Statistical Models In Toxicology And Biochemistry

毒理学和生物化学的统计模型

• 批准号: 7168889
• 负责人: Christopher J Portier
• 金额: --
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7168889
• 关键词: biochemistry; cancer risk; cell cycle; chemical carcinogen; chemical carcinogenesis; dioxins; endocrinology; environmental contamination; environmental exposure; environmental toxicology; gene environment interaction; gene expression; growth /development; immunology; laboratory rat; mathematical model; model design /development; neurology; pharmacology; statistics /biometry; thyroid hormones; vital statistics

This Project focuses on (1) The development of methodology for incorporating "cutting-edge" research findings into future risk assessments; (2) The development of methods for designing studies to improve risk estimates, especially when mechanistic data is involved; (3) The development of methods for the evaluation of exposure, dose-response shape and potency; (4) The development of methods for evaluating mixtures when multiple mechanisms are involved; (5) The development of methods which harmonize cancer and non-cancer health risk assessments; (6) Direct engagement of the regulatory community through expert panels, peer review and collaborative research; (7) Practical improvement of stochastic processes through careful linkage of theoretical developments with computational methods that are accurate and convenient; (8) Collaboration with research groups within the NIEHS and research groups doing similar work to improve the biological understanding of disease incidence; (9) Iterative improvement of the biological basis for disease incidence models through a process of hypothesis testing and laboratory research; (10) Linkage of disease incidence models to toxicokinetics models in a scientifically credible manner; (11) Use of the broadest array of data in both the development of the model and its application; (12) Support of the National Toxicology Program. We developed a quantitative, statistically sound methodology for the analysis of suspected gene regulatory networks using gene expression data sets. The method is based on Bayesian networks and provides a means to directly quantify gene-expression networks and test hypotheses regarding the linkages between genes in this network. The method is currently being applied to a number of data sets including data on early brain development and acetaminophen toxicity.

本项目侧重于(1)发展将“前沿”研究成果纳入未来风险评估的方法；(2)发展设计研究的方法，以改进风险估计，特别是在涉及机械数据时；(3)开发评估暴露、剂量反应形状和效力的方法；(4)在涉及多种机制的情况下，开发评估混合物的方法；(5)制订协调癌症和非癌症健康风险评估的方法；(6)监管界通过专家小组、同行评审和合作研究直接参与；(7)通过将理论发展与精确和方便的计算方法仔细联系起来，对随机过程进行实际改进；(8)与NIEHS内的研究小组和从事类似工作的研究小组合作，以提高对疾病发病率的生物学认识；(9)通过假设检验和实验室研究，不断完善疾病发病率模型的生物学基础；（10）以科学可信的方式将疾病发生率模型与毒物动力学模型联系起来；（11）在模型的发展和应用中使用最广泛的数据；（12）国家毒理学计划的支持。