CN115035976A - Mezlocillin newborn group dosage optimization method and system - Google Patents
Mezlocillin newborn group dosage optimization method and system Download PDFInfo
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Abstract
The invention belongs to the field of pharmacokinetics, and provides a mezlocillin newborn group dosage optimization method and a mezlocillin newborn group dosage optimization system, which comprises the steps of obtaining clinical basic information, medication information and biochemical data information of a patient; preprocessing patient clinical basic information, medication information and biochemical data information, and constructing a sample data set and a test data set; constructing a mezlocillin newborn group pharmacokinetic model based on a sample data set; simulating by a mezlocillin newborn group pharmacokinetic model by utilizing a test data set to obtain the optimal dosage of the mezlocillin newborn group; the method evaluates the group pharmacokinetics of mezlocillin in the whole neonatal age range, establishes a dosage scheme based on development pharmacokinetics-pharmacodynamics evidence and obtains a best fit model of mezlocillin group pharmacokinetic data.
Description
Technical Field
The invention belongs to the technical field of pharmacokinetics, and particularly relates to a mezlocillin newborn group dosage optimization method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The newborn is not mature in self tissue and organ, the immune function is not healthy, the resistance is poor, and septicemia is easily caused by the invasion of pathogenic microorganisms. The neonatal septicemia incidence rate is high in China, accounts for about 1-10 per mill of the born infants, the number of patients died of illness is as high as 10% -20%, and the neonatal septicemia incidence rate and mortality rate are important reasons. Once sepsis is suspected, antibacterial drugs should be used immediately to reduce the incidence of serious consequences and even death. Mezlocillin is a semi-synthetic penicillin, has wide antibacterial activity and has good curative effect on neonatal septicemia. However, since the pharmacokinetics of this drug is less studied in the neonatal population, in clinical practice, the dosing regimen is often different depending on the adult. The lack of pharmacokinetic data in the target population may increase the risk of inappropriate use of the antibacterial drug, which may lead to treatment failure or serious adverse effects, and also contribute to the spread of antibacterial drug resistance.
There is therefore a need to assess the population pharmacokinetics of mezlocillin throughout the neonatal age range and establish a dosage regimen based on developmental pharmacokinetic-pharmacodynamic evidence.
That is, mezlocillin has less pharmacokinetic studies in the neonatal population and no recommended dose based on pharmacokinetic-pharmacodynamic basis is suggested, and thus there is a great difference in the neonatal mezlocillin dose schedule at each hospital of each center. There is no appropriate recommended dose for mezlocillin for use in neonates, which may lead to treatment failure or serious adverse effects, and also contribute to the spread of antibacterial drug resistance.
Disclosure of Invention
In order to solve the problems, the invention provides a mezlocillin neonatal population use dose optimization method and a mezlocillin neonatal population use dose optimization system.
According to some embodiments, the first aspect of the present invention provides a method for optimizing dosage for use in a neonatal population of mezlocillin, which comprises the following steps:
a method for optimizing dosage for use in a neonatal population of mezlocillin, comprising:
acquiring and preprocessing clinical basic information, medication information and biochemical data information of a patient;
simulating by a mezlocillin newborn group pharmacokinetic model to obtain the optimal dosage of mezlocillin newborn group;
the mezlocillin newborn group pharmacokinetic model is constructed by the following steps:
preprocessing patient clinical basic information, medication information and biochemical data information, and constructing a sample data set and a test data set;
constructing a mezlocillin newborn group pharmacokinetic model based on the sample data set;
and (3) simulating by using a test data set through a mezlocillin newborn group pharmacokinetic model to obtain the optimal dosage of the mezlocillin newborn group.
Further, the patient clinical basic information comprises birth weight, current weight, gestational age, day age and clinical diagnosis result;
the medication information comprises administration frequency, administration dosage, administration date, administration starting time, administration ending time, blood sampling date and blood sampling time;
the biochemical data information comprises serum creatinine values, urea nitrogen, albumin, glutamic-oxaloacetic transaminase and glutamic-pyruvic transaminase.
