Tag: EUCAST

I am very happy to report on our most recently published paper – and the first one I have co-authored with my PhD supervisor Joakim Larsson in more than six years – which is a product of the BIOCIDE project. In the paper, published in FEMS Microbiology Ecology, Daniel Jaén-Luchoro has been collecting hundreds of MIC data points for biocides and metals to create the first complete and relatively comparable (more on that in a bit) dataset of bacterial sensitivity data for biocides (1).

This was a much bigger undertaking that in might sound like, but it is important for out understanding of how potent metals and biocides are for inhibiting bacterial growth. Basically, Daniel has been putting together an entire EUCAST (2,3) for biocides and metals, which is potentially extremely useful. This in turn can be used to better estimate the risks for co-selection of resistance to antibiotics.

Daniel was able to collect data for 53 antibacterial biocides, 21 metals, and 17 related compounds, resulting in 20,378 MIC values across 164 bacterial species. Of course the data is very overrepresented by clinically relevant organisms, including Staphylococcus aureus and Escherichia coli. Also, certain biocides were studied more than others, such as chlorhexidine and benzalkonium chloride, which was also true for metals that were mostly represented by copper, zinc, arsenic, cadmium and silver.

Importantly, we saw that there is a variety of method to measure MICs for biocides and metals, which means that a lot of the data is not directly comparable. Also, some authors did not even report the exact conditions they measured MICs under. This emphasizes the urgent need for standardized susceptibility testing methodology and consistent terminology for research on biocide and metal resistance. By centralizing MIC data, we provide an important foundation for future efforts to assess ecological risks and co-selection with antibiotic resistance. We aim to integrate this into an update to the BacMet database (4,5) that hopefully will be come available in the near future!

References

1. Jaén-Luchoro D, Larsson DGJ, Bengtsson-Palme J: Bacterial sensitivity distributions for biocides and metals. FEMS Microbiology Ecology, Advance article fiag075 (2026). http://dx.doi.org/10.1093/femsec/fiag075
2. https://mic.eucast.org
3. Kahlmeter G, Turnidge J: Wild-type distributions of minimum inhibitory concentrations and epidemiological cut-off values—laboratory and clinical utility. Clinical Microbiology Reviews 36:e00100-22. https://doi.org/10.1128/cmr.00100-22
4. http://bacmet.biomedicine.gu.se
5. Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DGJ: BacMet: Antibacterial biocide and metal resistance genes database. Nucleic Acids Research, 42, D1, D737–D743 (2014). http://dx.doi.org/10.1093/nar/gkt1252

Yesterday was an intensive day for typesetters apparently, since they put two of my papers online on the same day. This second paper was published in Environment International, and focuses on predicting minimal selective concentrations for all antibiotics present in the EUCAST database (1).

Today (well, up until yesterday at least), we have virtually no knowledge of which environmental concentrations that can exert a selection pressure for antibiotic resistant bacteria. However, experimentally determining minimal selective concentrations (MSCs) in complex ecosystems would involve immense efforts if done for all antibiotics. Therefore, efforts to theoretically determine MSCs for different antibiotics have been suggested (2,3). In this paper we therefore estimate upper boundaries for selective concentrations for all antibiotics in the EUCAST database, based on the assumption that selective concentrations a priori must be lower than those completely inhibiting growth. Data on Minimal Inhibitory Concentrations (MICs) were obtained for 122 antibiotics and antibiotics combinations, the lowest observed MICs were identified for each of those across all tested species, and to compensate for limited species coverage, we adjusted the lowest MICs for the number of tested species. We finally assessed Predicted No Effect Concentrations (PNECs) for resistance selection using an assessment factor of 10 to account for the differences between MICs and MSCs. Since we found that the link between taxonomic similarity between species and lowest MIC was weak, we have not compensated for the taxonomic diversity that each antibiotic was tested against – only for limited number of species tested. In most cases, our PNECs for selection of resistance were below available PNECs for ecotoxicological effects retrieved from FASS. Also, concentrations predicted to be selective have, for some antibiotics, been detected in regular sewage treatment plants (4), and are greatly exceeded in environments polluted by pharmaceutical pollution (5-7), often with drastic consequences in terms of resistance gene enrichments (8-10). This is a central issue since in principle a transfer event of a novel resistance determinant from an environmental bacteria to an (opportunistic) human pathogen only need to occur once to become a clinical problem (11). Once established, the gene could then spread through human activities, such as trade and travel (7,13). Importantly, this paper:

