The path of least resistance: aggressive or moderate treatment?

The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance

Approximately 10% of antimicrobials used by humans in low- and middle-income countries are estimated to be substandard or falsified. In addition to their negative impact on morbidity and mortality, they may also be important drivers of antimicrobial resistance. Despite such concerns, our understanding of this relationship remains rudimentary. Substandard and falsified medicines have the potential to either increase or decrease levels of resistance, and here we discuss a range of mechanisms that could drive these changes. Understanding these effects and their relative importance will require an improved understanding of how different drug exposures affect the emergence and spread of resistance and of how the percentage of active pharmaceutical ingredients in substandard and falsified medicines is temporally and spatially distributed.

A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation

Adaptive therapy is a dynamic cancer treatment protocol that updates (or 'adapts') treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: (1) integrating the appropriate components into mathematical models (2) design and validation of dosing protocols, and (3) challenges and opportunities in clinical translation.

The future of evolutionary medicine: sparking innovation in biomedicine and public health

Evolutionary medicine - i.e. the application of insights from evolution and ecology to biomedicine - has tremendous untapped potential to spark transformational innovation in biomedical research, clinical care and public health. Fundamentally, a systematic mapping across the full diversity of life is required to identify animal model systems for disease vulnerability, resistance, and counter-resistance that could lead to novel clinical treatments. Evolutionary dynamics should guide novel therapeutic approaches that target the development of treatment resistance in cancers (e.g., via adaptive or extinction therapy) and antimicrobial resistance (e.g., via innovations in chemistry, antimicrobial usage, and phage therapy). With respect to public health, the insight that many modern human pathologies (e.g., obesity) result from mismatches between the ecologies in which we evolved and our modern environments has important implications for disease prevention. Life-history evolution can also shed important light on patterns of disease burden, for example in reproductive health. Experience during the COVID-19 (SARS-CoV-2) pandemic has underlined the critical role of evolutionary dynamics (e.g., with respect to virulence and transmissibility) in predicting and managing this and future pandemics, and in using evolutionary principles to understand and address aspects of human behavior that impede biomedical innovation and public health (e.g., unhealthy behaviors and vaccine hesitancy). In conclusion, greater interdisciplinary collaboration is vital to systematically leverage the insight-generating power of evolutionary medicine to better understand, prevent, and treat existing and emerging threats to human, animal, and planetary health.

Fast drug rotation reduces bacterial resistance evolution in a microcosm experiment

Drug rotation (cycling), in which multiple drugs are administrated alternatively, has the potential for limiting resistance evolution in pathogens. The frequency of drug alternation could be a major factor to determine the effectiveness of drug rotation. Drug rotation practices often have low frequency of drug alternation, with an expectation of resistance reversion. Here we, based on evolutionary rescue and compensatory evolution theories, suggest that fast drug rotation can limit resistance evolution in the first place. This is because fast drug rotation would give little time for the evolutionarily rescued populations to recover in population size and genetic diversity, and thus decrease the chance of future evolutionary rescue under alternate environmental stresses. We experimentally tested this hypothesis using the bacterium Pseudomonas fluorescens and two antibiotics (chloramphenicol and rifampin). Increasing drug rotation frequency reduced the chance of evolutionary rescue, and most of the finally surviving bacterial populations were resistant to both drugs. Drug resistance incurred significant fitness costs, which did not differ among the drug treatment histories. A link between population sizes during the early stages of drug treatment and the end-point fates of populations (extinction vs survival) suggested that population size recovery and compensatory evolution before drug shift increase the chance of population survival. Our results therefore advocate fast drug rotation as a promising approach to reduce bacterial resistance evolution, which in particular could be a substitute for drug combination when the latter has safety risks.

Drug resistance mechanism of kinase inhibitors in the treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer, and it usually occurs following chronic liver disease. Although some progress has been made in the treatment of HCC, the prognosis of patients with advanced HCC is not optimistic, mainly because of the inevitable development of drug resistance. Therefore, multi-target kinase inhibitors for the treatment of HCC, such as sorafenib, lenvatinib, cabozantinib, and regorafenib, produce small clinical benefits for patients with HCC. It is necessary to study the mechanism of kinase inhibitor resistance and explore possible solutions to overcome this resistance to improve clinical benefits. In this study, we reviewed the mechanisms of resistance to multi-target kinase inhibitors in HCC and discussed strategies that can be used to improve treatment outcomes.

Effects of epistasis and recombination between vaccine-escape and virulence alleles on the dynamics of pathogen adaptation

Pathogen adaptation to public health interventions such as vaccination may take tortuous routes and involve multiple mutations at different locations in the pathogen genome, acting on distinct phenotypic traits. Yet how these multi-locus adaptations jointly evolve is poorly understood. Here we consider the joint evolution of two adaptations: pathogen escape from the vaccine-induced immune response and adjustments to pathogen virulence affecting transmission or clearance. We elucidate the role played by epistasis and recombination, with an emphasis on the different protective effects of vaccination. We show that vaccines blocking infection, reducing transmission and/or increasing clearance generate positive epistasis between the vaccine-escape and virulence alleles, favouring strains that carry both mutations, whereas vaccines reducing virulence mortality generate negative epistasis, favouring strains that carry either mutation but not both. High rates of recombination can affect these predictions. If epistasis is positive, frequent recombination can prevent the transient build-up of more virulent escape strains. If epistasis is negative, frequent recombination between loci can create an evolutionary bistability, favouring whichever adaptation is more accessible. Our work provides a timely alternative to the variant-centred perspective on pathogen adaptation and captures the effect of different types of vaccine on the interference between multiple adaptive mutations.

