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

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.