肝移植患者术后口服他克莫司群体药动学研究

目的:考察肝移植患者术后口服他克莫司的群体药动学模型,为临床个体化用药提供参考。方法:回顾性收集天津市第一中心医院18例肝移植患者术后口服他克莫司12 h全血药浓度监测数据145个。运用非线性混合效应模型(nonlinear mixed effect model,NONMEM)建立他克莫司群体药动学模型,并考察了年龄、性别、移植术后天数、血清肌酐等固定效应对药动学参数的影响,得到最终模型方程,最后利用Bayesian反馈得到的个体药动学参数值进行个体化给药方案设计。结果:本次研究建立起了口服他克莫司一级吸收和消除的二房室群体药动学模型,并通过NONMEM模拟程序为1例患者进行了个体化给药设计。结论:NONMEM法建立的模型能较好地估算他克莫司的个体及群体药动学参数,为临床合理使用他克莫司提供参考依据。     

地高辛的个体化给药方案

目的在中国医院开展地高辛的临床药动学服务,达到地高辛用药个体化。方法根据不同患者的血清肌酐,计算患者的药动学参数。根据药动学参数,制定和调整给药方案。结果发现16例使用地高辛的患者不合理用药达到87.5%,及时调整了给药方案后,所有患者的血药浓度均在有效浓度范围。结论应根据地高辛的药动学参数开展临床药动学服务。     

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。     

哌拉西林他唑巴坦在肾内科患者中的群体药动学和蒙特卡罗模拟

目的:建立哌拉西林他唑巴坦在肾内科患者中的群体药动学模型,应用蒙特卡罗模拟优化其给药方案,以促进个体化给药.方法:采用高效液相色谱法测定50名肾内科患者静脉滴注哌拉西林-他唑巴坦的血清浓度310例次并收集相关临床指标,运用非线性混合效应模型(NONMEM)程序建立群体药动学模型.采用蒙特卡罗模拟(Monte Carlo simulation, MCS)比较哌拉西林他唑巴坦的不同给药方案对不同MIC群体的药效学目标到达.结果:哌拉西林、他唑巴坦的药动学符合一室模型,群体典型值及个体间差异(Between Subject Variability,BSV)分别为:哌拉西林CL/F=13.74 L·h^-1,BSV=11.1%;V/F=21.69 L,BSV=8.0%,他唑巴坦CL/F=9.32 L·h^-1, BSV=9.11%;V/F=16.0 L, BSV=5.28%;固定效应参数中,肌酐清除率对参数有影响.对于MIC值较大的细菌,延长输注的给药方案获得了更高的目标的累积反应分数(CFR).结论:群体药动学模型和蒙特卡罗模拟,可为调整哌拉西林他唑巴坦的治疗方案提供有效的分析手段.     

成人肝移植术后他克莫司群体药动学研究

目的：应用非线性混和效应模型考察中国肝移植患者他克莫司群体药动学特征。方法：回顾收集天津市第一中心医院成人肝移患者57例,1 094个他克莫司谷浓度点,验证组患者10例,183个谷浓度点。采用一房室模型,分析处理数据,采用NPDE、Bootstrap和外部验证的方法对模型进行评估。结果：最终模型显示血红蛋白（HGB）和术后时间（POD）为影响清除率的主要因素。药动学参数的群体典型值：清除率（CL/F）估算值为19.8 L·h^-1,表观分布容积估算值为597 L。模型评价显示该模型及所估算参数稳定。结论：本研究所建立的成人肝移植受者口服他克莫司的群体药动学模型能较好地估算患者的个体及群体药动学参数,为今后肝移植患者个体化给药方案的制订提供相关参考。     

左乙拉西坦儿童生理发育药动学模型的建立和剂量优化

目的:探讨生理发育因素对左乙拉西坦(LEV)在儿童体内药动学特征的影响,评价和优化LEV给药剂量,促进个体化用药.方法:根据203例0.42～15岁癫痫患者的LEV血药浓度及相关临床资料建立群体药动学模型,考察生理发育因素对LEV药动学参数的影响方式和强度.基于模型,模拟和评价不同体质量和年龄段的儿童患者的LEV给药方...     

