Computational Approaches for the Prediction of Environmental Transformation Products: Chlorination of Steroidal Enones.

Christopher J Knutson,James B Gloer,Eric V Patterson,Wyanna Yeung,Matthew Grobstein,David M Cwiertny,Nicholas C Pflug

doi:10.1021/acs.est.1c04659

Christopher J Knutson, James B Gloer + Show 5 more

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https://doi.org/10.1021/acs.est.1c04659

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Abstract

There is growing interest in the fate and effects of transformation products generated from emerging pollutant classes, and new tools that help predict the products most likely to form will aid in risk assessment. Here, using a family of structurally related steroids (enones, dienones, and trienones), we evaluate the use of density functional theory to help predict products from reaction with chlorine, a common chemical disinfectant. For steroidal dienones (e.g., dienogest) and trienones (e.g., 17β-trenbolone), computational data support that reactions proceed through spontaneous C4 chlorination to yield 4-chloro derivatives for trienones and, after further reaction, 9,10-epoxide structures for dienones. For testosterone, a simple steroidal enone, in silico predictions suggest that C4 chlorination is still most likely, but slow at environmentally relevant conditions. Predictions were then assessed through laboratory chlorination reactions (0.5–5 mg Cl2/L) with product characterization via HRMS and NMR, which confirmed near exclusive 4-chloro and 9,10-epoxide products for most trienones and all dienones, respectively. Also consistent with computational expectations, testosterone was effectively unreactive at these same chlorine levels, although products consistent with in silico predictions were observed at higher concentrations (in excess of 500 mg Cl2/L). Although slight deviations from in silico predictions were observed for steroids with electron-rich substituents (e.g., C17 allyl-substituted altrenogest), this work highlights the potential for computational approaches to improve our understanding of transformation products generated from emerging pollutant classes.

Highlights

Hazards associated with chemical contaminants are often assumed to be mitigated through their environmental transformation, bioactive and potentially harmful reaction products often persist and can complicate risk assessment for some emerging pollutant classes.[1−5] A good example are synthetic steroids widely used in medicine and agriculture, for which we have previously demonstrated retained and/or altered bioactivity in products generated from environmental reactions including photolysis in sunlit surface waters and chemical disinfection with free chlorine.[3,4]
Yuan et al recently extended the capability of Chemical Transformation Simulator (CTS) by demonstrating that its cheminformatics approach can predict transformation products of direct photolysis based on reported reaction pathways.[7]
We focused on chlorination reactions due to the widespread use of free chlorine in chemical disinfection of water and wastewater,[18−20] the well-recognized ability of chlorination of the steroid ring structure to amplify anabolic activity,[21] and the limited amount of existing work using computational tools to predict chlorination products from emerging pollutant classes, including aromatic hydrocarbons, various amines, and naproxen.[22−26] other steroid classes have been extensively investigated, far less is known about the reaction of free chlorine with steroidal enones

Summary

Introduction

Hazards associated with chemical contaminants are often assumed to be mitigated through their environmental transformation, bioactive and potentially harmful reaction products often persist and can complicate risk assessment for some emerging pollutant classes.[1−5] A good example are synthetic steroids widely used in medicine and agriculture, for which we have previously demonstrated retained and/or altered bioactivity in products generated from environmental reactions including photolysis in sunlit surface waters and chemical disinfection with free chlorine.[3,4] a major challenge associated with bioactive transformation products is that environmental reactions can have a multiplicative effect on the sheer number of species that need to be evaluated for ecological and human health impacts, including many that lack readily available analytical standards. We explored the use of in silico modeling for identifying the most probable environmental reaction products generated during the reaction of free chlorine with a family of structurally related steroidal trienones (i.e., 17α-trenbolone, 17β-trenbolone, methyltrenbolone, gestrinone, and altrenogest), dienones (i.e., dienogest, dienedione, and methyldienolone), and enones (i.e., testosterone) (see structures in Figure S1 of the Supporting Information, SI).

Results

Conclusion