National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia.
Molecules. 2010 Mar 22;15(3):1987-99. doi: 10.3390/molecules15031987.
Reproductive toxicity is an important regulatory endpoint, which is required in registration procedures of chemicals used for different purposes (for example pesticides). The in vivo tests are expensive, time consuming and require large numbers of animals, which must be sacrificed. Therefore an effort is ongoing to develop alternative In vitro and in silico methods to evaluate reproductive toxicity. In this review we describe some modeling approaches. In the first example we describe the CAESAR model for prediction of reproductive toxicity; the second example shows a classification model for endocrine disruption potential based on counter propagation artificial neural networks; the third example shows a modeling of relative binding affinity to rat estrogen receptor, and the fourth one shows a receptor dependent modeling experiment.
生殖毒性是一个重要的监管终点，这是用于不同目的（例如农药）的化学品注册程序所必需的。体内试验昂贵、耗时且需要大量动物，这些动物必须被牺牲。因此，人们正在努力开发替代的体外和计算方法来评估生殖毒性。在这篇综述中，我们描述了一些建模方法。在第一个例子中，我们描述了 CAESAR 模型，用于预测生殖毒性；第二个例子展示了基于反向传播人工神经网络的内分泌干扰潜力分类模型；第三个例子展示了相对结合亲和力到大鼠雌激素受体的建模，第四个例子展示了受体依赖的建模实验。
