Shockley Keith R
Biostatistics and Computational Biology Branch, The National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA.
Drug Discov Today. 2015 Mar;20(3):296-300. doi: 10.1016/j.drudis.2014.10.005. Epub 2014 Oct 23.
In vitro HTS holds much potential to advance drug discovery and provide cell-based alternatives for toxicity testing. In quantitative HTS, concentration-response data can be generated simultaneously for thousands of different compounds and mixtures. However, nonlinear modeling in these multiple-concentration assays presents important statistical challenges that are not problematic for linear models. The uncertainty of parameter estimates obtained from the widely used Hill equation model can be extremely large when using standard designs. Failure to properly consider standard errors of these parameter estimates would greatly hinder chemical genomics and toxicity testing efforts. In this light, optimal study designs should be developed to improve nonlinear parameter estimation; or alternative approaches with reliable performance characteristics should be used to describe concentration-response profiles.
体外高通量筛选在推进药物发现以及为毒性测试提供基于细胞的替代方法方面具有很大潜力。在定量高通量筛选中，可以同时为数千种不同的化合物和混合物生成浓度-反应数据。然而，这些多浓度测定中的非线性建模带来了重要的统计挑战，而线性模型则不存在这些问题。使用标准设计时，从广泛使用的希尔方程模型获得的参数估计值的不确定性可能极大。未能正确考虑这些参数估计值的标准误差将极大地阻碍化学基因组学和毒性测试工作。有鉴于此，应开发最佳研究设计以改进非线性参数估计；或者应使用具有可靠性能特征的替代方法来描述浓度-反应曲线。
