Evaluation of predictability for vancomycin dosage regimens by the Bayesian method with Japanese population pharmacokinetic parameters

The predictability of serum vancomycin (VCM) concentrations by means of the Bayesian method was evaluated to establish whether the method can be used to select safe and effective VCM treatment regimens for individual patients. Serum VCM concentrations at the trough and 2 h after the end of infusion (peak) were measured. Pharmacokinetic parameters were calculated for VCM dosage regimens based on a two-compartment model with the Bayesian method, using the Japanese population pharmacokinetic parameters estimated by Yasuhara et al. (1998). The predictive performance for serum VCM concentrations and the dosage regimens were analyzed using two points of serum VCM concentration in 41 patients whose serum creatinine and age were in the ranges of 0.4-4.6 mg/dl and 24-92 years, respectively. Although the predicted values for trough and peak VCM concentrations were slightly lower than measured VCM concentrations, the predictive performance was generally good. There were no differences among the groups classified by serum creatinine or age. An examination of predicted data that differed markedly from the measured serum VCM concentrations indicated that a larger difference in volume of distribution at the steady state (Vdss) calculated from serum VCM concentrations at the beginning and revision of dosage regimens resulted in a poorer correlation of predicted values and measured values. This finding indicates that therapeutic drug monitoring should be conducted frequently, and the dosage regimen revised accordingly, in the case of patients who may have a change of Vdss of VCM, for example, due to a complication such as heart failure or edema.     

基于群体药动学模型的万古霉素个体化给药软件研制及应用

目的:研发基于建立的成人和老年群体药动学(population pharmacokinetics,PPK)模型的万古霉素(vancomycin,VCM)个体化给药软件。方法:根据已建立的成人和老年VCM的PPK模型信息,运用MyEclipse、SQL Server、JRE等工具软件研发VCM给药软件。软件开发方案包括需求分析,概要设计,详细设计,软件编码,软件测试以及软件维护和二次开发。结果:研制的VCM给药软件可实现感染患者信息输入和管理,软件通过接口调用非线性混合效应模型(NONMEM)软件,不仅能预测多种具体VCM给药方案下的血药浓度,供临床医师制定初始用药方案参考,而且能结合已有的血药浓度监测信息和贝叶斯反馈法更精准地预测血药浓度,辅助临床医师进一步优化给药方案。软件应用于VCM血药浓度解读,药师向临床做出剂量调整建议。采纳建议组患者复查的血药浓度均达到目标血药浓度范围。结论:本研究基于VCM的PPK模型研制的给药软件能快速方便地辅助成人和老年感染患者VCM的个体化给药。     

Population pharmacokinetics of vancomycin in Chinese elderly patients and its application for dose individualisation

In the present study, we aimed to develop a population pharmacokinetics (PPK) model of vancomycin (VCM) and proposethe individualised dosage regimen for Chinese elderly patients. The data were collected prospectively from Chinese elderly patients receiving VCM therapy. Steady-state trough concentrations of VCM were determined using an enzyme-multiplied immunoassay. Patients’ sex, age, body weight, concomitant medications, infection type, and laboratory findingswere recorded. The PPK model was developed using nonlinear mixed-effects model software. Moreover, we used Monte Carlo simulations to develop an initial dosage regimen targeting various VCM through concentration ranges based on the final model. We found that VCM clearance (CL) was significantly influenced by post-craniotomy meningitis (PCM) and glomerular filtration rate in elderly patients. Additionally, a new dosage regimen was proposed to individualise VCM regimen for PCM and non-PCM elderly patients. A PPK model was established to estimate the individual VCM CL for elderly patients, which could be applied for individualising doses in the target population.     

