Benigni R, Richard A M
Laboratory of Comparative Toxicology and EcoToxicology, Istituto Superiore di Sanitá, Rome, Italy.
Mutat Res. 1996 Nov 4;371(1-2):29-46. doi: 10.1016/s0165-1218(96)90092-0.
There is a strong motivation to develop QSAR models for toxicity prediction for use in screening, for setting testing priorities, and for reducing reliance on animal testing. Decisions must be made daily by toxicologists in governments and industry to direct limited testing to the most urgent public health problems, and to direct the types of chemical synthesis and product development efforts undertaken. This need has motivated attempts to construct general QSAR models (e.g., for rodent carcinogenicity), not tailored to congeneric series of chemicals. These various attempts have provided interesting and important scientific evidence; however, they have also shared a limited overall performance. The goal of this paper is to illustrate, by two unrelated actual examples of QSARs for mutagens and carcinogens, some fundamental problems relative to the application of general QSAR approaches to noncongeneric chemicals. Both examples consider data sets that are noncongeneric in a chemical structure and mechanism of action sense: in the first case, a mean mutagenic potency defined as an average over multiple genetic toxicity endpoints, and, in the second case, the NTP two-sexes, two species rodent carcinogenicity bioassay results for 280 carcinogens and noncarcinogens. The problems encountered with the QSAR analyses of these two cases indicate that a successful approach to the problem of QSAR modeling of noncongeneric data will need to consider the multidimensional nature of the problem in both a chemical and a biological sense. Since different chemical classes represent largely independent action mechanisms, some means for extracting local QSARs for constituent classes will be necessary. Alternatively, a general QSAR derived for a noncongeneric data set will need to be scrutinized and decomposed along chemical class lines in order to establish boundaries for application and confidence levels for prediction.
开发用于毒性预测的定量构效关系（QSAR）模型具有强烈的动机，可用于筛选、确定测试优先级以及减少对动物试验的依赖。政府和行业的毒理学家每天都必须做出决策，将有限的测试导向最紧迫的公共卫生问题，并指导所进行的化学合成和产品开发工作的类型。这种需求促使人们尝试构建通用的QSAR模型（例如用于啮齿动物致癌性），而不是针对同类化学物质系列量身定制。这些不同的尝试提供了有趣且重要的科学证据；然而，它们的整体表现也很有限。本文的目的是通过两个不相关的实际例子，即关于诱变剂和致癌物的QSAR，来说明将通用QSAR方法应用于非同类化学物质时存在的一些基本问题。这两个例子都考虑了在化学结构和作用机制方面非同类的数据集：在第一个例子中，平均诱变效力被定义为多个遗传毒性终点的平均值，在第二个例子中，是280种致癌物和非致癌物的美国国家毒理学计划（NTP）两性、两种物种的啮齿动物致癌性生物测定结果。这两个案例的QSAR分析中遇到的问题表明，对于非同类数据的QSAR建模问题，成功的方法需要从化学和生物学角度考虑该问题的多维性质。由于不同的化学类别代表了很大程度上独立的作用机制，因此有必要采用一些方法来提取组成类别的局部QSAR。或者，对于从非同类数据集中导出的通用QSAR，需要沿着化学类别线进行审查和分解，以便确定应用边界和预测的置信水平。
