药代动力学人体预测及其在新药研发中的应用

药物代谢和药代动力学(DMPK)通过揭示药物的体内代谢处置过程,理解药物药理效应和毒副反应的体内物质基础,是连接药物分子及其性质与生物学效应的桥梁。DMPK人体预测应用模型拟合技术,由人体外试验数据和动物体内外数据预测人体药代动力学性质,并与药效动力学和毒性评价相关联,可提高新药研发效率、降低临床失败率和节省资源。经典的异速放大法和体外-体内外推法主要用于预测人体清除率和稳态表观分布容积等重要的药代动力学参数。近10年来,基于生理的药代动力学模型(PBPK)的快速发展和应用实践,推动了DMPK人体预测在新药研发、药物监管、临床合理和个体化用药中的应用。PBPK模型不仅能预测消除和分布等参数,还能用于药物人体药代动力学行为的预测,包括血药浓度-时间曲线和药物-药物相互作用,以及不同人群体内药代动力学和药代-药效预测。作为新药研发的转化科学技术以及个体化用药的指导工具,DMPK人体预测将具有更为广泛的应用价值。     

生理药动学模型的研究与应用进展

摘要：生理药动学(PBPK)模型可以利用临床前数据预测药物在人体内的药动学行为,还可以探讨各种生理参数,如年龄、种族或疾病状况对人体药动学的影响,从而指导用药剂量和给药方案,进行药物相互作用的风险评估。本文简要介绍了PBPK模型的概念、建模方法和常用软件,以及在指导新药研发、毒理学和风险评估、评价药物-药物相互作用、药品监管领域等方面的应用进展,以期了解候选药物的药代动力学,为提高药物研发效率提供方法。     

奥卡西平的药代动力学及其立体选择性

奥卡西平是一种治疗癫痫部分发作和全身强直阵孪性癫痫发作的新药。与传统抗癫痫药物相比，奥卡西平有不良反应少、自身诱导及对肝药酶的诱导作用小等优点。本文系统地介绍了奥卡西平在人体内吸收、分布、代谢和排泄的药代动力学特征，对年龄、性别、肝肾功能损伤等影响因素进行了描述，同时综述了奥卡西平及其活性代谢产物10-羟基卡马西平在动物及人体内药代动力学的立体选择性。     

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．     

阿托氟啶在肿瘤患者体内的药代动力学研究

目的：通过对肿瘤患者服用阿托氟啶后的械代动力学研究,探讨氟啶在人体内的药代动力学特征。方法：18例恶性肿瘤者随机分成3组,分别po阿托氟啶800,1000和1200mg,采用高效液相色谱法测定用药后不同时间血清中阿托氟啶代谢产物的浓度,并对所测得的血药浓度－时间数据进行拟合,计算药代动力学参数。结果：陈托氟啶在人体内的代谢物为3－邻甲基苯甲酰基－5－氟尿嘧啶（TFU）,后者在体内的代谢符合一房室模型的特征,不同患者对阿托氟啶的代谢存在较大的个体差异。高中低剂量组的阿托氟啶的药代动力学参数没有显著性差异。结论：陈托氟啶的体内代谢存在极大的个体差异,有必要进行治疗药物监测,实现临床用药个体化。     

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     

时辰药代动力学在药物治疗中的应用

目的：通过阐明影响口服药物药代动力学的时辰因素,以指导临床合理用药。方法从药物的吸收、分布、代谢和排泄四方面讨论药物在人体内的时辰变化。结果药物在体内的药代动力学过程受到生理活动周期昼夜节律变化的影响。结论结合药物的时辰药代动力学和人体生理功能的节律变化制订合理的给药方案,从而提高疗效,减少药品不良反应。     

基于生理药代动力学模型在药物评价中的应用进展

基于生理的药代动力学(PBPK)模型是当前药物研究领域的重要方法,已被广泛应用于药物发现和开发的各个阶段。在药物发现阶段,利用PBPK模型对药物药代动力学性质进行预测,完成对候选药物的筛选;在临床前阶段,通过结合体外数据和生理放大系数,利用PBPK模型预测候选药物在动物和人的整体药代动力学行为,并结合体外代谢实验,可提前预测药物药物相互作用;在临床阶段,PBPK模型有助于预测不同参照人群(不同年龄、不同疾病状态、不同种族)的差异,尤其是对儿童给药剂量及采样时间的预测。目前,PBPK模型的输入参数多为群体均值,难以达到服务个体的目的。在个体化需求前提下,要求模型的输入参数更能反映个体特征,且导入更加符合实际生理条件的时间参数。本文综述了PBPK模型的原理和特征,及其在药物发现阶段、临床前开发阶段、临床开发阶段、药物相互作用和个体化用药的应用,并简要介绍了常用的PBPK软件的特点。     

