Zhang Xiaowei, Newsted John L, Hecker Markus, Higley Eric B, Jones Paul D, Giesy John P
Toxicology Centre, University of Saskatchewan, 44 Campus Drive, Saskatoon SK S7N 5B3, Canada.
Environ Sci Technol. 2009 May 15;43(10):3926-32. doi: 10.1021/es8029472.
Concentration-dependent response relationships provide essential information on the characteristics of chemical-induced effects on toxicological end points, which include effect (inhibition or induction), potency, and efficacy of the chemical. Recent developments in systems biology and high throughputtechnologies have allowed simultaneous examination of many chemicals at multiple end point levels. While this increase in the quantity of information generated offers great potential, it also poses a significant challenge to environmental scientists to efficiently manage and interpret these large data sets. Here we present a novel method, ToxClust, that allows clustering of chemicals on the basis of concentration-response data derived with single or multiple end points. This method utilizes a least distance-searching algorithm (LDSA) to measure the pattern dissimilarity of concentration-response curves between chemicals and their relative toxic potency. ToxClust was tested using simulated data and chemical test data collected from the human H295R cell-based in vitro steroidogenesis assay. ToxClust effectively identified similar patterns of simulated data and responses to the exposure with the five model chemicals and separated them into different groups on the basis of their dissimilarities. These observations demonstrate that ToxClust not only provides an effective data analysis and visualization tool, but also has value in hypothesis generation and mechanism-based chemical classification.
浓度依赖性反应关系提供了有关化学物质对毒理学终点产生影响的特性的重要信息，这些特性包括效应（抑制或诱导）、效力和化学物质的功效。系统生物学和高通量技术的最新发展使得能够在多个终点水平上同时检测多种化学物质。虽然所产生的信息量的增加提供了巨大的潜力，但这也给环境科学家带来了重大挑战，即如何有效地管理和解释这些大型数据集。在此，我们提出了一种新方法ToxClust，该方法能够基于单终点或多终点得出的浓度-反应数据对化学物质进行聚类。此方法利用最小距离搜索算法（LDSA）来测量化学物质之间浓度-反应曲线的模式差异及其相对毒性效力。使用从基于人H295R细胞的体外类固醇生成试验收集的模拟数据和化学测试数据对ToxClust进行了测试。ToxClust有效地识别了模拟数据的相似模式以及对五种模型化学物质暴露的反应，并根据它们的差异将它们分为不同的组。这些观察结果表明，ToxClust不仅提供了一种有效的数据分析和可视化工具，而且在假设生成和基于机制的化学物质分类方面也具有价值。