基于群体药动学模型的万古霉素个体化给药辅助决策平台的研制及应用

目的 研发一款适合临床实际应用场景的万古霉素个体化给药辅助决策平台,并为万古霉素的合理使用提供个体化给药建议。方法 基于当前已构建并验证可行的万古霉素群体药动学模型,运用Idea2019、JDK1.8、ETL等软件研发万古霉素个体化给药辅助决策平台。平台开发历经(1)需求分析;(2)模块设计;(3)软件测试、优化完善3个主要阶段。结果成功研制并应用的万古霉素个体化给药辅助决策平台具有页面简约,功能完善,操作便捷的优势,按功能分为4个主要模块,分别是检索模块、信息模块、浓度预测模块以及报告模块。该平台可以连接医院内网平台,自动获取患者信息、用药信息、血药浓度检测结果等,并基于内嵌的群体药动学模型,结合患者个体信息,计算出个体药动学参数用于后续的万古霉素浓度预测。浓度预测模块将贝叶斯反馈法与患者用药信息、药物浓度测定结果及相关协变量参数值相结合,以指南推荐的谷浓度及AUC范围为目标值,计算满足目标浓度范围下的个体化给药方案,并考虑临床实际应用场景,设定自定义模拟功能,更具推广应用价值。结论 基于前期已构建的万古霉素群体药动学模型,在Idea2019、JDK1.8、ETL等软件工具辅助下,成...     

卡培他滨在结直肠癌患者中的群体药动学研究

目的评价卡培他滨在肠癌患者的群体药动学特征及可能的影响因素。方法选取56例肠癌(包括结肠癌及直肠癌)患者为研究对象,餐后单次口服卡培他滨0.6g(0.15g×4片)后进行多点采集血样,以高效液相色谱-质谱联用法(HPLC-MS/MS)检测受试者给药后血浆中的卡培他滨的血药浓度,以非线性混合效应模型及程序(NONMEM)对检测数据进行分析,建立卡培他滨群体药动学模型并获得其群体药动学参数。结果最终模型为一级吸收和消除的一室模型,模型的药动学参数群体典型值为:清除率(CL)为265 L/h,表观分布容积(V)为329 L,吸收速率常数(Ka)为2.15 h~(-1)。人口统计学因素(年龄、性别、体质量等)及肝肾清除相关因素(肌酐清除率、胆红素及白蛋白等)对卡培他滨的清除均无显著性影响。结论所得模型稳定,能较好地拟合卡培他滨在肠癌患者的的群体药动学特征,可用于临床个体化给药方案的制订。     

吗替麦考酚酯在原发性肾病综合征患者中的群体药动学研究

目的：研究吗替麦考酚酯在原发性肾病综合征患者中的群体药动学模型,为临床个体化用药提供参考。方法选取2011年1月~2014年2月期间我院收治的56例原发性肾病综合征患者作为本组研究的观察对象,所有患者在给予吗替麦考酚酯对症治疗后,采集其血液样本,通过非线性混合效应模型构建出麦考酚酯的群体药动学模型,从而探讨患者的年龄、性别、身高、体重、剂量、血清肌酐、治疗时间等对药动学参数的影响。结果药动学基本模型采用一级吸收和消除的二房室模型最为有效;患者体重与治疗时间是吗替麦考酚酯清除率的主要因素;最终群体药动学模型为：清除率=0.476×体重×(1-e-1.17POD)。结论吗替麦考酚酯在原发性肾病综合征患者中的药动学模型具有明显的代表性,为个体化给药方案的确定提供依据。     

