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。     

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。     

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。     

药代动力学和药效学结合模型的研究进展

药代动力学-药效学结合模型是将药代动力学和药效学结合起来研究的模型，能描述和预测一定剂量方案下药物的效应-时间过程，还能解释造成这种效应一时间过程的原因。这种结合模型可应用于药物开发的临床前和临床试验的各个阶段。在临床前试验阶段可用于评价药物的体内效价和固有活性、剂型和给药方案的选择及优化等；在临床试验阶段则可用于估算给药剂量一浓度一效应或毒性之间的关系，以及年龄、性另q等对药效的影响等，从而满足新药开发和临床试验的要求。本文综述了近年来的药代动力学一药效学结合模型及其在药物研究开发领域中的应用。     

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...     

药代动力学体外测定方法新进展

药物代谢和药代动力学面临的一个主要挑战应尽可能早地由体外模型数据对人体做出预测。近期在美国佛罗里达州的奥兰多召开药物代谢的体外筛选会议，报道了此领域的新进展。任何可预测性的体外系统必须发展新型的测定法、正确的体内系统模型及增加对体内生物学知识的了解。细胞色素P450（CYP）表达对许多化学物质代谢过程起关键作用，因此可用于预测代谢能力。     

人体关键药代动力学参数预测方法

分布容积、清除率、半衰期和生物利用度是重要的药物代谢动力学参数,决定着药物在体内的暴露程度与暴露时间,在新药开发过程中尽早预测人体内这些参数对选择与优化潜在新药有重要价值。本文综述了采用临床前药代动力学实验数据、体外吸收与代谢数据、化合物理化性质、计算机模拟等多种方法预测人体内关键药代动力学参数的研究及其进展。     

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。     

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。     

药代动力学-药效动力学结合模型在中药研究中的应用

药代动力学-药效动力学(Pharmacokinetic-pharmacodynamic,PK/PD)结合模型是研究中药体内代谢过程、药物效应及二者联系的有效工具,对于中药作用机制研究、临床用药优化有重要的参考价值。建立能体现中医药特色的PK/PD结合模型十分必要。该文针对目前PK/PD结合模型在中药研究领域的应用现状作了系统的阐述,并就中药效应物质基础的确定、效应指标的选择等关键问题进行探讨并提出建议,以期为今后的相关研究提供参考。     

Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro,In Vivo,PBPK and QSAR Methods

Volume of distribution at steady state (V_ss) is an important pharmacokinetic parameter of a drug candidate. In this study, V_ss prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods. Tissue fraction unbound (f_u,t) method with adipose and muscle also provided high V_ss prediction accuracy. Overall, the high performing methods for human V_ss prediction are the global QSAR models, Øie-Tozer and equivalency methods from scaling of preclinical species, as well as PBPK methods with Kp scalar from preclinical species. Certain input parameter ranges rendered PBPK models inaccurate due to mass balance issues. These were addressed using appropriate theoretical limit checks. Prediction accuracy of tissue Kp were also examined. The f_u,t method predicted Kp values more accurately than the PBPK methods for adipose, heart and muscle. All the methods overpredicted brain Kp and underpredicted liver Kp due to transporter effects. Successful V_ss prediction involves strategic integration of in silico, in vitro and in vivo approaches.     

PhRMA CPCDC initiative on predictive models of human pharmacokinetics,part 2: Comparative assessment of prediction methods of human volume of distribution

The objective of this study was to evaluate the performance of various empirical, semimechanistic and mechanistic methodologies with and without protein binding corrections for the prediction of human volume of distribution at steady state (V_ss). PhRMA member companies contributed a set of blinded data from preclinical and clinical studies, and 18 drugs with intravenous clinical pharmacokinetics (PK) data were available for the analysis. In vivo and in vitro preclinical data were used to predict V_ss by 24 different methods. Various statistical and outlier techniques were employed to assess the predictability of each method. There was not simply one method that predicts V_ss accurately for all compounds. Across methods, the maximum success rate in predicting human V_ss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed V_ss. Generally, the methods that made use of in vivo preclinical data were more predictive than those methods that relied solely on in vitro data. However, for many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. It is recommended to initially use the in vitro tissue composition‐based equations to predict V_ss in preclinical species and humans, putting the assumptions and compound properties into context. As in vivo data become available, these predictions should be reassessed and rationalized to indicate the level of confidence (uncertainty) in the human V_ss prediction. The top three methods that perform strongly at integrating in vivo data in this way were the Øie–Tozer, the rat –dog–human proportionality equation, and the lumped‐PBPK approach. Overall, the scientific benefit of this study was to obtain greater characterization of predictions of human V_ss from several methods available in the literature. © 2011 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4074–4089, 2011     

Successful Prediction of Human Pharmacokinetics by Improving Calculation Processes of Physiologically Based Pharmacokinetic Approach