Further, preprocessing the patient clinical basic information, the medication information and the biochemical data information, and constructing a sample data set and a test data set, wherein the method comprises the following steps:
carrying out mezlocillin concentration measurement on a sample of biochemical data information to obtain mezlocillin blood concentration information;
storing mezlocillin blood concentration information, patient clinical basic information, medication information and biochemical data information according to a two-dimensional arrangement format with row and column data;
adjusting all data to numbers except for specific alphabetic characters, TIME and DATE;
distinguishing and classifying the obtained mezlocillin data variables, defining specific variables, and assigning different numbers to different patients;
enumerating and distinguishing one or more events contained in each patient data based on the event type;
arranging and sorting the patient records according to the time sequence, and establishing a time-dependent modeling system;
filling missing data and perfecting specific variables, so that the data record of each patient must contain the same number of variables to obtain a final patient data set;
the patient data set is divided into a sample data set and a test data set.
Further, the constructing a mezlocillin newborn population pharmacokinetic model based on the sample data set comprises the following steps:
respectively measuring different chamber models based on a sample data set, and determining a mezlocillin chamber model as a biventricular model according to a model goodness diagnostic diagram and an objective function value;
constructing a mezlocillin newborn group pharmacokinetic basic model based on inter-individual pharmacokinetic parameters and a residual model considering inter-individual variability and by combining a two-chamber model;
carrying out covariate evaluation on the mezlocillin newborn group pharmacokinetic basic model, and determining patient pharmacokinetic influence parameters of the mezlocillin newborn group pharmacokinetic basic model;
and correcting the mezlocillin newborn group pharmacokinetic basic model based on the patient pharmacokinetic influence parameters to obtain a mezlocillin newborn group pharmacokinetic model.
Further, the performing covariate evaluation on the mezlocillin newborn group pharmacokinetic basic model to determine the patient pharmacokinetic influence parameters of the mezlocillin newborn group pharmacokinetic basic model comprises:
evaluating covariates by adopting a method of gradually eliminating forwards and backwards;
in the process of forward incorporation, adding and evaluating all covariates one by one, and if the addition of one covariate can enable the objective function value to be reduced to be larger than a preset value, adding the covariate into a mezlocillin newborn group pharmacokinetic basic model;
in the process of backward deletion, all covariates are deleted and evaluated one by one, and if the deletion of one covariate can increase the objective function value to be more than a preset value, the covariate is kept in a mezlocillin newborn group pharmacokinetic basic model;
the final patient pharmacokinetic influencing parameters are determined by two processes, including forward inclusion and backward deletion.
Further, the mezlocillin newborn group pharmacokinetic model specifically comprises the following steps:
CL＝0.180×(CW/2300) 0.75 ×(PMA/35.86) 0.212 ×(CREA/73) 0.155
V1＝0.019×(CW/2300)
V2＝0.404×(CW/2300)
Q＝0.205×(CW/2300) 0.75 ；
wherein CL is the mezlocillin clearance, V1 is the apparent distribution volume of the central compartment, V2 is the apparent distribution volume of the peripheral compartment, Q is the inter-compartment clearance, CW is the current body weight, PMA is the corrected gestational age, and CREA is the serum creatinine concentration.
Further, the optimal dosage of the mezlocillin newborn group is obtained by simulating a mezlocillin newborn group pharmacokinetic model, and the method comprises the following steps:
carrying out multiple Monte Carlo simulations on the test data set based on a Mezlocillin newborn group pharmacokinetic model to obtain the total concentration of Mezlocillin;
calculating the concentration of free drug of each patient in the total concentration of mezlocillin according to the protein binding rate;
increasing the dose and/or dosing frequency if the free drug concentration of the current dosing regimen is less than a preset value;
if the free drug concentration of the current dosing regimen reaches a preset value, the dosage is reduced.
According to some embodiments, the second aspect of the present invention provides a dosage optimization system for a neonatal population of mezlocillin, which adopts the following technical scheme:
a system for optimizing dosage for use in a neonatal population of mezlocillin, comprising:
the data acquisition module is configured to acquire and preprocess clinical basic information, medication information and biochemical data information of a patient;
the dosage optimization module is configured to perform simulation through a mezlocillin newborn group pharmacokinetic model to obtain the optimal dosage of mezlocillin newborn group;
the mezlocillin newborn group pharmacokinetic model is constructed by the following steps:
preprocessing patient clinical basic information, medication information and biochemical data information, and constructing a sample data set and a test data set;
constructing a mezlocillin newborn group pharmacokinetic model based on the sample data set;
and simulating by using a mezlocillin newborn group pharmacokinetic model by using a test data set to obtain the optimal dosage of mezlocillin newborn groups.
According to some embodiments, a third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of a method for optimizing the dosage for use in a neonatal population of mezlocillin as described in the first aspect above.
