Mila Goronova, Gabriela Ochoa, Patrick Maier, Andrew Hoyle

Evolutionary optimisation of antibiotic dosing regimens for bacteria
with different levels of resistance

ABSTRACT:
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.

KEYWORDS:
Antimicrobial resistance;
Evolutionary algorithms;
Differential evolution;
Optimisation;
Mathematical modelling;
Pharmacokinetics/pharmacodynamics modelling;
MIC;
Antibiotic dosing regimens