用NONMEM法估算肾移植患者术后口服普乐可复的群体药代动力学特征

目的 考察肾移植患者口服普乐可复(FK506)胶囊常规监测群体药代动力学特征，为临床调整个体化给药方案提供依据。方法 回顾性收集我院88例肾移植患者的366个FK506全血稳态浓度样本，建立FK506群体分析数据库，用非线性混合效应模型法进行模型优化，确定FK506的药代动力学模型和统计学模型。分别考察性别、年龄、体重、术后时间等因素对药代动力学参数的影响，得到模型方程并进行个体化给药方案设计。结果 FK506数据用稳态药代动力学模型进行描述，通过模型优化表明，剂量、肌酐和尿素氮对药代动力学参数有显著性影响。结论 本方法可用于临床调整给药方案。     

群体药代动力学在临床药学中的应用

群体药代动力学(Population Pharmacoki-netics)较经典药代动力学(Classical Pharma-cokinetics)是一年轻的药学分支学科,它的发展仅有十几年的历史。Sheiner等将经典的药代动力学模型与群体统计学模型(Population Statis-tical Model)结合起来,提供了群体药代动力学理论,主要研究人体药代动力学参数的群体值,再结合病人的个体信息的反馈,得到病人的个体药代动力学参数,以优化给药方案,指导临床个体化用药。1 清除率概念在药代动力学中的应用 药物从人体内清除是多途径的。通常是以肾清除和肝清除为主。当一个药物经主要的代谢途径消除时,病人间消除的个体差异是普遍存在的。药物主要以肾排泄消除时,则肌酐清除     

药代动力学建模的人工神经网络新方法

人工神经网络(ANN)在药代动力学领域主要用于血药浓度预测、药物结构和药代动力学定量关系、体内体外相关关系研究,群体药物动力学数据分析、药代动力学-药效动力学统一模型研究等。本文就ANN的基本理论及其在药代动力学研究的应用作简要综述。     

群体药代动力学在临床药学中的应用

群体药代动力学(Population Pharmacokinetics)较经典药代动力学(Classical Pharmacokinetics)是一年轻的药学分支学科．它的发展仅有十几年的历史。Sheiner等将经典的药代动力学模型与群体统计学模型(Population Statistical Model)结合起来，提供了群体药代动力学理论，主要研究人体药代动力学参数的群体值，再结合病人的个体信息的反馈，得到病人的个体药代动力学参数，以优比给药方案，指导临床个体化用药。     

MTX药代动力学以及组织与全血浓度相关性研究

目的 研究MTX药代动力学以及组织与全血药物浓度相关性。方法 用30mg/m~2 MTX的给药剂量,在肿瘤病人体内研究其药代动力学过程,并对肿瘤局部血及外周血中的药物浓度进行了相关性比较,同时对该剂量下的药品不良反应作了观察。结果 用同30mg/m~2剂量MTX静脉滴注后,肿瘤病人体内药代动力学为一级动力学三房室模型。肿瘤局部与外周血药浓度有显著相关性。应用本剂量药品不良反应发生率较低。结论 治疗药物浓度监测可为临床治疗提供客观有效的数据,有指导意义,使用30mg/m~2剂量为一安全有效的治疗剂量。     

头孢克洛在中国健康志愿者体内的群体药代动力学研究

目的:建立头孢克洛口服给药在健康志愿者体内的群体药代动力学模型,探讨个体因素对代谢反应的影响。方法:基于头孢克洛生物等效性试验数据,应用非线性混合效应模型的群体方法分析头孢克洛口服给药的生物等效性试验数据,估算相关药代动力学参数及变异。结果:头孢克洛在健康志愿者中符合一级吸收的二室模型。药物表观清除率(CL)、中央分布容积(V2)、中央分布容积(V3)、吸收速率常数(KA)的群体典型值分别为0.219L/min、35.9L、598L和0.042min-1。体重对清除率(CL)有显著影响。结论:群体药代动力学最终模型可对个体药代参数做出精确的估计,体重对表观清除率有影响。     

群体药代动力学及其在新药研究中的应用

近年来新药临床研究越来越重视群体药代动力学的应用。群体药代动力学可以定量地描述病理、生理、合并用药等多种因素对药物代谢的影响，可将PK参数中的各种变异区分开，指导用药方案的调整，从而增强对新药有效性和安全性的评价。本文对群体药代动力学的研究方法及其在新药研究中的应用进行综述．     

局部给药长效缓释的药代模型初探

目的创建局部给药长效缓释治疗系统的药代动力学模型。方法分析体内过程,建立微分方程并特别关注其中局部靶位的数学表达,进而对药物在血循环中及局部吸收位的分布作理论比较。结果缓释植入剂局部给药治疗系统的药代动力学二室模型为:Cc(t)=A1e-K rt A2e-Kat A3e-αt A4e-βt,吸收位与血循环中药物分布的比值为:ACc/ACa≈(Va/V)(Ka/Ke)。结论该模型立意严谨,有一定的理论意义和实用价值。     