According to some embodiments, a fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program performing the steps in a method for neonatal population dose optimization of mezlocillin as defined in the first aspect above.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, through the research on the infants at the whole neonatal age group, the pharmacokinetic analysis of the mezlocillin neonatal group is completed, the dosage is simulated and optimized, the bicompartmental model of the first-stage elimination is confirmed to be the best fitting model for describing the pharmacokinetic data of the mezlocillin group, and the corrected gestational age, the current weight and the serum creatinine concentration are important covariates.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are included to illustrate an exemplary embodiment of the invention and not to limit the invention.
FIG. 1 is a flow chart of a method for optimizing the dosage of mezlocillin in a neonatal group according to a first embodiment of the present invention;
FIG. 2 is a diagram of a two-chamber model according to a first embodiment of the present invention;
FIG. 3(a) is a scatter plot of observed value concentration (DV) versus population prediction value (PRED) according to a first embodiment of the present invention;
FIG. 3(b) is a scatter plot of observed value concentration (DV) versus predicted value of Individuals (IPRED) according to a first embodiment of the present invention;
FIG. 3(c) is a scatter plot of the Conditional Weighted Residuals (CWRES) and population Predictors (PREDs) according to an embodiment of the present invention;
FIG. 3(d) is a scatter plot of a Conditional Weighted Residual (CWRES) versus dosing time plot in accordance with a first embodiment of the invention;
FIG. 3(e) is a Normalized Prediction Distribution Error (NPDE) histogram according to one embodiment of the present invention;
FIG. 3(f) is a quantile-quantile graph (QQ plot) according to one embodiment of the present invention;
fig. 4 shows the result of the mezlocillin dose simulation according to the embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
The pharmacokinetics of mezlocillin in the newborn population is less studied, and the recommended dosage based on the pharmacokinetics-pharmacodynamics basis is not proposed, so that the dosage scheme of mezlocillin for newborn in each hospital in each center has large difference.
Faced with this problem, we evaluated the population pharmacokinetics of mezlocillin over the entire neonatal age range and established a dosage regimen based on developmental pharmacokinetic-pharmacodynamic evidence.
Example one
The present embodiment provides a method for optimizing dosage for use in a neonatal population of mezlocillin, the method comprising the steps of:
acquiring clinical basic information, medication information and biochemical data information of a patient;
preprocessing patient clinical basic information, medication information and biochemical data information, and constructing a sample data set and a test data set;
constructing a mezlocillin newborn pharmacokinetic model based on a sample data set;
and simulating by using a mezlocillin newborn drug-induced dynamic model by using a test data set to obtain the optimal dosage of mezlocillin newborn groups.
As shown in fig. 1, the method of this embodiment specifically includes the following steps:
first, data acquisition
The patient clinical basic information, the medication information and the biochemical data information are collected through a medical order sheet and a hospital His system, and the patient clinical basic information comprises: birth weight; current body weight; gestational age; the age in days; and (5) clinical diagnosis. The medication information includes: the frequency of administration; the dose administered; the date of administration; the time of initiation of administration; the end time of administration; the date of blood collection; and (5) blood sampling time. The biochemical data information includes: serum creatinine, urea nitrogen, albumin, glutamic-oxaloacetic transaminase, and glutamic-pyruvic transaminase. The biochemical data is mainly collected within +/-48 hours of the day of blood collection. And (4) carrying out concentration measurement on the collected blood sample to obtain the concentration data of mezlocillin.
The plasma concentration of mezlocillin is determined by high performance liquid chromatography (metronidazole as an internal standard). The non-linear hybrid model NONMEM software was used for population pharmacokinetic analysis.
Population pharmacokinetic analysis was performed using a non-linear mixed effects model (NONMEM V7.4, Icon Development Solutions, USA) software program. The NONMEM software is used for modeling group pharmacokinetics and then analyzing and optimizing (namely, a nonlinear mixed effect model method), the nonlinear mixed effect model method is a commonly used group pharmacokinetic research method at present, but the established model is a brand new model of mezlocillin neonates. The concentration, administration information, infant basic information and the like of mezlocillin are measured by using a high performance liquid chromatography method to serve as a data set for modeling, and the data set participates in the construction of a model.
Opportunistic blood sampling is an analysis that utilizes opportunistic samples (samples collected from blood after routine clinical laboratory testing as part of clinical care) in conjunction with population pharmacokinetics.
