Suppr超能文献

制药研究和制造商协会(PhRMA)关于人体药代动力学预测模型的合作项目,第2部分:人体分布容积预测方法的比较评估

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

作者信息

Jones Rhys Do, Jones Hannah M, Rowland Malcolm, Gibson Christopher R, Yates James W T, Chien Jenny Y, Ring Barbara J, Adkison Kimberly K, Ku M Sherry, He Handan, Vuppugalla Ragini, Marathe Punit, Fischer Volker, Dutta Sandeep, Sinha Vikash K, Björnsson Thorir, Lavé Thierry, Poulin Patrick

机构信息

Modeling and Simulation, DMPK, AstraZeneca Ltd., Macclesfield, Cheshire SK10 3JL, UK.

出版信息

J Pharm Sci. 2011 Oct;100(10):4074-89. doi: 10.1002/jps.22553. Epub 2011 Mar 30.

Abstract

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 (Vss ). 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 Vss 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 Vss accurately for all compounds. Across methods, the maximum success rate in predicting human Vss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed Vss . 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 Vss 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 Vss 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 Vss from several methods available in the literature.

摘要

本研究的目的是评估各种经验性、半机制性和机制性方法在有或无蛋白质结合校正情况下预测人体稳态分布容积(Vss)的性能。制药研究和制造商协会(PhRMA)的成员公司提供了一组来自临床前和临床研究的盲态数据,共有18种具有静脉注射临床药代动力学(PK)数据的药物可用于分析。体内和体外临床前数据通过24种不同方法用于预测Vss。采用了各种统计和异常值技术来评估每种方法的可预测性。没有一种方法能准确预测所有化合物的Vss。在所有方法中,预测人体Vss的最大成功率分别为:预测值在观察到的Vss的10倍、3倍和2倍误差范围内的化合物比例为100%、94%和78%。一般来说,利用体内临床前数据的方法比仅依赖体外数据的方法更具预测性。然而,对于许多化合物,仅可获得来自两个物种(通常是大鼠和狗)的体内数据和/或缺少所需的体外数据,这意味着一些方法无法得到恰当评估。建议最初使用基于体外组织组成的公式来预测临床前物种和人体的Vss,并结合假设和化合物特性进行考量。随着体内数据的获得,应重新评估并合理化这些预测,以表明人体Vss预测中的置信度(不确定性)水平。以这种方式在整合体内数据方面表现出色的前三种方法是Øie-Tozer法、大鼠-狗-人体比例方程法和集总式生理药代动力学(PBPK)方法。总体而言,本研究的科学益处在于从文献中可用的几种方法中更全面地描述人体Vss的预测情况。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验