National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia.
Mol Divers. 2010 Aug;14(3):581-94. doi: 10.1007/s11030-009-9190-4. Epub 2009 Aug 15.
The new European chemicals regulation Registration, Evaluation, Authorization and Restriction of Chemicals entered into force in June 2007 and accelerated the development of quantitative structure-activity relationship (QSAR) models for a variety of endpoints, including carcinogenicity. Here, we would like to present quantitative (continuous) and qualitative (categorical) models for non-congeneric chemicals for prediction of carcinogenic potency. A dataset of 805 substances was obtained after a preliminary screening of findings of rodent carcinogenicity for 1,481 chemicals accessible via Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network originated from the Lois Gold Carcinogenic Potency Database (CPDB). Twenty seven two-dimensional MDL descriptors were selected using Kohonen mapping and principal component analysis. The counter propagation artificial neural network (CP ANN) technique was applied. Quantitative models were developed exploring the relationship between the experimental and predicted carcinogenic potency expressed as a tumorgenic dose TD(50) for rats. The obtained models showed low prediction power with correlation coefficient less than 0.5 for the test set. In the next step, qualitative models were developed. We found that the qualitative models exhibit good accuracy for the training set (92%). The model demonstrated good predicted performance for the test set. It was obtained accuracy (68%), sensitivity (73%), and specificity (63%). We believe that CP ANN method is a good in silico approach for modeling and predicting rodent carcinogenicity for non-congeneric chemicals and may find application for other toxicological endpoints.
新的欧洲化学品法规——注册、评估、授权和限制化学品于 2007 年 6 月生效，加速了各种终点（包括致癌性）的定量构效关系（QSAR）模型的开发。在这里，我们想为非同系化学品呈现定量（连续）和定性（分类）模型，以预测致癌效力。在对通过分布式结构可搜索毒性（DSSTox）公共数据库网络可获得的 1481 种化学品的啮齿动物致癌性研究结果进行初步筛选后，获得了 805 种物质的数据集，这些化学品来源于 Lois Gold 致癌效力数据库（CPDB）。使用 Kohonen 映射和主成分分析选择了 27 个二维 MDL 描述符。应用了反向传播人工神经网络（CP ANN）技术。开发了定量模型，探索了实验和预测致癌效力之间的关系，以大鼠的肿瘤形成剂量 TD（50）表示。所得模型对于测试集的相关系数小于 0.5，表明预测能力较低。在下一步中，开发了定性模型。我们发现定性模型对于训练集具有良好的准确性（92％）。该模型对测试集的预测性能也很好。它的准确率为 68％，灵敏度为 73％，特异性为 63％。我们认为 CP ANN 方法是对非同系化学品进行建模和预测啮齿动物致癌性的良好计算方法，并且可能适用于其他毒理学终点。
