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

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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.