Goranova M, Ochoa G, Maier P & Hoyle A (2022) Evolutionary optimisation of antibiotic dosing regimens for bacteria with different levels of resistance. Artificial Intelligence in Medicine, 133, Art. No.: 102405. https://doi.org/10.1016/j.artmed.2022.102405
Antimicrobial resistance is one of the biggest threats to global health, food security, and development. Antibiotic overuse and misuse are the main drivers for the emergence of resistance. It is crucial to optimise the use of existing antibiotics in order to improve medical outcomes, decrease toxicity and reduce the emergence of resistance. We formulate the design of antibiotic dosing regimens as an optimisation problem, and use an evolutionary algorithm suited to continuous optimisation (differential evolution) to solve it. Regimens are represented as vectors of real numbers encoding daily doses, which can vary across the treatment duration. A stochastic mathematical model of bacterial infections with tuneable resistance levels is used to evaluate the effectiveness of evolved regimens. The objective is to minimise the treatment failure rate, subject to a constraint on the maximum total antibiotic used. We consider simulations with different levels of bacterial resistance, two ways of administering the drug (orally and intravenously), as well as coinfections with two strains of bacteria. Our approach produced effective dosing regimens, with an average improvement in lowering the failure rate 30%, when compared with standard fixed-daily-dose regimens with the same total amount of antibiotic.
Antimicrobial resistance; Evolutionary algorithms; Differential evolution; Optimisation; Mathematical modelling; Pharmacokinetics/pharmacodynamics modelling; MIC; Antibiotic dosing; regimens
Artificial Intelligence in Medicine: Volume 133
|Publication date online||24/09/2022|
|Date accepted by journal||15/09/2022|