Enslein K, Gombar V K, Blake B W, Maibach H I, Hostynek J J, Sigman C C, Bagheri D
Health Designs, Inc., Rochester, New York, USA.
Food Chem Toxicol. 1997 Oct-Nov;35(10-11):1091-8. doi: 10.1016/s0278-6915(97)87277-8.
We have developed quantitative structure-toxicity relationship (QSTR) models for assessing dermal sensitization using guinea pig maximization test (GPMT) results. The models are derived from 315 carefully evaluated chemicals. There are two models, one for aromatics (excluding one-benzene-ring compounds), and the other for aliphatics and one-benzene-ring compounds. For sensitizers, the models can resolve whether they are weak/moderate or severe sensitizers. The statistical methodology, based on linear discriminant analysis, incorporates an optimum prediction space (OPS) algorithm. This algorithm ensures that the QSTR model will be used only to make predictions on query structures which fall within its domain. Calculation of the similarities between a query structure and the database compounds from which the applicable model was developed are used to validate each skin sensitization assessment. The cross-validated specificity of the equations ranges between 81 and 91%, and the sensitivity between 85 and 95%. For an independent test set, specificity is 79%, and sensitivity 82%.
我们利用豚鼠最大化试验（GPMT）结果开发了用于评估皮肤致敏性的定量结构-毒性关系（QSTR）模型。这些模型源自315种经过仔细评估的化学品。有两个模型，一个用于芳香族化合物（不包括单苯环化合物），另一个用于脂肪族化合物和单苯环化合物。对于致敏剂，这些模型可以区分它们是弱/中度致敏剂还是严重致敏剂。基于线性判别分析的统计方法纳入了最优预测空间（OPS）算法。该算法确保QSTR模型仅用于对属于其定义域的查询结构进行预测。通过计算查询结构与开发适用模型所依据的数据库化合物之间的相似度来验证每次皮肤致敏性评估。这些方程的交叉验证特异性在81%至91%之间，灵敏度在85%至95%之间。对于一个独立测试集，特异性为79%，灵敏度为82%。
