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The global crisis we are facing with regard to antibiotic resistance has been largely attributed to the overuse and misuse of antibiotics in healthcare and agriculture. However, there is also growing global concern about cross-resistance between biocides and antibiotics. This has made clear the need for more research in this area along with easy-to-perform, but realistic, methods to characterise the potential risk associated with cross-resistance to antibiotics due to biocide use. The primary aim of this work was to develop a repeat-exposure method for predicting bacterial resistance to microbicides, including their cross-resistance to antibiotics. Realism is incorporated in the presented protocol through the use of relevant concentrations and contact times, validated neutralisers, appropriate test organisms and repeat-exposures. The protocol can be applied to formulated microbicides, as shown in the liquid handwash case study presented here. Five bacterial strains were included in the study: Staphylococcus aureus ATCC 6538, Pseudomonas aeruginosa ATCC 15442, Staphylococcus epidermidis ATCC 14990, Escherichia coli ATCC 10536 and Enterococcus hirae ATCC 10541. The protocol parameters used in the case study reflected a worst-case exposure scenario (in terms of contact time and concentration). The results demonstrated that repeated exposure to the liquid handwash would not be expected to lead to development of bacterial resistance or cross-resistance to antibiotics. It is envisaged that this protocol could be used by manufacturers of microbicidal formulations to assess whether repeated use of the test products would contribute to bacterial resistance development or cross-resistance to antibiotics. Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

Citation

J A Shepherd, M D Parker. Repeat-exposure in vitro protocol to assess the risk of antimicrobial resistance (AMR) development from use of personal care products: Case study using an antibacterial liquid handwash. Journal of microbiological methods. 2023 Dec;215:106851

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PMID: 37907118

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