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

Abstract

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 follows fixed-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 a Galleria mellonella (insect) model of bacterial infection to create a fully parametrised mathematical model of a systemic Vibrio 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 'loading' dose followed by doses of incremental reductions in antibiotic quantity (dose 'tapering'). Moreover, application of the entire antibiotic in a single dose at the start of treatment was never optimal, except when the total quantity of antibiotic was very low. Importantly, the range of optimal regimens identified was broad enough to allow the antibiotic prescriber to choose a regimen based on additional criteria or preferences. Our findings demonstrate the utility of an insect host to model antibiotic therapies in vivo and the approach lays a foundation for future regimen optimisation for patient and societal benefits.

Effective antibiotic dosing in the presence of resistant strains

“…Hoyle et al study single and multiple fixed-dose regimens as well as tapered dosing [ 23 ]. They conclude that a single large dose is never optimal, while the optimal dosing when we also want to minimize the total antibiotic quantity follows a tapering pattern; in a follow-up work [ 24 ] they find similar results and also validate their findings using biological experiments. Penna-Miller et al use optimal control to study dosing strategies in the case where commensal and pathogenic bacteria are present; they find that an ‘intermittent’ or pulsed dosing is optimal [ 25 ].…”

Unravelling microbial efflux through mathematical modelling

“…While understanding efflux pumps is clearly important in combatting antimicrobial resistance, it is not the only thing that matters and mathematical models can be expanded to account for additional aspects: the bactericidal/bacteriostatic action of the antibiotic, interplay with the host immune response, dosing regimens and patient heterogeneity are just a few examples. In particular, mathematical modelling has a bright future in personalized medicine and the optimization of treatment regimes [44][45][46]. Mathematics is of course not the only discipline that can help -physics and chemistry are already routinely used in many laboratories, and collaborations with behavioural scientists will help to predict and counteract lack of adherence to proposed treatment regimes in personalized medicine, for example.…”

“…While understanding efflux pumps is clearly important in combatting antimicrobial resistance, it is not the only thing that matters and mathematical models can be expanded to account for additional aspects: the bactericidal/bacteriostatic action of the antibiotic, interplay with the host immune response, dosing regimens and patient heterogeneity are just a few examples. In particular, mathematical modelling has a bright future in personalized medicine and the optimization of treatment regimes [44–46].…”

The utilization of Blaptica dubia cockroaches as an in vivo model to test antibiotic efficacy

“…Probably the most extensively utilized insect model for studies involving microbial pathogenesis and antibiotic efficacy is the waxworm, G. mellonella 14 . The waxworm model has been used to investigate traditional antibiotics, novel anti-biofilm compounds, and even bacteriophage therapy 24 , 25 , 31 , 32 . Although not nearly as developed as the G. mellonella model, the B. dubia OS cockroach model has potential for use as a model for pathogenesis 8 .…”