妊娠期药物代谢个体差异与药物基因组学研究进展

妊娠期女性的特殊生理状态以及个体独特的遗传背景,均使其在接受药物治疗时对药物治疗的反应差异较大。目前,以妊娠期女性为研究对象的药代动力学和药物基因组学研究相对滞后,精准治疗所需的药物基因组学证据较少。妊娠期间重要的药物代谢酶的活性变化是导致药代动力学差异的重要原因,并且药物基因组的遗传多态性也会影响妊娠期妇女对常用药物的反应。因此,妊娠期女性的治疗药物选择不但需要考虑妊娠期特殊的、动态的生理和代谢变化,而且应当重视药物基因组学的重要研究成果及其对个体化治疗的指导意义。     

心血管药物代谢酶及其遗传药理学研究进展

目的:为心血管药物的临床合理应用和个体化给药提供参考。方法:检索近年来国内、外遗传药理学和药物基因组学在心血管药物代谢酶研究方面的论文,对其进行分析、归纳和总结。结果与结论:药物代谢酶的基因多态性是心血管药物疗效和不良反应产生个体差异的重要原因,是个体化给药的依据。     

生理药代动力学软件模拟技术在药物研发和评价中的应用

生理药代动力学(physiologically-based pharmacokinetic, PBPK)软件模拟技术是通过计算机软件构建模型,模拟机体生理或病理环境,用以预测药物在体内的药代动力学行为(吸收、分布、代谢、排泄)。本综述概述了生理药代动力学模型的常用功能模块、在药物早期研发和新制剂研究、食物影响药物相互作用、儿童药物研究、吸入制剂开发及仿制药一致性评价等多方面的应用,并简要介绍了常用生理药代动力学软件的特点。讨论了生理药代动力学模型的当前挑战,包括模型建立的适用范围,模型准确性评估的判断标准等方面内容。     

肝脏“代谢-转运互作”及其对药物药代动力学、疗效和毒性影响的研究进展

肝脏是药物代谢和排泄的主要器官。肝脏药物代谢酶和膜转运体对肝细胞内药物处置及其临床疗效和毒性产生重要影响。近年来,国内外学者发现被称为"代谢-转运互作"的动力学现象,其对药物药代动力学(生物利用度)、药物相互作用具有显著影响。药物代谢酶与转运体间的功能相互作用是目前药物代谢和药代动力学研究的热点之一。本文对肝脏代谢-转运互作进行了探究,并系统阐述了这种互作对药物(特别是Ⅱ相药物代谢)的药物相互作用、药代动力学、临床疗效和毒性反应的影响。今后应进一步阐明肝脏代谢-转运互作机制,有助于研究体内药物处置及药物相互作用,为临床合理用药提供新思路和新技术。     

生物大分子药物代谢消除途径及体外代谢研究方法进展

由于理化及生物学性质的差异,生物大分子药物与传统小分子药物相比,药代动力学机制更加复杂,在体内表现出不同的吸收、分布、代谢、排泄过程。大分子药物一般不经CYP 450酶代谢,其体内消除途径主要有肾小球滤过、酶水解、受体介导的胞吞消除和抗药物抗体介导的消除。近年来,除了常用的免疫分析法、放射性同位素示踪法、LC-MS/MS等分析方法外,还有计算机模型被开发用于模拟预测大分子药物药代动力学性质的研究并发挥越加重要的作用。本文主要综述了大分子药物主要代谢消除途径及体外研究模型TMDD、PBPK的应用和发展。     

阿托伐他汀疗效及安全性相关基因多态性的研究进展

阿托伐他汀是临床上应用最广泛的一种调血脂药物,在心血管疾病的预防和治疗中起到了重要作用。近年来,药代动力学和药效学相关基因多态性对阿托伐他汀疗效和安全性的个体化影响研究在国内外广泛开展并取得了丰硕成果。本文对药物代谢酶、转运蛋白和脂质代谢靶点等与阿托伐他汀疗效及安全性相关的基因多态性研究进展进行了综述,以期为其个体化临床应用提供理论依据。     

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。     

儿童生理药代动力学模型及其在儿科药物研究中的应用

生理药代动力学(physiologically based pharmacokinetic, PBPK)模型是预测药物在特殊人群中的药代动力学、药效学和安全性的重要工具。尤其对于儿童这类不易开展临床试验的人群, PBPK模型的应用更是能有效促进儿科药物的开发以及儿童的临床用药。目前, PBPK模型在儿科药物开发中的主要应用有以下几种:临床试验设计、药物相互作用(drug-drug interaction, DDI)的风险评估和儿童给药剂量的确立等。本综述简介了儿童生理药动学模型在儿科药物研究中的优越性,总结了PBPK模型如何实现从成人到儿童的外推,儿童生理药动学模型的理论基础,建模过程及所要注意的重要生理参数,列举了目前PBPK模型在儿科药物研究中的一些应用实例。最后简述了儿童PBPK模型当前的局限性和未来发展方向。     