计算机辅助设计地高辛个体化给药方案

目的建立一种简便的计算机辅助设计地高辛个体化给药方案。方法根据患者的性别、年龄、身高、体重、血肌酐浓度,计算患者的肌酐清除率、表观分布容积、药物清除率和消除速率常数,以药动学模型理论和药效学原理为依据,用Excel编写程序计算达到理想血药浓度的给药剂量。结果只要输入患者生理参数,可计算出男性和女性达到理想血药浓度的给药剂量与给药频率。小儿剂量可由小儿体表面积与成人体表面积之比折算而成。由于Excel具有重复计算功能,改变输入的参数值,可一步得到结果供临床决策。该方法适用于其他主要从肾脏消除药物的个体化给药设计。结论根据药动学参数和患者的生理特点,可利用Excel函数设计地高辛个体化给药方案,该法简单、可靠、直观。     

Effect of itraconazole on digoxin clearance in patients with congestive heart failure

We showed a digoxin-itraconazole interaction in three patients in whom digoxin serum concentrations were increased. Their electrocardiograms revealed arrhythmias such as ventricular premature contraction, atrioventricular block, and ST depression. The elimination half-life of digoxin in case 3 patient who continued itraconazole therapy was 8.4 days, which was estimated by nonlinear least squares method from the serum concentrations of digoxin versus time curve. In order to evaluate the influence of itraconazole on pharmacokinetic parameters of digoxin, we estimated digoxin clearance by the Bayesian method using the population pharmacokinetic parameters in Japanese patients. During the concomitant use of itraconazole and digoxin, the digoxin clearance in all patients decreased to 50.5 +/- 8.8% (mean +/- S.D.) of the clearance without itraconazole. When digoxin and itraconazole are used concomitantly, careful monitoring of digoxin serum concentrations is necessary. Based on our results of digoxin clearance evaluation, the dose of digoxin should be reduced to 50% of original dose after itraconazole is started, and digoxin serum concentration might be controlled at the same level before the concomitant use.     

Population pharmacokinetics of digoxin in Japanese patients: a 2-compartment pharmacokinetic model.

OBJECTIVE: To clarify the observed variability of digoxin disposition by performing a population pharmacokinetic analysis in a Japanese population. DESIGN: Retrospective analysis of clinical pharmacokinetic data. PATIENTS AND PARTICIPANTS: Data were obtained from 106 patients with heart failure and atrial fibrillation (43 males and 63 females). METHODS: Digoxin concentrations in serum were measured by fluorescence polarisation immunoassay. Population pharmacokinetic analysis was performed using a 2-compartment open pharmacokinetic model with the computer program NONMEM. RESULTS: 246 serum concentrations were obtained. Final pharmacokinetic parameters were: CL (L/h) = (0.036 x TBW + 0.112 x CL(CR)) x 0.77SPI x 0.784CCB, V1 = 1.83 L/kg, V2 = 22.6 L/kg and Q = 0.629 L/h/kg, where CL is total body clearance, V1 and V2 are the apparent volumes of distribution in the central and peripheral compartments, Q is intercompartmental clearance, TBW is total bodyweight (in kg), CL(CR) is creatinine clearance (in ml/min), SPI = 1 for concomitant administration of spironolactone (and zero otherwise) and CCB = 1 for concomitant administration of calcium antagonists (and zero otherwise). Concomitant administration of digoxin and spironolactone resulted in a 23% decrease in digoxin clearance. Concomitant administration of digoxin and calcium antagonists (diltiazem, nicardipine, nifedipine or verapamil) resulted in a 21.6% decrease in digoxin clearance. CONCLUSIONS: The estimated population parameter values may assist clinicians in the individualisation of digoxin dosage regimens.     

Digoxin population pharmacokinetics from routine clinical data: role of patient characteristics for estimating dosing regimens.

