Optimising Antibiotic Usage to Treat Bacterial Infections

Abstract

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.

Conditionally Redundant Bacteriocin Targeting byPseudomonas syringae

“…With the widely recognized breakdown in antibiotic-mediated control of human and agricultural pathogens 29–31 , resulting from the selection for and dissemination of antibiotic-resistance genes, there has been a push to both rethink how antibiotics are used, as well as the adoption of new control methods. To this end, researchers have explored several potentially intersecting approaches, none of which have yet been widely adopted, including: applying evolutionary principles to use antibiotics in temporal combinations to slow the emergence of resistance 32–34 , using antibiotic combinations that result in ‘collateral sensitivity’ 35 , combining antibiotics with bacteriophages (phage-therapy) in a synergistic combination 36 , and utilizing bacteriocins (including combinations thereof) as narrow-spectrum antibacterials 21,23 . Inherent in all of these approaches is the recognition that, regardless of the selective agent, there are always paths to the evolution of resistance.…”

Nisin Z and Lacticin 3147 Improve Efficacy of Antibiotics Against Clinically Significant Bacteria

“…Furthermore, treatment duration is an important consideration for antimicrobial treatment regimens with repeated doses usually administered for a specified duration. Indeed, Paterson et al [45] reported that high initial doses followed by extended tapering of doses improved antibiotic efficacy. Thus, the model presented here could be further optimized.…”

Metal self-assembly mimosine peptides with enhanced antimicrobial activity: towards a new generation of multitasking chelating agents

“…85 It is difficult to remove biofilm formation on medical devices, therefore necessitating the removal of the infected device or use of high doses of antibiotics, 86 which increases costs and further antibiotic resistance and cytotoxicity. 87 Therefore, the identification of new compounds (e.g. peptides) capable of inhibiting biofilm formation is required.…”