After the sample collection work is finished, researchers need to accurately record the start and stop time of drug administration and the blood sampling time when the samples are collected on a special registration table, and the samples without complete drug administration information are excluded. And verifying the collected sample information, and finally bringing the sample into the research. In clinical practice, collected blood samples were centrifuged at 12000 rpm for 10 min at 4 ℃. And respectively extracting, subpackaging and numbering the upper layer plasma and the lower layer blood cells obtained by centrifugation, and storing in a refrigerator at the temperature of-80 ℃. If the collected sample cannot be immediately processed, the sample should be temporarily stored in a refrigerator (4 ℃). Within 48 hours after sample collection, centrifugation and storage must be completed.
Centrifugation and storage work are steps that need to be taken to obtain drug concentrations. Separating plasma from blood cells by centrifugation, wherein the concentration measured by high performance liquid chromatography is the drug concentration in the plasma, and mezlocillin in the plasma can be decomposed after being placed at room temperature for a period of time, and should be timely treated and stored in a refrigerator at-80 deg.C.
Population Pharmacokinetics (Population Pharmacokinetics) analysis was performed using the nonlinear mixed effects model (NONMEM V7.4, Icon Development Solutions, USA) software program.
Second, data summarization and review
And summarizing, settling and examining the measured mezlocillin blood concentration information and clinically collected patient clinical basic information, medication information and biochemical data information. The NONMEM (NM-TRAN) system has certain structural requirements for analyzing data sets. And arranging the mezlocillin data format.
1) The data file has a two-dimensional arrangement of rows and columns of data.
2) All data is adjusted to numbers except for specific alphabetic characters, TIME and DATE, etc.
3) The required variables are created. And carrying out distinguishing classification according to the obtained mezlocillin data variables, and defining or classifying specific variables (dependent variables and independent variables). Different patients are given different numbers.
4) Event types are distinguished. The events may be of the following types: a dosing event, an observation event, or other type of event. One or more of the above events contained in each patient's record are listed and distinguished.
5) And establishing a time-dependent modeling system. The patient records are arranged in chronological order.
6) Filling missing data and improving specific variables. Each data set record must contain the same number of variables. The order of the variables is arbitrary, but must be consistent across all records (both within and between subjects). Replace missing values with ". quadrature..
Careful and detailed analysis of mezlocillin data is required prior to modeling to identify potential outliers or high impact points that may be of significant interest for modeling. Hidden anomalies and errors in the dataset are identified and isolated metrics are identified from the aggregated points. The observed concentration of the treatment, the time relative to the last administration and the dose administered were analyzed for rationality.
Thirdly, constructing a model
The mezlocillin newborn group pharmacokinetics model is constructed based on a sample data set, and comprises the following steps:
respectively measuring different chamber models based on a sample data set, and determining a mezlocillin chamber model as a biventricular model according to a model goodness diagnostic diagram and an objective function value;
constructing a mezlocillin newborn group pharmacokinetic basic model based on inter-individual pharmacokinetic parameters and a residual model considering inter-individual variability and by combining a two-chamber model;
a compartment model: a two-chamber model;
inter-individual variation: theta i ＝θ mean *exp(ηi)；
Wherein, theta mean As fixed effect parameters (population-typical values); theta i An estimate of a parameter for the ith individual; eta i is the inter-individual variability of the drug parameters, eta i has a mean of 0 and a variance of omega 2 Normal distribution of (2);
residual error model: y ═ f (x) (+ e 1) + e 2. Wherein Y is an observed value, f (x) is a model predicted value, and epsilon is a residual variation;
carrying out covariate evaluation on the mezlocillin newborn group pharmacokinetic basic model, and determining patient pharmacokinetic influence parameters of the mezlocillin newborn group pharmacokinetic basic model;
and correcting the mezlocillin newborn group pharmacokinetic basic model based on the patient pharmacokinetic influence parameters to obtain a mezlocillin newborn group pharmacokinetic model.
The mezlocillin newborn group pharmacokinetic model specifically comprises the following components:
CL＝0.180×(CW/2300)0.75×(PMA/35.86)0.212×(CREA/73)0.155
V1＝0.019×(CW/2300)
V2＝0.404×(CW/2300)
Q＝0.205×(CW/2300)0.75；
wherein CL is the mezlocillin clearance, V1 is the apparent distribution volume of the central compartment, V2 is the apparent distribution volume of the peripheral compartment, Q is the inter-compartment clearance, CW is the current body weight, PMA is the corrected gestational age, and CREA is the serum creatinine concentration.
