Evaluating batteries of short-term genotoxicity tests.
Evaluating batteries of short-term genotoxicity tests.
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评估短期遗传毒性测试的电池组。
DOI：
10.1093/mutage/1.4.293
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发表时间：
1986
期刊：
影响因子：
2.7
通讯作者：
Rosenkranz,HS
中科院分区：
文献类型：
--
作者：
Ennever,FK;Rosenkranz,HS
选择一组适合于筛选致癌未知化学物质的短期遗传毒性测试可能是一项巨大的组合任务，因为目前有大量的短期测试可用。生物标准，如对不同目标和终点的要求，可以减少可能的组合数量。我们开发了一种独立但具有潜在互补性的方法，它使用贝叶斯定理根据短期测试的结果(阳性或阴性)预测致癌性。电池可以通过它们的预测性来评估，根据贝叶斯定理根据组件测试的灵敏度和特异度计算得出。我们的分析表明，对电池的预测性贡献最大的测试是那些既敏感又特异的测试，我们称之为I类测试。由于目前可用的测试很少是I类测试，我们扩展了我们的分析，以考虑何时必须用其他类型的测试来替代I类测试，特定测试计划的目的将影响选择。为了获得对所有化学物质的最佳预测，电池应包括同等数量的II类测试(即敏感但不特异的测试)和III类测试(即不敏感但特异的测试)。然而，为了减少被错误归类为非致癌物质的数量，II类测试对电池的预测能力的贡献大约是III类测试的两倍，而为了减少被错误归类为致癌物质的非致癌物质的数量，II类测试的贡献大约是III类测试的一半。(测试的确切等价性取决于敏感度和特异度的值。)我们还开发了电池预测性的图形表示，它考虑了一组结果的出现频率，这很容易根据它们的灵敏度和特异度计算出来。这些二维表示法允许根据许多不同的标准对电池进行评估。贝叶斯方法可以与其他测试选择方法结合使用，以构建满足生物标准并具有高度预测性的电池。
Selecting a battery of short-term genotoxicity tests suitable for screening unknown chemicals for carcinogenicity can be a large combinatorial task because of the great number of short-term tests currently available. Biological criteria, such as requirements for different targets and endpoints, can reduce the number of possible combinations. An independent yet potentially complementary approach which we have developed uses Bayes' theorem to predict carcinogenicity from results (positive or negative) in short-term tests. Batteries can be evaluated by their predictivity, calculated with Bayes' theorem from the sensitivities and specificities of the component tests. Our analyses indicate that tests which contribute most to a battery's predictivity are those which are both sensitive and specific, which we call Class I tests. Because few of the currently available tests are Class I, we have extended our analyses to consider when other types of tests must be substituted for Class I tests, the purpose of a particular test program will influence the choice. In order to obtain the best predictions for all chemicals, a battery should include an equal number of Class II tests (i.e. those that are sensitive but are not specific) and Class III tests (i.e. those that are not sensitive but are specific). However, for the purpose of reducing the number of carcinogens erroneously classified as noncarcinogenic, Class II tests contribute about twice as much to the predicitivity of a battery as do Class III tests, and for the purpose of reducing the number of non-carcinogens erroneously classified as carcinogenic, Class II tests contribute about half as much as Class III tests. (Exact equivalences of tests depend upon the values of sensitivity and specificity.) We have also developed a graphical representation of the predictivity of batteries, which takes into consideration the frequency of occurrence of a set of results, which is easily calculated from their sensitivities and specificities. These twodimensional representations allow the evaluation of batteries based on a number of different criteria. The Bayesian approach can be used in conjunction with other methods of test selection to construct batteries which satisfy biological criteria and which are also highly predictive.