MTX药代动力学以及组织与全血浓度相关性研究

目的 研究MTX药代动力学以及组织与全血药物浓度相关性。方法 用30mg／m^2 MTX的给药剂量，在肿瘤病人体内研究其药代动力学过程，并对肿瘤局部血及外周血中的药物浓度进行了相关性比较，同时对该剂量下的药品不良反应作了观察。结果 用同30mg／m^2剂量MTX静脉滴注后，肿瘤病人体内药代动力学为一级动力学三房室模型。肿瘤局部与外周血药浓度有显相关性。应用本剂量药品不良反应发生率较低。结论 治疗药物浓度监测可为临床治疗提供客观有效的数据，有指导意义，使用30mg／m^2剂量为一安全有效的治疗剂量。     

抗癫痫药的群体药代动力学与CYP450基因多态性的研究进展

抗癫痫药代谢的个体差异较大，需要个体化用药。群体药代动力学的研究是设计个体化治疗方案的有效方法。国内外对新老抗癫痫药的群体药代动力学进行了广泛研究，分析了一般生物学特征对药物代谢的影响。CYP450基因多态性是影响抗癫痫药物代谢的主要遗传因素，是个体差异的重要原因。目前，已有研究将CYP450基因多态性的因素引入群体药代动力学的模型，将其对抗癫痫药物代谢的影响进行了量化，并且，可以依据不同的基因型选择不同的初始剂量，促进个体化治疗，取得了新的进展。但是，有项研究提示基因多态性对群体药代动力学（PPK）参数的影响没有统计学意义。因此，目前的结论尚不完全一致，需要进一步研究。     

The application of population pharmacokinetic modeling to individualized antibiotic therapy

This paper describes applications of population pharmacokinetic modeling to the optimization of antibiotic dosing. Parametric and nonparametric pharmacokinetic modeling approaches are discussed. Population models can be important extensions of therapeutic drug monitoring (TDM) in infectious disease. The concept of population model-based individualized antimicrobial therapy is described. With the availability of population modeling for obtaining PK parameter estimates, the focus has shifted to quantifying the antimicrobial effect and linking kinetics to drug effects. Examples of integrated pharmacokinetic-pharmacodynamic (PK-PD) models to describe bacterial killing as a function of drug concentration are discussed. Application of PK-PD mathematical models that correlate with microbiological and clinical outcomes will provide us with a better rationale for the proper dose selection of anti-infective therapy in different patient populations.     

PBPK模型在抗感染药物研发及临床评价中的应用

摘要：生理药动学( physiologically based pharmacokinetic，PBPK) 模型是一种模拟药物在人或动物体内吸收、分布、代谢 和排泄过程的数学模型，集成了药物的理化和系统(生理)信息，能描述药物在靶组织器官中的经时变化，用于药物研究的各个 阶段。本文将综述PBPK模型在抗感染药物研发及临床评价中的应用，为抗感染药物研发及临床合理应用提供参考。     

Population pharmacokinetics of melphalan in paediatric blood or marrow transplant recipients

AIM: To develop a population pharmacokinetic model for melphalan in children with malignant diseases and to evaluate limited sampling strategies for melphalan. METHODS: Melphalan concentration data following a single intravenous dose were collected from 59 children with malignant diseases aged between 0.3 and 18 years. The data were split into two sets: the model development dataset (39 children, 571 concentration observations) and the model validation dataset (20 children, 277 concentration observations). Population pharmacokinetic modelling was performed with the NONMEM software. Stepwise multiple linear regression was used to develop a limited sampling model for melphalan. RESULTS: A two-compartment model was fitted to the concentration-vs.-time data. The following covariate population pharmacokinetic models were obtained: (i) Clearance (l h(-1)) = 0.34.WT - 3.17.CPT + 0.0377.GFR, where WT = weight (kg), CPT = prior carboplatin therapy (0 = no, 1 = yes), and GFR = glomerular filtration rate (ml min(-1) 1.73 m(-2)); (ii) Volume of distribution (l) = 1.12 + 0.178.WT. Interpatient variability (coefficient of variation) was 27.3% for clearance and 33.8% for volume of distribution. There was insignificant bias and imprecision between observed and model-predicted melphalan concentrations in the validation dataset. A three-sample limited sampling model was developed which adequately predicted the area under the concentration-time curve (AUC) in the development and validation datasets. CONCLUSIONS: A population pharmacokinetic model for melphalan has been developed and validated and may now be used in conjunction with pharmacodynamic data to develop safe and effective dosing guidelines in children with malignant diseases.     

