Cronin M T, Basketter D A
School of Pharmacy, Liverpool John Moores University, UK.
SAR QSAR Environ Res. 1994;2(3):159-79. doi: 10.1080/10629369408029901.
There is a regulatory requirement for the potential of a new chemical to cause skin sensitization to be assessed. This requirement is presently fulfilled by the use of animal tests. In this study a data base of heterogeneous organic compounds from the guinea pig maximization test has been subjected to multivariate QSAR analysis. The compounds were described both by whole molecule parameters and structural features associated with likely sites of reactivity. Principal component analysis was applied to the data set and although it functions reasonably well to reduce the dimensionality of a large data matrix, it is only moderately useful as a predictive tool when descriptors were chosen rationally. Stepwise discriminant analysis produces a fourteen parameter model, of which twelve were structural features associated with reactivity. This however predicts only 82.6% of compounds correctly after cross validation. There is trend for the linear discriminant analysis model to predict compounds as non sensitizers, suggesting that the parameters incorporated were not wholly suitable for discriminating between the two classes. Another criticism of linear discriminant analysis is that it may be unable to cope with the likely embedded data structure. With this in mind, the structural alerts may be better employed in an expert system, to identify potential hazard, where they will not suffer the limitations of a statistical model.
对于评估新化学物质引起皮肤致敏的可能性存在监管要求。目前，这一要求通过动物试验来满足。在本研究中，对来自豚鼠最大化试验的异类有机化合物数据库进行了多变量定量构效关系（QSAR）分析。这些化合物通过全分子参数以及与可能的反应位点相关的结构特征来描述。主成分分析应用于数据集，尽管它在合理降低大数据矩阵维度方面功能良好，但在合理选择描述符时，作为预测工具的作用仅为中等。逐步判别分析产生了一个包含14个参数的模型，其中12个是与反应性相关的结构特征。然而，交叉验证后该模型仅能正确预测82.6%的化合物。线性判别分析模型有将化合物预测为非致敏剂的趋势，这表明纳入的参数并不完全适合区分这两类化合物。对线性判别分析的另一个批评是它可能无法应对可能存在的嵌入式数据结构。考虑到这一点，结构警示可能更适合在专家系统中用于识别潜在危害，因为在专家系统中它们不会受到统计模型的限制。
