Informatics and Computational Safety Analysis Staff, Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993-0002, USA.
Mol Nutr Food Res. 2010 Feb;54(2):186-94. doi: 10.1002/mnfr.200900259.
Computational toxicology employing quantitative structure-activity relationship (QSAR) modeling is an evidence-based predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological endpoints of interest such as rodent carcinogenicity. Computational toxicology is being tested for its usefulness to support the safety assessment of drug-related substances (e.g. active pharmaceutical ingredients, metabolites, impurities), indirect food additives, and other applied uses of value for protecting public health including safety assessment of environmental chemicals. The specific use of QSAR as a chemoinformatic tool for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources is investigated here by an external validation study, which is the most stringent scientific method of measuring predictive performance. The external validation statistics for predicting rodent carcinogenicity of 43 phytochemicals, using two computational software programs evaluated at the FDA, are discussed. One software program showed very good performance for predicting non-carcinogens (high specificity), but both exhibited poor performance in predicting carcinogens (sensitivity), which is consistent with the design of the models. When predictions were considered in combination with each other rather than based on any one software, the performance for sensitivity was enhanced, However, Chi-square values indicated that the overall predictive performance decreases when using the two computational programs with this particular data set. This study suggests that complementary multiple computational toxicology software need to be carefully selected to improve global QSAR predictions for this complex toxicological endpoint.
运用定量构效关系（QSAR）模型的计算毒理学是一种基于证据的预测方法，正在被监管机构评估，用于对感兴趣的毒理学终点（如啮齿动物致癌性）进行风险评估和科学决策支持。计算毒理学正在接受测试，以支持与药物相关物质（如活性药物成分、代谢物、杂质）、间接食品添加剂以及其他用于保护公众健康的应用（包括环境化学品的安全评估）的安全性评估。这里通过外部验证研究调查了 QSAR 作为一种化学生信工具用于评估植物药、草药和天然膳食来源中的植物化学物质的啮齿动物致癌潜力的用途，这是衡量预测性能的最严格的科学方法。讨论了在 FDA 评估的两个计算软件程序中，使用外部验证统计数据预测 43 种植物化学物质的啮齿动物致癌性。一个软件程序在预测非致癌物方面表现出非常好的性能（高特异性），但两个程序在预测致癌物方面的性能都很差（敏感性），这与模型的设计一致。当综合考虑预测结果而不是基于任何一个软件程序时，敏感性的性能得到了提高。然而，卡方值表明，当使用这两个计算程序处理这个特定数据集时，整体预测性能会下降。这项研究表明，需要仔细选择互补的多种计算毒理学软件，以改善对这个复杂毒理学终点的全局 QSAR 预测。
