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。     

用NONMEM法建立中国癫痫儿童丙戊酸钠的群体药动学/药效学结合模型

目的:建立中国癫痫儿童应用丙戊酸钠(VPA)的群体药动学药效学(PPKPD)结合模型,为设计个体化用药方案奠定基础。方法:回顾性收集246例癫痫患儿应用VPA的临床数据。血药浓度是常规监测的稳态浓度。用246例患儿的数据,通过NONMEM法已经自行成功建立PPK模型。现将246例中单用VPA的69例的数据与已经建立的PPK模型结合,建立PPKPD模型。药效指标用癫痫发作次数减少百分比,分为5级。应用Logistic回归分析,拟合线性药效模型,用NONMEM法建立PPKPD模型,求算血药浓度获得某一级疗效的概率。结果:应用Logistic回归分析,拟合线性药效模型,求算出血药浓度获得某一级疗效的概率:血药浓度超过23μg·ml-1时,5级的概率小于50%,获得4、3、2级的最大概率及浓度为(30μg·ml-1,32.3%)、(50μg·ml-1,26.3%)、(65μg·ml-1,36.5%);血药浓度超过78μg·ml-1时,1级的概率大于50%;浓度为100μg·ml-1时,1级的概率约84.2%。结论:用NONMEM法成功地建立了中国癫痫儿童应用VPA的PPKPD模型,定量地求出某一血药浓度获得不同疗效等级的概率。     

儿童丙戊酸群体药动学

目的:研究仅有临床稀疏数据的儿童丙戊酸群体药动学.方法:收集667例儿童癫痫患者的常规治疗药物监测资料,将794对血药浓度-时间数据利用群体药动学理论经CPKDP程序处理,提取儿童群体药动学参数,再经Bayesian反馈法及二步迭代计算出儿童个体药动学参数.结果:儿童丙戊酸群体药动学主要参数Ke、Vm、CL在单用丙戊酸组分别为(4.5±2.2)h-1、(1.4±0.5)L·kg-1和(14.5±4.3)mL·h-1·kg-1;在合并其他抗癫痫药物时分别为(7.3±3.4)h-1、(1.4±0.6)L·kg-1、(24.8±9.3)mL·h-1·kg-1.预测浓度和实测浓度显著相关;合并PB、PTH、CBZ、CNP对丙戊酸的药动学有显著影响.结论:本研究对儿童丙戊酸给药方案个体化提供有价值的参考依据.     

丙戊酸清除率的群体药动学模型的建立

目的:建立中国癫痫病患者丙戊酸清除率的群体药动学模型.方法:前瞻性收集上海、北京两地4所医院服用丙戊酸的350名患者的稳态血药浓度(n=435).数据分析采用非线性混合效应模型.结果:最终模型为:CL(L·h-1)=0.0422·Dose·BSA0.269·1.41(如果合用卡马西平,否则为1)·1.37(如果合用苯妥因,否则为1)·(0.00735·PBS 0.807)(如果合用苯巴比妥,否则为1)·(Dose/950)(如果Dose大于950 mg·m-2·d-1,否则为1)·1.21(如果BSA大于1.7 m2,否则为1)·1.24(如果年龄小于6,否则为1).上式中Dose为日剂量(mg·m-2·d-1);BSA为体表面积(m2);PBS为苯巴比妥的日剂量(mg·m-2·d-1).结论:根据患者的生理用药资料,结合上述模型,可估算其清除率,为制定给药方案提供依据.     

丙戊酸群体药动学模型的建立与临床应用

目的建立门诊癫痫患者应用丙戊酸的群体药代动力学模型,并进行血药浓度预测。方法162例门诊癫痫患者连续服用丙戊酸钠达稳态,测定其谷浓度附近血样标本共196个。用非线性混合效应模型（NONMEM）考察固定效应对丙戊酸相对清除率的影响。结果体重、丙戊酸钠日剂量、合并用药等因素与清除率CL（L/h）之间的拟合模型为CL=0.00482×WT＋0.110×TAMT＋0.394×CBZS＋0.108×PHT＋0.0822×PB＋0.0583。将11例患者血药浓度预测值与实测值作线性回归,其方程为DV=1.0632×PRED—3.2665（μg/ml）,r=0.     