• Provides upper boundaries for selective concentrations (MSCs) for 111 antibiotics
• Predicts no effect concentrations (PNECs) for resistance selection
• Can guide implementation of compound-specific emission limits based on the provided concentrations

The paper is available under open access here. We hope, and believe, that the data will be of great use in environmental risk assessments, in efforts by industries, regulatory agencies or purchasers of medicines to define acceptable environmental emissions of antibiotics, in the implementation of environmental monitoring programs, for directing mitigations, and for prioritizing future studies on environmental antibiotic resistance.

References:

1. Bengtsson-Palme J, Larsson DGJ: Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environment International, 86, 140-149 (2016). doi: 10.1016/j.envint.2015.10.015 [Paper link]
2. Ågerstrand M, Berg C, Björlenius B, Breitholtz M, Brunstrom B, Fick J, Gunnarsson L, Larsson DGJ, Sumpter JP, Tysklind M, Rudén C: Improving environmental risk assessment of human pharmaceuticals. Environmental Science and Technology (2015). doi:10.1021/acs.est.5b00302
3. Tello A, Austin B, Telfer TC: Selective pressure of antibiotic pollution on bacteria of importance to public health. Environmental Health Perspectives, 120, 1100–1106 (2012). doi:10.1289/ehp.1104650
4. Michael I, Rizzo L, McArdell CS, Manaia CM, Merlin C, Schwartz T, Dagot C, Fatta-Kassinos D: Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: a review. Water Research, 47, 957–995 (2013). doi:10.1016/j.watres.2012.11.027
5. Larsson DGJ, de Pedro C, Paxeus N: Effluent from drug manufactures contains extremely high levels of pharmaceuticals. Journal of Hazardous Materials, 148, 751–755 (2007). doi:10.1016/j.jhazmat.2007.07.008
6. Fick J, Söderström H, Lindberg RH, Phan C, Tysklind M, Larsson DGJ: Contamination of surface, ground, and drinking water from pharmaceutical production. Environmental Toxicology and Chemistry, 28, 2522–2527 (2009). doi:10.1897/09-073.1
7. Larsson DGJ: Pollution from drug manufacturing: review and perspectives. Philosophical Transactions of the Royal Society London, Series B Biological Sciences, 369 (2014). doi:10.1098/rstb.2013.0571
8. Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, Volume 5, Issue 648 (2014). doi: 10.3389/fmicb.2014.00648 [Paper link]
9. Kristiansson E, Fick J, Janzon A, Grabic R, Rutgersson C, Weijdegård B, Söderström H, Larsson DGJ: Pyrosequencing of antibiotic-contaminated river sediments reveals high levels of resistance and gene transfer elements. PLoS ONE, Volume 6, e17038 (2011). doi:10.1371/journal.pone.0017038.
10. Marathe NP, Regina VR, Walujkar SA, Charan SS, Moore ERB, Larsson DGJ, Shouche YS: A Treatment Plant Receiving Waste Water from Multiple Bulk Drug Manufacturers Is a Reservoir for Highly Multi-Drug Resistant Integron-Bearing Bacteria. PLoS ONE, Volume 8, e77310 (2013). doi:10.1371/journal.pone.0077310
11. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1 [Paper link]
12. Bengtsson-Palme J, Angelin M, Huss M, Kjellqvist S, Kristiansson E, Palmgren H, Larsson DGJ, Johansson A: The human gut microbiome as a transporter of antibiotic resistance genes between continents. Antimicrobial Agents and Chemotherapy, 59, 10, 6551-6560 (2015). doi: 10.1128/AAC.00933-15 [Paper link]