非线性混合效应模型法研究卡马西平在癫痫儿童中的群体药动学

目的:考察卡马西平(CBZ)在癫痫儿童中的群体药动学参数。方法:采集我院的866例儿童癫痫患者服用CBZ常规治疗及监测的资料数据,利用Michaelis-Menten一级消除药物动力学模型,非线性混合效应模型程序估算癫痫儿童服用CBZ的群体药动学参数。结果:癫痫儿童卡马西平群体药动学主要参数Ke、Vd、CL在单用CBZ组分别为0.091h-1、0.502L.kg-1和0.046L.h-1.kg-1;性别、身高以及合并氯硝西泮、妥吡酯对CBZ清除率未见明显影响;儿童年龄、体质量、肝肾功能异常以及合并丙戊酸、苯巴比妥、苯妥因为CBZ清除率影响的重要因素,并且均增加CBZ的清除率。结论:根据癫痫儿童的群体药动学模型,结合患儿的年龄、体质量、肝肾功能、服药剂量以及合并用药等资料,估算其清除率,预测患儿体内的药物浓度,制定个体化给药方案。     

非线性混合效应模型法估算癫痫患者卡马西平的群体药动学参数

目的:建立癫痫患者卡马西平(CBZ)的群体药动学(PPK)模型。方法:采集我院服用CBZ的270例门诊癫痫患者的稳态血药浓度数据(共316个样本)以及患者相关资料数据。应用非线性混合效应模型(NONMEM)法估算癫痫患者CBZ的PPK参数值,建立PPK模型。并运用自举法(Bootstrap)验证模型的可靠性。结果:年龄(AGE)、每日服药剂量(DKG)、体质量(BW)均为CBZ清除率(CL)的影响因素。最终模型:当AGE≤14岁时,CL(L/h)=[2.55+0.013×(AGE-15)]×(DKG/0.011)0.443×(BW/40)0.392;AGE>14岁时,CL(L/h)=2.55×(DKG/0.011)0.443×(BW/40)0.392。表观分布容积(Vd)=85L。经Bootstrap法验证,本模型稳定、可靠。结论:用NONMEM软件成功建立我院癫痫患者服用CBZ的PPK模型。根据本院癫痫患者的PPK模型,结合患者DKG、BW和合并用药可估算其CL,优化临床个体化用药方案。     

卡马西平在癫痫患儿中的群体药动学研究

目的:研究卡马西平(CBZ)在癫痫患儿中的群体药动学。方法:回顾性收集我院119例服用CBZ的门诊癫痫患儿的稳态血药浓度(n=122)。用非线性混合效应模型(NONMEM)法进行数据分析,定量考察年龄、性别、体重、日剂量和合用其他抗癫痫药对CBZ清除率的影响。采用一房室开放模型和一级吸收和消除的药动学模型,按照固定吸收速率常数文献值,最终求算CBZ的清除率。结果:最终拟合群体药动学模型为:CL=0.593·(体重/28.5)0.63·日剂量0.569。性别、合用丙戊酸钠不影响CBZ的清除率。结论:用NONMEM法估算CBZ的清除率和推荐剂量,可为临床制订个体化给药方案、提高疗效、降低药物的毒副作用提供依据。     

NONMEM法估算中国癫痫患者卡马西平的清除率

目的　考察中国癫痫患者卡马西平的群体药动学参数。方法　癫痫病例来自上海、北京两地 4所医院 ,采集服用卡马西平的 5 92例患者的稳态血药浓度 (n =70 3)。NONMEM程序估算分析时 ,采用一级吸收和消除的药动学模型并固定吸收速率、生物利用度和表观分布体积参数。结果　体重 (TBW )、剂量 (Dose)、合用丙戊酸钠 (VPA)且其日剂量大于 2 0mg·kg-1·d-1、苯妥英 (PHT)、苯巴比妥 (PB)和年龄大于 6 5岁的老年人 (ELDER)均为卡马西平清除率(CL)的影响因素。性别、合用氯硝西泮、妥吡酯不改变卡马西平的清除率。最终模型为 :CL(CL/F) (L/h) =1 32·Dose(g·kg-1·d-1) 0 42 1·TBW (kg) 0 .691·1 2 0 VPA·1 4 3PHT·1 14 PB·0 836 ELDER。讨论　根据中国癫痫患者的群体药动学模型 ,结合患者服用的剂量、体重和合并用药可估算其清除率 ,制定给药方案     

