应用生理药动学模型评估雷尼替丁片剂生物等效豁免的影响因素

目的：建立雷尼替丁片剂在健康人体内的生理药动学（PBPK）模型,并用该模型模拟各因素对盐酸雷尼替丁片剂生物等效性的影响,为生物等效豁免标准的制定提供参考。方法：查询不同数据库关于雷尼替丁理化参数的相关文献,遵照美国食品与药物监督管理局（FDA）的生物等效研究的指导原则,采用GastroPlus^TM9.5软件建立雷尼替丁注射给药和口服给药的PBPK模型,并通过倍数误差来评价模型的有效性;再根据已建立的PBPK模型对可能影响雷尼替丁片剂生物等效性的各因素进行体内模拟。结果：PBPK模型预测雷尼替丁的药-时曲线与实测值拟合良好。药动学参数最大血药浓度（C_max）、达峰时间（t_max）、血药浓度-时间曲线下总面积（AUC_0-inf）和截止至终末观察点时的血浆药物浓度-时间曲线下面积（AUC_0-t）与实测值接近,倍数误差 < 2。影响因素沉淀时间（90~9 000 s）、胃pH（0.5~6）、十二指肠pH（0.5~8）、溶解度（100~10 000 g·L^-1）对C_max和AUC_0-t值几乎无影响;当胃排空时间在0.125~0.5 h内时,随着胃排空时间延长,C_max略有下降,AUC_0-t基本不变,C_max和AUC_0-t均符合生物等效（BE）标准;当渗透性在（0.62~2.48）×10^-4 cm·s^-1内时,随着渗透性增加,C_max和AUC_0-t均增加;C_max在渗透性为（0.84~1.82×10^-4 ）cm·s^-1时符合BE标准,在渗透性研究范围之内AUC_0-t均符合BE标准;当小肠转运时间在1.586~6.344 h内时,随着小肠转运时间增加,C_max略有增加,AUC_0-t也略有增加,C_max和AUC_0-t都符合BE标准;在120 min内药物溶出度达到85%时,不同制剂与口服溶液在体内的C_max和AUC_0-t是一致的,不会受溶出的影响。结论：所建PBPK模型准确可靠,可用于模拟和评价各因素对BE试验的影响程度,为生物等效豁免标准的制定提供参考。     

基于生理药代动力学模型研究达格列净对肠道和肾脏SGLT蛋白抑制作用

建立并优化达格列净的生理药代动力学(PBPK)模型,预测相关组织药物分布浓度,计算对应浓度对肠段和肾脏近端小管钠葡萄糖协同转运蛋白(SGLTs)的抑制率。根据文献报道的相关数据,建立健康成年人口服给药的PBPK模型,将预测的血药浓度-时间曲线特征、主要药物代谢动学参数(pharmacokinetics, PK)及尿中药物排出量与实测数据进行比较对建立的模型进行验证和优化,为了进一步验证组织分布浓度预测的准确性,建立药物效应动力学模型(pharmacodynamics, PD)对相应时间内尿葡萄糖排泄量(urine glucose excretion, UGE)进行模拟。通过建立成功的模型预测药物在体内各个组织和器官的分布暴露量。模型预测药时曲线特征与实测曲线特征相似,主要PK参数与实测值比值在2倍范围内,表明建立的PBPK模型精确性良好。10 mg达格列净对十二指肠和空肠段钠葡萄糖协同转运蛋白1 (SGLT1s)最大抑制率为1.6%～4.7%,对肾脏近端小管处钠葡萄糖协同转运蛋白2 (SGLT2s)的抑制率高达99.9%。达格列净在10 mg剂量下延缓肠道葡萄糖吸收能力差,可占据肾脏S...     

