Bioengineering partnership to improve chemical hazard testing paradigms

描述（由申请人提供）：目前的化学危害测试程序没有考虑到可能处于暴露风险的人群的遗传多样性；然而，我们最近的数据表明，小鼠近交系之间的遗传差异对毒性物质的时间、剂量和器官特异性效应有深远的影响。因此，我们假设有关有毒物质作用模式的知识可以有效地与组织工程、计算机建模、遗传学和基因组学的进展相结合，以开发新的方法来改进体外和体内化学危害测试范式，优先选择环境物质，并以生物学上有意义和合理的方式增加筛选的通量。该应用程序建议在环境卫生科学家、生物工程师、化学信息学家、生物统计学家和遗传学家之间建立伙伴关系。这个多学科团队将采用一种综合的系统方法：(1)开发一种可用于高通量化学物质筛选的3D微型小鼠肝组织生物反应器。这是一项设计导向的努力，旨在生产一种独特的工具，可以提高测试的吞吐量，同时减少动物的数量；(2)建立、测试和验证定量结构-毒性关系（QSTR）模型，该模型采用分子结构的化学和生物学描述符，并考虑个体之间的遗传多样性。这一目标是一种发现驱动的方法，它将产生一种基于化学结构、多维毒性数据（包括组学端点）和群体遗传多样性信息的化合物优先级计算方法；(3)通过产生以种群内存在的遗传变异性为基础的知识，验证一类烯丙苯衍生物的体内和体外毒性筛选范式在财政上是合理的。这一目标将检验这样一种假设，即个体之间的遗传变异是化学危害毒性效应的主要决定因素，而且易感性的遗传基础可以通过一组小鼠近交系成功阐明。该合作伙伴关系将由Ivan Rusyn医学博士（北卡罗来纳大学公共卫生）领导，他是一名环境健康科学家，在肝脏毒理学和小鼠毒性模型方面具有丰富的经验。其他主要研究人员有：Linda Griffith博士（麻省理工学院），世界知名的肝组织工程研究人员；Alex Tropsha博士（北卡罗来纳大学药房），定量结构活性/毒性关系建模领域的领导者；以及遗传学家David Threadgill博士（北卡罗来纳大学医学院），他的工作是将小鼠遗传学应用于癌症研究和毒理学的先驱。总的来说，这一努力将在理解遗传多样性人群中化学特性和化学危害之间的关系方面取得重大而及时的进展。它还将说明如何将环境卫生、遗传学、化学信息学和生物工程方面的基础科学结合起来，通过提高我们推断和将化学测试结果转化为人类人群的能力，以及通过告知限制接触与疾病有关的环境因子的监管决策，来预防人类疾病。

DESCRIPTION (provided by applicant): Current chemical hazard testing procedures do not take into account the genetic diversity of the population that may be at risk of exposure; however, our recent data show that genetic differences between mouse inbred strains have a profound impact on time-, dose- and organ-specific effects of toxic agents. Thus, we hypothesize that the knowledge on the modes of action of toxic agents can be efficiently combined with advances in tissue engineering, in silico modeling, genetics and genomics to develop novel approaches that improve in vitro and in vivo chemical hazard testing paradigms, prioritize environmental agents for selection and increase throughput of screening in a biologically meaningful and sensible way. This application proposes to establish a partnership between environmental health scientists, biological engineers, chem- informaticians, biostatisticians and geneticists. This multidisciplinary team will apply an integrative, systems approach to: (1) Develop a 3D microscale mouse liver tissue bioreactor that can be applied to high- throughput screening of chemicals. This is a design-directed effort to produce a unique tool that can increase throughput of testing while reducing the number of animals; (2) To build, test and validate Quantitative Structure-Toxicity Relationship (QSTR) models that employ both chemical and biological descriptors of molecular structures and take into account genetic diversity between individuals. This aim is a discovery- driven approach that will produce a computational method for compound-prioritization based on the chemical structure, multi-dimensional toxicity data that includes -omics endpoints, and information on genetic diversity of the population; and (3) Validate a fiscally sensible in vivo and in vitro toxicity screening paradigm for a class of allylbenzene derivatives by producing knowledge anchored on the genetic variability present within the population. This aim will test the hypothesis that genetic variability among individuals is a major determinant in the toxic effects of chemical hazards and that the genetic basis for susceptibility can be successfully elucidated using a panel of mouse inbred strains. The partnership will be directed by Ivan Rusyn, M.D., Ph.D. (UNC Public Health) who is an environmental health scientist with experience in liver toxicology and mouse models of toxicity. Other Lead Investigators are: Linda Griffith, Ph.D. (Massachusetts Institute of Technology), a world-renowned researcher in the field of liver tissue engineering; Alex Tropsha, Ph.D., (UNC Pharmacy), a leader in the field of quantitative structure activity/toxicity relationship modeling; and David Threadgill, Ph.D. (UNC Medicine), a geneticist whose work pioneers the applications of mouse genetics into cancer research and toxicology. Collectively, this effort will lead to significant and timely advances in understanding the relationship between chemical identity and chemical hazard within a genetically diverse population. It will also illustrate how basic science in environmental health, genetics, chem-informatics and bioengineering can be combined to prevent human disease by improving our ability to extrapolate and translate findings from chemical testing to human populations and by informing regulatory decisions that limit exposures to disease-associated environmental agents.