Population pharmacokinetics of amphotericin B in children with malignant diseases

AIMS: To construct a population pharmacokinetic model for the antifungal agent, amphotericin B (AmB), in children with malignant diseases. METHODS: A two compartment population pharmacokinetic model for AmB was developed using concentration-time data from 57 children aged between 9 months and 16 years who had received 1 mg kg(-1) day(-1) doses in either dextrose (doseform=1) or lipid emulsion (doseform=2). P-Pharm (version 1.5) was used to estimate the basic population parameters, to identify covariates with significant relationships with the pharmacokinetic parameters and to construct a Covariate model. The predictive performance of the Covariate model was assessed in an independent group of 26 children (the validation group). RESULTS: The Covariate model had population mean estimates for clearance (CL), volume of distribution into the central compartment (V) and the distributional rate constants (k12 and k21) of 0.88 l h(-1), 9.97 l, 0.27 h(-1) and 0.16 h(-1), respectively, and the intersubject variability of these parameters was 19%, 49%, 55% and 48%, respectively. The following covariate relationships were identified: CL (l h(-1)) = 0.053 + 0.0456 weight (0.75) (kg) + 0.242 doseform and V (l) = 7.11 + 0.107 weight (kg). Our Covariate model provided unbiased and precise predictions of AmB concentrations in the validation group of children: the mean prediction error was 0.0089 mg l(-1) (95% confidence interval: -0.0075, 0.0252 mg l(-1)) and the root mean square prediction error was 0.1245 mg l(-1) (95% confidence interval: 0.1131, 0.1349 mg l(-1)). CONCLUSIONS: A valid population pharmacokinetic model for AmB has been developed and may now be used in conjunction with AmB toxicity and efficacy data to develop dosing guidelines for safe and effective AmB therapy in children with malignancy.     

临床药师在抗感染目标治疗中的作用

目的:探讨临床药师在抗感染目标治疗中的作用。方法:总结药师在临床工作中解决的有关目标治疗。结果:临床药师应关注药敏试验报告中病原学诊断是否准确;合理地分析和引用药敏试验结果;监测治疗过程中可能产生的耐药性;结合药物的药动学特性确定临床用药和用药方法;参与药敏试验备选品种的确定。结论:临床抗感染不再是单一学科可以处理好的工作,药师在抗感染目标性治疗中可以大有作为。     

Intravenous indometacin in preterm infants with symptomatic patent ductus arteriosus. A population pharmacokinetic study

AIMS: To characterize the population pharmacokinetics of indometacin in preterm infants with symptomatic patent ductus arteriosus and to investigate the influence of various factors on the response to treatment. METHODS: Data were collected from 35 infants (gestational age 25-34 weeks; postnatal age 1-77 days) in neonatal units in Belfast and Copenhagen. Infants received an initial course of up to three doses of intravenous indometacin (0.1-0.2 mg kg(-1)) as considered appropriate by the treating physician. For those infants who did not respond to therapy or in whom the ductus reopened, a second course was sometimes given. Population analysis of the 185 plasma concentrations obtained was conducted using NONMEM and pharmacokinetic and demographic differences between responders and nonresponders were compared. RESULTS: The concentration-time course of indometacin was best described by a one-compartment model. The final population parameter estimates of clearance (CL) and volume of distribution (V) (standardized to the median weight of 1.17 kg) were 0.00711 l h(-1) and 0.266 l, respectively. CL increased from birth by approximately 3.38% per day and V by approximately 1.47% per day. Concomitant digoxin therapy resulted in a 30% decrease in V. Interindividual variability in CL and V was 41% and 21%, respectively. Interoccasion variability for CL was 43%. Residual variability corresponded to a standard deviation of 0.148 mg l(-1). Closure occurred in 75% of infants with a plasma concentration > or = 0.4 mg l(-1) 24 h after the last dose. CONCLUSIONS: Dosing regimens for indometacin should take into account the weight and postnatal age of the infant and any concomitant digoxin therapy. The population estimates can be used to determine typical values of CL and V allowing the prediction of individualized doses of indometacin that should increase the probability of achieving a 24 h plasma concentration > or = 0.4 mg l(-1). Although the pharmacokinetic estimates will be affected by both interindividual and within-individual variation, it is anticipated that this approach will decrease the variability of exposure and optimize treatment outcome.     