通过非线性混合效应模型法建立多西他赛的群体药动学模型

目的 建立多西他赛在肿瘤患者中的群体药动学(PPK)模型,考察固定效应因素对多西他赛清除率的影响.方法 以接受多西他赛静脉滴注的肿瘤患者为研究对象,回顾性分析80例患者服药后的210个多西他赛的稳态血药浓度及相应的实验室指标检测数据,多西他赛的血药浓度采用高效液相色谱法测定,应用非线性混合效应模型(NONMEM)软件进行PPK数据分析,Bootstrap重复抽样用于模型的内部验证.结果 建立最终模型方程为:CL'=θ1×(BSA/1.58)02×(ALB/3.6)θ3× HEP,患者体表面积、白蛋白和肝功能对多西他赛的清除率影响显著.结论 利用NONMEM软件建立针对肿瘤患者的多西他赛PPK模型,并结合患者体表面积、白蛋白和肝功能可估算其清除率,为临床个体化用药方案的优化提供参考.     

基于非线性混合效应模型法建立的拉莫三嗪群体药动学模型研究进展

目的 探讨拉莫三嗪在不同群体中的群体药动学特征及其影响因素,为建立更精准的群体药动学模型提供参考。方法 系统检索PubMed、Embase、Web of Science、Science Direct及Cochrane Library数据库中采用非线性混合效应模型(NONMEM)法在人体内进行的拉莫三嗪群体药动学的研究,时间为1995年1月—2021年7月。结果 共纳入研究20项,其中17项研究将拉莫三嗪的药动学特征描述为一室模型结构。伴随抗癫痫药物、体质量和基因多态性被认为是影响拉莫三嗪清除的3个最常见显著协变量,其他显著协变量研究较少。结论 在建立新的拉莫三嗪群体药动学模型时,应考虑合用其他抗癫痫药物、体质量、基因型等重要协变量因素;对于妊娠妇女等特殊人群,应纳入特殊人群的生理病理特征作为协变量因素,同时进行内部验证和外部验证增加模型的普适性。     

丙戊酸钠在癫痫患者中的群体药动学

目的建立丙戊酸钠在癫痫患者治疗中的群体药动学模型,为临床个体化给药提供参考。方法收集我院门诊60名癫痫患者丙戊酸钠稳态血药浓度监测数据和相应的人口学数据,应用非线性混合效应模型(non linearm ixed-effectmodel,NONMEM)程序对收集的数据进行分析,建立群体药动学模型。结果建立了癫痫患者口服丙戊酸钠群体药代动力学模型:CL/F=0.959×1.04x,(x=0,1),V/F=1.35,ka=2.38 h-1,说明丙戊酸的清除率与患者性别相关,即男性患者的清除率大于女性。结论初步建立癫痫患者口服丙戊酸钠群体药动学模型,为丙戊酸钠个体化用药提供理论基础。     

非线性混合效应模型法在群体药代动力学和群体药效学中的研究进展

群体药代动力学和群体药效学是近年来得到快速发展的药学领域，具有广阔的应用前景。本文对群体药代动力学和群体药效学重要估算方法非线性混合效应模型法进行综述，包括基本概念、常用模型、模型确定方法、数值计算和应用等方面。     

苯巴比妥在癫(癎)儿童中的群体药动学研究

目的 通过临床数据研究儿童苯巴比妥的群体药动学。方法采集298例儿童癫痫患者服用苯巴比妥常规治疗的监测资料数据,利用CPKDP程序分析药动学参数，结合Ｂayesian反馈法及二步迭代估算儿童个体药动学参数。结果癫痫儿童苯巴比妥群体药动学主要参数Ke、Vd、CL在单用苯巴比妥组分别为0.351 h-1、0.452 L·kg-1和5.135 L·h-1·kg-1；其中性别、身高以及辅助用药、用药持续时间未见明显影响；儿童年龄、体重、合并丙戊酸（vaproic， VPA）、氯硝西泮（clonazepam ， CNP）、托吡酯（topiramate ，TPM）、苯妥因（phenytion ，PHT）、卡马西平（carbamazepine， CBZ）为影响苯巴比妥清除率的重要因素，其中VPA、CBZ和PHT均增加PB的清除率，而CNP、TPM则会降低其清除率。结论 根据癫痫儿童的群体药动学模型，结合患儿的年龄、体重、服药剂量以及合并药等资料，可估算其清除率，预测患儿体内的药物浓度，制定个体化给药方案。     