Specifically, the method comprises the following steps:
1) selection of a Chamber model
After the mezlocillin data set is constructed and is checked, the next step is to construct a chamber model (namely a structural model) by using NONMEM software. The basis of population pharmacokinetics is the selection of a structural model (i.e., a classical compartmental model) that is at a critical position throughout the data analysis. Two types of atrioventricular models are selected, and one type of the atrioventricular models is a medicine for which the pharmacokinetic characteristics are reported in related documents and can be directly adopted; and the other method is to test the first-chamber model and the second-chamber model in sequence and compare the models through a model goodness diagnostic map and an objective function value.
In this embodiment, the first-order eliminated two-chamber model can better complete the fitting of the data by comparing the objective function values with the fitting goodness diagnostic map, and the two-chamber model has lower objective function values compared with the one-chamber model (objective function value of the one-chamber model: 442.477; objective function value of the two-chamber model: 438.083). This study showed that the two-compartment model of primary elimination is the best choice to describe the pharmacokinetics of the neonatal population. Correcting gestational age, current body weight and serum creatinine concentration are important covariates.
A two-chamber model: the body is considered as two compartments, blood and organs rich in blood flow such as liver and kidney are considered as the same compartment, called central compartment, while tissues deficient in blood flow such as muscle, fat, etc. are considered as the same compartment, called peripheral compartment, and it is assumed that drug exchange between the central and peripheral compartments and elimination of drugs from the central compartment are first-order rate processes. In the case of a rapid intravenous dose of drug, as shown in FIG. 2, where X 0 For the administration dosage, Xc is the central chamber dosage, Vc is the apparent distribution volume of the central chamber, Vp is the apparent distribution volume of the peripheral chamber, Xp is the peripheral chamber dosage, K 12 Is the rate constant of drug transport from the central compartment to the peripheral compartment, K 21 Is the transport rate constant from the peripheral chamber to the central chamber.
According to the structural characteristics of a two-chamber model, the study parameterization is carried out on the clearance rate (CL) of mezlocillin, the apparent distribution volume of a central chamber (V1), the apparent distribution volume of a peripheral chamber (V2) and the clearance rate (Q) among compartments, and a proper model is selected and constructed to estimate the difference among individuals and the residual error. The process of estimating the inter-individual differences and the residual residuals is the description of the inter-individual differences and the selection of the residual model.
2) Description of inter-Individual variability
Inter-individual variability the influence of inter-individual pharmacokinetic parameters was examined as the deviation of individual parameters from population typical values. Pharmacokinetic parameters were predicted using first-order conditional evaluation of interactions (FOCE). The index model is used for analyzing and estimating the inter-individual variation of the medicine, and the specific index equation is as follows:
θ i ＝θ mean *exp(η i )
wherein, theta mean Is a fixed effect parameter (population-typical value), i.e.; theta.theta. i An estimate of a parameter for the ith individual; eta i Is the inter-individual variability, eta, of a drug parameter i The coincidence mean is 0 and the variance is omega 2 Normal distribution of。
3) Selection of residual model
The residual variation reflects the magnitude of random variation between predicted and measured values, including measurement error, differences between different laboratories, etc. The residual variation is evaluated by using an addition, a proportion method, an exponential method and a mixed method residual model, and the residual formula is as follows:
the addition model is Y ═ f (x) + epsilon,
exponential model Y ═ f (x) × exp (epsilon),
scale model Y ═ f (x) (1+ epsilon),
mixed model of Y ═ f (x) (1+ ε) 1 )+ε 2 ；
Wherein, Y is an observed value, f (x) is a model predicted value, and epsilon is a residual variation.
And evaluating a residual error model through the size of the residual error variation, the fitting goodness map and the size of the objective function value.
4) Covariate evaluation
Covariates (body weight, age, serum creatinine, transaminase, etc.) were evaluated. Covariate analysis uses a stepwise forward and backward elimination approach. In the process of forward inclusion, if the addition of one covariate can drop the Objective Function Value (OFV) by more than 3.84, this covariate is proved to be significant (p <0.05), which is statistically significant. All covariates were evaluated one by one, and finally all significant covariates were incorporated into the model together. And then carrying out backward deletion, wherein the covariates are deleted one by one, and if one covariate is deleted, so that the OFV value is increased by more than 6.635(p <0.01), the covariate is continuously kept in the final model.
As shown in table 1, inter-and intra-individual variability of patient pharmacokinetic parameters was described and explained by covariate assessment, while also increasing the potential for individualized dosing regimens.
TABLE 1 Mezlocillin covariate analysis results
5) Model validation
The drug population pharmacokinetic model was validated by statistical and graphical methods.