应用群体药动学模型制订患儿抗癫痫给药方案的药学会诊实践

目的 为优化癫痫患儿卡马西平和丙戊酸给药方案提供参考。方法 药师参与1例脑外伤术后癫痫患儿的药学会诊,总结分析其使用卡马西平和丙戊酸血药浓度异常的原因,应用群体药动学模型为患儿调整给药剂量,并评价笔者团队所建模型的预测能力。结果 患儿病情得到了有效控制。结论 药师应用群体药动学模型优化抗癫痫药物治疗方案,为临床合理用药提供参考。     

群体药动学原理建立卡马西平和丙戊酸的定时定量给药模型及临床应用

目的:建立国人卡马西平和丙戊酸的群体药动学模型,并将其应用于临床,建立定时定量给药的癫痫临床药学服务模式。方法:筛选国内多中心卡马西平(carbamazepine,CBZ)和丙戊酸(valproic acid,VPA)的稳态谷浓度数据,建立适合神经内科癫痫患者个体化给药的群体药动学(population pharmacokinetics,PPK)模型,利用建模中心外数据评价所建模型的预测能力。结果:建立了CBZ的PPK最终模型: K_a(h^-1)=1.2, CL(CL/F)(L·h^-1)=0.074×TAMT_CBZ^0.41×WT^0.267×1.42(若合用苯妥因钠,否则为1)×1.18(若合用苯巴比妥,否则为1)×0.84(若年龄>65岁,否则为1),V(V/F)(L)=1.21×WT; VPA的PPK最终模型:K_a(h^-1)=1.9,CL(CL/F)(L·h^-1)=0.102×(WT/60)^0.696×TAMT_VPA^0.197×1.36(若合用CBZ,否则为1)×1.25(若合用苯妥英钠,否则为1)×1.11(若合用苯巴比妥,否则为1),V(V/F)(L)=0.14×WT;其中,K_a为吸收速率常数,CL为表观清除率,V为表观分布容积,F为生物利用度,TAMT_CBZ、TAMT_VPA为CBZ、VPA的日剂量(mg·d^-1),WT为体质量(kg)。经建模中心外数据验证,所建模型预测能力较强。建立的定时定量药学服务应用于临床后,取得了较好的临床疗效(案例略)。结论:新临床药学服务有助于医疗团队提高抗癫痫治疗质量。     

左乙拉西坦在癫痫患儿中的群体药动学研究

目的建立癫痫患儿口服左乙拉西坦的群体药动学(PPK)模型,并用此模型探讨左乙拉西坦在儿童患者群体内的药动学特征。方法收集344例癫痫患儿口服左乙拉西坦后的血药浓度数据和临床资料。将患儿随机分为两组,模型组(n=259)采用NLME程序进行PPK分析,建立一房室药动学模型(个体间变异采用指数模型,残差变异采用加法模型表示),考察各协变量对参数Ka、Vd和CL的影响。用拟合优度、自举法对最终模型的性能进行内部验证。采用最终模型预测验证组(n=85)患儿的血药浓度,计算平均预测误差(MPE)、平均绝对预测误差(MAE)、平均预测误差平方(MSE)和均方根预测误差(RMSE)对最终模型进行外部验证。结果 PPK最终模型为:Ka(h-1)=1.01×eηKa,Vd(L·kg-1)=[0.42+0.000 35×(AGE-56)]×eηVd,CL(L·kg-1·h-1)=[0.05-0.009 5×(ln WT-2.83)]×eηCl;年龄(AGE)正相关影响Vd,体重的自然对数值(ln WT)负相关影响CL。拟合优度、自举验证的评价结果表明最终模型稳定、预测结果可靠。外部验证最终模型结果为:MPE=0.01 mg·L-1,MAE=0.91 mg·L-1,MSE=1.16(mg·L-1)2,RMSE=1.08 mg·L-1。血药浓度实测值和最终模型的个体预测值的决定系数R2=0.990 6。外部验证说明最终模型预测准确度高。结论本研究成功建立了癫痫患儿口服左乙拉西坦后的PPK模型,模型结构表明左乙拉西坦体重校正的清除率随患儿年龄和体重的增加有下降趋势。     

