STATISTICAL MODELS IN TOXICOLOGY AND BIOCHEMISTRY

该项目旨在增加我们对使用的理解， 数学和统计模型在毒理学中的应用 生物化学和实施新的数学模型，以帮助 解释目前的研究成果。研究工作探索了一个 生物学领域的多样性，包括致癌作用， 药理学、发育生物学、神经学和免疫学。在癌症中 模型，主要成果包括（1）两个新的模型， 癌发生进行了详细研究（一个模型与真正的干细胞， 皮肤癌发生和多途径、多阶段模型研究 结肠癌和肝癌）;（2）开发新的方法， 在建立机制时， 模型;（3）将机制模型应用于癌前病变的数据 和肿瘤同时进行;（4）开发一种通用算法， 从任意复杂的多阶段模型估计肿瘤发病率 （5）建立了将PBPK模型与肿瘤发病率模型相联系的方法。 在基于生理学的药代动力学建模中，该组有（1） 基本上改进了TCDD的PBPK模型，并将其应用于研究 TCDDs对甲状腺激素代谢的影响;（2）制定规则， 在风险评估中使用机械模型。在风险评估中， 该小组已（1）与EPA合作重新评估TCDD;（2） 开发了使用剖面图估算基准剂量的代码 可能性

This project is intended to increase our understanding of the use and application of mathematical and statistical models in toxicology and biochemistry and to implement new mathematical models to aid in explaining current research findings. The research effort explores a diverse range of biological areas, including carcinogenesis, pharmacology, developmental biology, neurology and immunology. In cancer modeling, major accomplishments include (1) two new models of carcinogenesis were studied in detail (a model with true stem cells for skin carcinogenesis and a multipathway, multistage model for studying colon and liver cancer); (2) development of new methods for utilizing data on malignant and premalignant states when building a mechanistic model; (3) applying mechanistic models to data on premalignant lesions and tumors simultaneously; (4) the development of a general algorithm for estimating tumor incidence from arbitrarily complicated multistage models and (5) developed methods to link PBPK models to tumor incidence models. In physiologically-based pharmacokinetic modeling, this group has (1) substantially improved a PBPK model for TCDD and applied it to study TCDDs effects on thyroid hormone metabolism; and (2)developed rules for using mechanistic models in risk assessment. In risk assessment, this group has (1) collaborated with EPA on the reasessment of TCDD; (2) developed code for estimating benchmarks doses using a profile likelihood.