Suppressing selection for antibiotic resistance in the environment: A transparent, ecology-based approach to predicted no-effect concentrations

Kneis, D.; de la Cruz Barron, M.; Konyali, D.; Westphal, V.; Schröder, P.; Westphal-Settele, K.; Schönfeld, J.; Jungmann, D.; Berendonk, T. U.; Klümper, U.

doi:10.1101/2025.04.04.647007

bioRxiv · 10.1101/2025.04.04.647007

Suppressing selection for antibiotic resistance in the environment: A transparent, ecology-based approach to predicted no-effect concentrations

Kneis, D.·de la Cruz Barron, M.·Konyali, D.·Westphal, V.·Schröder, P.·Westphal-Settele, K.·Schönfeld, J.·Jungmann, D.·Berendonk, T. U.·Klümper, U.

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Abstract

Selection for antibiotic resistance has been demonstrated at low, environmentally-relevant antibiotic concentrations. Over the past decade, the concept of minimum selective concentrations (MSC) has been adopted in environmental regulation to define maximum permissible antibiotic concentrations. Such empirically determined MSC values often fail to reflect the complexity of natural communities, where susceptibility and resistance-associated fitness costs vary widely across species. To address this limitation, computational approaches have been developed to predict no-effect concentrations for selection of antibiotic resistance (PNECres) from routinely collected minimum inhibitory concentration (MIC) data. However, these approaches often lack a strong ecological basis, undermining confidence in their predictions. Here, we propose a simple but biologically consistent framework to derive PNECres values by integrating MIC data with probabilistic estimates of resistance-related fitness costs. Our results suggest that current regulatory environmental threshold concentrations should be lowered by at least one order of magnitude to guard against selection for antibiotic resistance.

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Kneis, D., de la Cruz Barron, M., Konyali, D., Westphal, V., Schröder, P., Westphal-Settele, K., Schönfeld, J., Jungmann, D., Berendonk, T. U., Klümper, U.. 2025-04-04. Suppressing selection for antibiotic resistance in the environment: A transparent, ecology-based approach to predicted no-effect concentrations. https://doi.org/10.1101/2025.04.04.647007

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