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。     

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。     

Population pharmacokinetics of valproate in Chinese children with epilepsy

Aim： The aim of the present study is to establish a population pharmacokinetic （PPK） model of valproate （VPA） in Chinese epileptic children to promote the reasonable use of anti-epileptic drugs. Methods： Sparse data of VPA serum concentrations from 417 epileptic children were collected. These patients were divided into 2 groups： the PPK model group （n=317） and the PPK valid group （n=100）. The PPK parameter values of VPA were calculated by NONMEM software using the data of the PPK model group. A basic model and a final model were set up. To validate the 2 models, the concentrations of PPK valid group were predicted by each model, respectively. The mean prediction error （MPE）, mean squared prediction error （MSPE）, root mean squared prediction error （RMSPE）, weight residues （WRES）, and the 95% confidence intervals （95% CI） were also calculated. Then, the values between the 2 models were compared. Results： The PPK of VPA was determined by a 1-compartment model with a first-order absorption process. The basic model was： Ka=3.09 （h^-1）, V/F=20.4 （L）, CL/F=0.296 （L/h）. The final model was： Ka=0.251＋2.24-（1-HS） （h^-l）, V/F=2.8 8＋0.157-WT （L）, CL/F=0.106^0.98.CO＋ 0.0157·AGE （L/h）. For the basic model, the MPE, MSPE, RMSPE, WRES, and the 95% CI were -23.53 （-30.36,-16.70）, 3728.96 （2872.72, 4585.20）, 39.62 （34.34, 44.90）, and-0.06 （-0.14, 0.02）, respectively. For the final model, the MPE, MSPE, RMSPE, WRES, and the 95% CI were -1.16 （-4.85, 2.53）, 1002.83 （1050.64,1143.61）, 23.04 （21.12, 24.96）, and 0.08 （-0.04, 0.20）, respectively. The final model was more optimal than the basic model. Conclusion： The PPK model of VPA in Chinese epileptic children was successfully established. It will be valuable to facilitate individualized dosage regimens.     

非线性混合效应模型法估算门诊癫痫患者丙戊酸的相对清除率

目的 :用非线性混合效应模型 (NONMEM)法估算门疹癫患者丙戊酸 (VPA)的相对清除率。方法 :5 6例门诊癫患者 po VPA达稳态 ,得到谷浓度数据 71点 ,用 FPIA法进行检测。采用 NONMEM法估算其清除率 (CL ) ,并定量考察体重、合并用药、VPA剂量对清除率的影响。结果 :按口服一房室开放模型得到 CL(L/ h)的最终回归方程为 :CL=(0 .0 0 896· WT 0 .0 919·L 0 .787· DCBZ)· (1 0 .2 96· m)。其中 WT为患者的体重 (kg) ;L 的值当体重小于 30 kg时为 1,其余为 0 ;DCBZ的值为卡马西平 (CBZ)按体表面积折算的日剂量 (g· m- 2 · d- 1 ) ;m的值当 VPA剂量大于 1.3g/ d时为 1,否则为 0。结论 :当VPA剂量 >1.3g/ d或合并使用 CBZ时 ,CL 增加 ,儿童 CL/ WT大于成人。     

Physiologically based pharmacokinetics of valproic acid in rabbits

Protein-binding parameters of valproic acid (VPA) in rabbit serum were determined. Due to the non-linear binding, the binding percentage decreased from 91 to 41% when the serum concentration of VPA rose from 10 to 1000 μg/ml. The hepatic clearance of VPA as unbound drug followed Michaelis-Menten kinetics. A physiologically based pharmacokinetics model was adopted to interpret the overall disposition of VPA in rabbits which incorporated the non-linear plasma protein binding and non-linear intrinsic hepatic clearance. The predicted plasma and tissue concentrations were found to be in good agreement with the observed concentrations. The reason why the plasma concentration versus time curves appear to be apparently parallel in spite of remarkable changes in the unbound concentration among three different doses could be explained by the association effects in which an increase in the total plasma concentration may produce a decrease in intrinsic hepatic clearance consistent with Michaelis-Menten kinetics resulting in an increase in the unbound fraction of VPA in plasma.     

Population pharmacokinetics of lamotrigine in Chinese children with epilepsy

Aim:

To establish a population pharmacokinetics (PPK) model for lamotrigine (LTG) in Chinese children with epilepsy in order to formulate an individualized dosage guideline.