应用非线性混合效应模型法考察儿童疾病因素对环孢素药动学的影响

目的:考察疾病因素对于环孢素A(CsA)在儿童体内药动学的影响,促进个体化用药。方法:收集150例包括再生障碍性贫血(AA)、嗜血细胞综合征(HPS)和难治性肾病综合征(RNS)不同病种患儿的CsA血药浓度数据和临床资料。采用非线性混合效应模型法考察疾病种类因素对于CsA药动学的影响。采用Bayesian最大后验概率法获取并比较CsA在不同病种患者中药动学参数的差异。用拟合优度(goodness-of-fit)、自举法(bootstrap)、直观预测检验法(VPC)、正态化预测分布误差(NPDE)对最终模型的预测性能进行验证。结果:最终模型药动学参数的群体典型值分别为:吸收速率常数(k_a)1.22 h^-1,吸收时滞时间(T_lag)0.45 h,表观分布容积(V_d)218.18 L,口服清除率(CL)14.45 L·h^-1。拟合优度、自举验证、VPC和NPDE结果表明最终模型稳定,预测结果可靠。模型结构显示只有患者的体质量和AST值是影响CsA清除率的显著性因素。CsA在AA、HPS和RNS患者中的药动学参数差异无显著性(P>0.05)。结论:本研究成功获取了CsA在儿童AA、HPS和RNS患者中的药动学参数,上述疾病因素不会显著影响CsA在儿童体内的药动学过程。     

中国成年患者万古霉素群体药动学研究

目的:利用万古霉素治疗药物监测(TDM)数据建立群体药动学(PPK)模型,用于估算个体化药动学参数。方法:选择使用万古霉素成年患者,详细记录用药、TDM数据以及病理生理资料。采用非线性混合效应模型(NONMEM)法建立万古霉素群体药动学模型。结果:169例患者数据来源于血液科及重症监护(ICU)病房等9个科室,共获得385个血药浓度数据,其中峰浓度39个,谷浓度346个。根据文献资料及TDM数据建立二室PPK模型,万古霉素清除率(CL)、中央室(V1)及外周室(V2)分布容积、室间清除率分别为4.08 L·h-1、21.7 L、65.3 L、5.95 L·h-1,患者肌酐清除率及体重分别对CL及V2具有显著影响。根据模型预测169位患者AUC0-24h为(450.1±231.8)mg·L-1·h。结论:本研究建立的万古霉素PPK模型可以用于中国成年患者个体化药动学参数估算。     

Population Pharmacokinetics of Carbamazepine in Adults with Epilepsy

Study Objective . To conduct a population pharmacokinetic analysis of carbamazepine (CBZ). Design . Retrospective chart review. Setting . Ambulatory neurology clinics at three medical centers. Patients . Patients diagnosed with epilepsy from 1991–1995. The index set included 829 adults receiving CBZ. A separate validation set consisted of 50 patients. Interventions . None. Measurements and Main Results . Final regression equations were apparent oral clearance (Cl/F) (L/hr) = (0.0134 • TBW + 3.58), • 1.42 if receiving phenytoin only; • 1.17 if receiving phenobarbital or felbamate; • 1.62 if receiving phenytoin and phenobarbital or felbamate; • 0.749 if age ≥ 70 years; apparent volume of distribution (V_d/F) (L) = 1.97 • total body weight; absorption rate constant (hr⁻¹) = 0.441. Interindividual variability in Cl/F and V_d/F was 26% and 82%, respectively. Residual variability was 1.8 mg/L. Predictive performance analysis of the validation set provided a mean prediction error of 0.6 mg/L and median absolute error of 2.4 mg/L. Conclusions . These routinely collected data provided quantitative estimates of changes in CBZ Cl/F due to comedication and an age-related decrease in Cl/E The derived regression equations reasonably predicted concentrations in a separate validation set.     