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。     

基因多态性与磺脲类药物不良反应的相关性研究

磺脲类药物是治疗2型糖尿病的主要口服药之一．但是其诱发低血糖的风险在一定程度制约着临床使用。为探究如何制定个体化绘药方案．减少不良反应的发生．近年来关于基因多态性与磺脲类药物不良反应相关性的研究不断增多。前期大多集中于代谢酶基因多态性对磺脲类药物药代动力学的影响．近期则扩展至磺脲类靶基因及糖尿病风险基因的多态性研究。已有的研究认为Serl369Ala会增加格列齐特诱发低血糖风险．但对格列本脲和格列美脲无影响,CYP2C9‘3与SUs诱发低血糖风险相关：E23K是否降低低血糖风险及其他代谢酶基因多态性是否与磺脲类诱发低血糖风险相关．仍需进一步研究。     

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。     

Pharmacokinetic interplay of phase II metabolism and transport: a theoretical study

Understanding of the interdependence of cytochrome P450 enzymes and P-glycoprotein in disposition of drugs (also termed "transport-metabolism interplay") has been significantly advanced in recent years. However, whether such "interplay" exists between phase II metabolic enzymes and efflux transporters remains largely unknown. The objective of this article is to explore the role of efflux transporters (acting on the phase II metabolites) in disposition of the parent drug in Caco-2 cells, liver, and intestine via simulations utilizing a catenary model (for Caco-2 system) and physiologically based pharmacokinetic (PBPK) models (for the liver and intestine). In all three models, "transport-metabolism interplay" (i.e., inhibition of metabolite efflux decreases the metabolism) can be observed only when futile recycling (or deconjugation) occurred. Futile recycling appeared to bridge the two processes (i.e., metabolite formation and excretion) and enable the interplay thereof. Without futile recycling, metabolite formation was independent on its downstream process excretion, thus impact of metabolite excretion on its formation was impossible. Moreover, in liver PBPK model with futile recycling, impact of biliary metabolite excretion on the exposure of parent drug [(systemic (reservoir) area under the concentration-time curve (AUC(R1))] was limited; a complete inhibition of efflux resulted in AUC(R1) increases of less than 1-fold only. In intestine PBPK model with futile recycling, even though a complete inhibition of efflux could result in large elevations (e.g., 3.5-6.0-fold) in AUC(R1), an incomplete inhibition of efflux (e.g., with a residual activity of ≥ 20% metabolic clearance) saw negligible increases (<0.9-fold) in AUC(R1). In conclusion, this study presented mechanistic observations of pharmacokinetic interplay between phase II enzymes and efflux transporters. Those studying such "interplay" are encouraged to adequately consider potential consequences of inhibition of efflux transporters in humans.     

An integrated approach to model hepatic drug clearance.

It has been well accepted that hepatic drug extraction depends on the blood flow, vascular binding, transmembrane barriers, transporters, enzymes and cosubstrate and their zonal heterogeneity. Models of hepatic drug clearances have been appraised with respect to their utility in predicting drug removal by the liver. Among these models, the "well-stirred" model is the simplest since it assumes venous equilibration, with drug emerging from the outflow being in equilibrium with drug within the liver, and the concentration is the same throughout. The "parallel tube" and dispersion models, and distributed model of Goresky and co-workers have been used to account for the observed sinusoidal concentration gradient from the inlet and outlet. Departure from these models exists to include heterogeneity in flow, enzymes, and transporters. This article utilized the physiologically based pharmacokinetic (PBPK) liver model and its extension that include heterogeneity in enzymes and transporters to illustrate how in vitro uptake and metabolic data from zonal hepatocytes on transport and enzymes may be used to predict the kinetics of removal in the intact liver; binding data were also necessary. In doing so, an integrative platform was provided to examine determinants of hepatic drug clearance. We used enalapril and digoxin as examples, and described a simple liver PBPK model that included transmembrane transport and metabolism occurring behind the membrane, and a zonal model in which the PBPK model was expanded three sets of sub-compartments that are arranged sequentially to represent zones 1, 2, and 3 along the flow path. The latter model readily accommodated the heterogeneous distribution of hepatic enzymes and transporters. Transport and metabolic data, piecewise information that served as initial estimates, allowed for the unknown efflux and other intrinsic clearances to be estimated. The simple or zonal PBPK model provides predictive views on the hepatic removal of drugs and metabolites.     

Evaluation of uncertainty in input parameters to pharmacokinetic models and the resulting uncertainty in output

Physiologically-based pharmacokinetic (PBPK) models may be used to predict the concentrations of parent chemical or metabolites in tissues, resulting from specified chemical exposures. An important application of PBPK modeling is in assessment of carcinogenic risks to humans, based on animal data. The parameters of a PBPK model may include metabolic parameters, blood/air and tissue/blood partition coefficients, and physiological parameters, such as organ weights and blood flow rates. Uncertainty in estimates of these parameters results in uncertainty regarding tissue concentrations and resulting risks. Data are reviewed relevant to the quantification of these uncertainties, for a PBPK model-based risk assessment for tetrachloroethylene. Probability distributions are developed to express uncertainty in model parameters, and uncertainties are propagated by a sequence of operations that simulates processes recognized as contributing to estimates of human risk. Distributions of PBPK model output and human risk estimates are used to characterize uncertainty resulting from uncertainty in model parameters.     