Systematic external evaluation of published population pharmacokinetic models of tacrolimus in adult liver transplant recipients

OBJECITVE Diverse tacrolimus population pharmacokinetic(pop PK) models in adult liver transplant recipients have been established to describe its PK characteristics in the last two decades. However, their extrapolated predictive performance remains unclear.Therefore, in this study, we aimed to evaluate their external predictability and identify their potential influencing factors.METHODS The external predictability of each selected pop PK model was evaluated using an independent dataset of 59 patients with 404 trough concentrations prospectively collected from Huashan Hospital. Prediction-and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate the model predictability. Furthermore, the influence of the model structures on predictive performance was investigated. RESULTS Sixteen published pop PK models were assessed. In prediction-based diagnostics, the prediction error within ±30% was below 50%in all the published models. The simulation-based normalised prediction distribution error test and prediction-and variability-corrected visual predictive check indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting demonstrated improvement in model predictability with 2-3 prior observations. Additionally, the predictive performance of the nonlinear Michaelis-Menten model was superior to that of linear one-and two-compartment models with first-order elimination, indicating the underlying nonlinear kinetics of tacrolimus in liver transplant recipients, which was consistent with the findings in adult kidney transplant recipients.CONCLUSION The published models performed inadequately in prediction-and simulation-based diagnostics.Bayesian forecasting could improve predictive performance of the models. Furthermore, incorporating non-linear kinetics in tacrolimus popPK modelling should be considered to improve model predictability.     

Clinical usefulness of the new Japanese glomerular filtration rate equation for initial and individualized dosage adjustment concentrations of vancomycin

To clarify whether the new Japanese glomerular filtration rate (eGFR) equation was able to accurately determine the initial and individualized dosage adjustment concentrations of vancomycin (VCM), the predictive performance for VCM concentrations using the eGFR and Cockcroft-Gault (CG) equations was compared. Data were retrospectively collected from clinical records of 90 patients with MRSA infection whose trough and peak VCM concentrations had been determined. The predicted VCM initial and individualized dosage adjustment concentrations were performed with the 2-compartment linear model using pharmacokinetic parameter means and their individual values via Bayesian estimation, respectively. The prediction error (PE) and its absolute value (APE) between the observed and predicted VCM concentrations were calculated as indices of bias and accuracy in predictive performance, respectively. In the initial dosage adjustment of VCM, the PE value, calculated with the eGFR equation in trough and peak VCM concentrations of patients whose BMI were 18.5 kg/m(2) and higher, was significantly smaller than that calculated with the CG equation. In particular, both PE and APE values obtained from the eGFR calculated concentrations from nonelderly patients (younger than 65 years old) were significantly improved compared with those from the CG equation. In the individualized dosage adjustment of VCM, the eGFR equation gave a significantly smaller PE value in nonelderly patients' trough concentrations than the CG equation. These findings provide useful information for adjusting the VCM dosage to achieve optimal therapeutic efficacy in patients with MRSA infection.     

基于群体药动学的儿童奥卡西平给药方案优化和漏服药物补救方案研究

目的:建立奥卡西平在儿童群体中的药动学模型,辅助制定个体化给药方案和漏服药物后的补救方案。方法:收集124例4个月~18岁儿童癫痫患者口服奥卡西平后体内主要活性代谢产物羟基卡马西平(MHD)的药物浓度数据和临床资料,采用非线性混合效应建模法建立群体药动学(PPK)模型。应用自举法、预测值校准的直观预测检验(pc-VPC)、正态化预测分布误差检验(NPDE)评价PPK模型的预测性能。基于最终模型参数,模拟不同特征儿童患者的最佳给药方案和不同漏服场景下的补救给药方案。结果:本研究发现奥卡西平在儿童体内的药动学特征符合一级吸收和消除的一房室模型,按标准体质量70 kg成人校正的群体典型值为:吸收速率常数(K_a)=0.83 h^-1,表观分布容积(V_d/F)=16.70 L,清除率(CL/F)=1.92 L·h^-1。体质量是影响V_d/F和CL/F的显著性协变量。自举法、pc-VPC和NPDE检验显示模型预测准度高,稳定性好。模拟结果显示,按本研究推荐的维持方案和漏服后补救方案给药可提高药物浓度的达标概率,保证治疗的安全性和有效性。结论:本研究通过群体药动学研究,制定了不同体质量分层儿童患者的奥卡西平优化给药方案以及漏服药物后的补救方案,可为临床药物治疗决策提供参考。     

Evaluation of Bayesian predictability of vancomycin concentration in patients with various degrees of renal function.

