The optimal deployment of synergistic antibiotics: a control-theoretic approach

Peña‐Miller, Rafael; Laehnemann, David; Schulenburg, Hinrich; Ackermann, Martin; Beardmore, Robert

doi:10.1098/rsif.2012.0279

Cited by 24 publications

(23 citation statements)

References 23 publications

(25 reference statements)

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“…For both logistic growth models, the optimum treatment in each therapy is found when Δ = 0.5, i.e., when the drugs are applied alternately. This result is consistent with the findings from Peña-Miller et al (2012) for synergistic drug therapies.…”

Section: Probability Of Double Resistance For Time-dependent Dosing Ssupporting

confidence: 93%

Competition delays multi-drug resistance evolution during combination therapy

Berríos-Caro

Gifford

Galla

2020

Preprint

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A R T I C L E I N F O Keywords: probability of drug resistance combination therapy simulation of individual-based models stochastic processes A B S T R A C T Combination therapies have shown remarkable success in preventing the evolution of resistance to multiple drugs, including HIV, tuberculosis, and cancer. Nevertheless, the rise in drug resistance still remains an important challenge. The capability to accurately predict the emergence of resistance, either to one or multiple drugs, may help to improve treatment options. Existing theoretical approaches often focus on exponential growth laws, which may not be realistic when scarce resources and competition limit growth. In this work, we study the emergence of single and double drug resistance in a model of combination therapy of two drugs. The model describes a sensitive strain, two types of single-resistant strains, and a double-resistant strain. We compare the probability that resistance emerges for three growth laws: exponential growth, logistic growth without competition between strains, and logistic growth with competition between strains. Using mathematical estimates and numerical simulations, we show that between-strain competition only affects the emergence of single resistance when resources are scarce. In contrast, the probability of double resistance is affected by between-strain competition over a wider space of resource availability. This indicates that competition between different resistant strains may be pertinent to identifying strategies for suppressing drug resistance, and that exponential models may overestimate the emergence of resistance to multiple drugs. A by-product of our work is an efficient strategy to evaluate probabilities of single and double resistance in models with multiple sequential mutations. This may be useful for a range of other problems in which the probability of resistance is of interest.

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Section: Probability Of Double Resistance For Time-dependent Dosing Ssupporting

confidence: 93%

Competition delays multi-drug resistance evolution during combination therapy

Berríos-Caro

Gifford

Galla

2020

Preprint

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“…While reducing or inverting selection for resistance, antagonistic and suppressive combinations require higher doses of drugs and longer treatment time. This poses toxicity issues and can increase the potential for accumulating additional resistance mutations (49–53). Conversely, synergistic combinations increase the selective advantage of resistance mutations, but can also clear infections faster using less drug, reducing toxicity and the time in which resistance can arise (52).…”

Section: Selection Inversion Using Suppressive Drug Interactionsmentioning

confidence: 99%

Multidrug evolutionary strategies to reverse antibiotic resistance

Baym

Stone

Kishony

2016

Science

599

571

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Antibiotic treatment has two conflicting effects: the desired, immediate effect of inhibiting bacterial growth and the undesired, long-term effect of promoting the evolution of resistance. Although these contrasting outcomes seem inextricably linked, recent work has revealed several ways by which antibiotics can be combined to inhibit bacterial growth while, counterintuitively, selecting against resistant mutants. Decoupling treatment efficacy from the risk of resistance can be achieved by exploiting specific interactions between drugs, and the ways in which resistance mutations to a given drug can modulate these interactions or increase the sensitivity of the bacteria to other compounds. Although their practical application requires much further development and validation, and relies on advances in genomic diagnostics, these discoveries suggest novel paradigms that may restrict or even reverse the evolution of resistance.

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“…1B and D), can even select against drug resistance (Chait et al, 2007). Another strategy to prevent resistance evolution may be deploying synergistic drugs sequentially rather than simultaneously (Peña-Miller et al, 2012). Finding drug combinations or treatment strategies that minimize resistance evolution is an active field of research (Yeh et al, 2009;Lázár et al, 2013).…”

Section: Combinations Of Drugs Can Lead To Drug Interactionsmentioning

confidence: 99%

Bacterial responses to antibiotics and their combinations

Mitosch

Bollenbach

2014

Environ Microbiol Rep

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Antibiotics affect bacterial cell physiology at many levels. Rather than just compensating for the direct cellular defects caused by the drug, bacteria respond to antibiotics by changing their morphology, macromolecular composition, metabolism, gene expression and possibly even their mutation rate. Inevitably, these processes affect each other, resulting in a complex response with changes in the expression of numerous genes. Genome-wide approaches can thus help in gaining a comprehensive understanding of bacterial responses to antibiotics. In addition, a combination of experimental and theoretical approaches is needed for identifying general principles that underlie these responses. Here, we review recent progress in our understanding of bacterial responses to antibiotics and their combinations, focusing on effects at the levels of growth rate and gene expression. We concentrate on studies performed in controlled laboratory conditions, which combine promising experimental techniques with quantitative data analysis and mathematical modeling. While these basic research approaches are not immediately applicable in the clinic, uncovering the principles and mechanisms underlying bacterial responses to antibiotics may, in the long term, contribute to the development of new treatment strategies to cope with and prevent the rise of resistant pathogenic bacteria.

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The optimal deployment of synergistic antibiotics: a control-theoretic approach

Cited by 24 publications

References 23 publications

Competition delays multi-drug resistance evolution during combination therapy

Competition delays multi-drug resistance evolution during combination therapy

Multidrug evolutionary strategies to reverse antibiotic resistance

Bacterial responses to antibiotics and their combinations

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