, and each group of sampling points is 3-4;
2) determining the covariates to be investigated: taking physiological and pathological information of the rat as covariates to be investigated, and investigating the influence of the factors on drug metabolism;
3) a certain number of test samples were included: aiming at the covariate inclusion sample, the included sample is confirmed to be uniformly distributed on the covariate, the testing range is wider, and the later prediction range is met;
4) administration, sample collection and information collection, sample determination: determining the administration scheme of the compound red sage root dripping pill aqueous solution, collecting blood samples in a preset sampling time range, recording corresponding collecting time, recording individual related information aiming at a preset covariate, and completing sample determination work by applying a detection method;
5) Constructing a model:
a, data inspection is carried out, and the basic characteristics of acquisition time and blood concentration are determined;
b, covariate analysis: using the SPSS as a matrix diagram and calculating correlation coefficients among all factors, and performing frequency distribution and correlation investigation on the covariates to be investigated;
c, selecting a structural model: respectively adopting different atrioventricular extravascular administration models for fitting, adopting an extended least square method (FOCE-ELS) for calculation, comparing the objective function values of the models and determining a structural model;
d, selecting a statistical model: selecting a suitable inter-individual/intra-individual random variation model;
e, screening a fixed effect and finally modeling;
f, internal verification of the model: and internally verifying the final model by using a Bootstrap method and a VPC verification method.
2. The pharmacokinetic model building method according to claim 1, wherein: 1) and (3) drawing up sampling point time: the sampling points were divided into 4 groups.
3. The pharmacokinetic model building method according to claim 1, wherein: 3) rats of 6-21 weeks of age, male and female halves, were enrolled, with rats of the same week of age and sex randomly distributed to 4 sampling groups.
4. The pharmacokinetic model building method according to claim 1, wherein: 4) drawing a scatter diagram of relationship between collection time and blood concentration of effective components of the compound Saviae Miltiorrhizae radix dripping pill, i.e. tanshinol, ginsenoside Rb1 and ginsenoside Rg 1.
5. The pharmacokinetic model building method according to claim 1, wherein: 5) selecting a proportion model from random variation models in individuals of danshensu and ginsenoside Rb1, wherein CObs represents observed value, C represents fitting value, CEps represents mean 0, and variance is sigma2Random error of (2).
6. The pharmacokinetic model building method according to claim 1, wherein: 5) ginsenoside Rg1 due to low in vivo exposure, random variation in individuals selected addition model, expressed as CObs ═ C + CEps, wherein CObs represents observed value, C represents fitting value, CEps represents mean 0, and variance is σ2Random error of (2).
7. The pharmacokinetic model building method according to claim 1, comprising the steps of:
1) and (3) drawing up sampling point time: designing sampling points according to the half-life time of ginsenoside Rb1, and dividing the sampling points into 4 groups (group A: 5min, 1.5h, 6h) (group B: 15min, 2h, 8h, 24h) (group C: 30min, 3h, 10h, 48h) (group D: 1h, 4h, 12h, 72 h);
2) determining the covariates to be investigated: taking physiological and pathological information of the rat as covariates to be investigated, investigating the influence of the factors on drug metabolism, and finally determining the sex, the week age and the weight of the rat as the covariates to be investigated;
3) A certain number of test samples were included: rats of 6-21 weeks of age are imported, half each male and female, wherein rats of the same week of age and the same sex are randomly distributed to 4 sampling groups, and the requirement that the three components have 2-3 effective data in each tested individual is met;
4) administration, sample collection and information collection, sample determination: preparing 0.3g/mL of compound salvia miltiorrhiza dropping pill aqueous solution, weighing a rat, performing single intragastric administration according to 1.5g/kg of dose, collecting a blood sample in a preset sampling time range, recording corresponding sampling time, recording individual related information, namely sex, week age and weight of the rat, and completing sample determination work;
5) construction of models
a, data inspection is carried out, basic characteristics of acquisition time and blood concentration are determined, a scatter diagram of the relationship between the acquisition time and the blood concentration of active ingredients of the compound salvia miltiorrhiza dripping pill, namely danshensu, ginsenoside Rb1 and ginsenoside Rg1 is drawn, a Y axis is logarithmic form concentration, an X axis is corresponding time after administration, and model structures of the danshensu, the ginsenoside Rb1 and the ginsenoside Rg1 are determined;
b, covariate analysis: using the SPSS as a matrix diagram and calculating correlation coefficients among all factors, and performing frequency distribution and correlation investigation on the covariates to be investigated; proper covariate inclusion requires that the corresponding factors are wide in distribution, have no obvious deviation phenomenon, are close to normal distribution and have enough representativeness;
c, selecting a structural model: fitting is carried out by respectively adopting a linearly eliminated one-atrioventricular extravascular administration model and a linearly eliminated two-atrioventricular extravascular administration model, calculation is carried out by adopting an extended least square method (FOCE-ELS), and the better fitting of the linearly eliminated one-atrioventricular extravascular administration model of the danshensu and the ginsenoside Rb1 and the better fitting of the linearly eliminated two-atrioventricular extravascular administration model of the ginsenoside Rg1 are determined;
d, selecting a statistical model: random variation among individuals assumes that the individual parameters conform to the lognormal distribution with the group parameters as typical values, so that the index model is selected for random variation among individuals of effective components, specifically Pij＝tvPij×exp(ηij) Represents;
wherein P isijIs the jth pharmacokinetic parameter of the ith individual, tvPijIs a population-typical value for the jth pharmacokinetic parameter modified by covariates. EtaijRepresentative of individual parameters PijFor population parameter tvPijHas a value of 0 as a center and a variance of ω2Normal distribution of (2);
selecting a proportion model from random variation models in individuals of danshensu and ginsenoside Rb1, wherein CObs represents observed value, C represents fitting value, CEps represents mean 0, and variance is sigma2Random error of (2). Ginsenoside Rg1 due to low in vivo exposure, random variation in individuals selected addition model, expressed as CObs ═ C + CEps, wherein CObs represents observed value, C represents fitting value, CEps represents mean 0, and variance is σ 2Random error of (2);
e fixed effect screening and final model: introducing covariates to be considered into the model one by one, screening by adopting a forward inclusion method (P <0.01) and a backward elimination method (P <0.001) to obtain a fixed effect which has a large influence on pharmacokinetic parameters, and establishing a final model;
f, internal verification of the model: and internally verifying the final model by using a Bootstrap method and a VPC verification method.
8. A pharmacokinetic model building method according to claim 1, the resulting model being:
salvianic acid was fitted with a linear abolished one-compartment extravascular dosing model with PK parameters as follows:
Ka＝tvKa*(week/14)^dKadweek*exp(nKa)
V＝tvV*(weight/260)^dVdweight*exp(nV)
Cl＝tvCl*(weight/260)^dCldweight*exp(dCldsex0*(sex＝＝0))*exp(nCl)
CObs＝C*(1+CEps)
ginsenoside Rb1 was fitted in a linear abrogation one-compartment extravascular dosing model with PK parameters as follows:
Ka＝tvKa*exp(nKa)
V＝tvV*(weight/260)^dVdweight*exp(nV)
Cl＝tvCl*(weight/260)^dCldweight*exp(nCl)
CObs＝C*(1+CEps)
ginsenoside Rg1 was fitted with a linear-elimination biventricular extravascular dosing model with PK parameters as follows:
Ka＝tvKa*exp(nKa)
V＝tvV*exp(nV)
V2＝tvV2*exp(nV2)
Cl＝tvCl*(weight/260)^dCldweight*exp(nCl)
Cl2＝tvCl2*exp(nCl2)
CObs＝C+CEps。
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