To assess the usefulness of the population pharmacokinetic parameters of vancomycin (VCM) based on a two-compartment model in Japanese adult patients, predictability by a Bayesian method was evaluated using a concentration time course after single dosing to 22 patients with various degrees of renal function. Using one or two points from the observed data for each patient, the concentrations predicted by a Bayesian method were compared with the observed data for each sampling time. The patients were separated into five groups based on their renal functions indicated by creatinine clearance, and the mean prediction error (MPE) and root mean squared error (RMSE) were calculated for each group as measures of accuracy and precision, respectively. In both one- and two-point methods, the absolute MPE values at each sampling time in the elimination phase were less than 2.5 microg/ml, and the RMSE values were also small. No clear differences were found in MPE and RMSE among the groups. In the distribution phase, the MPE and RMSE were somewhat greater, and RMSE in some groups was around 15 microg/ml when trough data was used to predict the peak concentration. Also, the theoretical RMSE using this population parameter setting could well explain the observed RMSE. These results confirmed this population parameter setting is useful for at least predicting concentration in the elimination phase after single dosing, and the predictability was independent of renal function.     

基于NONMEM法建立紫杉醇群体药动学模型

目的：建立国人紫杉醇（paclitaxel,PTX）群体药动学（population pharmacokinetic,PPK）模型,为制定个体化给药方案提供理论支持。方法：收集138例接受紫杉醇治疗的肿瘤患者（建模组105例,验证组33例）210个血样,HPLC法测定紫杉醇血药浓度,PCR-RFLP法检测MDR1 C3435T。应用非线性混合效应模型（NONMEM）法,考察MDR1 C3435T基因多态性、合并用药及病理生理因素对紫杉醇药动学参数的影响,建立紫杉醇PPK模型。对模型进行拟合优度诊断、自举法（Bootstrap）内部验证,正态预测分布误差法（NPDE）及外部验证考察模型预测能力。结果：紫杉醇清除率（CL）和表观分布容积（V_d）的群体典型值分别为64.7 L·h^-1和1 240 L,患者内生肌酐清除率（CLcr）和给药速率（RATE）显著影响紫杉醇清除率。最终模型Bootstrap法验证结果与模型计算值相符,拟合优度、准确度及精密度均优于最简模型。结论：紫杉醇PPK最终模型稳定、有效,可结合Bayesian反馈法为临床优化给药方案提供科学依据。     

Predictive performance of the Bayesian analysis: Effects of blood sampling time,population parameters,and pharmacostatistical model

The present paper reports theoretical equations for the predictive performance of the Bayesian forecasting method. The precision of parameter estimates and predicted concentrations for an individual was described by general equations with the aid of a variance-covariance matrix of parameter estimates that involved the Bayes theorem. The equations were applied to assess the predictive performance of the one-point Bayesian method in association with blood sampling time, the population parameters, and the pharmacostatistical model. The simulation study showed that the prediction error in parameter estimates essentially depended upon the sampling time but the magnitude of dependency was affected by the size of inter-and intraindividual variances. With a smaller value of interindividual variance, the dependency on sampling time was less apparent. Effects of sampling time were further examined using clinical data obtained from 20 patients taking theophylline, and the results were in good agreement with the theoretical consideration. The present general equations are useful to investigate the sampling strategy as well as structural and variance modeling on the predictive performance of the Bayesian method.     

Limited sampling strategy using Bayesian estimation for estimating individual exposure of the once-daily prolonged-release formulation of tacrolimus in kidney transplant children

Background

A limited sampling strategy (LSS) for estimating the area under the curve (AUC) of the prolonged-release formulation of tacrolimus (tacrolimus^PR) is not available in pediatric patients, although the method is of real benefit to children. The objective of this study was to develop and validate a reliable and clinically applicable LSS using Bayesian estimation for estimating tacrolimus^PR AUC in pediatric kidney transplant patients

Methods

The original tacrolimus pharmacokinetic dataset consisted of 22 full profiles from 22 pediatric kidney transplant patients. The Bayesian estimation method was used to develop the LSS. External validation was performed in an independent validation group which consisted of 20 full pharmacokinetic profiles from 12 pediatric kidney transplant patients.