Pharmacokinetics of Teicoplanin in An ICU Population of Children and Infants

PURPOSE: Better dosing is needed for antibiotics, including teicoplanin (TEI), to prevent emergence of resistant bacterial strains. Here, we assess the TEI pharmacokinetics (PK) related to a 10 mg/l minimum inhibitory concentration (MIC) target in ICU children (4 to 120 months; n = 20) with gram+ infections. METHODS: Standard administration of TEI was with three 10 mg/kg Q12h, loading infusions, and maintainance with 10 mg/kg or 15 mg/kg Q24h. During maintenance, 9 samples (3/day) were collected per patient and the PK analyzed with Nonlinear Mixed Effects Model (NONMEM). RESULTS: Thirty-five percent of concentrations in older children (> or =2 months) vs. 8% in younger infants (<12 months) were below the target MIC. The global bicompartmental population PK parameters were [mean (interindividual CV%)] CL = 0.23 l/h [72%], V = 3.16 l [58%], k12 = 0.23 h(-1), and k21 = 0.04 h(-1). Two PK subpopulations were identified. The older children had CL = 0.29 [23%] l/h, V = 3.9 l and the younger infants, CL = 0.09 [37%] l/h, V = 1.05 l. Residual error was reduced from 52% to around 30% in the final models. CONCLUSIONS: Older children in the ICU may require relatively higher doses of teicoplanin. However, a study in a larger population is needed.     

Population Pharmacokinetic Modeling of Steady State Carbamazepine Clearance in Children, Adolescents, and Adults

Carbamazepine (CBZ) clearance decreases from childhood to adulthood and the factors determining this change could include age, size, autoinduction, or maturational changes. This study aims to describe the population pharmacokinetics of CBZ in children and young adults and test the hypothesis that CBZ clearance correlates with weight, surface area, and age. CBZ therapeutic drug monitoring data (sparse data) were collected from child and adult epileptics, and rich data were obtained from a bioequivalence study of CBZ in young adults. Population pharmacokinetic analysis was performed using NONMEM V. Forward stepwise, multiple regression was performed on the covariates. Bootstrap validation was performed. A total of 946 observations from 91 subjects, ages 0.7–37 years, were collected and analyzed. A one-compartment, first-order absorption and elimination model, with exponential interindividual error and additive residual error models was developed. The population model was: Clearance (Lhr ^–1)=((2.24 · Surface area (m ²))+(0.047 · Dose (mg · kg ^–1)); Volume of distribution (L)=0.37 · weight (kg); Absorption rate constant=0.013 (hr ^–1). CBZ clearance increased with surface area and dose.     

The Effect of Collinearity on Parameter Estimates in Nonlinear Mixed Effect Models

Purpose. To demonstrate how correlations among predictor variables in a population pharmacokinetic model affect the ability to discern which covariates should enter into the structural pharmacokinetic model. Methods. Monte Carlo simulation was used to generate multiple-dose concentration-time data similar to that seen in a Phase III clinical trial. The drugs' pharmacokinetics were dependent on two covariates. Five data sets were simulated with increasing correlation between the two covariates. All data sets were analyzed using NONMEM both with and without inclusion of the covariates in the structural pharmacokinetic model. Summary measures for ill-conditioning and sensitivity analysis were used to examine how increasing correlation among covariates affects the accuracy and precision of the parameter estimates. Results. When covariates were included in the structural pharmacokinetic model and the correlation between covariates increased, the standard error of the parameter estimates increased and the value of parameter estimates themselves became increasingly biased. When the correlation between predictor variables was 0.75, the standard errors of the parameter estimates were too large to declare statistical significance. Conclusions. Correlations among predictor variables greater than 0.5 when entered into the model simultaneously should be a warning to researchers because the (1) the accuracy of the parameter estimates themselves may be biased and (2) the precision of the estimates may be inflated due to ill-conditioning.     

