Suppr超能文献

利用物理化学测量和血浆蛋白结合数据预测中性和碱性药物在人体中的分布容积值

Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data.

作者信息

Lombardo Franco, Obach R Scott, Shalaeva Marina Y, Gao Feng

机构信息

Molecular Properties Group, Pfizer Global Research and Development, Groton Laboratories, Groton, CT 06340, USA.

出版信息

J Med Chem. 2002 Jun 20;45(13):2867-76. doi: 10.1021/jm0200409.

Abstract

We present a method for the prediction of volume of distribution in humans, for neutral and basic compounds. It is based on two experimentally determined physicochemical parameters, ElogD(7.4) and f(i(7.4)), the latter being the fraction of compound ionized at pH 7.4 and on the fraction of free drug in plasma (f(u)). The fraction unbound in tissues (f(ut)), determined via a regression analysis from 64 compounds using the parameters described, is then used to predict VD(ss) via the Oie-Tozer equation. Accuracy of this method was determined using a test set of 14 compounds, and it was demonstrated that human VD(ss) values could be predicted, on average, within or very close to 2-fold of the actual value. The present method is as accurate as reported methods based on animal pharmacokinetic data, using a similar set of compounds, and ranges between 1.62 and 2.20 as mean-fold error. This method has the advantage of being amenable to automation, and therefore fast throughput, it is compound and resources sparing, and it offers a rationale for the reduction of the use of animals in pharmacokinetic studies. A discussion of the potential errors that may be encountered, including errors in the determination of f(u), is offered, and the caveats about the use of computed vs experimentally determined logD and pK(a) values are addressed.

摘要

我们提出了一种预测人体中中性和碱性化合物分布容积的方法。该方法基于两个通过实验测定的物理化学参数,即ElogD(7.4)和f(i(7.4)),后者是化合物在pH 7.4时的离子化分数,以及血浆中游离药物的分数(f(u))。通过使用所述参数对64种化合物进行回归分析确定的组织中未结合分数(f(ut)),然后通过Oie-Tozer方程用于预测稳态分布容积(VD(ss))。使用14种化合物的测试集确定了该方法的准确性,结果表明人体VD(ss)值平均可以在实际值的2倍以内或非常接近实际值的范围内预测。本方法与基于动物药代动力学数据、使用类似化合物集的已报道方法一样准确,平均误差倍数在1.62至2.20之间。该方法的优点是易于自动化,因此通量高,节省化合物和资源,并且为减少药代动力学研究中动物的使用提供了理论依据。文中讨论了可能遇到的潜在误差,包括f(u)测定中的误差,并阐述了关于使用计算得到的logD和pK(a)值与实验测定值的注意事项。

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