(1) The internal verification is mainly based on a model diagnostic diagram of goodness of fit, and comprises the following steps:
when a scatter diagram of the Detection Value (DV) and the population predicted value (PRED) is observed, as shown in fig. 3(a), the data points are distributed symmetrically on both sides of the line where y is equal to x, and there is no significant bias, and the model fitting degree is good. As shown in fig. 3(b), the scattergram of the observed value concentration (DV) and the individual predicted value concentration (IPRED) shows that the data points have no significant bias on both sides of the y ═ x straight line, and the degree of model fitting is good.
Conditional Weighted Residual (CWRES) and PRED scattergrams, as shown in fig. 3(c), CWRES and TIME after first dose (TIME) scattergrams, as shown in fig. 3(d), CWRES are symmetrically and uniformly distributed around a reference line where CWRES is 0, most of CWRES are within ± 2, and do not exhibit a significant trend with TIME, and there is no significant fitting error.
(2) Bootstrapping method
Model performance and stability were evaluated using an alternative unparameterized bootstrap method and a repeatability test. By resampling from the original data set (in units of subject), a pilot data set of the same size as the original data set is generated. This process is repeated hundreds or thousands of times to generate a large number of resampled data sets (typically 1000 times). A model of interest is fitted to each pilot replication dataset. Summary statistics are calculated for each final parameter estimate in the pilot dataset using the successfully minimized model. (e.g., based on the mean, median, minimum, maximum, 90% prediction interval, etc. of the 5 th and 95 th percentiles of the distribution). The summary statistics for each parameter estimate were compared to the raw model fit results, as shown in table 2.
TABLE 2 Mezlocillin group pharmacokinetic parameters and bootstrap validation
(3) Normalized predictive distribution error checking
Model diagnostics are based on Normalized Prediction Distribution Errors (NPDE): the performance of the final model was evaluated by statistical methods and the original data set was simulated 1000 times using the final model parameters. And performing visual analysis on the simulation result and the observation result by using R language (v3.6.3) to generate a QQ-plot diagram and an NPDE histogram of normal distribution. The NPDE results are shown in FIG. 3(e), and fit to the N (0,1) normal distribution. The QQ-plot of the normal distribution is shown in FIG. 3 (f). The NPDE results and the normal distribution QQ-plot show that the model can be well fitted to individual data.
(4) Predictive correction visual predictive review
Model diagnostics generate a prediction-corrected simulated concentration data set from the final model, model parameters, etc. using PsN (v5.0.0) software based on a prediction-corrected visual prediction check (pcVPC), calculate 5, 50, and 95 percentiles of the data set and corresponding 95% confidence intervals, plot the prediction-corrected simulated concentrations and the prediction-corrected observed concentration points against time, observe the distribution of the prediction-corrected observed concentration points within the prediction-corrected simulated concentration range, with a majority (about 90%) of the concentration points falling within the 5 and 95 percentile ranges of the prediction-corrected simulated concentrations.
6) Dose simulation
Mezlocillin belongs to the beta-lactam antibacterial drugs, is a time-dependent antibiotic, and the pharmacodynamic target of the beta-lactam antibacterial drugs, namely the time when the unbound drug concentration is higher than the MIC (i.e. 70% fT > MIC) during 70% of the dosing interval, is used to evaluate the efficacy of mezlocillin. The antimicrobial dose adjustment needs to take into account not only the concentration of the drug itself, but also the MIC of the pathogenic pathogen. Common pathogens in newborns in china are enterobacter (e.g. escherichia coli) and staphylococcus aureus. The MIC is 4 mug/mL, covers the MIC values of most pathogenic bacteria (such as Escherichia coli, Staphylococcus aureus) and can be used for dosage optimization.
The final model was used to perform 1000 monte carlo simulations, where the concentration of the monte carlo simulation was the total concentration of mezlocillin, and the free drug concentration of each patient was calculated according to the protein binding rate, which was about 42%. If the current dosing regimen is inadequate, the dosage and/or frequency of dosing can be increased. If the current dosing regimen is adequate, an attempt is made to reduce the dosage. Thus, various possible dosing regimens were tested, and the percentage of patients with fT > MIC over 70% was subsequently calculated to optimize the dosing regimen.
Results
1) By comparing the objective function values with the goodness-of-fit diagnostic map, a two-chamber model with one-chamber model with lower objective function values (one-chamber model objective function values: 442.477, respectively; two-chamber model objective function values: 438.083). This study showed that the two-compartment model of primary elimination is the best choice to describe the pharmacokinetics of the neonatal population. Correcting gestational age, current body weight and serum creatinine concentration are important covariates.