Covariate Detection in Population Pharmacokinetics Using Partially Linear Mixed Effects Models

No HeadingPurpose. To introduce partially linear mixed effects models (PLMEMs), to illustrate their use, and to compare the power and Type I error rate in detecting a covariate effect with nonlinear mixed effects modeling using NONMEM.Methods. Sparse concentration-time data from males and females (1:1) were simulated under a 1-compartment oral model where clearance was sex-dependent. All possible combinations of number of subjects (50, 75, 100, 150, 250), samples per subject (2, 4, 6), and clearance multipliers (1 to 1.25) were generated. Data were analyzed with and without sex as a covariate using PLMEM (maximum likelihood estimation) and NONMEM (first-order conditional estimation). Four covariate screening methods were examined: NONMEM using the likelihood ratio test (LRT), PLMEM using the LRT, PLMEM using Walds test, and analysis of variance (ANOVA) of the empirical Bayes estimates (EBEs) for CL treating sex as a categorical variable. The percent of simulations rejecting the null hypothesis of no covariate effect at the 0.05 level was determined. 300 simulations were done to calculate power curves and 1000 simulations were done (with no covariate effect) to calculate Type I error rate. Actual implementation of PLMEMs is illustrated using previously published teicoplanin data.Results. Type I error rates were similar between PLMEM and NONMEM using the LRT, but were inflated (as high as 36%) based on PLMEM using Walds test. Type I error rate tended to increase as the number of observations per subject increased for the LRT methods. Power curves were similar between the PLMEM and NONMEM LRT methods and were slightly more than the power curve using ANOVA on the EBEs of CL. 80% power was achieved with 4 samples per subject and 50 subjects total when the effect size was approximately 1.07, 1.07, 1.08, and 1.05 for LRT using PLMEMs, LRT using NONMEM, ANOVA on the EBEs, and Walds test using PLMEMs, respectively.Conclusions. PLMEM and NONMEM covariate screening using the LRT had similar Type I error rates and power under the data generating model. PLMEMs offers a viable alternative to NONMEM-based covariate screening.     

用NONMEM法建立癫痫患者丙戊酸群体药代动力学模型

目的 建立丙戊酸(VPA)在癫痫患者中的群体药代动力学(PPK)模型,考察固定效应因素对VPA清除率(CL/F)的影响.方法 回顾性收集贵州省人民医院111名癫痫患者VPA稳态血药浓度数据及相应的人口学、合并用药及CYP2A6基因型等资料,随机将患者分成建模组(74名)及验证组(37名),使用建模组数据通过非线性混合效应模型(NONMEM)程序建立VPA的PPK模型.使用验证组数据来验证模型的准确度和精密度,比较基础模型和最终模型的平均预测误差(MPE)、平均绝对误差(MAE)、平均根方差(RMSE).结果 建立的最终模型包含了日用药剂量(DDO)及CYP2A6基因型,模型方程为:CL/F=0.363·DD00.525·1.29GENECYP2A6.最终模型有更好的精密度及准确度,基础模型MPE、MAE、RMSE值为- 10.63、14.40、22.55,最终模型相应值为-6.11、9.06、14.17.结论 本研究初步建立癫痫患者VPA的PPK模型,VPA清除率随日给药剂量的增大而增大,CYP2A6野生型(CYP2A6*1/*1)组患者较CYP2A6突变型(CYP2A6* 1/*4、CYP2A6* 4/*4)组患者有更高的VPA清除率.     

用NONMEM法建立癫痫患者丙戊酸群体药代动力学模型

目的建立丙戊酸（VPA）在癫痫患者中的群体药代动力学（PPK）模型,考察固定效应因素对VPA清除率（CL/F）的影响。方法回顾性收集贵州省人民医院111名癫痫患者VPA稳态血药浓度数据及相应的人口学、合并用药及CYP2A6基因型等资料,随机将患者分成建模组（74名）及验证组（37名）,使用建模组数据通过非线性混合效应模型（NONMEM）程序建立VPA的PPK模型。使用验证组数据来验证模型的准确度和精密度,比较基础模型和最终模型的平均预测误差（MPE）、平均绝对误差（MAE）、平均根方差（RMSE）。结果建立的最终模型包含了日用药剂量（DDO）及CYP2A6基因型,模型方程为：CL/F=0.363.DDO0.525.1.29GENECYP2A6。最终模型有更好的精密度及准确度,基础模型MPE、MAE、RMSE值为-10.631、4.40、22.55,最终模型相应值为-6.11、9.06、14.17。结论本研究初步建立癫痫患者VPA的PPK模型,VPA清除率随日给药剂量的增大而增大,CYP2A6野生型（CYP2A6＊1/＊1）组患者较CYP2A6突变型（CYP2A6＊1/＊4、CYP2A6＊4/＊4）组患者有更高的VPA清除率。