Kruhlak Naomi L, Contrera Joseph F, Benz R Daniel, Matthews Edwin J
US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
Adv Drug Deliv Rev. 2007 Jan 10;59(1):43-55. doi: 10.1016/j.addr.2006.10.008. Epub 2006 Nov 15.
Active ingredients in pharmaceutical products undergo extensive testing to ensure their safety before being made available to the American public. A consideration during the regulatory review process is the safety of pharmaceutical contaminants and degradents which may be present in the drug product at low levels. Several published guidances are available that outline the criteria for further testing of these impurities to assess their toxic potential, where further testing is in the form of a battery of toxicology assays and the identification of known structural alerts. However, recent advances in the development of computational methods have made available additional resources for safety assessment such as structure similarity searching and quantitative structure-activity relationship (QSAR) models. These methods offer a rapid and cost-effective first-pass screening capability to assess toxicity when conventional toxicology data are limited or lacking, with the potential to identify compounds that would be appropriate for further testing. This article discusses some of the considerations when using computational toxicology methods for regulatory decision support and gives examples of how the technology is currently being applied at the US Food and Drug Administration.
药品中的活性成分在提供给美国公众之前要经过广泛测试以确保其安全性。监管审查过程中要考虑的一个因素是药品中可能以低水平存在的污染物和降解产物的安全性。有几份已发布的指南概述了对这些杂质进行进一步测试以评估其潜在毒性的标准，进一步测试采用一系列毒理学分析和已知结构警示识别的形式。然而，计算方法开发方面的最新进展提供了用于安全评估的额外资源，如结构相似性搜索和定量构效关系（QSAR）模型。当传统毒理学数据有限或缺乏时，这些方法提供了一种快速且经济高效的初筛能力来评估毒性，有可能识别出适合进一步测试的化合物。本文讨论了使用计算毒理学方法进行监管决策支持时的一些注意事项，并举例说明了该技术目前在美国食品药品监督管理局的应用情况。
