Collaborative Research: Integrated In Silico and Non-Target Analytical Framework for High Throughput Prioritization of Bioactive Transformation Products

合作研究：集成计算机和非目标分析框架，用于生物活性转化产品的高通量优先排序

• 批准号: 1609791
• 负责人: David Cwiertny
• 金额: $ 20万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609791&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrated; Silico; Non

Today, water resources are threatened by a complex mixture of chemical pollutants, many of which are poorly removed by traditional water and wastewater treatment technologies. These include potent pharmaceutical classes including synthetic steroids, whose bioactivity can persist in the environment despite their transformation through natural and engineered processes. In this project funded by the Environmental Chemical Sciences Program of the Chemistry Division at the National Science Foundation, a collaborative team of researchers at the University of Iowa, University of Washington at Tacoma and Seattle, University of California at San Diego, and Stony Brook University develops a predictive framework to help catalyze more robust water regulations and improved chemical risk assessment. Ultimately, outcomes of this project moves society toward more safe and sustainable water supplies, particularly as society becomes more reliant on reuse of treated wastewater to bridge the widening gap in supply and demand. The broader impacts of this work include advancing undergraduate education by enabling the participation of under-represented groups in research activities, integrating modern computational tools into student learning, and promoting scientific literacy in non-technical audiences through general education coursework development. This work represents a new paradigm in water quality management. Focusing on a widely utilized abiotic treatment process, chlorination, and ubiquitous but understudied pollutant classes, potent synthetic progestins and glucocorticoids, this project will develop a high-throughput framework built upon computational and experimental methods for the a priori prediction of high risk, bioactive transformation products. This approach integrates (i) theoretical calculations to identify probable chlorination products using descriptors for both parent (partial charges, oxidation potentials) and likely product (thermodynamic stability) species. (ii) Potential product species are prioritized based on bioactivity (i.e., risk) using high throughput virtual ligand screening. Once identified, formation and yield of high risk products are evaluated (iii) in bench-scale experiments across a range of chlorination conditions and (iv) via high resolution mass spectrometric detection in wastewaters and receiving waters. Research outcomes ensures that when emerging pollutant classes are inevitably regulated, a more holistic approach is available that also addresses risks posed by their bioactive products. This collaborative project provides transdisciplinary training of 2 graduate students, 2 postdocs, and several undergraduates at the interface of environmental chemistry, computational chemistry, and biochemistry.

今天，水资源受到化学污染物复杂混合物的威胁，其中许多污染物无法通过传统的水和废水处理技术去除。这些包括有效的药物类别，包括合成类固醇，其生物活性可以在环境中持续存在，尽管它们通过自然和工程过程转化。在这个由美国国家科学基金会化学部环境化学科学计划资助的项目中，爱荷华州大学、华盛顿大学塔科马分校和西雅图分校、加州大学圣地亚哥分校和斯托尼布鲁克大学的研究人员组成的合作小组开发了一个预测框架，以帮助促进更健全的水法规和改进的化学风险评估。最终，该项目的成果将推动社会走向更安全和可持续的供水，特别是随着社会越来越依赖于处理后废水的再利用，以弥补供需之间日益扩大的差距。这项工作的更广泛影响包括通过使代表性不足的群体能够参与研究活动来推进本科教育，将现代计算工具纳入学生学习，并通过普通教育课程开发促进非技术受众的科学素养。这项工作代表了水质管理的新范式。该项目侧重于广泛使用的非生物处理过程、氯化、普遍存在但研究不足的污染物类别、有效的合成孕激素和糖皮质激素，将开发一个基于计算和实验方法的高通量框架，用于先验预测高风险、生物活性转化产物。该方法整合了（i）理论计算，以使用母体（部分电荷、氧化电位）和可能的产物（热力学稳定性）物质的描述符来识别可能的氯化产物。(ii)基于生物活性（即，风险）。 一旦确定，高风险产品的形成和产量进行评估（iii）在实验室规模的实验，在一系列的氯化条件和（iv）通过高分辨率质谱检测废水沃茨和接收沃茨。研究成果确保了当新出现的污染物类别不可避免地受到监管时，可以采用更全面的方法来解决其生物活性产品带来的风险。这个合作项目提供了跨学科的培训2研究生，2博士后，和几个本科生在环境化学，计算化学和生物化学的接口。