Kulkarni S A, Zhu J
Chemistry Research Division, Safe Environments Programme, Health Canada, Ottawa, Ontario, Canada.
SAR QSAR Environ Res. 2008 Jan-Mar;19(1-2):39-54. doi: 10.1080/10629360701843508.
An integrated framework of data analysis has been proposed to systematically address the determination of the domain of applicability (DA) of some commercial Quantitative Structure Activity Relationship ((Q)SAR) models based on the structure of test chemicals. This framework forms one of the important steps in dealing with the growing concerns on reliability of model-based predictions on toxicity of chemicals specifically in the regulatory context. The present study uses some of the well-known mutagenicity and carcinogenicity models that are available within the Casetox (MultiCASE Inc.) and TOPKAT (Accelrys Software Inc.) programs. The approach enumerated in this paper employs chemoinformatics tools that facilitate comparisons of key structural features as well as application of cluster analysis techniques. The approach has been illustrated using a set of eleven chemical structures selected from the Canadian Domestic Substances List (DSL) that are not present in the model training sets, and the efficacy of the approach has also been assessed using seven chemicals with known toxicities. The methodologies presented here could help address the issue of DA of complex (Q)SAR models and at the same time, serve as useful tools for regulators to make a preliminary assessment of (Q)SAR based systems thereby helping the process of hazard-based regulatory assessments of chemicals.
已经提出了一个数据分析的综合框架，以系统地解决基于测试化学品结构确定一些商业定量构效关系（(Q)SAR）模型的适用域（DA）的问题。该框架是应对日益增长的对基于模型的化学品毒性预测可靠性的关注的重要步骤之一，特别是在监管背景下。本研究使用了Casetox（MultiCASE公司）和TOPKAT（Accelrys软件公司）程序中可用的一些著名的致突变性和致癌性模型。本文列举的方法采用了化学信息学工具，便于比较关键结构特征以及应用聚类分析技术。该方法已通过从加拿大国内物质清单（DSL）中选取的一组11种化学结构进行了说明，这些结构不在模型训练集中，并且还使用了7种已知毒性的化学品对该方法的有效性进行了评估。这里提出的方法可以帮助解决复杂(Q)SAR模型的适用域问题，同时，作为监管机构对基于(Q)SAR的系统进行初步评估的有用工具，从而有助于化学品基于危害的监管评估过程。
