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。其中包括强效药物类，包括合成类固醇，尽管它们通过自然和工程过程进行了转化，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，以帮助促进更健全的水法规和改进的化学品风险评估。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，特别是在社会越来越依赖于处理后的废水的再利用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。这项工作代表了水质管理的新范式。该项目将重点关注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、强效合成黄体酮和糖皮质激素，并将开发一个基于计算和实验方法的高通量框架，用于对高风险、生物活性转化产物进行先验预测。该方法整合了(1)理论计算，利用母体（部分电荷、氧化电位）和可能产物（热力学稳定性）的描述符来识别可能的氯化产物。（ii）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，将评估高风险产品的形成和产量（iii）在一系列氯化条件下的实验中进行，（iv）在废水和接收水中进行高分辨率质谱检测。研究结果确保，当新兴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。本合作项目为环境化学、计算化学和生物化学的交叉领域提供2名研究生、2名博士后和数名本科生的跨学科培训。

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 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.