Results

Bayesian estimator using C_0h C_2h and C_3h gave the best predictive performance with a mean prediction error of 2.2 % in the external validation dataset. There was no correlation between the prediction error and age. The Bland–Altman analysis showed that the mean difference between the reference and Bayesian-estimated AUC_0-24 was 3.5 (95 % confidence interval ?3.5–10.5) ng h/mL

Conclusions

A reliable and clinically applicable LSS for estimating AUC_0–24 of tacrolimus^PR was determined and validated in children. The prediction was unbiased and precise. It can be used as a routine procedure to perform AUC-based tacrolimus^PR dosage optimization in pediatric renal transplant patients.     

Aminoglycoside dosing in pediatric patients

We assessed the performance of a predictive algorithm for dosing aminoglycoside antibiotics in 75 pediatric patients and Bayesian feedback in 36. The absolute errors for peak and trough concentrations were 1.83 and 0.80 micrograms/ml, respectively, which seem clinically acceptable for most patients. However, the algorithm had significant negative bias for both peaks and troughs. Implementation of Bayesian feedback eliminated bias in a second set of concentrations and significantly decreased its magnitude for both peaks (p = 0.028) and troughs (p = 0.005). This method may allow more accurate dosing of aminoglycoside antibiotics in pediatric patients, though it would most likely be improved by better definition of population parameters and their variability.     

重症感染患者多黏菌素B群体药代动力学模型的外部验证

目的:对既往发表的多黏菌素B群体药代动力学(population pharmacokinetics,PPK)模型进行外部验证,评估各模型对重症感染患者血药浓度的预测性能。方法:在PubMed数据库、Web of Science核心合集、中国知网、万方数据库检索建库至2022年3月公开发表的多黏菌素B PPK模型的相关文献,提取模型结构和参数信息。同时收集南京大学医学院附属鼓楼医院接受多黏菌素B治疗重症感染患者的临床数据作为外部数据集。根据患者是否采用连续肾脏替代疗法(continuous renal replacement therapy,CRRT)分为非CRRT组和CRRT组。利用2组数据对已发表模型进行基于模型预测和模型模拟的评价。结果:检索到14个多黏菌素B PPK模型。CRRT组,预测误差检验结果显示,所有已发表模型均不满足选定的标准。非CRRT组,有一个模型具有较好的血药浓度预测性能。结论:可以尝试利用预测结果良好的模型在非CRRT患者进行前瞻性个体化给药。     

用群体药代动力学预测癫痫儿童丙戊酸钠血药浓度

目的 用群体药代动力(PPK)模型和贝叶斯法预测血药浓度。方法 用癫痫儿童丙戊酸钠PPK模型和USC~*PACK软件中的贝叶斯程序,对100例新癫痫患儿丙戊酸钠的血药浓度进行预测。将预测值与实测值做配对t检验,相关和回归分析,计算平均预测误差、预测误差的百分比、不同预测误差百分比的符合率及其95％可信区间、构成比和评价预测的准确程度。结果 预测值与实测值的相关系数为0.99,P<0.001,线性回归Y_(OBS)=0.99)Y_(PRED),决定系数为0.98,P<0.001;平均预测误差为-0.43μg·mL~(-1),预测误差百分比分别为5％,10％,15％,20％,25％,30％的符合率为62％,74％,82％,85％,89％,93％。结论 PPK模型和贝叶斯法可准确预测丙戊酸钠稳态血药浓度。     

Predictive performance of Sawchuk-Zaske and Bayesian dosing methods for tobramycin

We evaluated the reliability of pharmacokinetic parameter estimations determined by the Sawchuk-Zaske method and by a Bayesian method for predicting steady-state tobramycin concentrations on day 4 and day 10 of therapy in 30 patients treated for gram-negative infections. We also assessed the effect of using only trough tobramycin concentrations on the predictive performance of the Bayesian method. The mean estimation of tobramycin volume of distribution was significantly different for the Bayesian methods compared with the Sawchuk-Zaske methods when the same number of L/kg). Comparing the Bayesian and Sawchuk-Zaske methods when the same number of serum concentrations was available, the Bayesian method overpredicted peak concentrations on day 4 of therapy. When only trough concentration data were used, the Bayesian method significantly overpredicted peak concentrations compared with the Sawchuk-Zaske method on days 4 and 10 of therapy. Each method predicted steady-state trough concentrations without significant differences in bias or accuracy. The Bayesian method may be useful in providing aminoglycoside dosing recommendations.     