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。     

CYP 3A5*3基因多态性对肾移植术后他克莫司药代动力学的影响

目的探讨CYP3A5*3基因多态性对肾移植术后他克莫司(免疫抑制药)剂量校正给药2h后浓度的影响。方法选取61例肾移植术后患者,用聚合酶链式反应-限制性片段长度多态性的方法,分析CYP 3A5*3基因型;用微粒酶联免疫吸附法,测定患者他克莫司浓度。并分析CYP 3A5*3基因多态性与他克莫司给药剂量、给药2h浓度(C2)及剂量校正给药2h后浓度(C2/D)的相关性。结果肾移植术后1周及1、3个月,CYP 3A5*1/*1 CYP 3A5*1/*3组和CYP3A5*3/*3组他克莫司剂量比较均无显著性差异。术后1周和1个月,2组间他克莫司C2比较无显著性差异;术后3个月,CYP 3A5*1/*1 CYP 3A5*1/*3组的C2显著低于CYP 3A5*3/*3组(P<0.05)。术后1周及1、3个月,CYP 3A5*1/*1 CYP 3A5*1/*3组的C2/D均明显低于CYP 3A5*3/*3组(P<0.05)。结论肾移植术后,他克莫司C2/D的个体化差异与患者CYP3A5*3基因型密切相关。     

生理药动学模型预测阿戈美拉汀口服给药的体内药动学过程

目的:建立阿戈美拉汀在人体内的生理药动学(PBPK)模型,预测其口服给药后的体内药动学过程。方法:测定不同基因型群体的健康男性空腹口服阿戈美拉汀后的血药浓度,采用Gastro PlusTM软件建立阿戈美拉汀口服给药的PBPK模型,并进行模型的优化和验证。结果:模型拟合阿戈美拉汀的药-时曲线与实测值比较R2均>0.95。预测阿戈美拉汀口服给药后绝对生物利用度为1%~7%;给药后其在人体内广泛分布,各组织/器官的暴露量以肝、脑和红骨髓中为最高,约为血中药物暴露量的2~4倍;食物、年龄、性别均可对阿戈美拉汀口服给药后的药动学过程产生一定的影响。结论:该试验所建立的PBPK模型可较好模拟阿戈美拉汀的体内药动学过程。     

用GastroPlus构建茶碱的早产儿生理药代动力学模型研究

目的构建茶碱的早产儿生理药代动力学(PBPK)模型,并进行药代动力学(PK)行为预测。方法通过文献收集茶碱的理化性质参数和临床试验数据,用Gastro Plus软件搭建PBPK模型,验证在不同人群的准确性及适用性,并用于预测早产儿人群,通过参数优化预测茶碱在早产儿个体的PK行为。结果经验证模型有良好的准确性和适用性,药物浓度-时间曲线下面积AUC_预测/AUC_观测比值在0.85~1.15,且决定系数(R^2)>0.85,观测值与预测曲线的重合度良好,误差较小。结论用Gastro Plus构建的茶碱早产儿PBPK模型能够较好地模拟药物在体内的PK过程,为个体化给药提供参考;同时,补充中国早产儿和婴幼儿PBPK建模的重要生理和解剖学数据,可以提高模拟的准确性。     

PBPK模型在抗菌药物与其他药物相互作用中的应用

细菌感染是临床上常见的并发症，而抗菌药物作为广泛应用的抗感染类药物，常与免疫抑制剂、抗肿瘤类药物等联合使用。由于存在潜在的药物-药物相互作用(DDIs)，抗菌药物与其他药物的共同给药仍然具有挑战性。近年来，基于生理的药动学(PBPK)模型越来越多地用于预测药物-药物相互作用，从而更好的指导治疗方案和药物剂量的选择。本文将对PBPK模型的概念及其在抗菌药物与其他药物相互作用方面的应用作一综述，以期为临床抗菌药物的合理使用提供理论和循证依据。     