The physiologically based pharmacokinetics (PBPK) model is a major mechanistic approach for predicting human pharmacokinetics (PK) using drug-specific and physiological parameters but has been difficult to use for human PK prediction with acceptable accuracy. Here, we report a newly developed PBPK approach that incorporates the mechanism of albumin-mediated membrane penetration in the liver and interspecies correlation for unbound tissue fractions. To verify the utility of our PBPK approach, we used 12 drugs that are mainly eliminated by hepatic metabolism to compare the prediction accuracy with a conventional PBPK approach and to observe human PK parameters. We found the predictive accuracy for total clearance (CL_tot), distribution volume at the steady state (V_ss), elimination half-life (t_1/2), and plasma concentration at the last measurable time point (C_last) of our PBPK approach to show better absolute average fold error and percentage within 2-fold error (1.6-1.8 and 67%-92%, respectively) compared with values obtained from the conventional PBPK approach (2.1-2.4 and 42%-67%, respectively). As our approach can use parameters obtained in early drug screening, it could help accelerate successful nomination of drug candidates by optimizing the pharmacokinetics of new chemical entities by directly using predicted human PK profiles.     

A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V_ss) in humans under in vivo conditions. This correlation method demonstrated inaccurate predictions of V_ss for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V_ss of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.     

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.     

Microdialysis: the Key to Physiologically Based Model Prediction of Human CNS Target Site Concentrations

Despite the enormous research efforts that have been put into the development of central nervous system (CNS) drugs, the success rate in this area is still disappointing. To increase the successful rate in the clinical trials, first the problem of predicting human CNS drug distribution should be solved. As it is the unbound drug that equilibrates over membranes and is able to interact with targets, especially knowledge on unbound extracellular drug concentration-time profiles in different CNS compartments is important. The only technique able to provide such information in vivo is microdialysis. Also, obtaining CNS drug distribution data from human subjects is highly limited, and therefore, we have to rely on preclinical approaches combined with physiologically based pharmacokinetic (PBPK) modeling, taking unbound drug CNS concentrations into account. The next step is then to link local CNS pharmacokinetics to target interaction kinetics and CNS drug effects. In this review, system properties and small-molecule drug properties that together govern CNS drug distribution are summarized. Furthermore, the currently available approaches on prediction of CNS pharmacokinetics are discussed, including in vitro, in vivo, ex vivo, and in silico approaches, with special focus on the powerful combination of in vivo microdialysis and PBPK modeling. Also, sources of variability on drug kinetics in the CNS are discussed. Finally, remaining gaps and challenges are highlighted and future directions are suggested.     

Dose Selection Based on Physiologically Based Pharmacokinetic (PBPK) Approaches

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration–time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.     

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein‐bound drugs and the impact of errors in plasma protein binding

Predicting the pharmacokinetics of highly protein‐bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration–time profiles for 22 highly protein‐bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well‐stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration–time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2‐fold error was obtained for the terminal elimination half‐life (t_1/2, 100% of drugs), peak plasma concentration (C_max, 100%), area under the plasma concentration–time curve (AUC_0‐t, 95.4%), clearance (CL_h, 95.4%), mean residence time (MRT, 95.4%) and steady state volume (V_ss, 90.9%). The impact of f_up errors on CL_h and V_ss prediction was evaluated. Errors in f_up resulted in proportional errors in clearance prediction for low‐clearance compounds, and in V_ss prediction for high‐volume neutral drugs. For high‐volume basic drugs, errors in f_up did not propagate to errors in V_ss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd.     

Prediction of Human Pharmacokinetics from Preclinical Information of Rhein,an Antidiabetic Nephropathy Drug,Using a Physiologically Based Pharmacokinetic Model

The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model of rhein to predict human pharmacokinetics before dosing for the first time in human beings. After oral administration of rhein at the doses of 35, 70 and 140 mg/kg in rat, rhein had the following mean plasma pharmacokinetic properties: t_1/2 of 3.2, 3.6 and 4.3 hr, AUC_∞ of 69.5, 164.3 and 237.8 μg/h/ml and CL/F of 503.4, 426.1 and 588.8 ml/hr/kg, respectively. In vitro, the intrinsic clearance (Cl_int) of rhein in cytochrome P450 (CYP450), UDP‐glucuronosyltransferase (UGT) and sulfotransferase (SULT) metabolism of rat was 0.6, 7.8, and 5.5 μl/min/mg protein, respectively. The Cl_int of rhein in CYP450, UGT and SULT of human beings was 0.10, 1.36 and 0.68 μl/min/mg protein. The rat pharmacokinetics and the metabolism data in vitro were used to construct the PBPK model of rhein, and the observed plasma drug concentration profiles of rhein in rat were validated by a PBPK model. Subsequently, the plasma drug concentration profiles of human beings by the present PBPK model were validated by experimental data in human beings accurately.