创新性皮肤局部外用药物研发分析及对皮肤药代动力学的影响研究

人体皮肤药代动力学研究可以直接反映药物在皮肤中的变化规律.从而为药效的强度、作用量与靶点的关系和药效代谢规律提供直接数据的手段.有助于确定合理的剂量及间隔时间,为合理用药提供依据,但目前皮肤药代动力学研究不足、不完整,是皮肤外用药物研发中普遍遇到的问题.为此本文探讨了新药研发与评价中可采用的检测技术和方法及其优点与不足...     

艾瑞昔布的药物动力学研究

本实验对新药艾瑞昔布进行健康人体药代动力学研究。研究结果表明,艾瑞昔布在人体内发生较强的首过代谢,主要生成羟基代谢产物M1和羧基代谢产物M2。饮食影响的药动学研究表明,餐后给药能显著提高艾瑞昔布的生物利用度。建议餐后给药。     

用临床前和体外代谢数据预测创新药物的人体药动学参数

目的:介绍采用临床前和体外代谢数据预测人体药动学参数的方法和进展.方法:对近年来有关文献进行分析、归纳.结果:应用临床前动物实验测得的药动学参数、药物的理化参数以及体外代谢数据,采用合适的方程、模型可以预测人体的主要药动学参数,包括分布容积、清除率、半衰期和生物利用度.结论:采用动物试验和体外代谢数据对人体药动学参数进行预测,将会大大提高新药筛选的效率,消除盲目性,节约时间和成本.     

Defining drug disposition determinants: a pharmacogenetic-pharmacokinetic strategy

In preclinical and early clinical drug development, information about the factors influencing drug disposition is used to predict drug interaction potential, estimate and understand population pharmacokinetic variability, and select doses for clinical trials. However, both in vitro drug metabolism studies and pharmacogenetic association studies on human pharmacokinetic parameters have focused on a limited subset of the proteins involved in drug disposition. Furthermore, there has been a one-way information flow, solely using results of in vitro studies to select candidate genes for pharmacogenetic studies. Here, we propose a two-way pharmacogenetic-pharmacokinetic strategy that exploits the dramatic recent expansion in knowledge of functional genetic variation in proteins that influence drug disposition, and discuss how it could improve drug development.     

Current Methods for Predicting Human Food Effect

Food can impact the pharmacokinetics of a drug product through several mechanisms, including but not limited to, enhancement in drug solubility, changes in GI physiology, or direct interaction with the drug. Significant food effects complicate development of new drugs, especially when clinical plans require control and/or monitoring of food intake in relation to dosing. The prediction of whether a drug or drug product will show a human food effect is challenging. In vitro models which consider physical-chemical properties can classify the potential for a compound to demonstrate a positive, negative or no food effect, and may be appropriate for screening compounds at early stages of drug discovery. When comparing various formulations, dissolution tests in biorelevant media can serve as a predictor of human drug performance under fasted and fed conditions. Few in vivo models exist which predict the magnitude of change in pharmacokinetic parameters in humans when dosing in the presence of food, with the dog appearing to be the most studied species for this purpose. Control of gastric pH, as well as the amount and composition of the fed state in dogs are critical parameters to improving the predictability of the dog overall as a food effect model. No single universal model is applicable for all drugs at all stages of drug development. One or more models may be required depending whether the goal is to assess potential for a food effect, determine the magnitude of change in pharmacokinetic parameters in the fed/fasted state, or whether formulation efforts have the ability to mitigate an observed food effect.     

