Diller David J, Hobbs Doug W
Department of Molecular Modeling, Pharmacopeia Inc, CN5350, Princeton, NJ 08543-5350, USA.
J Comput Aided Mol Des. 2007 Jul;21(7):379-93. doi: 10.1007/s10822-007-9122-2. Epub 2007 Jun 5.
Blockage of the potassium channel encoded by the human ether-a-go-go related gene (hERG) is well understood to be the root cause of the cardio-toxicity of numerous approved and investigational drugs. As such, a cascade of in vitro and in vivo assays have been developed to filter compounds with hERG inhibitory activity. Quantitative structure activity relationship (QSAR) models are used at the very earliest part of this cascade to eliminate compounds that are likely to have this undesirable activity prior to synthesis. Here a new QSAR technique based on the one-dimensional representation is described in the context of the development of a model to predict hERG inhibition. The model is shown to perform close to the limits of the quality of the data used for model building. In order to make optimal use of the available data, a general robust mathematical scheme was developed and is described to simultaneously incorporate quantitative data, such as IC50 = 50 nM, and qualitative data, such as inactive or IC50 > 30 microM into QSAR models without discarding any experimental information.
人类醚 - 去极化相关基因(hERG)编码的钾通道阻滞是众多已批准和正在研究的药物产生心脏毒性的根本原因,这一点已得到充分理解。因此,已经开发了一系列体外和体内试验来筛选具有hERG抑制活性的化合物。在这一系列试验的最早期阶段,使用定量构效关系(QSAR)模型来在合成之前排除可能具有这种不良活性的化合物。在此,在开发预测hERG抑制的模型的背景下,描述了一种基于一维表示的新QSAR技术。该模型的表现接近用于模型构建的数据质量极限。为了充分利用现有数据,开发并描述了一种通用的稳健数学方案,该方案可在不丢弃任何实验信息的情况下,将定量数据(如IC50 = 50 nM)和定性数据(如无活性或IC50> 30 microM)同时纳入QSAR模型。