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

Optimizing Antimicrobial Treatment Schedules: Some Fundamental Analytical Results

This work studies fundamental questions regarding the optimal design of antimicrobial treatment protocols, using pharmacodynamic and pharmacokinetic mathematical models. We consider the problem of designing an antimicrobial treatment schedule to achieve eradication of a microbial infection, while minimizing the area under the time-concentration curve (AUC), which is equivalent to minimizing the cumulative dosage. We first solve this problem under the assumption that an arbitrary antimicrobial concentration profile may be chosen, and prove that the ideal concentration profile consists of a constant concentration over a finite time duration, where explicit expressions for the optimal concentration and the time duration are given in terms of the pharmacodynamic parameters. Since antimicrobial concentration profiles are induced by a dosing schedule and the antimicrobial pharmacokinetics, the 'ideal' concentration profile is not strictly feasible. We therefore also investigate the possibility of achieving outcomes which are close to those provided by the 'ideal' concentration profile, using a bolus+continuous dosing schedule, which consists of a loading dose followed by infusion of the antimicrobial at a constant rate. We explicitly find the optimal bolus+continuous dosing schedule, and show that, for realistic parameter ranges, this schedule achieves results which are nearly as efficient as those attained by the 'ideal' concentration profile. The optimality results obtained here provide a baseline and reference point for comparison and evaluation of antimicrobial treatment plans.

Unravelling microbial efflux through mathematical modelling

AbstractMathematical modelling is a useful tool that is increasingly used in the life sciences to understand and predict the behaviour of biological systems. This review looks at how this interdisciplinary approach has advanced our understanding of microbial efflux, the process by which microbes expel harmful substances. The discussion is largely in the context of antimicrobial resistance, but applications in synthetic biology are also touched upon. The goal of this paper is to spark further fruitful collaborations between modellers and experimentalists in the efflux community and beyond.

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

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.

The potential of 4D's approach in curbing antimicrobial resistance among bacterial pathogens

INTRODUCTION

Antibiotics are life-saving drugs but irrational/inappropriate use leads to the emergence of antibiotic-resistant bacterial superbugs, making their treatment extremely challenging. Increasing antimicrobial resistance (AMR) among bacterial pathogens is becoming a serious public health concern globally. If ignorance persists, there would not be any antibiotics available to treat even a common bacterial infection in future.

AREA COVERED

This article intends to collate and discuss the potential of 4D's (right Drug, Dose, Duration, and De-escalation of therapy) approach to tackle the emerging problem of AMR. For this, we searched PubMed, Google Scholar, Medline, and clinicaltrials.gov databases primarily using keywords 'optimal antibiotic therapy,' 'antimicrobial resistance,' 'higher versus lower dose antibiotic treatment,' 'shorter versus longer duration antibiotic treatment,' 'de-escalation study', and 'antimicrobial stewardship measures' and based on the findings, form and expressed our opinion.

EXPERT OPINION

More efforts are needed for developing diagnostics for rapid, accurate, point-of-care, and cost-effective pathogen identification and antimicrobial susceptibility testing (AST) to facilitate rational use of antibiotics. Current dosing and duration of therapies also need to be redefined to maximize their impact. Furthermore, de-escalation approaches should be developed and encouraged in the clinic. This altogether will minimize selection pressure on the pathogens and reduce emergence of AMR.

Effective antibiotic dosing in the presence of resistant strains

Mathematical models can be very useful in determining efficient and successful antibiotic dosing regimens. In this study, we consider the problem of determining optimal antibiotic dosing when bacteria resistant to antibiotics are present in addition to susceptible bacteria. We consider two different models of resistance acquisition, both involve the horizontal transfer (HGT) of resistant genes from a resistant to a susceptible strain. Modeling studies on HGT and study of optimal antibiotic dosing protocols in the literature, have been mostly focused on transfer of resistant genes via conjugation, with few studies on HGT via transformation. We propose a deterministic ODE based model of resistance acquisition via transformation, followed by a model that takes into account resistance acquisition through conjugation. Using a numerical optimization algorithm to determine the 'best' antibiotic dosing strategy. To illustrate our optimization method, we first consider optimal dosing when all the bacteria are susceptible to the antibiotic. We then consider the case where resistant strains are present. We note that constant periodic dosing may not always succeed in eradicating the bacteria while an optimal dosing protocol is successful. We determine the optimal dosing strategy in two different scenarios: one where the total bacterial population is to be minimized, and the next where we want to minimize the bacterial population at the end of the dosing period. We observe that the optimal strategy in the first case involves high initial dosing with dose tapering as time goes on, while in the second case, the optimal dosing strategy is to increase the dosing at the beginning of the dose cycles followed by a possible dose tapering. As a follow up study we intend to look at models where 'persistent' bacteria may be present in additional to resistant and susceptible strain and determine the optimal dosing protocols in this case.

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

Insects are now well recognized as biologically relevant alternative hosts for dozens of mammalian pathogens and they are routinely used in microbial pathogenesis studies. Unfortunately, these models have yet to be incorporated into the drug development pipeline. The purpose of this work was to begin to evaluate the utility of orange spotted (Blaptica dubia) cockroaches in early antibiotic characterization. To determine whether these model hosts could exhibit mortality when infected with bacteria that are pathogenic to humans, we subjected B. dubia roaches to a range of infectious doses of Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, and Acinetobacter baumannii to identify the medial lethal dose. These results showed that lethal disease did not develop following infection of high doses of S. aureus, and A. baumannii. However, cockroaches infected with E. coli and K. pneumoniae succumbed to infection (LD50s of 5.82 × 10⁶ and 2.58 × 10⁶ respectively) suggesting that this model may have limitations based on pathogen specificity. However, because these cockroaches were susceptible to infection from E. coli and K. pneumoniae, we used these bacterial strains for subsequent antibiotic characterization studies. These studies suggested that β-lactam antibiotic persistence and dose was associated with reduction of hemolymph bacterial burden. Moreover, our data indicated that the reduction of bacterial CFU was directly due to the drug activity. Altogether, this work suggests that the orange-spotted cockroach infection model provides an alternative in vivo setting from which antibiotic efficacy can be evaluated.