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。     

肺移植患者术后使用他克莫司1年CYP3A5、CYP3A4、ABCB1、POR~*28基因多态性与他克莫司个体化用药的关系研究

目的:研究肺移植患者术后使用他克莫司1年CYP3A5、CYP3A4、ABCB1、POR*28基因多态性与他克莫司给药剂量(D)和稳态血药浓度/给药剂量比值(c0/D)的关系。方法:采用回顾性分析方法,选取2017年5月-2018年5月期间在中日友好医院接受肺移植术的46例受试者为研究对象,统计受试者术后使用他克莫司1年后他克莫司的c0和D,并计算c0/D。收集受试者CYP3A5(rs776746)、CYP3A4(rs2242480、rs28371759)、ABCB1(rs1045642、rs2032582、rs1128503)和POR*28(rs1057868)位点的基因型,对基因多态性与D、c0/D的关系进行统计学分析。结果:本研究中涉及位点的基因型频率均符合Hardy-Weinberg平衡(P>0.05)。维持他克莫司c0在治疗窗范围内的条件下,受试者的CYP3A5(rs776746)和CYP3A4(rs2242480)基因型多态性对他克莫司的D、c0/D有显著影响(P<0.05);其他位点各基因型之间的D、c0/D差异均无统计学意义(P>0.05)。联合CYP3A5(rs776746)和CYP3A4(rs2242480)两个位点分析受试者CYP3A代谢型发现,同时携带CYP3A5(rs776746)*1和CYP3A4(rs2242480)*1G等位基因的快代谢型受试者与只携带CYP3A5(rs776746)*1或CYP3A4(rs2242480)*1G等位基因的正常代谢型受试者和不携带CYP3A5(rs776746)*1和CYP3A4(rs2242480)*1G等位基因的慢代谢受试者比较,D、c0/D差异具有统计学意义(P<0.05),其中快代谢型受试者的他克莫司D最高,慢代谢型受试者的他克莫司D最低。结论:检测CYP3A5(rs776746)和CYP3A4(rs2242480)基因多态性对肺移植患者术后使用他克莫司1年后他克莫司的个体化给药具有指导意义。     

与细胞色素P_(450)2D6相关的药物相互作用研究

细胞色素P450（CYP）是一组结构和功能相关的超家族基因编码同工酶。人体内参与药物代谢的CYP有17个基因家族，42个亚家族，64个酶。众所周知，在人体内许多因素如遗传因素、年龄、性别、疾病和环境都可以影响CYP的活性。本文着重综述了近年来国内、外文献报道的与CYP2D6相关的药物相互作用。CYP2D6是一种重要的细胞色素药物代谢酶，除参与代谢一些内源性物质和某些环境中毒性化合物外，主要是参与多种药物的代谢。CYP2D6参与代谢的药物占总CYP代谢药物的30％，同时对手性药物的代谢还呈现立体选择性。CYP2D6是临床上重要的肝药酶之一，与之相关的药物相互作用也十分多见。了解CYP2D6的底物以及相关的药物相互作用对临床合理用药具有十分重要的意义。     

A Mechanistic Physiologically Based Pharmacokinetic-Enzyme Turnover Model Involving both Intestine and Liver to Predict CYP3A Induction-Mediated Drug–Drug Interactions

Cytochrome P450 (CYP) 3A induction-mediated drug–drug interaction (DDI) is one of the major concerns in drug development and clinical practice. The aim of the present study was to develop a novel mechanistic physiologically based pharmacokinetic (PBPK)-enzyme turnover model involving both intestinal and hepatic CYP3A induction to quantitatively predict magnitude of CYP3A induction-mediated DDIs from in vitro data. The contribution of intestinal P-glycoprotein (P-gp) was also incorporated into the PBPK model. First, the pharmacokinetic profiles of three inducers and 14 CYP3A substrates were predicted successfully using the developed model, with the predicted area under the plasma concentration–time curve (AUC) [area under the plasma concentration–time curve] and the peak concentration (C_max) [the peak concentration] in accordance with reported values. The model was further applied to predict DDIs between the three inducers and 14 CYP3A substrates. Results showed that predicted AUC and C_max ratios in the presence and absence of inducer were within twofold of observed values for 17 (74%) of the 23 DDI studies, and for 14 (82%) of the 17 DDI studies, respectively. All the results gave us a conclusion that the developed mechanistic PBPK-enzyme turnover model showed great advantages on quantitative prediction of CYP3A induction-mediated DDIs. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:2819–2836, 2013     

Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling

The occurrence of drug–drug interactions (DDIs) can significantly affect the safety of a patient, and thus assessing DDI risk is important. Recently, physiologically based pharmacokinetic (PBPK) modeling has been increasingly used to predict DDI potential. Here, we present a PBPK modeling concept and strategy. We also surveyed PBPK-related articles about the prediction of DDI potential in humans published up to October 10, 2017. We identified 107 articles, including 105 drugs that fit our criteria, with a gradual increase in the number of articles per year. Studies on antineoplastic and immunomodulatory drugs (26.7%) contributed the most to published PBPK models, followed by cardiovascular (20.0%) and anti-infective (17.1%) drugs. Models for specific products such as herbal products, therapeutic protein drugs, and antibody–drug conjugates were also described. Most PBPK models were used to simulate cytochrome P450 (CYP)-mediated DDIs (74 drugs, of which 85.1% were CYP3A4-mediated), whereas some focused on transporter-mediated DDIs (15 drugs) or a combination of CYP and transporter-mediated DDIs (16 drugs). Full PBPK, first-order absorption modules and Simcyp^® software were predominantly used for modeling. Recently, DDI predictions associated with genetic polymorphisms, special populations, or both have increased. The 107 published articles reasonably predicted the DDI potentials, but further studies of physiological properties and harmonization of in vitro experimental designs are required to extend the application scope, and improvement of DDI predictions using PBPK modeling will be possible in the future.     

Application of a physiologically based pharmacokinetic model for the prediction of mirabegron plasma concentrations in a population with severe renal impairment

We previously verified a physiologically based pharmacokinetic (PBPK) model for mirabegron in healthy subjects using the Simcyp Simulator by incorporating data on the inhibitory effect on cytochrome P450 (CYP) 2D6 and a multi‐elimination pathway mediated by CYP3A4, uridine 5′‐diphosphate‐glucuronosyltransferase (UGT) 2B7 and butyrylcholinesterase (BChE). The aim of this study was to use this PBPK model to assess the magnitude of drug–drug interactions (DDIs) in an elderly population with severe renal impairment (sRI), which has not been evaluated in clinical trials. We first determined the system parameters, and meta‐analyses of literature data suggested that the abundance of UGT2B7 and the BChE activity in an elderly population with sRI was almost equivalent to and 20% lower than that in healthy young subjects, respectively. Other parameters, such as the CYP3A4 abundance, for an sRI population were used according to those built into the Simcyp Simulator. Second, we confirmed that the PBPK model reproduced the plasma concentration–time profile for mirabegron in an sRI population (simulated area under the plasma concentration–time curve (AUC) was within 1.5‐times that of the observed value). Finally, we applied the PBPK model to simulate DDIs in an sRI population. The PBPK model predicted that the AUC for mirabegron with itraconazole (a CYP3A4 inhibitor) was 4.12‐times that in healthy elderly subjects administered mirabegron alone, and predicted that the proportional change in AUC for desipramine (a CYP2D6 substrate) with mirabegron was greater than that in healthy subjects. In conclusion, the PBPK model was verified for the purpose of DDI assessment in an elderly population with sRI.     