Routine clinical pharmacokinetic data collected from patients receiving digoxin have been analysed to evaluate the role of patient characteristics for estimating dosing regimens. The data were analysed using NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data. The pharmacokinetic model of digoxin was described using a one-compartment steady-state model. The effect of a variety of developmental and demographic factors on clearance was investigated. NONMEM estimates indicate that digoxin clearance was influenced by the demographic variables of age, total body weight, serum creatinine and sex. The interindividual variability in digoxin clearance was modelled with additive error with an estimated standard deviation of 46.15 L day-1 and the intraindividual variability, or residual error was 0.209 ng mL-1. The dosing method based on clearance values obtained by NONMEM analysis allowed the prediction of the steady-state concentration as a function of maintenance dose with acceptable error for therapeutic drug monitoring.     

Population pharmacokinetics of digoxin in Korean patients

AIM: Digoxin possesses a narrow therapeutic index and shows a large inter-patient pharmacokinetic variability. The purpose of this study was to develop a population model for the pharmacokinetics of digoxin in Korean patients. METHODS: Plasma concentrations of digoxin after multiple administration at varying dosing schedules in Korean patients were used for population modeling. Data analysis was performed with the P-Pharm software. The data were best fitted by a one-compartment model. The effect of demographic and clinical factors like sex, age, weight, disease state, and renal function on the pharmacokinetic parameters of digoxin was investigated. RESULTS: The study indicated that the clearance of digoxin was influenced by creatinine clearance, while body weight and creatinine clearance were the covariates for its volume of distribution. The population mean estimates for CL and V were 4.4 l/h and 535 l, respectively. Absorption rate constant was lower in females and in the presence of concomitant drug treatment. CONCLUSION: A population pharmacokinetic model for the digoxin pharmacokinetics in a section of Korean patients was developed. The relationships between the pharmacokinetic parameters and the demographic data and the patient-specific covariates were established.     

Absorption of digoxin in severe right heart failure

Summary The absorption of digoxin has been investigated in 8 patients before and after successful treatment of severe right heart failure.³H-digoxin 0.1 mg as a solution, and un-labelled digoxin 0.25 mg as a tablet, were given to fasted patients. Blood samples were taken at various time intervals up to 120 hours and urine was collected over the same period. The concentrations of labelled digoxin in plasma and urine were measured in a liquid scintillation counter, unlabelled digoxin was estimated by radioimmunoassay, and various pharmacokinetic parameters were calculated. There was no significant difference in the plasma concentration curves in severe right heart failure and after its successful treatment, nor did any of the calculated pharmacokinetic parameters change significantly. Therefore, inhibition of the absorption of digoxin appears unlikely. In an additional study to estimate absolute bioavailability two different groups of patients in severe right heart failure were given³H-digoxin 0.1 mg or unlabelled digoxin 0.25 mg i. v. and the pharmacokinetic parameters were compared with those from the previous study. The bioavailability of the³H-digoxin solution and of the digoxin tablet were in the same range as values previously published for healthy volunteers, and patients both with and without cardiac failure.     

Population pharmacokinetics of digoxin in pediatric patients

Digoxin pharmacokinetics were studied in a pediatric population with an age range of 6 days to 1 year using the population pharmacokinetic approach. Digoxin data were analyzed by mixed-effects modeling according to a one-compartment steady-state pharmacokinetic model using NONMEM software. The final model selected for the population prediction of digoxin clearance in pediatric patients was as follows: [equation: see text] Individual empirical Bayesian estimates were generated on the basis of the population estimates and were used to correlate the optimum dose of digoxin and patient age according to the following equation: [equation: see text] This equation and its derived nomogram may be used for the initial dosing of digoxin in children aged between 0 and 1 year. The use of this nomogram in routine monitoring requires further pharmacokinetic and clinical validation.     