Methods:

LTG steady-state plasma concentration data from therapeutic drug monitoring (TDM) were collected retrospectively from 284 patients, with a total of 404 plasma drug concentrations. LTG concentrations were determined using a HPLC method. The patients were divided into 2 groups: PPK model group (n=116) and PPK valid group (n=168). A PPK model of LTG was established with NONMEM based on the data from PPK model group according to a one-compartment model with first order absorption and elimination. To validate the basic and final model, the plasma drug concentrations of the patients in PPK model group and PPK valid group were predicted by the two models.

Results:

The final regression model for LTG was as follows: CL (L/h)=1.01^*(TBW/27.87)^0.635*e^−0.753*VPA*e^0.868*CBZ*e^0.633*PB, Vd (L)= 16.7^*(TBW/27.87). The final PPK model was demonstrated to be stable and effective in the prediction of serum LTG concentrations by an internal and external approach validation.

Conclusion:

A PPK model of LTG in Chinese children with epilepsy was successfully established with NONMEM. LTG concentrations can be predicted accurately by this model. The model may be very useful for establishing initial LTG dosage guidelines.     

Bayesian法估算丙戊酸动力学参数

目的采用丙戊酸(VPA)群体药动学参数结合Bayesian法估算癫疒间患者VPA的个体药动学参数。方法60例癫疒间患者口服VPA达稳态,取其每天早晨服药前10 min血样,用荧光偏振免疫法(FPIA)测得血清中VPA血药浓度谷值。用Bayesian法估算其药动学参数,并用逐步回归法分析个体的性别、年龄等18种因素对其药动学参数的影响。结果按口服一级吸收和消除的一房室开放模型用Bayesian法计算得VPA药动学参数清除率CL为(8.98±2.50)mL.h-1.kg-1,逐步回归CL方程:CL=5.858+1.423X5+1.593X11,式中X5表示身高(cm)体重(kg)比,X11表示当合并用苯妥英钠时系数为1,否则为0。估算的个体稳态VPA血药浓度谷值平均为(54.19±20.50)mg.L-1,实测结果平均为(53.58±20.85)mg.L-1,估算结果与实测结果之间无显著性差异(P>0.05)。结论可采用Bayesian法估算癫疒间患者VPA动力学参数CL和预测癫疒间患者个体稳态VPA血药浓度谷值;当癫疒间患者身高体重比增加或合并使用苯妥英时VPA动力学参数CL增加。     

Population pharmacokinetics modeling of levetiracetam in Chinese children with epilepsy

Aim:

To establish a population pharmacokinetics (PPK) model of levetiracetam in Chinese children with epilepsy.

Methods:

A total of 418 samples from 361 epileptic children in Peking University First Hospital were analyzed. These patients were divided into two groups: the PPK model group (n=311) and the PPK validation group (n=50). Levetiracetam concentrations were determined by HPLC. The PPK model of levetiracetam was established using NONMEM, according to a one-compartment model with first-order absorption and elimination. To validate the model, the mean prediction error (MPE), mean squared prediction error (MSPE), root mean-squared prediction error (RMSPE), weight residues (WRES), and the 95% confidence intervals (95% CI) were calculated.

Results:

A regression equation of the basic model of levetiracetam was obtained, with clearance (CL/F)=0.988 L/h, volume of distribution (V/F)=12.3 L, and K_a=1.95 h⁻¹. The final model was as follows: K_a=1.56 h⁻¹, V/F=12.1 (L), CL/F=1.04×(WEIG/25)^0.583 (L/h). For the basic model, the MPE, MSPE, RMSPE, WRES, and the 95%CI were 9.834 (−0.587–197.720), 50.919 (0.012–1286.429), 1.680 (0.021–34.184), and 0.0621 (−1.100–1.980). For the final model, the MPE, MSPE, RMSPE, WRES, and the 95% CI were 0.199 (−0.369–0.563), 0.002082 (0.00001–0.01054), 0.0293 (0.001−0.110), and 0.153 (−0.030–1.950).

Conclusion:

A one-compartment model with first-order absorption adequately described the levetiracetam concentrations. Body weight was identified as a significant covariate for levetiracetam clearance in this study. This model will be valuable to facilitate individualized dosage regimens.