Quantifying Uncertainty in Predictions of Hepatic Clearance

Preclinical predictions of human pharmacokinetic parameters are routinely used in pharmaceutical research and development. In particular, pharmacokinetic predictions are critical in the decision to advance a potential drug to the clinic, to determine appropriate dosing regimens for first-in-human studies, and as a component of translational pharmacology models. Although the associated biological and mathematical models have been extensively discussed in the pharmacokinetic literature, relatively little work has been done to explicitly relate the estimation error of these methods to the underlying experimental variability. This article proposes and evaluates Bayesian models for this purpose.

We apply our methodology to a dataset describing both preclinical and clinical pharmacokinetic experimentation for 12 different anonymized drugs. For each drug and for each preclinical mode of prediction, a credible interval is computed and compared against estimates obtained by direct experimentation with human subjects in the clinic. We conclude that many apparent translational differences may be readily explained as a function of experimental error.

We view this problem as representative of a larger class of statistical problems in translational medicine, where the mathematics of translation from one species to another requires multiple experimentally estimated scaling factors.     

Flip-flop pharmacokinetics--delivering a reversal of disposition: challenges and opportunities during drug development

Flip-flop pharmacokinetics is a phenomenon often encountered with extravascularly administered drugs. Occurrence of flip-flop spans preclinical to human studies. The purpose of this article is to analyze both the pharmacokinetic interpretation errors and opportunities underlying the presence of flip-flop pharmacokinetics during drug development. Flip-flop occurs when the rate of absorption is slower than the rate of elimination. If it is not recognized, it can create difficulties in the acquisition and interpretation of pharmacokinetic parameters. When flip-flop is expected or discovered, a longer duration of sampling may be necessary in order to avoid overestimation of fraction of dose absorbed. Common culprits of flip-flop disposition are modified dosage formulations; however, formulation characteristics such as the drug chemical entities themselves or the incorporated excipients can also cause the phenomenon. Yet another contributing factor is the physiological makeup of the extravascular site of administration. In this article, these causes of flip-flop pharmacokinetics are discussed with incorporation of relevant examples and the implications for drug development outlined.     

Allometric issues in drug development

The concept of correlating pharmacokinetic parameters with body weight from different animal species has become a useful tool in drug development. The allometric approach is based on the power function, where the body weight of the species is plotted against the pharmacokinetic parameter(s) of interest. Clearance, volume of distribution, and elimination half-life are the three most frequently extrapolated pharmacokinetic parameters. Over the years, many approaches have been suggested to improve the prediction of these pharmacokinetic parameters in humans from animal data. A literature review indicates that there are different degrees of success with different methods for different drugs. Overall, though interspecies scaling requires refinement and better understanding, the approach has lot of potential during the drug development process.     

The Impact of Lag Time on the Estimation of Pharmacokinetic Parameters. I. One-Compartment Open Model

Lag time in pharmacokinetics corresponds to the finite time taken for a drug to appear in systemic circulation following extravascular administration. Lag time is a reflection of the processes associated with the absorption phase such as drug dissolution and/or release from the delivery system and drug migration to the absorbing surface. Failure to specify the lag time can lead to inappropriate or erroneous estimates of pharmacokinetic parameters. This has been demonstrated in the case of a one-compartment open model by the pharmacokinetic analysis of bioequivalence data from a study involving the administration of propoxyphene napsylate to human volunteers. Subsequently, pharmacokinetic and statistical analyses of data obtained from a series of 49 simulations involving a wide range of absorption and elimination rate constants (0.05 to 5.00 and 0.01 to 0.95 hr^–1, respectively) showed that lag time has a substantial effect on several primary and secondary pharmacokinetic parameters.     

Non-compartmental estimation of pharmacokinetic parameters in serial sampling designs

Pharmacokinetic studies are commonly analyzed using a two-stage approach where the first stage involves estimation of pharmacokinetic parameters for each subject separately and the second stage uses the individual parameter estimates for statistical inference. This two-stage approach is not applicable in sparse sampling situations where only one sample is available per subject. Nonlinear models are often applied to analyze pharmacokinetic data assessed in such serial sampling designs. Modelling approaches are suitable provided that the form of the true model is known, which is rarely the case in early stages of drug development. This paper presents an alternative approach to estimate pharmacokinetic parameters based on non-compartmental and asymptotic theories in the case of serial sampling when a drug is given as an intravenous bolus. The statistical properties of estimators of the pharmacokinetic parameters are investigated and evaluated using Monte Carlo simulations.