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

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.

Influence of multiple courses of therapy on aminoglycoside clearance in adult patients with cystic fibrosis.

OBJECTIVES: To investigate factors affecting aminoglycoside clearance in adult patients with cystic fibrosis who received multiple courses of antibiotic therapy over a period of up to 15 years. METHODS: Aminoglycoside concentration-time data and relevant clinical characteristics were collated from clinical pharmacokinetic databases established in Glasgow, Scotland and The Hague, The Netherlands. Data from Glasgow (1993-2009) were used for population model development; data from The Hague (2002-11) were used for model validation. NONMEM was used to determine structural and covariate models, with a particular focus on between-occasion variability and changes in aminoglycoside handling over multiple courses of therapy. RESULTS: The Glasgow dataset comprised 1075 courses of aminoglycoside therapy (96% tobramycin and 4% gentamicin) in 166 patients and included 2238 concentration measurements. The data were best described by a two-compartment model with creatinine clearance and height influencing aminoglycoside clearance, and height influencing volume of distribution. Between-subject and between-occasion variabilities in clearance were low, at 18% and 11%, respectively; between-subject variability was 12% for volume of distribution. Internal and external model validations were satisfactory. Multiple courses of therapy (ranging from 2 to 28 courses per patient) were not associated with any systematic changes in aminoglycoside clearance. CONCLUSIONS: Height was a better descriptor of aminoglycoside pharmacokinetics than weight in adult patients with cystic fibrosis. No changes in clearance were observed over time, even in patients who had received multiple courses of therapy over several years.

Tezacaftor-ivacaftor use in routine care of adults with cystic fibrosis: a medicine use evaluation.

OBJECTIVES: Cystic fibrosis is a devastating life-limiting genetic condition characterised by a progressive decline in lung function, respiratory infections and premature death. Tezacaftor-ivacaftor is a combined cystic fibrosis transmembrane conductance regulator (CFTR) modulator that targets the underlying cause of the disease. This study aimed to assess the impact of tezacaftor-ivacaftor use in routine clinical practice for adults with cystic fibrosis. METHODS: A retrospective observational longitudinal cohort study design was applied to examine the clinical effect of tezacaftor-ivacaftor in routine practice in the West of Scotland Adult Cystic Fibrosis Unit. Adults receiving tezacaftor-ivacaftor for at least 4 weeks were included in this medicine use evaluation.A standardised data form was used to collect patient-level data: demographics, genotype, complications of cystic fibrosis, medicine access process. Fifty-two weeks pre and post tezacaftor-ivacaftor initiation data: lung function, body mass index (BMI), days spent in hospital, days receiving antibiotic treatment for respiratory exacerbations. Anonymised data were collated and analysed using SPSS V.26. RESULTS: Of 121 potential patients, 45 received treatment with tezacaftor-ivacaftor; median age 30 years (range 17-64) at initiation, 56% were male, 76% were deemed to be homozygote and 41 patients continued treatment for at least 52 weeks. There was no significant change in % predicted FEV1; median difference 0 (IQR -3 to 6). There was a significant improvement in BMI, mean 0.6 kg/m2 (95% CI 0.2 to 1.0), as well as a median 4 (IQR -17 to 0) day reduction in days in hospital and 21 (IQR -42 to 0) day reduction in days receiving antibiotics. CONCLUSIONS: The use of tezacaftor-ivacaftor in routine practice for people with cystic fibrosis was associated with improvements in weight, as well as reducing the number of days people needed to spend in hospital and receive antibiotics.

Optimising Antibiotic Usage to Treat Bacterial Infections.

The increase in antibiotic resistant bacteria poses a threat to the continued use of antibiotics to treat bacterial infections. The overuse and misuse of antibiotics has been identified as a significant driver in the emergence of resistance. Finding optimal treatment regimens is therefore critical in ensuring the prolonged effectiveness of these antibiotics. This study uses mathematical modelling to analyse the effect traditional treatment regimens have on the dynamics of a bacterial infection. Using a novel approach, a genetic algorithm, the study then identifies improved treatment regimens. Using a single antibiotic the genetic algorithm identifies regimens which minimise the amount of antibiotic used while maximising bacterial eradication. Although exact treatments are highly dependent on parameter values and initial bacterial load, a significant common trend is identified throughout the results. A treatment regimen consisting of a high initial dose followed by an extended tapering of doses is found to optimise the use of antibiotics. This consistently improves the success of eradicating infections, uses less antibiotic than traditional regimens and reduces the time to eradication. The use of genetic algorithms to optimise treatment regimens enables an extensive search of possible regimens, with previous regimens directing the search into regions of better performance.