Translational aspects of cytochrome P450-mediated drug–drug interactions: A case study with clopidogrel

Multimorbidity, polypharmacotherapy and drug interactions are increasingly common in the ageing population. Many drug–drug interactions (DDIs) are caused by perpetrator drugs inhibiting or inducing cytochrome P450 (CYP) enzymes, resulting in alterations of the plasma concentrations of a victim drug. DDIs can have a major negative health impact, and in the past, unrecognized DDIs have resulted in drug withdrawals from the market. Signals to investigate DDIs may emerge from a variety of sources. Nowadays, standard methods are widely available to identify and characterize the mechanisms of CYP-mediated DDIs in vitro. Clinical pharmacokinetic studies, in turn, provide experimental data on pharmacokinetic outcomes of DDIs. Physiologically based pharmacokinetic (PBPK) modelling utilizing both in vitro and in vivo data is a powerful tool to predict different DDI scenarios. Finally, epidemiological studies can provide estimates on the health outcomes of DDIs. Thus, to fully characterize the mechanisms, clinical effects and implications of CYP-mediated DDIs, translational research approaches are required. This minireview provides an overview of translational approaches to study CYP-mediated DDIs, going beyond regulatory DDI guidelines, and an illustrative case study of how the DDI potential of clopidogrel was unveiled by combining these different methods.     

Physiologically Based Modeling of Pravastatin Transporter-Mediated Hepatobiliary Disposition and Drug-Drug Interactions

Purpose

To develop physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics and drug-drug interactions (DDI) of pravastatin, using the in vitro transport parameters.

Methods

In vitro hepatic sinusoidal active uptake, passive diffusion and canalicular efflux intrinsic clearance values were determined using sandwich-culture human hepatocytes (SCHH) model. PBPK modeling and simulations were implemented in Simcyp (Sheffield, UK). DDI with OATP1B1 inhibitors, cyclosporine, gemfibrozil and rifampin, was also simulated using inhibition constant (Ki) values.

Results

SCHH studies suggested active uptake, passive diffusion and efflux intrinsic clearance values of 1.9, 0.5 and 1.2?μL/min/10⁶cells, respectively, for pravastatin. PBPK model developed, using transport kinetics and scaling factors, adequately described pravastatin oral plasma concentration-time profiles at different doses (within 20% error). Model based prediction of DDIs with gemfibrozil and rifampin was similar to that observed. However, pravastatin-cyclosporine DDI was underpredicted (AUC ratio 4.4 Vs ~10). Static (R-value) model predicted higher magnitude of DDI compared to the AUC ratio predicted by the PBPK modeling.

Conclusions

PBPK model of pravastatin, based on in vitro transport parameters and scaling factors, was developed. The approach described can be used to predict the pharmacokinetics and DDIs associated with hepatic uptake transporters.     

Dealing with the complex drug–drug interactions: Towards mechanistic models

Unmanageable severe adverse events caused by drug‐drug interactions (DDIs), leading to market withdrawals or restrictions in the clinical usage, are increasingly avoided with the improvement in our ability to predict such DDIs quantitatively early in drug development. However, significant challenges arise in the evaluation and/or prediction of complex DDIs caused by inhibitor drugs and/or metabolites that affect not one but multiple pathways of drug clearance. This review summarizes the discussion topics at the 2013 AAPS symposium on “Dealing with the complex drug‐drug interactions: towards mechanistic models”. Physiologically based pharmacokinetic (PBPK) models, in combination with the established in vitro‐to‐in vivo extrapolations of intestinal and hepatic disposition, have been successfully applied to predict clinical pharmacokinetics and DDIs, especially for drugs with CYP‐mediated metabolism, and to explain transporter‐mediated and complex DDIs. Although continuous developments are being made towards improved mechanistic prediction of the transporter‐enzyme interplay in the hepatic and intestinal disposition and characterizing the metabolites contribution to DDIs, the prediction of DDIs involving them remains difficult. Regulatory guidelines also recommended use of PBPK modeling for the quantitative prediction and evaluation of DDIs involving multiple perpetrators and metabolites. Such mechanistic modeling approaches culminate to the consensus that modeling is helpful in predicting DDIs or quantitatively rationalizing the clinical findings in complex situations. Furthermore, they provide basis for the prediction and/or understanding the pharmacokinetics in populations like patients with renal impairment, pediatrics, or various ethnic groups where the conduct of clinical studies might not be feasible in early drug development stages and yet some guidance on management of dosage is necessary. Copyright © 2014 John Wiley & Sons, Ltd.     

