Benigni R
Istituto Superiore di Sanità, Laboratory of Comparative Toxicology and Ecotoxicology, Rome, Italy.
Mutat Res. 1995 Feb;334(1):103-13. doi: 10.1016/0165-1161(95)90036-5.
Within a recent comparative exercise, different approaches to the prediction of rodent carcinogenicity were challenged on a common set of chemicals bioassayed by the U.S. National Toxicology Program. The approaches were of very different natures. Some prediction systems looked for relationships between carcinogenicity and other, more quickly detectable biological events (activity-activity relationships, AAR). Some approaches tended to find structure-activity relationships (SAR). To give an objective evaluation of the results of the exercise, we have analyzed the rodent results and the predictions with the multivariate data analysis methods. The calculated performances varied according to the adopted carcinogenicity classification of the chemicals. When the four rodent results were summarized into a final + or - call, the Tennant approach (AAR method) showed the best performance (about 75% accuracy), whereas the best SAR systems had 60-65% accuracy. A common limitation of almost all the systems was the lack of specificity (too many false positives). Based on these results, better concordance was obtained when the input information was the very costly (and closer to the final endpoint) biological data, rather than the inexpensive (and farther from the endpoint) knowledge of the chemical structure. However, when the rodent results were summarized into a carcinogenicity classification that maintained, to some extent, the gradation intrinsic to the original experimental data, the performance of the AAR systems declined, and the SAR approaches showed a better performance. The difficulty in evaluating the various approaches was further complicated because of a fundamental difference in the approaches themselves: some approaches were 'pure' prediction methods (i.e. their predictions were rigorously based on information not inclusive of carcinogenicity); other approaches (e.g. Tennant, Weisburger) used 'mixed' information, inclusive of known carcinogenicity results from experiments performed before the NTP bioassays. As far as the SAR systems are concerned, their sets of predictions showed a fundamental similarity. This happened in spite of the extremely different procedures adopted to treat the chemical formula (initial information): very simple calculations (Benigni), intuition of the experts (Weisburger and Lijinsky), sophisticated computer programs (TOPKAT and CASE). The results of the Bakale Ke method, based on the experimental measurement of the chemical electrophilicity, and of the Salmonella typhimurium mutagenicity assay were similar to the patterns of predictions of the SAR methods.
在最近的一项对比研究中,针对美国国家毒理学计划生物测定的一组常见化学品,对预测啮齿动物致癌性的不同方法提出了挑战。这些方法性质迥异。一些预测系统寻找致癌性与其他更易检测的生物学事件之间的关系(活性-活性关系,AAR)。一些方法倾向于寻找构效关系(SAR)。为了对该研究结果进行客观评估,我们使用多元数据分析方法分析了啮齿动物实验结果和预测结果。计算得出的性能根据所采用的化学品致癌性分类而有所不同。当将四项啮齿动物实验结果总结为最终的阳性或阴性判定时,坦南特方法(AAR方法)表现最佳(准确率约为75%),而最佳的SAR系统准确率为60 - 65%。几乎所有系统的一个共同局限是缺乏特异性(假阳性过多)。基于这些结果,当输入信息是成本高昂(且更接近最终终点)的生物学数据而非成本低廉(且离终点较远)的化学结构知识时,能获得更好的一致性。然而,当将啮齿动物实验结果总结为某种程度上保留原始实验数据固有等级的致癌性分类时,AAR系统的性能下降,而SAR方法表现出更好的性能。由于这些方法本身存在根本差异,评估各种方法的难度进一步加大:一些方法是“纯粹”的预测方法(即其预测严格基于不包含致癌性的信息);其他方法(如坦南特、魏斯伯格方法)使用“混合”信息,包括NTP生物测定之前进行的实验中已知的致癌性结果。就SAR系统而言,它们的预测集显示出基本的相似性。尽管处理化学式(初始信息)所采用的程序极为不同:非常简单的计算(贝尼尼方法)、专家的直觉(魏斯伯格和利金斯基方法)、复杂的计算机程序(TOPKAT和CASE方法),但情况依然如此。基于化学亲电性实验测量的巴卡莱·凯方法的结果以及鼠伤寒沙门氏菌致突变性测定的结果与SAR方法的预测模式相似。