Evaluation of AUC(0-4) predictive methods for cyclosporine in kidney transplant patients

Cyclosporine (CyA) is the most commonly used immunosuppressive agent in patients who undergo kidney transplantation. Dosage adjustment of CyA is usually based on trough levels. Recently, trough levels have been replacing the area under the concentration-time curve during the first 4 h after CyA administration (AUC(0-4)). The aim of this study was to compare the predictive values obtained using three different methods of AUC(0-4) monitoring. AUC(0-4) was calculated from 0 to 4 h in early and stable renal transplant patients using the trapezoidal rule. The predicted AUC(0-4) was calculated using three different methods: the multiple regression equation reported by Uchida et al.; Bayesian estimation for modified population pharmacokinetic parameters reported by Yoshida et al.; and modified population pharmacokinetic parameters reported by Cremers et al. The predicted AUC(0-4) was assessed on the basis of predictive bias, precision, and correlation coefficient. The predicted AUC(0-4) values obtained using three methods through measurement of three blood samples showed small differences in predictive bias, precision, and correlation coefficient. In the prediction of AUC(0-4) measurement of one blood sample from stable renal transplant patients, the performance of the regression equation reported by Uchida depended on sampling time. On the other hand, the performance of Bayesian estimation with modified pharmacokinetic parameters reported by Yoshida through measurement of one blood sample, which is not dependent on sampling time, showed a small difference in the correlation coefficient. The prediction of AUC(0-4) using a regression equation required accurate sampling time. In this study, the prediction of AUC(0-4) using Bayesian estimation did not require accurate sampling time in the AUC(0-4) monitoring of CyA. Thus Bayesian estimation is assumed to be clinically useful in the dosage adjustment of CyA.     

Comparison of a Bayesian program with three microcomputer programs for predicting gentamicin concentrations

A recently developed Bayesian regression program was compared with three other aminoglycoside pharmacokinetic dosing programs available for clinical use. From 30 adult patients, 152 measured serum gentamicin concentrations (SGC) were evaluated retrospectively (78 peak and 74 trough). Predictive performance was compared for each method by using the first peak and trough SGC pair to predict subsequent serum concentrations, making a total of 92 predictions (48 peak and 44 trough). The two Bayesian programs (Brater and Koup) were further evaluated using only one initial peak or trough SGC to make the same predictions. Mean predicted error (ME), mean absolute error (MAE), and root mean squared error (RMSE) were calculated for each method. Prediction bias and precision were compared statistically, between each method, by calculating the 95% confidence intervals for the delta ME and delta MAE, respectively. No statistically significant differences were found in the MAEs among any of the methods for predicting peak SGCs, with the exception of the Brater program, using a single trough SGC, which was statistically less precise (less than 0.05). There were few statistically significant differences in the MAEs for trough SGCs; however, Koup's Bayesian program using a single trough concentration yielded statistically more precise predictions than the other methods. The ME was found to differ significantly (p less than 0.05) among estimates for peak and trough SGCs provided by some of the predictive methods.(ABSTRACT TRUNCATED AT 250 WORDS)     

Evaluation of limited sampling designs to estimate maximal concentration and area under the curve of mizoribine in pediatric patients with renal disease

