Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times

by Andy Hoyle, David Cairns, Iona Paterson, Stuart McMillan, Gabriela Ochoa, Andrew P. Desbois

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 followsfixed-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 aGalleria mellonella (insect) model of bacterial infection, to create a fully parametrised mathematical model of a systemicVibrio 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 ‘loa...

Source: PLoS Computational Biology - August 2, 2020 Category: Biology Authors: Andy Hoyle Source Type: research