How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient

Hansen, Elsa; Woods, Robert J.; Read, Andrew F.

doi:10.1371/journal.pbio.2001110

Cited by 117 publications

(194 citation statements)

References 116 publications

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“…In recent years, significant efforts have been devoted to designing evolutionarily sound strategies that balance short-term drug efficacy with the long-term potential to develop resistance. These approaches describe a number of different factors that could modulate resistance evolution, including interactions between bacterial cells (3,4,5,6,7,8), synergy with the immune system (9), spatial heterogeneity (10,11,12,13,14,15), epistasis between resistance mutations (16,17), precise temporal scheduling (18,19,20,21), and statistical correlations between resistance profiles for different drugs (22,23,24,25,26,27,28,29,30,31).…”

Section: Introductionmentioning

confidence: 99%

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Dean

Maltas

Wood

2019

Preprint

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Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identi ed, much less is known about how di erent combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting di erent classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the speci c interplay between drug interaction and cross resistance. For example, selection in a dual -lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance pro les are nearly identical. On the other hand, populations evolved in an aminoglycoside and -lactam combination exhibit decreased growth adaptation and resistant pro les that depend on the speci c drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance pro les selected by drug combinations correspond to (nearly) optimized linear combinations of those selected by the component drugs. Our results highlight tradeo s between drug interactions and collateral e ects during the evolution of multi-drug resistance and emphasize evolutionary bene ts and disadvantages of particular drug pairs targeting enterococci.

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Section: Introductionmentioning

confidence: 99%

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Dean

Maltas

Wood

2019

Preprint

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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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“…Using 387 a general stochastic model which includes variations of both composition and size of 388 the microbial population, we have shed light on the impact of periodic alternations of 389 presence and absence of antimicrobial on the probability that resistance evolves de novo 390 and rescues a microbial population from extinction. The majority of previous studies of 391 periodic antimicrobial treatments [10,[50][51][52][53][54][55] neglect stochastic effects, while they can 392 have a crucial evolutionary impact [13,15], especially on population extinction [18,20]. 393 In addition, established microbial populations are structured, even within a single 394 patient [56], and competition is local, which decreases their effective size, thus making 395 stochasticity relevant.…”

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confidence: 99%

Resist or perish: fate of a microbial population subjected to a periodic presence of antimicrobial

Marrec

Bitbol

2019

Preprint

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AbstractThe evolution of antimicrobial resistance can be strongly affected by variations of antimicrobial concentration. Here, we study the impact of periodic alternations of absence and presence of antimicrobial on resistance evolution in a microbial population, using a stochastic model that includes variations of both population composition and size, and fully incorporates stochastic population extinctions. We show that fast alternations of presence and absence of antimicrobial are inefficient to eradicate the microbial population and strongly favor the establishment of resistance, unless the antimicrobial increases enough the death rate. We further demonstrate that if the period of alternations is longer than a threshold value, the microbial population goes extinct upon the first addition of antimicrobial, if it is not rescued by resistance. We express the probability that the population is eradicated upon the first addition of antimicrobial, assuming rare mutations. Rescue by resistance can happen either if resistant mutants preexist, or if they appear after antimicrobial is added to the environment. Importantly, the latter case is fully prevented by perfect biostatic antimicrobials that completely stop division of sensitive microorganisms. By contrast, we show that the parameter regime where treatment is efficient is larger for biocidal drugs than for biostatic drugs. This sheds light on the respective merits of different antimicrobial modes of action.Author summaryAntimicrobials select for resistance, which threatens to make antimicrobials useless. Understanding the evolution of antimicrobial resistance is therefore of crucial importance. Under what circumstances are microbial populations eradicated by antimicrobials? Conversely, when are they rescued by resistance? We address these questions employing a stochastic model that incorporates variations of both population composition and size. We consider periodic alternations of absence and presence of antimicrobial, which may model a treatment. We find a threshold period above which the first phase with antimicrobial fully determines the fate of the population. Faster alternations strongly select for resistance, and are inefficient to eradicate the microbial population, unless the death rate induced by the treatment is large enough. For longer alternation periods, we calculate the probability that the microbial population gets eradicated. We further demonstrate the different merits of biostatic antimicrobials, which prevent sensitive microbes from dividing, and of biocidal ones, which kill sensitive microbes.

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How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient

Cited by 117 publications

References 116 publications

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

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

Resist or perish: fate of a microbial population subjected to a periodic presence of antimicrobial

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