The use of in vitro metabolic parameters and physiologically based pharmacokinetic (PBPK) modeling to explore the risk assessment of trichloroethylene

A physiologically based pharmacokinetic (PBPK) model has been developed for trichloroethylene (1,1,2-trichloroethene, TRI) for rat and humans, based on in vitro metabolic parameters. These were obtained using individual cytochrome P450 and glutathione S-transferase enzymes. The main enzymes involved both for rats and humans are CYP2E1 and the μ- and π-class glutathione S-transferases. Validation experiments were performed in order to test the predictive value of the enzyme kinetic parameters to describe ‘whole-body’ disposition. Male Wistar rats were dosed orally or intravenously with different doses of trichloroethylene. Obtained exhaled radioactivity, excreted radioactivity in urine, and obtained blood concentration–time curves of trichloroethylene for all dosing groups were compared to predictions from the PBPK model. Subsequently, using the scaling factor derived from the rat experiments predictions were made for the extreme cases to be expected in humans, based on interindividual variations of the key enzymes involved. On comparing these predictions with literature data a very close match was found. This illustrates the potential application of in vitro metabolic parameters in risk assessment, through the use of PBPK modeling as a tool to understand and predict in vivo data. From a hypothetical 8 h exposure scenario to 35 ppm trichloroethylene in rats and humans, and assuming that the glutathione S-transferase pathway is responsible for the toxicity of trichloroethylene, it was concluded that humans are less sensitive for trichloroethylene toxicity than rats.     

Early identification of drug-induced impairment of gastric emptying through physiologically based pharmacokinetic (PBPK) simulation of plasma concentration-time profiles in rat

Inhibition of gastric emptying rate can have adverse effects on the absorption of food and nutrients. The absorption phase of the plasma concentration-time profile of a compound administered orally to pre-clinical species reflects among others, the gastric and intestinal transit kinetics, and can thus assist in the early identification of delayed gastric emptying. The purpose of this article is to demonstrate the value of Physiologically Based Pharmacokinetic (PBPK) modelling in the early identification of drug induced impairment of gastric emptying from pharmacokinetic profiles. To our knowledge, this is first time that the value of a generic PBPK model for hypothesis testing has been demonstrated with examples. A PBPK model built in-house using MATLAB package and incorporating absorption, metabolism, distribution, biliary and renal elimination models has been employed for the simulation of concentration-time profiles. PBPK simulations of a few compounds that are currently in drug discovery projects show that the observed initial absorption phase of their concentration-time profiles in rat were consistent with reduced gastric emptying rates. The slow uptake of these compounds into the systemic circulation is reflected in their pharmacokinetic profiles but it is not obvious until PBPK simulations are done. Delayed gastric emptying rates of these compounds in rats were also independently observed in x-ray imaging. PBPK simulations can provide early alerts to drug discovery projects, besides aiding the understanding of complex mechanisms that determine the lineshapes of pharmacokinetic profiles. The application of PBPK simulations in the early detection of gastric emptying problems with existing data and without the need to resort to additional animal studies, is appealing both from an economic and ethical standpoint.     

The use of PBPK modeling across the pediatric age range using propofol as a case

The project SAFEPEDRUG aims to provide guidelines for drug research in children, based on bottom-up and top-down approaches. Propofol, one of the studied model compounds, was selected because it is extensively metabolized in liver and kidney, with an important role for the glucuronidation pathway. Besides, being a lipophilic molecule, it is distributed into fat tissues, from where it redistributes into the systemic circulation. In the past, both bottom-up (Physiologically based pharmacokinetic, PBPK) and top-down approaches (population pharmacokinetic, popPK) were applied to describe its pharmacokinetics (PK). In this work, a combination of the two was used to check their performance to describe PK in children and neonates (both term and preterm) using propofol as a case compound. First, in vitro data was generated in human liver microsomes and recombinant enzymes and used to develop an adult PBPK model in Simcyp^®. Activity adjustment factors (AAFs) were calculated to account for differences between in vitro and in vivo enzyme activity. Clinical data were analyzed using a 3-compartment model in NONMEM. These data were used to construct a retrograde PBPK model and for qualification of the PBPK models. Once an accurate in vivo clearance was obtained accounting for the contribution of the different metabolic pathways, the resulting PBPK models were challenged with new data for qualification. After that, the constructed adult PPBK model for propofol was extrapolated to the pediatric population. Both the default built-in and in vivo derived ontogeny functions were used to do so. The models were qualified by comparing their predicted PK parameters to published values, and by comparison of predicted concentration–time profiles to available clinical data. Clearance values were predicted well, especially when compared with values obtained from trials where long-term sampling was applied, whereas volume of distribution was lower compared to the most common popPK model predictions. Concentration–time profiles were predicted well up until and including the preterm neonatal population. In this work, it was thus shown that PBPK can be used to predict the PK up to and including the preterm neonatal population without the use of pediatric in vivo data. This work adds weight to the need for further development of PBPK models, especially regarding distribution modeling and the use of in vivo derived ontogeny functions.