2) The mixed model was found to better describe the inter-individual differences in Clearance (CL) and apparent volume of distribution (V2) of the peripheral chamber. The current body weight was added to the basal model as a fixed influencing factor using the method of the growth-at-different-speed model (apparent distribution volume-at-different-speed coefficient of 1, clearance-at-different-speed coefficient of 0.75). The results show a significant reduction in the value of the objective function (Δ OFV: 4.78). Correcting gestational age is the most important covariate influencing factor, with a significant reduction in the value of the objective function (Δ OFV: 8.43) compared to combinations of gestational age and day age (Δ OFV: 8.06). Compared to corrected gestational age effect factors alone. None of the other covariate factors had a significant effect on the model.
The drug population pharmacokinetic model is verified by a statistical and graphical method, and internal verification shows that the final model is stable and reliable.
3) The objectives of the present example for the mezlocillin dose simulation optimization are: patients with drug concentrations above the MIC had a target achievement rate of over 70% over the 70% time between dosing, with a MIC of 4mg/L being selected. And (3) constructing a virtual data set by using the basic information of the patient of the original data set, carrying out simulation operation 1000 times on the dose by adopting a Monte Carlo simulation method, and calculating the time when the blood concentration of each patient is higher than MIC. As shown in FIG. 4, the currently used dosing regimen (50mg/kg q12h) allowed 89.2% of neonates to reach therapeutic targets by simulating the combination of different dosing doses and dosing frequency, and 74.3% of patients to reach therapeutic targets by increasing the dosing frequency to q8h at a dose of 20 mg/kg. Thus, for newborns with bacterial infections (e.g., escherichia coli, staphylococcus aureus, etc.), we provide a model-based dosing regimen, i.e., a 20mg/kg q8h dosing regimen that achieves both therapeutic efficacy and reduces the dose administered and mitigates the incidence of drug resistance.
Example two
The present embodiment provides a system for optimizing dosage for use in a neonatal population of mezlocillin, comprising:
the data acquisition module is configured to acquire and preprocess clinical basic information, medication information and biochemical data information of a patient;
the dosage optimization module is configured to perform simulation through a mezlocillin neonatal drug-induced power model to obtain the optimal dosage of mezlocillin neonatal population;
the mezlocillin newborn drug-induced power model is constructed by the following steps:
preprocessing patient clinical basic information, medication information and biochemical data information, and constructing a sample data set and a test data set;
constructing a mezlocillin newborn pharmacokinetic model based on the sample data set;
and simulating by using a mezlocillin newborn drug-induced dynamic model by using a test data set to obtain the optimal dosage of mezlocillin newborn groups.
The modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method for optimizing the dosage for use in a neonatal population of mezlocillin as described in the first embodiment above.
Example four
The present embodiment provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the steps of the method for optimizing the dosage for a neonatal population of mezlocillin as described in the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A method for optimizing dosage for use in a neonatal population of mezlocillin, comprising:
acquiring and preprocessing clinical basic information, medication information and biochemical data information of a patient;
simulating by a mezlocillin newborn group pharmacokinetic model to obtain the optimal dosage of mezlocillin newborn group;
the mezlocillin newborn group pharmacokinetic model is constructed by the following steps:
preprocessing patient clinical basic information, medication information and biochemical data information, and constructing a sample data set and a test data set;
constructing a mezlocillin newborn group pharmacokinetic model based on a sample data set;
and simulating by using a mezlocillin newborn group pharmacokinetic model by using a test data set to obtain the optimal dosage of mezlocillin newborn groups.
2. The method of claim 1, wherein the patient clinical basic information comprises birth weight, current weight, gestational age, day age, and clinical diagnosis results;
the medication information comprises administration frequency, administration dosage, administration date, administration starting time, administration ending time, blood sampling date and blood sampling time;
the biochemical data information comprises serum creatinine values, urea nitrogen, albumin, glutamic-oxalacetic transaminase and glutamic-pyruvic transaminase.