The aim of this study was to evaluate limited sampling designs to estimate the maximal concentration (C(max)) and area under the curve (AUC) of mizoribine in pediatric patients with renal disease. We utilized 48 serum mizoribine concentration profiles obtained from the full (6-point) sampling pharmacokinetic test, and estimated 48 individual C(max) and AUC values accurately with Bayesian analysis using the full sampling data. We then developed limited sampling models (LSM) for C(max) and AUC using 1-4 serum mizoribine concentration data points. The C(max) and AUC estimation performance of the Bayesian and LSM analysis was fairly good in the 3-point (2, 3, and 6 hr after the dose) sampling design. In addition, the C(max) estimation performance of the Bayesian and LSM analysis deteriorated only marginally even in the 1-point (3 hr) sampling design. On the other hand, the AUC estimation performance seemed to be inadequate in the 1-point (3 hr) sampling design; however, it improved markedly in the 2-point (3 and 6 hr) sampling design. These findings suggested that the 1-point (3 hr) sampling design is promising for approximate C(max) estimation, but that the 2-point (3 and 6 hr) sampling design is preferable to estimate the AUC of mizoribine.     

去甲万古霉素成人药动学数据外推至儿童群体和剂量优化

目的：获得去甲万古霉素在儿童群体中的药动学特征,优化给药方案以指导临床个体化用药。方法：将成人去甲万古霉素群体药动学（PPK）模型外推得到儿童模型;通过拟合优度图（goodness-of-fit）、可视化预测检验（VPC）及正态化预测分布误差（NPDE）验证外推模型的稳定性和预测性能。采用贝叶斯法获取个体药动学参数,通过蒙特卡洛模拟法评价和优化给药方案。结果：去甲万古霉素在儿童群体中药动学参数的群体均值分别为总体清除率（CL）0.11 L·kg^-1·h^-1、中央室分布容积（V₁）6.08 L、周边室分布容积（V₂）6.21 L、室间清除率（Q）2.32 L·h^-1。拟合优度、VPC和NPDE结果表明外推模型稳定性和预测性能均较好。蒙特卡洛模拟结果提示对于肾功能正常的患儿,去甲万古霉素用于治疗不同MIC（0.25,0.5,0.75和1 mg·L^-1）细菌感染时,要使体内暴露水平的目标获得概率（PTA）达到90%以上,对葡萄球菌属的理想日剂量应分别为16,32和48 mg·kg^-1及以上,对肠球菌属的理想日剂量应分别为16,24,32和40 mg·kg^-1。结论：本研究成功外推得到去甲万古霉素在儿童群体的药动学模型和参数,模拟结果显示,现行去甲万古霉素给药剂量可能偏低。     

Bayesian反馈法估算静脉输注伏立康唑的群体药动学参数

目的用Bayesian反馈法估算临床患者静脉输注伏立康唑的群体药动学(PPK)参数。方法收集静脉输注给药不同时间后的血样,采用HPLC法测定伏立康唑血药浓度,用Bayesian反馈法估算PPK参数。结果以二室模型拟合伏立康唑的药动学过程,得PPK参数为Vss为(46.58±19.35)L,CL为(4.76±2.64)L/h,k10为(0.187±0.006)h-1k,12为(4.97±0.02)h-1,k21为(0.895±0.308)h-1。结论此PPK模型能够较准确地描述伏立康唑在临床患者静脉输注的药动学特征,其预测能力尚待进一步评估。     

Monte Carlo法用于新生儿阿米卡星的群体药动学分析和认证(英文)

目的:用Monte Carlo算法编制群体药动学分析程序并认证该方法估计药动学参数和预测血药浓度的能力.方法:用阿米卡星作为模型药物,对来自42名新生儿共142对血药浓度时间数据进行分析;根据Sheiner等提出的群体药动学思想,我们编制了估计群体参数和个体参数的程序,目标函数最小值以Monte Carlo算法求得,方法的认证采用经典药动学 程序3p87作为对照,预测能力通过计算预测血药浓度的均方根误差(RMSD)和偏性(BIAS)来考察.结果:我们自编的程序运行稳定;本法提取的群体参数与3p87得到的一致,学习样本与认证样本的预测浓度与实测浓度显著相关(相关系数分别为0.995和0.990),预测误差大多数小于1 mg/L,认证样本RMSD和BIAS分别为0.58和-0.07 mg/L.结论:本法估计参数准确,预测血药浓度能力令人满意.