Atenolol Biotransformation by Denitrifying Mixed Culture Communities

The biotransformation of atenolol, sotalol, and metoprolol by mixed culture denitrifying communities was examined as a function of carbon availability and pharmaceutical concentration to explore the possible roles of cometabolism and direct substrate utilization. Sotalol and metoprolol were not found to be transformed. A matrix of conditions explored the biotransformation of water resource recovery facility relevant (25 μg/L) and chemical oxygen demand (COD)-equivalent (10–100 mg/L) atenolol concentrations in batch reactors (1 L) containing 25 mg-N/L nitrate. Carbon (MicroC® 2000) conditions included Nonlimiting (COD:N ratio >10), Partially Limiting (COD:N ratio = 2–3), and Limiting (COD:N ratio <1). Reactors without atenolol served as denitrification control experiments. Results suggest that atenolol did not appreciably influence nitrate reduction. The extent of atenolol degradation was independent of carbon availability. In the experiments conducted in the μg/L range, 30–40% biotransformation was observed. Scant degradation at higher concentrations suggested that direct utilization of the pharmaceutical was either unlikely or occurring slowly and independent of the pharmaceutical concentration. Process-based modeling using the Activated Sludge Model framework assessed denitrification across carbon availability conditions and incorporated three individual pharmaceutical biotransformation modeling strategies: (1) direct metabolism model; (2) cometabolic model, and (3) biotransformation kinetic model. Results suggest that there was insufficient evidence to suspect direct metabolism meaningfully contributed to the reduction in atenolol concentration. The cometabolic process-based model, including both growth and nongrowth-linked rate coefficients offered the best model performance, although growth-linked cometabolism was limited and not readily apparent across all experiments (growth-linked transformation coefficient <0.4 g-atenolol·g-COD−1). Thus, the biotransformation kinetic model is favored for monitoring and forecasting biotransformation in larger-scale systems because use of the parsimonious model (rate coefficient of 7 mg-atenolol·g-COD−1·d−1) resulted in only modest performance reductions. The biotransformation model represents an improvement over pseudo first-order models as it is a mixed-order kinetic model that is linked to transient biomass conditions.