DataSheet1.pdf

Abstract

Oxytetracycline (OTC), one of the most important antibiotics in aquaculture industry, has been linked to emergence of antibiotic resistance genes in aquatic environment. Given rapid growth of aquaculture industry and unregulated use of antibiotics, it is needed to implement measures to mitigate the impact of antibiotic resistance risk on environmental and human health. However, there is a lack of quantitative models to properly assess risk of antibiotic resistance associated with environmentally relevant antibiotic residues. To address this issue, here we developed a computational framework to assess antibiotic resistance risk posed by low-concentration OTC in aquaculture ponds and rivers across Taiwan regions. To this end, estimated amount of aquaculture used OTC as a crucial input parameter was incorporated into a multimedia fugacity model to predict environmental concentrations of OTC in surface water/sediment. A pharmacodynamic-based dose–response model was used to characterize the OTC concentration–antibiotic resistance relationships. The risk of antibiotic resistance selection in aquatic environment could be assessed based on a probabilistic risk model. We also established a control measure model to manage the risks of substantial OTC-induced antibiotic resistance impacts. We found that OTC residues were likely to pose a high risk of tetracycline resistance (tetR) genes selection in aquaculture ponds among all the study basins, whereas risk of tetR genes selection in rivers experienced a variably changing fashion. We also showed that it was extremely difficult to moderate the tetR genes selection rates to less than 10% increase in aquaculture ponds situated at northeastern river basins in that the minimum reductions on OTC emission rates during spring, summer, and autumn were greater than 90%. On the other hand, water concentrations of OTC during spring and summer in southwestern rivers should be prioritized to be severely limited by reducing 67% and 25% of OTC emission rate, respectively. Overall, incorporating a computational fugacity model into a risk assessment framework can identify relative higher risk regions to provide the risk-based control strategies for public health decision-making and development of robust quantitative methods to zero-in on environment with high risk of tetR genes selection in relation to aquaculture-used pharmaceutical residues.