Quantifying trade-offs between therapeutic efficacy and resistance dissemination for enrofloxacin dose regimens in cattle

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Quantifying trade-offs between therapeutic efficacy and resistance dissemination for enrofloxacin dose regimens in cattle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantifying trade-offs between therapeutic efficacy and resistance dissemination for enrofloxacin dose regimens in cattle Liton Chandra Deb, Archana Timsina, Suzanne Lenhart, Derek Foster, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4166888/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The use of antimicrobial drugs in food-producing animals increases the selection pressure on pathogenic and commensal bacteria to become resistant. This study aims to evaluate the existence of trade-offs between treatment effectiveness, cost, and the dissemination of resistance in gut commensal bacteria. We developed a within-host ordinary differential equation model to track the dynamics of antimicrobial drug concentrations and bacterial populations in the site of infection (lung) and the gut. The model was parameterized to represent enrofloxacin treatment for bovine respiratory disease (BRD) caused by Pastereulla multocida in cattle. Three approved enrofloxacin dosing regimens were compared for their effects on resistance on P. multocida and commensal E. coli : 12.5 mg/kg and 7.5 mg/kg as a single dose, and 5 mg/kg as three doses. Additionally, we explored non-approved regimes. Our results indicated that both 12.5 mg/kg and 7.5 mg/kg as a single dose scenario increased the most the treatment costs and prevalence of P. multocida resistance in the lungs, while 5 mg/kg as three doses increased resistance in commensal E. coli bacteria in the gut the most out of the approved scenarios. A proposed scenario (7.5 mg/kg, two doses 24 hours apart) showed low economic costs, minimal P. multocida , and moderate effects on resistant E. coli. Overall, the scenarios that decrease P. multocida , including resistant P. multocida did not coincide with the scenarios that decrease resistant E. coli the most, suggesting a trade-off between both outcomes. The sensitivity analysis indicates that bacterial populations were the most sensitive to drug conversion factors into plasma (𝛽), elimination of the drug from the colon (𝜗), fifty percent sensitive bacteria ( P. multocida ) killing effect (L s50 ), fifty percent of bacteria ( E. coli ) above ECOFF killing effect (C r50 ), and net drug transfer rate in the lung (𝛾) parameters. Health sciences/Health care/Public health/Epidemiology Biological sciences/Computational biology and bioinformatics/Computational models Enrofloxacin dosing regimens in cattle Antimicrobial drug resistance Pk-Pd model Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure1.docx Cite Share Download PDF Status: Published Journal Publication published 04 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 Jul, 2024 Reviews received at journal 11 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviews received at journal 18 Jun, 2024 Reviewers agreed at journal 09 Jun, 2024 Reviewers agreed at journal 09 Jun, 2024 Reviewers invited by journal 09 Jun, 2024 Editor assigned by journal 09 Jun, 2024 Editor invited by journal 04 Apr, 2024 Submission checks completed at journal 04 Apr, 2024 First submitted to journal 26 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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