Peroxidase-mediated removal of endocrine disrupting compound mixtures from water

Several classes of oxidative enzymes have shown promise for efficient removal of endocrine disrupting compounds (EDCs) that are resistant to conventional wastewater treatments. Although the kinetics of reactions between individual EDCs and selected oxidative enzymes are well documented in the literature, there has been little investigation of reactions with EDC mixtures. This makes it impossible to predict how enzyme-mediated treatment systems will perform since wastewater effluents generally contain multiple EDCs. This paper reports pseudo-first order rate constants for a model oxidative enzyme, horseradish peroxidase (HRP), during single-substrate (k1) and mixed-substrate (k1-MIX) reactions. Measured values are compared with literature values of three Michaelis–Menten parameters: half-saturation constant (KM), enzyme turnover number (kCAT), and the ratio kCAT/KM. Published reports had suggested that each of these could be correlated with HRP reactivity towards EDCs in mixtures, and empirical results from this study show that KM can be used to predict the sequence of EDC removal reactions within a particular mixture. We also observed that k1-MIX values were generally greater than k1 values and that compounds exhibiting greatest estrogenic toxicities reacted most rapidly in a given mixture. Finally, because KM may be tedious to measure for every EDC of interest, we have constructed a quantitative structure–activity relationship (QSAR) model to predict these values. This model predicts KM quite accurately (R2 = 89%) based on two molecular characteristics: molecular volume and hydration energy. Its accuracy makes this QSAR a useful tool for predicting which EDCs will be removed most efficiently during enzyme treatment of EDC mixtures.

► Single- and multiple-substrate results suggest KM correlates with HRP reactivity in mixtures.
► Rate constants for mixtures (k1-MIX) greater than respective single-substrate rate k1.
► Regression model facilitates prediction of KM for wastewater EDCs.