Regan, H.C., Taylor, A.F. and Karunakaran, E. orcid.org/0000-0003-1913-4319 (2022) A novel high-throughput ex vivo ovine skin wound model for testing emerging antibiotics. Journal of Visualized Experiments (187). e64041. ISSN 1940-087X
Abstract
The development of antimicrobials is an expensive process with increasingly low success rates, which makes further investment in antimicrobial discovery research less attractive. Antimicrobial drug discovery and subsequent commercialization can be made more lucrative if a fail-fast-and-fail-cheap approach can be implemented within the lead optimization stages where researchers have greater control over drug design and formulation. In this article, the setup of an ex vivo ovine wounded skin model infected with Staphylococcus aureus is described, which is simple, cost-effective, high throughput, and reproducible. The bacterial physiology in the model mimics that during infection as bacterial proliferation is dependent on the pathogen's ability to damage the tissue. The establishment of wound infection is verified by an increase in viable bacterial counts compared to the inoculum. This model can be used as a platform to test the efficacy of emerging antimicrobials in the lead optimization stage. It can be contended that the availability of this model will provide researchers developing antimicrobials with a fail-fast-and-fail-cheap model, which will help increase success rates in subsequent animal trials. The model will also facilitate the reduction and refinement of animal use for research and ultimately enable faster and more cost-effective translation of novel antimicrobials for skin and soft tissue infections to the clinic.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 JoVE. Article available under a Creative Commons Attribution-Non Commercial 3.0 License (https://creativecommons.org/licenses/by-nc/3.0/) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Funding Information: | Funder Grant number InnovateUK 86267-540271 INNOVATE UK TS/P011616/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Oct 2022 13:50 |
Last Modified: | 04 Oct 2022 13:50 |
Status: | Published |
Publisher: | Journal of Visualized Experiments |
Refereed: | Yes |
Identification Number: | 10.3791/64041 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191606 |
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