Mechanistic Modeling to Predict the Transporter- and Enzyme-Mediated Drug-Drug Interactions of Repaglinide

Purpose

Quantitative prediction of complex drug-drug interactions (DDIs) is challenging. Repaglinide is mainly metabolized by cytochrome-P-450 (CYP)2C8 and CYP3A4, and is also a substrate of organic anion transporting polypeptide (OATP)1B1. The purpose is to develop a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics and DDIs of repaglinide.

Methods

In vitro hepatic transport of repaglinide, gemfibrozil and gemfibrozil 1-O-β-glucuronide was characterized using sandwich-culture human hepatocytes. A PBPK model, implemented in Simcyp (Sheffield, UK), was developed utilizing in vitro transport and metabolic clearance data.

Results

In vitro studies suggested significant active hepatic uptake of repaglinide. Mechanistic model adequately described repaglinide pharmacokinetics, and successfully predicted DDIs with several OATP1B1 and CYP3A4 inhibitors (<10% error). Furthermore, repaglinide-gemfibrozil interaction at therapeutic dose was closely predicted using in vitro fraction metabolism for CYP2C8 (0.71), when primarily considering reversible inhibition of OATP1B1 and mechanism-based inactivation of CYP2C8 by gemfibrozil and gemfibrozil 1-O-β-glucuronide.

Conclusions

This study demonstrated that hepatic uptake is rate-determining in the systemic clearance of repaglinide. The model quantitatively predicted several repaglinide DDIs, including the complex interactions with gemfibrozil. Both OATP1B1 and CYP2C8 inhibition contribute significantly to repaglinide-gemfibrozil interaction, and need to be considered for quantitative rationalization of DDIs with either drug.     

Prediction of drug‐drug interactions using physiologically‐based pharmacokinetic models of CYP450 modulators included in Simcyp software

In recent years, physiologically based PharmacoKinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics. It has been demonstrated to be informative and helpful to quantify the modification in drug exposure due to specific physio‐pathological conditions, age, genetic polymorphisms, ethnicity and particularly drug–drug interactions (DDIs). In this paper, the prediction success of DDIs involving various cytochrome P450 isoenzyme (CYP) modulators namely ketoconazole (a competitive inhibitor of CYP3A), itraconazole (a competitive inhibitor of CYP3A), clarithromycin (a mechanism‐based inhibitor of CYP3A), quinidine (a competitive inhibitor of CYP2D6), paroxetine (a mechanism‐based inhibitor of CYP2D6), ciprofloxacin (a competitive inhibitor of CYP1A2), fluconazole (a competitive inhibitor of CYP2C9/2C19) and rifampicin (an inducer of CYP3A) were assessed using Simcyp^® software. The aim of this report was to establish confidence in each CYP‐specific modulator file so they can be used in the future for the prediction of DDIs involving new victim compounds. Our evaluation of these PBPK models suggested that they can be successfully used to evaluate DDIs in untested scenarios. The only noticeable exception concerned a quinidine inhibitor model that requires further improvement. Additionally, other important aspects such as model validation criteria were discussed.     

塞来昔布对阿戈美拉汀抑制作用的体内外研究

目的：研究塞来昔布对阿戈美拉汀的体内外抑制作用及机制。方法：鼠肝微粒体体外实验得到塞来昔布对阿戈美拉汀的抑制机制,并在大鼠体内得到验证。因此,可通过人肝脏微粒体和重组CYP2C9.1蛋白体外实验预测塞来昔布在人体中对阿戈美拉汀的抑制作用和可能机制。结果：塞来昔布表现出明显抑制作用,该作用在CYP2C9.1中呈现浓度依赖性,而在鼠肝微粒体中表现为非竞争性抑制。结论：塞来昔布联合阿戈美拉汀进行抑郁治疗时可能会对后者产生非竞争抑制,这一作用能允许降低阿戈美拉汀的使用剂量,从而降低不良反应的发生,提高治疗的安全性。