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面临暴露风险的种群的遗传多样性；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剂的时间、剂量和器官特定的影响具有深远的影响。因此，我们假设毒物作用模式的知识可以有效地与组织工程、硅胶模型、遗传学和基因组学方面的进展相结合，以开发新的方法来改进体外和体内化学危险测试范式，优先选择环境毒剂，并以生物意义和敏感的方式增加筛查的吞吐量。这项申请建议在环境健康科学家、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。这个多学科团队将应用一种综合的、系统的方法来：(1)开发一种3D微型小鼠肝组织生物反应器，可以应用于高通量的化学品筛选。这是一项以设计为导向的努力，目的是生产一种独特的工具，可以在减少动物数量的同时增加测试的吞吐量；(2)建立、测试和验证量化结构-毒性关系(QSTR)模型，该模型使用分子结构的化学和生物描述符，并考虑到个体之间的遗传多样性。这一目标是一种发现驱动的方法，将根据化学结构、包括组学终点的多维毒性数据和关于种群遗传多样性的信息产生一种计算方法来确定化合物的优先顺序；以及(3)通过产生基于种群内存在的遗传可变性的知识，验证一类烯基苯衍生物在体内和体外具有财务敏感性的毒性筛选范例。这一目标将检验这样的假设，即个人之间的遗传变异性是化学危险毒性影响的主要决定因素，并且可以使用一组小鼠近亲交配品系成功地阐明易感性的遗传基础。该合作伙伴关系将由伊万·鲁辛(医学博士、博士(北卡罗来纳大学公共卫生))指导，他是一位环境健康科学家，在肝脏毒理学和小鼠毒性模型方面有经验。其他主要研究人员包括：世界著名的肝脏组织工程研究人员琳达·格里菲斯博士(麻省理工学院)；定量结构活性/毒性关系建模领域的领导者亚历克斯·特罗普沙博士(北卡罗来纳大学药房)；以及遗传学家大卫·斯里德吉尔博士(北卡罗来纳大学医学院)，他的工作开创了将老鼠遗传学应用于癌症研究和毒理学的先河。总体而言，这一努力将在理解遗传多样性人群中化学特性和化学危险之间的关系方面取得重大和及时的进展。它还将说明如何将环境健康、遗传学、化学信息学和生物工程方面的基础科学结合起来，通过提高我们推断化学测试结果并将其转化为人类群体的能力，以及通过向限制暴露于与疾病相关的环境制剂的监管决定提供信息，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.