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Abstract：
Mixture risk assessment is often hampered by the lack of dose-response information on the mixture being assessed, forcing reliance on component formulas such as dose addition. We present a four-step approach for evaluating chemical mixture data for consistency with dose addition for use in supporting a component based mixture risk assessment. Following the concepts in the U.S. EPA mixture risk guidance (U.S. EPA, 2000a,b), toxicological interaction for a defined mixture (all components known) is departure from a clearly articulated definition of component additivity. For the common approach of dose additivity, the EPA guidance identifies three desirable characteristics, foremost of which is that the component chemicals are toxicologically similar. The other two characteristics are empirical: the mixture components have toxic potencies that are fixed proportions of each other (throughout the dose range of interest), and the mixture dose term in the dose additive prediction formula, which we call the combined prediction model (CPM), can be represented by a linear combination of the component doses. A consequent property of the proportional toxic potencies is that the component chemicals must share a common dose-response model, where only the dose coefficients depend on the chemical components. A further consequence is that the mixture data must be described by the same mathematical function (\"mixture model\") as the components, but with a distinct coefficient for the total mixture dose. The mixture response is predicted from the component dose-response curves by using the dose additive CPM and the prediction is then compared with the observed mixture results. The four steps are to evaluate: (1) toxic proportionality by determining how well the CPM matches the single chemical models regarding mean and variance; (2) fit of the mixture model to the mixture data; (3) agreement between the mixture data and the CPM prediction; and (4) consistency between the CPM and the mixture model. Because there are four evaluations instead of one, some involving many parameters or dose groups, there are more opportunities to reject statistical hypotheses about dose addition, thus statistical adjustment for multiple comparisons is necessary. These four steps contribute different pieces of information about the consistency of the component and mixture data with the two empirical characteristics of dose additivity. We examine this four-step approach in how it can show empirical support for dose addition as a predictor for an untested mixture in a screening level risk assessment. The decision whether to apply dose addition should be based on all four of those evidentiary pieces as well as toxicological understanding of these chemicals and should include interpretations of the numerical and toxicological issues that arise during the evaluation. This approach is demonstrated with neurotoxicity data on carbamate mixtures.
摘要：
混合物风险评估通常由于缺乏被评估混合物的剂量反应信息而受到阻碍，强迫依赖组分配方，如剂量添加。我们提出了一种四步方法，用于评估化学混合物数据与剂量添加的一致性，以支持基于成分的混合物风险评估。遵循美国EPA混合物风险指南中的概念(美国EPA，2000a,)定义混合物（所有已知成分）的毒理学相互作用偏离了成分可加性的明确定义。对于常用的剂量加性方法，EPA指南确定了三个理想特征，其中最重要的是成分化学物质在毒理学上相似。另外两个特征是经验性的：混合物成分具有彼此固定比例的毒性效力（在整个感兴趣的剂量范围内），和剂量添加剂预测公式中的混合剂量项，我们称之为组合预测模型(CPM)，可以由组分剂量的线性组合表示。成比例的毒性效力的一个随之而来的特性是，成分化学品必须共享一个共同的剂量反应模型，其中剂量系数仅取决于化学成分。进一步的结果是，混合数据必须由与组件相同的数学函数（“混合模型”）来描述，但总混合剂量的系数不同。通过使用剂量添加剂CPM从组分剂量-响应曲线预测混合物响应,然后将预测与观察到的混合物结果进行比较。四个步骤是评估:(1)通过确定CPM在均值和方差方面与单一化学模型匹配的程度来评估毒性比例;(2)混合物模型与混合物数据的拟合;(3)混合物数据与CPM预测之间的一致性;和(4)CPM与混合物模型之间的一致性。因为有四个评价而不是一个，一些涉及许多参数或剂量组，有更多的机会拒绝关于剂量添加的统计假设，因此，多重比较的统计调整是必要的。这四个步骤提供了关于组分和混合物数据的一致性的不同信息,具有剂量加性的两个经验特征。我们研究了这种四步方法，以了解它如何在筛查水平风险评估中显示对剂量添加作为未经测试的混合物的预测因子的经验支持。是否应用剂量添加的决定应基于所有这四个证据以及对这些化学品的毒理学理解，并应包括对评估过程中出现的数字和毒理学问题的解释。用氨基甲酸酯混合物的神经毒性数据证明了这种方法。
