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

今天，水资源受到化学污染物的复杂混合物的威胁，其中许多是传统的水和废水处理技术很难去除的。这些水污染物包括强效药物类，包括合成类固醇，尽管它们通过自然和人为过程转化为其他化合物，但其生物活性可以在环境中持续存在。在这个由美国国家科学基金会化学学部环境化学科学项目资助的项目中，一个由爱荷华大学、塔科马和西雅图华盛顿大学、加州大学圣地亚哥分校和石溪大学的研究人员组成的合作团队开发了一个预测框架，以帮助改进化学品风险评估。最终，该项目的成果可能会产生更安全和可持续的水供应，特别是在社会越来越依赖于再利用处理过的废水来弥合日益扩大的供需差距的情况下。这项工作的更广泛影响包括通过使代表性不足的群体参与研究活动来推进本科教育，将现代计算工具整合到学生学习中，并通过通识教育课程开发提高非技术受众的科学素养。这项研究试图改善水质。该项目着眼于广泛使用的非生物处理工艺、氯化、普遍存在但研究不足的污染物类别、强效合成孕激素和糖皮质激素，开发了一个基于计算和实验方法的高通量框架，用于对高风险、生物活性转化产物的先验预测。这种方法整合了理论计算，利用描述符来识别可能的氯化产物，包括母体（部分电荷、氧化电位）和可能的产物（热力学稳定性）。使用高通量虚拟配体筛选，根据生物活性（即风险）对潜在产品物种进行优先排序。一旦确定，高风险产品的形成和产量将在一系列氯化条件下的实验中进行评估。采用高分辨率质谱法检测废水和接收水。研究成果可用于预测新出现的污染物，并为解决其生物活性产品带来的风险提供更全面的方法。该合作项目为两名研究生、两名博士后和几名本科生提供环境化学、计算化学和生物化学交叉领域的跨学科培训。

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 water pollutants include potent pharmaceutical classes including synthetic steroids, whose bioactivity can persist in the environment despite their transformation to other compounds through natural and man-made 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 improve chemical risk assessment. Ultimately, outcomes of this project may produce 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 research attempts to improve water quality. Focusing on a widely utilized abiotic treatment process, chlorination, and ubiquitous but understudied pollutant classes, potent synthetic progestins and glucocorticoids, this project develops a high-throughput framework built upon computational and experimental methods for the a priori prediction of high risk, bioactive transformation products. This approach integrates theoretical calculations to identify probable chlorination products using descriptors for both parent (partial charges, oxidation potentials) and likely product (thermodynamic stability) species. Potential product species are prioritized based on bioactivity (i.e., risk) using high throughput virtual ligand screening. Once identified, the formation and yield of high risk products are evaluated in bench-scale experiments across a range of chlorination conditions. High resolution mass spectrometric detection is used to examine wastewaters and receiving waters. Research outcomes may be used to predict emerging pollutants and provide a more holistic approach to addressing the risks posed by their bioactive products. This collaborative project provides transdisciplinary training of two graduate students, two postdocs, and several undergraduates at the interface of environmental chemistry, computational chemistry, and biochemistry.