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Multi-objective evolutionary design of antibiotic treatments.

Ochoa, Gabriela; Christie, Lee A.; Brownlee, Alexander E.; Hoyle, Andrew

Authors

Gabriela Ochoa

Alexander E. Brownlee

Andrew Hoyle



Abstract

Antibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but limited work is devoted to optimising dosage regimes when they are prescribed. The design of antibiotic treatments can be formulated as an optimisation problem where candidate solutions are encoded as vectors of dosages per day. The formulation naturally gives rise to competing objectives, as we want to maximise the treatment effectiveness while minimising the total drug use, the treatment duration and the concentration of antibiotic experienced by the patient. This article combines a recent mathematical model of bacterial growth including both susceptible and resistant bacteria, with a multi-objective evolutionary algorithm in order to automatically design successful antibiotic treatments. We consider alternative formulations combining relevant objectives and constraints. Our approach obtains shorter treatments, with improved success rates and smaller amounts of drug than the standard practice of administering daily fixed doses. These new treatments consistently involve a higher initial dose followed by lower tapered doses.

Citation

OCHOA, G., CHRISTIE, L.A., BROWNLEE, A.E. and HOYLE, A. 2020. Multi-objective evolutionary design of antibiotic treatments. Artificial intelligence in medicine [online], 102, article number 101759. Available from: https://doi.org/10.1016/j.artmed.2019.101759

Journal Article Type Article
Acceptance Date Nov 5, 2019
Online Publication Date Nov 17, 2019
Publication Date Jan 31, 2020
Deposit Date Nov 22, 2019
Publicly Available Date Nov 18, 2020
Journal Artificial intelligence in medicine
Print ISSN 0933-3657
Electronic ISSN 1873-2860
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 102
Article Number 101759
DOI https://doi.org/10.1016/j.artmed.2019.101759
Keywords Antibiotic resistance; Antimicrobial resistance; AMR; Evolutionary computation; Stochastic model
Public URL https://rgu-repository.worktribe.com/output/784417

Files

OCHOA 2020 Multi objective (1.1 Mb)
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/





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