Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31622
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times
Author(s): Hoyle, Andy
Cairns, David
Paterson, Iona
McMillan, Stuart
Ochoa, Gabriela
Desbois, Andrew P
Issue Date: 2020
Date Deposited: 1-Sep-2020
Citation: Hoyle A, Cairns D, Paterson I, McMillan S, Ochoa G & Desbois AP (2020) Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times. PLoS Computational Biology, 16 (8), Art. No.: e1008037. https://doi.org/10.1371/journal.pcbi.1008037
Abstract: Mass production and use of antibiotics has led to the rise of resistant bacteria, a problem possibly exacerbated by inappropriate and non-optimal application. Antibiotic treatment often follows fixed-dose regimens, with a standard dose of antibiotic administered equally spaced in time. But are such fixed-dose regimens optimal or can alternative regimens be designed to increase efficacy? Yet, few mathematical models have aimed to identify optimal treatments based on biological data of infections inside a living host. In addition, assumptions to make the mathematical models analytically tractable limit the search space of possible treatment regimens (e.g. to fixed-dose treatments). Here, we aimed to address these limitations by using experiments in a Galleria mellonella (insect) model of bacterial infection, to create a fully parametrised mathematical model of a systemic Vibrio infection. We successfully validated this model with biological experiments, including treatments unseen by the mathematical model. Then, by applying artificial intelligence, this model was used to determine optimal antibiotic dosage regimens to treat the host to maximise survival while minimising total antibiotic used. As expected, host survival increased as total quantity of antibiotic applied during the course of treatment increased. However, many of the optimal regimens tended to follow a large initial ‘loading’ dose followed by doses of incremental reductions in antibiotic quantity (dose ‘tapering’). Moreover, application of the entire antibiotic in a single dose at the start of treatment was never optimal, except when the total quantity of antibiotic was very low. Importantly, the range of optimal regimens identified was broad enough to allow the antibiotic prescriber to choose a regimen based on additional criteria or preferences. Our findings demonstrate the utility of an insect host to model antibiotic therapies in vivo and the approach lays a foundation for future regimen optimisation for patient and societal benefits.
DOI Link: 10.1371/journal.pcbi.1008037
Rights: © 2020 Hoyle et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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