Factors influencing the prediction of steady state concentrations of digoxin

The prediction error in the Bayesian analysis program for digoxin was evaluated in Japanese patients, and factors influencing the accuracy were investigated. Serum concentrations of digoxin were monitored two times and were compared with the predicted values obtained by using the Bayesian analysis program. The prediction error at the first time was 43.1%. Although this estimation error was reasonably restored at the second time of monitoring, the prediction error remained at 26.6%. These data suggested that unknown factors not included in the program affected the serum concentration of digoxin. Retrospective research of the digoxin serum concentrations in the patients suggested the coadministration of the drugs, which were the P-glycoprotein modulators, as well as the unexpected alteration of the serum creatinine, were the important factors influencing the prediction of the drug serum concentrations. We next examined the inhibitory effect of quinidine, verapamil and spironolactone on the transcellular transport of digoxin by using human P-glycoprotein overexpressing LLC-GA5-COL150 cells. Quinidine, verapamil and spironolactone could inhibit the transcellular transport of digoxin by 50%. In addition, the reduction of the renal clearance by 50%, which could possibly be caused by this inhibition, led to the increase of 36% in the steady state through concentrations of digoxin in the physiological pharmacokinetic model. In conclusion, the prediction of long-term serum concentration-time profiles of digoxin, based on the Bayesian analysis, will be disturbed by the coadministration of the P-glycoprotein modulators and the unexpected alteration of the serum creatinine.     

Population-based investigation of relative clearance of digoxin in Japanese neonates and infants by multiple-trough screen analysis

OBJECTIVE: The steady-state concentrations of digoxin at trough levels were studied to establish the role of patient characteristics in estimating doses for digoxin using routine therapeutic drug monitoring data. METHOD: The data (n = 448) showing steady state after repetitive oral administration in 172 hospitalized neonates and infants were analyzed using Nonlinear Mixed Effect Model (NONMEM), a computer program designed to analyze pharmacokinetics in study populations by allowing pooling of data. Analysis of the pharmacokinetics of digoxin was accomplished using a simple steady-state pharmacokinetic model. The effects of a variety of developmental and demographic factors on the clearance of digoxin were investigated. RESULTS: Estimates generated using NONMEM indicated that clearance of digoxin (l.h-1) was influenced by the demographic variables of age, total body weight, serum creatinine, the coadministration of spironolactone, and the presence or absence of congestive heart failure. The interindividual variability in digoxin clearance was modeled with proportional errors with an estimated coefficient of variation of 32.1%, and the residual variability was 28.9%. In the validation set of 66 patients, the performance (bias, precision) of the final population model was good (mean prediction error -0.04 ng.ml-1; mean absolute prediction error 0.20 ng.ml-1).     

Digoxin toxicity in the aged. Characterising and avoiding the problem.

Digoxin is one of the most frequently prescribed drugs, particularly in the elderly population where there is an increased prevalence of atrial fibrillation and cardiac failure. The drug has a narrow therapeutic range and has gained a reputation for producing adverse effects in older patients. The more frail elderly patients with coexistent disease, often taking other treatments, are more at risk from digoxin toxicity due to inappropriate dosing, noncompliance, or increased sensitivity to digoxin resulting from pharmacokinetic or pharmacodynamic interactions. Application of basic pharmacological principles may be helpful in anticipating these problems. Elderly patients more commonly receive digoxin than younger patients, which in part accounts for the higher rates of toxicity in this group. Numerous components contribute to the development of toxicity, and diagnosis of toxicity is difficult in this age group. The measurement of serum concentrations can contribute to the clinical diagnosis. A major problem is the accurate diagnosis of digoxin toxicity which may have numerous nonspecific clinical manifestations, many of which are related to coexisting disease in elderly patients. This diagnostic imprecision is well recognised but has been helped by the introduction of serum digoxin measurement. However, reliance on serum concentrations should not replace clinical judgement, since these do not always correlate with toxicity. The apparently decreasing incidence of toxicity over recent years probably reflects several factors: the improvement in digoxin formulations, awareness of digoxin pharmacology, utilisation of serum concentrations, and the realisation that digoxin withdrawal is a viable proposition in elderly patients. Greater knowledge about the causes and prevention of digoxin toxicity should further reduce the morbidity and mortality arising from digoxin overdose, especially in the elderly population.