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计算机辅助药物代谢动力学预测:数据、模型、事实与误区。

In silico ADME prediction: data, models, facts and myths.

作者信息

Lombardo Franco, Gifford Eric, Shalaeva Marina Y

机构信息

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

出版信息

Mini Rev Med Chem. 2003 Dec;3(8):861-75. doi: 10.2174/1389557033487629.

Abstract

A critical review of a very recent work in the field of in silico ADME prediction is presented with emphasis on the work published during the period 2000-2002, and several other review articles are mentioned in order to offer a broader view of the field. We find that not much progress has been made in developing robust and predictive models, and that the lack of accurate data, together with the use of questionable modeling end-points, has greatly hindered the real progress in defining generally applicable models. Due to the largely empirical nature of QSAR/QSPR approaches, general and truly predictive models for complex phenomena, such as absorption and clearance, may still be chimeric. The development of local models for use within focused chemical series may be the most appropriate way of utilizing in silico ADME predictions, once experience and data have been gained on a given project and/or structural class.

摘要

本文对计算机辅助药物代谢动力学(ADME)预测领域最近的一项工作进行了批判性综述,重点关注2000年至2002年期间发表的研究,并提及了其他几篇综述文章,以便更全面地了解该领域。我们发现,在开发强大且具有预测性的模型方面进展不大,准确数据的缺乏以及使用有问题的建模终点,极大地阻碍了定义通用模型方面的实际进展。由于定量构效关系/定量构性相关(QSAR/QSPR)方法在很大程度上具有经验性质,用于复杂现象(如吸收和清除)的通用且真正具有预测性的模型可能仍然是幻想。一旦在给定项目和/或结构类别上积累了经验和数据,开发适用于特定化学系列的局部模型可能是利用计算机辅助ADME预测的最合适方法。

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