利伐沙班导致出血事件的群体药动学模型建立及影响因素分析

目的：建立利伐沙班出血患者群体药动学模型并对出血事件的影响因素进行分析。方法：回顾性选取93例服用利伐沙班抗凝治疗发生出血事件的患者为研究对象,收集192例次利伐沙班稳态峰谷浓度数据,以非线性混合效应模型法建立群体药动学模型并以Bayesian反馈法计算药时曲线下面积（AUC）,同时分析该群体PK模型协变量与预估药物暴露量的相关性。结果：建立的群体药动学模型可对数据进行较好拟合,协变量肾小球滤过率（e-GFR）对清除率（CL）有显著影响,最终模型为CL=77.2×EGFR/71.56^θCLcr。协变量e-GFR和年龄影响利伐沙班的估算AUC和峰浓度（C_max）数值。结论：利伐沙班出血患者群体药动学模型稳定、有效。患者肾功能和年龄影响利伐沙班出血人群体内药物暴露量。     

Influence of clinical diagnosis in the population pharmacokinetics of amikacin in intensive care unit patients

The aim of the present study was to analyse the pharmacokinetic behaviour of amikacin in intensive care unit (ICU) patients using a mixed-effect model and sparse data collected during routine clinical care. The patient population comprised 158 medical ICU patients divided into two groups: one for computing the population model (n = 120) and the other for validation (n = 38). A 1-compartment model was used and the following covariates were tested for their influence on clearance (CL) and volume of distribution (Vd): age, gender, weight, parenteral nutrition, creatinine clearance, duration of therapy and clinical diagnosis. The nonlinear mixed-effect model (NONMEM) was used to assess the population pharmacokinetic model of amikacin in this patient population. In this study, the final population model accounting for amikacin pharmacokinetics in ICU patients was: CL = 0.93 CL(CR) (1 + 0.22 Trauma), Vd = 0.39 TBW (1 + 0.24 Sepsis), where CL(CR) and TBW corresponded to the patients' creatinine clearance and total bodyweight, respectively. The 'Trauma' and 'Sepsis' variables referred to the clinical diagnosis of the patients. This model was subsequently used to predict amikacin serum levels obtained in the validation population by a priori and Bayesian methods. The predictive performance was adequate for clinical purposes, pointing to the feasibility of our population model to provide reference values for a priori prediction as well as the Bayesian approach for individualisation of amikacin therapy in ICU patients.     

临床药师参与1例术后感染患者的抗感染治疗实践

目的:探讨临床药师在患者术后抗感染药治疗中如何选择药物,促进患者康复。方法:介绍临床药师参与1例术后感染患者的抗感染治疗过程,临床药师从抗感染药选择、给药剂量、给药方法、不良反应监测等方面为患者制订个体化给药方案并进行药学监护。结果与结论:临床药师发挥药学专业特长,使患者获得了更好的药物治疗。临床药师与临床医师合作可充分发挥团队作用,促进合理用药,提高药物治疗水平。     

Population pharmacokinetics. A regulatory perspective.

The application of population approaches to drug development is recommended in several US Food and Drug Administration (FDA) guidance documents. Population pharmacokinetic (and pharmacodynamic) techniques enable identification of the sources of inter- and intra-individual variability that impinge upon drug safety and efficacy. This article briefly discusses the 2-stage approach to the estimation of population pharmacokinetic parameters, which requires serial multiple measurements on each participant, and comprehensively reviews the nonlinear mixed-effects modelling approach, which can be applied in situations where extensive sampling is not done on all or any of the participants. Certain preliminary information, such as the compartment model used in describing the pharmacokinetics of the drug, is required for a population pharmacokinetic study. The practical design considerations of the location of sampling times, number of samples/participants and the need to sample an individual more than once should be borne in mind. Simulation may be useful for choosing the study design that will best meet study objectives. The objectives of the population pharmacokinetic study can be secondary to the objectives of the primary clinical study (in which case an add-on population pharmacokinetic protocol may be needed) or primary (when a stand-alone protocol is required). Having protocols for population pharmacokinetic studies is an integral part of 'good pharmacometric practice'. Real-time data assembly and analysis permit an ongoing evaluation of site compliance with the study protocol and provide the opportunity to correct violations of study procedures. Adequate policies and procedures should be in place for study blind maintenance. Real-time data assembly creates the opportunity for detecting and correcting errors in concentration-time data, drug administration history and covariate data. Population pharmacokinetic analyses may be undertaken in 3 interwoven steps: exploratory data analysis, model development and model validation (i.e. predictive performance). Documentation for regulatory purposes should include a complete inventory of key runs in the analyses undertaken (with flow diagrams if possible), accompanied by articulation of objectives, assumptions and hypotheses. Use of diagnostic analyses of goodness of fit as evidence of reliability of results is advised. Finally, the use of stability testing or model validation may be warranted to support label claims. The opinions expressed in this article were revised by incorporating comments from various sources and published by the FDA as 'Guidance for Industry: Population Pharmacokinetics' (see the FDA home page http:/(/)www.fda.gov for further information).