3. The method of claim 1, wherein the pre-processing of the patient clinical basic information, the medication information, and the biochemical data information to construct the sample data set and the test data set comprises:
carrying out mezlocillin concentration measurement on a sample of biochemical data information to obtain mezlocillin blood concentration information;
storing mezlocillin blood concentration information, clinical basic information of a patient, medication information and biochemical data information according to a two-dimensional arrangement format of data with rows and columns;
adjusting all data to numbers except for specific alphabetic characters, TIME and DATE;
distinguishing and classifying according to the obtained mezlocillin data variable, defining a specific variable, and giving different serial numbers to different patients;
listing and distinguishing one or more events contained in each patient data according to event type;
arranging and sorting patient records according to a time sequence, and establishing a time-dependent modeling system;
filling missing data and perfecting specific variables, so that the data record of each patient must contain the same number of variables to obtain a final patient data set;
the patient data set is divided into a sample data set and a test data set.
4. The method of claim 1, wherein constructing a mezlocillin neonatal population pharmacokinetic model based on the sample dataset comprises:
respectively measuring different chamber models based on a sample data set, and determining a mezlocillin chamber model as a biventricular model according to a model goodness diagnostic diagram and an objective function value;
constructing a mezlocillin newborn group pharmacokinetic basic model based on inter-individual pharmacokinetic parameters considering inter-individual variability and a residual model and combining a two-chamber model;
carrying out covariate evaluation on the mezlocillin newborn group pharmacokinetic basic model, and determining patient pharmacokinetic influence parameters of the mezlocillin newborn group pharmacokinetic basic model;
and correcting the mezlocillin newborn group pharmacokinetic basic model based on the patient pharmacokinetic influence parameters to obtain a mezlocillin newborn group pharmacokinetic model.
5. The method of claim 4, wherein the performing a covariate evaluation of the mezlocillin neonatal population pharmacokinetic base model to determine the patient pharmacokinetic influence parameters of the mezlocillin neonatal population pharmacokinetic base model comprises:
evaluating covariates by adopting a method of gradually eliminating forwards and backwards;
in the process of forward incorporation, adding and evaluating all covariates one by one, and if the addition of one covariate can enable the objective function value to be reduced to be larger than a preset value, adding the covariate into a mezlocillin newborn group pharmacokinetic basic model;
in the process of backward deletion, all covariates are deleted and evaluated one by one, and if the deletion of one covariate can increase the objective function value to be more than a preset value, the covariate is retained in a mezlocillin newborn group pharmacokinetic basic model;
the final patient pharmacokinetic impact parameters were determined by two processes, forward inclusion and backward deletion.
6. The method for optimizing dosage for use in a neonatal population of mezlocillin according to claim 4, wherein the mezlocillin neonatal population pharmacokinetic model is specifically:
CL＝0.180×(CW/2300) 0.75 ×(PMA/35.86) 0.212 ×(CREA/73) 0.155
V1＝0.019×(CW/2300)
V2＝0.404×(CW/2300)
Q＝0.205×(CW/2300) 0.75 ；
wherein CL is mezlocillin clearance, V1 is central compartment apparent distribution volume, V2 is peripheral compartment apparent distribution volume, Q is inter-compartment clearance, CW is current body weight, PMA is corrected gestational age, and CREA is serum creatinine concentration.
7. The method of claim 1, wherein the optimal dosage of mezlocillin for use in the neonatal group is obtained by performing simulation on a mezlocillin neonatal group pharmacokinetic model, and the method comprises:
carrying out multiple Monte Carlo simulations on the test data set based on a mezlocillin newborn group pharmacokinetic model to obtain the total concentration of mezlocillin;
calculating the free drug concentration of each patient in the total concentration of mezlocillin according to the protein binding rate;
increasing the dose and/or dosing frequency if the free drug concentration of the current dosing regimen is less than a preset value;
if the free drug concentration of the current dosing regimen reaches a preset value, the dose is reduced.
8. A system for optimizing dosage for use in a neonatal population of mezlocillin, comprising:
the data acquisition module is configured to acquire and preprocess clinical basic information, medication information and biochemical data information of a patient;
the dose optimization module is configured to simulate the mezlocillin neonatal group pharmacokinetic model to obtain the optimal using dose of the mezlocillin neonatal group;
the mezlocillin newborn group pharmacokinetic model is constructed by the following steps:
preprocessing patient clinical basic information, medication information and biochemical data information, and constructing a sample data set and a test data set;
constructing a mezlocillin newborn group pharmacokinetic model based on the sample data set;
and simulating by using a mezlocillin newborn group pharmacokinetic model by using a test data set to obtain the optimal dosage of mezlocillin newborn groups.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a method for optimizing the dosage for use in a neonatal population of mezlocillin as claimed in any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps in a method for optimizing dosage for use in a neonatal population of mezlocillin as claimed in any one of claims 1 to 7.
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