Giordanetto Fabrizio, Fossa Paola, Menozzi Giulia, Mosti Luisa
Centre for Computational Science, Department of Chemistry, Queen Mary, University of London, Mile End Road, London E1 4NS, United Kingdom.
J Comput Aided Mol Des. 2003 Jan;17(1):53-64. doi: 10.1023/a:1024557113083.
In PUVA (Psoralen plus UVA) chemotherapy 8-methoxypsoralen is the most widely used compound, although its efficacy is endowed with undesired side effects. In order to have an evident anti-proliferative activity with a reduced phototoxicity, many linear and angular derivatives have been synthesised. In this paper we describe a QSAR study in which, by means of the neural networks methodology, a useful model for predicting biological activity, expressed as ID50 (the UVA dose that reduces to 50% the DNA synthesis in Ehrlich cells), has been derived. A decision tree that is able to discriminate between active and inactive compounds has been built based on recursive partitioning. The study shows the key structural features responsible for the activity and could be a helpful tool in the rational design of new, less toxic, photochemotherapeuthic agents.
在补骨脂素加长波紫外线（PUVA）化疗中，8-甲氧基补骨脂素是应用最广泛的化合物，尽管其疗效伴有不良副作用。为了在降低光毒性的同时具有明显的抗增殖活性，人们合成了许多线性和角形衍生物。在本文中，我们描述了一项定量构效关系（QSAR）研究，通过神经网络方法，得出了一个预测生物活性的有用模型，该活性以ID50表示（使艾氏腹水癌细胞中DNA合成减少50%的长波紫外线剂量）。基于递归划分构建了一个能够区分活性和非活性化合物的决策树。该研究揭示了产生活性的关键结构特征，可能成为合理设计毒性更低的新型光化学治疗药物的有用工具。
