Multi-objective Evolutionary Design of Antibiotic Treatments

Citation

Ochoa G, Christie LA, Brownlee AE & Hoyle A (2020) Multi-objective Evolutionary Design of Antibiotic Treatments. Artificial Intelligence in Medicine, 102, Art. No.: 101759. https://doi.org/10.1016/j.artmed.2019.101759

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.

Keywords
antibiotic resistance; antimicrobial resistance; AMR; evolutionary computation; stochastic model

Journal
Artificial Intelligence in Medicine: Volume 102

People

Dr Sandy Brownlee

Senior Lecturer in Computing Science, Computing Science and Mathematics - Division

Dr Andrew Hoyle

Senior Lecturer, Mathematics

Professor Gabriela Ochoa

Professor, Computing Science