Predicting human pharmacokinetics from preclinical data: volume of distribution.

IF 1.1 Q4 PHARMACOLOGY & PHARMACY Translational and Clinical Pharmacology Pub Date : 2020-12-01 Epub Date: 2020-12-15 DOI:10.12793/tcp.2020.28.e19
Dong-Seok Yim, Suein Choi
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Abstract

This tutorial introduces background and methods to predict the human volume of distribution (Vd) of drugs using in vitro and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: Vd = Vp + ∑ T (VT × ktp ). In this equation, Vp (plasma volume) and VT (tissue volume) are known physiological values, and ktp (tissue plasma partition coefficient) is experimentally measured. Here, the ktp may be predicted by PBPK models because it is known to be correlated with the physicochemical property of drugs and tissue composition (fraction of lipid and water). Thus, PBPK models' evolution to predict human Vd has been the efforts to find a better function giving a more accurate ktp. When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human Vd. Unlike the PBPK method, many different models may be compared to find the best-fitting one in the allometry, a kind of empirical approach. Also, compartmental Vd parameters (e.g., Vc, Vp, and Q) can be predicted in the allometry. Although PBPK and allometric methods have long been used to predict Vd, there is no consensus on method choice. When the discrepancy between PBPK-predicted Vd and allometry-predicted Vd is huge, physiological plausibility of all input and output data (e.g., r2-value of the allometric curve) may be reviewed for careful decision making.

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从临床前数据预测人体药代动力学:分布容积。
本教程介绍利用体外和动物药代动力学(PK)参数预测药物人体分布容积(Vd)的背景和方法。基于生理学的 PK (PBPK) 方法基于我们熟悉的方程式:Vd = Vp + ∑ T (VT × ktp ) 。在该方程中,Vp(血浆容积)和 VT(组织容积)是已知的生理值,而 ktp(组织血浆分配系数)是通过实验测得的。在这里,ktp 可由 PBPK 模型预测,因为已知它与药物的理化性质和组织成分(脂质和水的比例)相关。因此,PBPK 模型在预测人体 Vd 方面的发展一直在努力寻找一个更好的函数来给出更准确的 ktp。如果能获得利用≥ 3 个物种的静脉注射 PK 数据估算出的动物 PK 参数,异速法也可用于预测人体 Vd。与 PBPK 方法不同的是,在异速法中可以对许多不同的模型进行比较,以找到最拟合的模型,这是一种经验方法。此外,在异构法中还可以预测分室 Vd 参数(如 Vc、Vp 和 Q)。尽管 PBPK 和异构法长期以来一直被用于预测 Vd,但在方法选择上并没有达成共识。当 PBPK 预测的 Vd 与异构法预测的 Vd 存在巨大差异时,可对所有输入和输出数据(如异构曲线的 r2 值)的生理学合理性进行审查,以便谨慎决策。
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来源期刊
Translational and Clinical Pharmacology
Translational and Clinical Pharmacology Medicine-Pharmacology (medical)
CiteScore
1.60
自引率
11.10%
发文量
17
期刊介绍: Translational and Clinical Pharmacology (Transl Clin Pharmacol, TCP) is the official journal of the Korean Society for Clinical Pharmacology and Therapeutics (KSCPT). TCP is an interdisciplinary journal devoted to the dissemination of knowledge relating to all aspects of translational and clinical pharmacology. The categories for publication include pharmacokinetics (PK) and drug disposition, drug metabolism, pharmacodynamics (PD), clinical trials and design issues, pharmacogenomics and pharmacogenetics, pharmacometrics, pharmacoepidemiology, pharmacovigilence, and human pharmacology. Studies involving animal models, pharmacological characterization, and clinical trials are appropriate for consideration.
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