A survey of within-host and between-hosts modelling for antibiotic resistance

Tetteh, Josephine N.A.; Matthäus, Franziska; Hernandez‐Vargas, Esteban A.

doi:10.1016/j.biosystems.2020.104182

Cited by 12 publications

(6 citation statements)

References 107 publications

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“…On the one hand, a clear evolutionary arms race is expected: antimicrobial compounds pose a selective pressure onto microbial communities, which allows for the selection of resistance determinants (Gullberg et al, 2011). Based on this, a series of mathematical models have been employed to describe or predict the outcome of antimicrobial use (reviews by Austin and Anderson, 1999;Blanquart, 2019;Tetteh et al, 2020). Such models often focus on the within-host dynamics of antimicrobial treatment in search of the optimal frequency and intensity to minimise resistance (e.g., Bonhoeffer et al, 1997;Levin et al, 1997;Colijn and Cohen, 2015;Bitbol, 2018, 2020).…”

Section: Introductionmentioning

confidence: 99%

Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance

et al. 2023

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The steep rise of infections caused by bacteria that are resistant to antimicrobial agents threatens global health. However, the association between antimicrobial use and the prevalence of resistance is not straightforward. Therefore, it is necessary to quantify the importance of additional factors that affect this relationship. We theoretically explore how the prevalence of resistance is affected by the combination of three factors: antimicrobial use, bacterial transmission, and fitness cost of resistance. We present a model that combines within-host, between-hosts and between-populations dynamics, built upon the competitive Lotka-Volterra equations. We developed the model in a manner that allows future experimental validation of the findings with single isolates in the laboratory. Each host may carry two strains (susceptible and resistant) that represent the host’s commensal microbiome and are not the target of the antimicrobial treatment. The model simulates a population of hosts who are treated periodically with antibiotics and transmit bacteria to each other. We show that bacterial transmission results in strain co-existence. Transmission disseminates resistant bacteria in the population, increasing the levels of resistance. Counterintuitively, when the cost of resistance is low, high transmission frequencies reduce resistance prevalence. Transmission between host populations leads to more similar resistance levels, increasing the susceptibility of the population with higher antimicrobial use. Overall, our results indicate that the interplay between bacterial transmission and strain fitness affects the prevalence of resistance in a non-linear way. We then place our results within the context of ecological theory, particularly on temporal niche partitioning and metapopulation rescue, and we formulate testable experimental predictions for future research.

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Section: Introductionmentioning

confidence: 99%

Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance

et al. 2023

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“…Tackling antibiotic resistance is an emerging topic throughout the world and requires urgent attention in both the policies and management of existing antibiotic-resistant bacteria ( Tetteh et al, 2020 ). The development of a novel strategy in eliminating antibiotic-resistant bacteria has been researched for decades.…”

Section: Discussionmentioning

confidence: 99%

Vibrio alginolyticus Survives From Ofloxacin Stress by Metabolic Adjustment

Yin

Yang

et al. 2022

Front. Microbiol.

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Antibiotic-resistant Vibrio alginolyticus becomes a worldwide challenge threatening both human health and food safety. The approach in managing such infection is largely absent, despite the fact that the mechanisms of antibiotic resistance have been extensively investigated. Metabolic modulation has been documented to be a novel approach in improving antibiotic efficacy. In this study, we characterize the metabolic signature of V. alginolyticus exposed to 0.3 or 0.5 μg/ml of ofloxacin (OFX). By profiling the metabolome, we find that bacteria treated by the two different concentrations of OFX generate different metabolic signatures. While a part of these metabolites was shared by both groups, the other metabolites represent their own signatures. The pathway enrichment analysis demonstrates that the pyruvate cycle is disrupted in the bacteria treated by the 0.3 μg/ml OFX as compared to those by the 0.5 μg/ml. Importantly, the disruption of pyruvate cycle confers the capability of bacteria to survive under 0.5 μg/ml of antibiotic stress. Further analysis identifies that the fatty acid biosynthesis is elevated in bacteria treated by 0.3 μg/ml OFX, and inhibition on fatty acid completely prevents the bacteria from survival even under such dose of antibiotic stress. Our study suggests that bacteria adapt to antibiotic stress by modulating the metabolic flux for survival, which could be targeted to increase antibiotic efficacy.

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“…Our modelling tools and understanding of biology have improved since Bonhoeffer’s publication. The significant impact of phenomena such as bystander selection [ 61 ] are better understood, and there has been a more recent push toward integrating both within host and between host models [ 62 ]. Similarly, in the area of clinical policy, diagnostic-informed strategies such as the ‘search and destroy’ ABR policy implemented in the Netherlands [ 48 , 49 , 60 ] have been mentioned only briefly in this article.…”

Section: Discussionmentioning

confidence: 99%

Comparing optimization criteria in antibiotic allocation protocols

2022

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Clinicians prescribing antibiotics in a hospital context follow one of several possible ‘treatment protocols’—heuristic rules designed to balance the immediate needs of patients against the long-term threat posed by the evolution of antibiotic resistance and multi-resistant bacteria. Several criteria have been proposed for assessing these protocols; unfortunately, these criteria frequently conflict with one another, each providing a different recommendation as to which treatment protocol is best. Here, we review and compare these optimization criteria. We are able to demonstrate that criteria focused primarily on slowing evolution of resistance are directly antagonistic to patient health both in the short and long term. We provide a new optimization criteria of our own, intended to more meaningfully balance the needs of the future and present. Asymptotic methods allow us to evaluate this criteria and provide insights not readily available through the numerical methods used previously in the literature. When cycling antibiotics, we find an antibiotic switching time which proves close to optimal across a wide range of modelling assumptions.

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A survey of within-host and between-hosts modelling for antibiotic resistance

Cited by 12 publications

References 107 publications

Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance

Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance

Vibrio alginolyticus Survives From Ofloxacin Stress by Metabolic Adjustment

Comparing optimization criteria in antibiotic allocation protocols

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