Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

Hansen, Elsa; Karslake, Jason; Woods, Robert J.; Read, Andrew F.; Wood, Kevin B.

doi:10.1101/638924

Cited by 16 publications

(25 citation statements)

References 87 publications

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“…Recall that the ideal MTD treatment depicted in Fig. 1a (dashed line) is an idealized version of MTD which instantly eliminates sensitive cells [18] [28]. The clinical gain from this regimen, compared to no treatment, is the time it takes for the tumor to grow from R 0 (the initial resistant population) back to its initial size N 0 ; that is, the time taken for the resistant population to increase by a factor of N /R 0 after being freed from competition with sensitive cells.…”

Section: Other Important Biological Parametersmentioning

confidence: 99%

The logic of containing tumors

Viossat¹,

Noble²

2020

Preprint

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Challenging the paradigm of the maximum tolerated dose, recent studies have shown that a strategy aiming for containment, not elimination, can control tumor burden more effectively in vitro, in mouse models, and in the clinic. These outcomes are consistent with the hypothesis that emergence of resistance to cancer therapy may be prevented or delayed by exploiting competitive ecological interactions between drug-sensitive and resistant tumor cell subpopulations. However, although various mathematical and computational models have been proposed to explain the superiority of particular containment strategies, this evolutionary approach to cancer therapy lacks a rigorous theoretical foundation. Here we combine extensive mathematical analysis and numerical simulations to establish general conditions under which a containment strategy is expected to control tumor burden more effectively than applying the maximum tolerated dose. We show that when resistant cells are present, an idealized strategy of containing a tumor at a maximum tolerable size maximizes time to treatment failure (that is, the time at which tumor burden becomes intolerable). These results are very general and do not depend on any fitness cost of resistance. We further provide formulas for predicting the clinical benefits attributable to containment strategies in a wide range of scenarios, and we compare outcomes of theoretically optimal treatments with those of more practical protocols. Our results strengthen the rationale for clinical trials of evolutionarily-informed cancer therapy.

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Section: Other Important Biological Parametersmentioning

confidence: 99%

The logic of containing tumors

Viossat¹,

Noble²

2020

Preprint

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“…It is thought that selective killing of sensitive cells during therapy removes competitive restrictions on resistant populations, allowing for their outgrowth and subsequent therapeutic failure. While intuitive in theory and observed in bacteria 29 , empiric evidence of the dynamics that underlie this phenomenon in cancer have, to our knowledge, yet to be elucidated.…”

Section: Introductionmentioning

confidence: 99%

Measuring competitive exclusion in non-small cell lung cancer

Farrokhian

Maltas

Dinh

et al. 2020

Preprint

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Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies work to delay this inevitability by promoting the maintenance of tumoral heterogeneity. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. Reproducibility of the success seen with this treatment paradigm is contingent on formal elucidation of underlying subclonal interactions. One such consequence of these interactions, “competitive release”, is an evolutionary phenomenon that describes the unopposed proliferation of resistant populations following maximally tolerated systemic therapies. While often assumed in evolutionary models of cancer, here we show the first empiric evidence of “competitive release” occurring in an in vitro tumor environment. We found that this phenomenon is modulated by both drug dose and initial population composition. As such, we observed that monotypic fitness differentials were insufficient to accurately predict the outcomes of this phenomenon. Instead, derivation of underlying frequency dependent evolutionary game dynamics is essential to understand resulting sub-population shifts through time. To evaluate the impact of these non-autonomous growth behaviors over longer time series, we used a range of commonly employed growth models, some of which are the foundation of ongoing clinical trials. While useful for identifying persistent qualitative features, we observed significant fragility and model specific behaviors that limited the ability of these models to make consistent quantitative predictions, even when the parameters were empirically derived.

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“…A number of recent studies illustrate how a deeper understanding of microbial population dynamics can lead to improved strategies for stalling the emergence of resistance. These anti-resistance approaches exploit different features of the population dynamics, including competitive suppression between sensitive and resistance cells (14,15), synergy with the immune system (16), precise timing of growth dynamics or dosing (17,18), responses to subinhibitory drug doses (19), and band-pass response to periodic dosing (10). Resistance-stalling strategies may also exploit spatial heterogeneity (20,21,22,23,24,25), epistasis between resistance mutations (26,27), hospital-level dosing protocols (28,29), and regimens of multiple drugs applied in sequence (28,30,18,31,19,32) or combination (33,34,35,36,37,38,39,40), which may allow one to leverage statistical correlations between resistance profiles for different drugs (41,42,43,44,39,37,45,46,47,48).…”

Section: Introductionmentioning

confidence: 99%

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

Hallinen

Karslake

Wood

2019

Preprint

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Bacteria exploit a diverse set of defenses to survive exposure to antibiotics.While the molecular and genetic underpinnings of antibiotic resistance are increasingly understood, less is known about how these molecular events influence microbial dynamics on the population scale. In this work, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where-for example-increasing population size or delaying drug exposure can promote population collapse. Specifically, we combine experiments in computer-controlled bioreactors with simple mathematical models to reveal density-dependent feedback loops that couple population growth and antibiotic efficacy when communities include drug-resistant (β-lactamase producing) subpopulations. The resulting communities exhibit a wide range of behavior, including population survival, population collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Guided by these results, we experimentally demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx (and lower average drug concentrations) collapse. These results illustrate that the spread of drug resistant determinants-even in a simplified single-species communities-may be governed by potentially counterintuitive dynamics driven by population-level interactions.

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Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

Cited by 16 publications

References 87 publications

The logic of containing tumors

The logic of containing tumors

Measuring competitive exclusion in non-small cell lung cancer

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

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