BIOLOGICALLY BASED CANCER RISK ASSESSMENT FOR MIXTURES

描述 在这个提案中，我们解决了估计和预测的问题 接触致癌物质混合物的致癌风险。我们的方法 要解决这一问题，将基于两个突变的克隆扩展(TMCE) 致癌模型。此模型可以显式地同时满足这两种需求 风险评估过程中的发起人和推动者。在这份提案中， 我们区分了简单混合物和复杂混合物。复合体 混合气体，如柴油废气、焦炉电池排放的废气和 香烟烟雾中含有数百种致癌化学物质。通常， 然而，当好的时候，复杂的混合物可以被视为单一的致癌物。 关于接触整个混合物的数据是可用的。因此，第一个 目标解决的问题是当一个小的 涉及多种成分致癌物质。关于角色的问题 致癌物类型(作用方式)、暴露模式、剂量延长、 以及对曝光开始和停止的依赖性的公式，以及它们的 探讨了对癌症风险的影响。具体地说，我们关注的是人类接触 以低LET和高LET辐射与肺癌(或肺癌死亡)为标准 终结点。三个大型数据集将用来说明其有用性 我们方法的有效性：科罗拉多高原铀矿工 队列，有关于联合接触香烟的详细个人信息 吸烟和氡暴露；中国锡矿工数据集， 关于三种肺癌致癌物质的详细个人信息：烟草烟雾， 氡子体和砷；原子弹的寿命研究 幸存者。第二个目标涉及制定适当的 使用生物学模型分析病例对照数据的方法。 这为评估致癌潜力提供了另一种工具 混合物。第三个目标涉及毒性当量系数。 (TEF)方法，适用于可能包含多种化学物质的复杂混合物 类似于上面提到的组件。来评估这些数据的有效性 我们建议用TEF方法分析阿勒格尼/非阿勒格尼焦炉 以队列数据为例。

DESCRIPTION In this proposal we address the problems of estimating and predicting carcinogenic risks from exposure to mixtures of carcinogens. Our approach to this problem will be based on the two mutation clonal expansion (TMCE) model of carcinogenesis. This model can explicitly accommodate both initiators and promoters in the risk assessment process. In this proposal, we make a distinction between simple and complex mixtures. Complex mixtures, such as diesel exhaust, emissions from coke oven batteries, and cigarette smoke, contain hundreds of cancer causing chemicals. Often, however, complex mixtures can be treated as single carcinogens when good data on exposure to the entire mixture are available. Thus, the first objective addresses the problem of estimating cancer risk when a small number of component carcinogens is involved. Questions regarding the roles of the carcinogen type (mode of action), exposure pattern, dose-protraction, and dependency on start and stop of exposures are formulated and their impact on cancer risk explored. Specifically, we focus on human exposures to low and high LET radiation and lung cancer (or death from lung cancer) as the endpoint. Three large data sets will serve to illustrate the usefulness and effectiveness of our approach: the Colorado Plateau Uranium Miners cohort, with detailed individual information on joint exposure to cigarette smoking and exposure to radon; the Chinese Tin Miners data set, with detailed individual information on three lung carcinogens: tobacco smoke, radon progeny and arsenic; and the Life Span Study of the atomic bomb survivors. The second objective concerns the development of appropriate methods for analyzing case-control data using biologically-based models. This provides another tool for assessing the carcinogenic potential of mixtures. The third objective concerns the toxicity equivalency factor (TEF) approach for complex mixtures that may contain numerous chemical components like those mentioned above. To evaluate the usefulness of the TEF approach we propose to analyze the Allegheny/non-Allegheny coke oven cohort data, as an example.