A probabilistic approach to assess antibiotic resistance development risks in environmental compartments and its application to an intensive aquaculture production scenario.
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TitleA probabilistic approach to assess antibiotic resistance development risks in environmental compartments and its application to an intensive aquaculture production scenario.
Published inEnviron Pollut 2017, 231:918-28
PubliekssamenvattingEstimating antibiotic pollution and antibiotic resistance development risks in environmental compartments is important to design management strategies that advance our stewardship of antibiotics. In this study we propose a modelling approach to estimate the risk of antibiotic resistance development in environmental compartments and demonstrate its application in aquaculture production systems. We modelled exposure concentrations for 12 antibiotics used in Vietnamese Pangasius catfish production using the ERA-AQUA model. Minimum selective concentration (MSC) distributions that characterize the selective pressure of antibiotics on bacterial communities were derived from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) Minimum Inhibitory Concentration dataset. The antibiotic resistance development risk (RDR) for each antibiotic was calculated as the probability that the antibiotic exposure distribution exceeds the MSC distribution representing the bacterial community. RDRs in pond sediments were nearly 100% for all antibiotics. Median RDR values in pond water were high for the majority of the antibiotics, with rifampicin, levofloxacin and ampicillin having highest values. In the effluent mixing area, RDRs were low for most antibiotics, with the exception of amoxicillin, ampicillin and trimethoprim, which presented moderate risks, and rifampicin and levofloxacin, which presented high risks. The RDR provides an efficient means to benchmark multiple antibiotics and treatment regimes in the initial phase of a risk assessment with regards to their potential to develop resistance in different environmental compartments, and can be used to derive resistance threshold concentrations.
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