Enhanced antibacterial effect of a novel Friunavirus phage vWU2001 in combination with colistin against carbapenem-resistant Acinetobacter baumannii

The emergence of carbapenem-resistant Acinetobacter baumannii (CRAB) has been increasingly reported, leading to greater challenges in treating infections. With the development of phage therapy and phage-antibiotic combinations, it is promising to improve the treatment of bacterial infections. In the present study, a novel vB_AbaP_WU2001 (vWU2001) phage-specific CRAB with a genome of 40,792 bp was isolated. Genomic analysis disclosed that it belongs to the Autographiviridae family of the order Caudovirales. Phage vWU2001 had a broad host range with a high adsorption rate, short latent period, large burst size and good stability. The phage could reduce preformed biofilms and inhibit biofilm formation. The combination of phage vWU2001 and colistin had significantly higher bacterial growth inhibition activity than that of phage, or colistin alone. The efficacy of the combined treatment was also evaluated in Galleria mellonella. Evaluation of its therapeutic potential showed that the combination of phage and colistin resulted in a significantly greater increase in G. mellonella survival and in bacterial clearance, as compared with that of phage or colistin alone, indicating that the combination was synergistic against CRAB. The results demonstrated that phage vWU2001 has the potential to be developed as an antibacterial agent.

Collateral Sensitivity Interactions between Antibiotics Depend on Local Abiotic Conditions

Mutations conferring resistance to one antibiotic can increase (cross-resistance) or decrease (collateral sensitivity) resistance to others. Antibiotic combinations displaying collateral sensitivity could be used in treatments that slow resistance evolution. However, lab-to-clinic translation requires understanding whether collateral effects are robust across different environmental conditions. Here, we isolated and characterized resistant mutants of Escherichia coli using five antibiotics, before measuring collateral effects on resistance to other paired antibiotics. During both isolation and phenotyping, we varied conditions in ways relevant in nature (pH, temperature, and bile). This revealed that local abiotic conditions modified expression of resistance against both the antibiotic used during isolation and other antibiotics. Consequently, local conditions influenced collateral sensitivity in two ways: by favoring different sets of mutants (with different collateral sensitivities) and by modifying expression of collateral effects for individual mutants. These results place collateral sensitivity in the context of environmental variation, with important implications for translation to real-world applications. IMPORTANCE When bacteria become resistant to an antibiotic, the genetic changes involved sometimes increase (cross-resistance) or decrease (collateral sensitivity) their resistance to other antibiotics. Antibiotic combinations showing repeatable collateral sensitivity could be used in treatment to slow resistance evolution. However, collateral sensitivity interactions may depend on the local environmental conditions that bacteria experience, potentially reducing repeatability and clinical application. Here, we show that variation in local conditions (pH, temperature, and bile salts) can influence collateral sensitivity in two ways: by favoring different sets of mutants during bacterial resistance evolution (with different collateral sensitivities to other antibiotics) and by modifying expression of collateral effects for individual mutants. This suggests that translation from the lab to the clinic of new approaches exploiting collateral sensitivity will be influenced by local abiotic conditions.

Adaptation at different points along antibiotic concentration gradients

Antibiotic concentrations vary dramatically in the body and the environment. Hence, understanding the dynamics of resistance evolution along antibiotic concentration gradients is critical for predicting and slowing the emergence and spread of resistance. While it has been shown that increasing the concentration of an antibiotic slows resistance evolution, how adaptation to one antibiotic concentration correlates with fitness at other points along the gradient has not received much attention. Here, we selected populations of Escherichia coli at several points along a concentration gradient for three different antibiotics, asking how rapidly resistance evolved and whether populations became specialized to the antibiotic concentration they were selected on. Populations selected at higher concentrations evolved resistance more slowly but exhibited equal or higher fitness across the whole gradient. Populations selected at lower concentrations evolved resistance rapidly, but overall fitness in the presence of antibiotics was lower. However, these populations readily adapted to higher concentrations upon subsequent selection. Our results indicate that resistance management strategies must account not only for the rates of resistance evolution but also for the fitness of evolved strains.

Clinically Relevant Concentrations of Polymyxin B and Meropenem Synergistically Kill Multidrug-Resistant Pseudomonas aeruginosa and Minimize Biofilm Formation

The emergence of antibiotic resistance has severely impaired the treatment of chronic respiratory infections caused by multidrug-resistant (MDR) Pseudomonas aeruginosa. Since the reintroduction of polymyxins as a last-line therapy against MDR Gram-negative bacteria, resistance to its monotherapy and recurrent infections continue to be reported and synergistic antibiotic combinations have been investigated. In this study, comprehensive in vitro microbiological evaluations including synergy panel screening, population analysis profiling, time-kill kinetics, anti-biofilm formation and membrane damage analysis studies were conducted to evaluate the combination of polymyxin B and meropenem against biofilm-producing, polymyxin-resistant MDR P. aeruginosa. Two phylogenetically unrelated MDR P. aeruginosa strains, FADDI-PA060 (MIC of polymyxin B [MICpolymyxin B], 64 mg/L; MICmeropenem, 64 mg/L) and FADDI-PA107 (MICpolymyxin B, 32 mg/L; MICmeropenem, 4 mg/L) were investigated. Genome sequencing identified 57 (FADDI-PA060) and 50 (FADDI-PA107) genes predicted to confer resistance to a variety of antimicrobials, as well as multiple virulence factors in each strain. The presence of resistance genes to a particular antibiotic class generally aligned with MIC results. For both strains, all monotherapies of polymyxin B failed with substantial regrowth and biofilm formation. The combination of polymyxin B (16 mg/L)/meropenem (16 mg/L) was most effective, enhancing initial bacterial killing of FADDI-PA060 by ~3 log10 CFU/mL, followed by a prolonged inhibition of regrowth for up to 24 h with a significant reduction in biofilm formation (* p < 0.05). Membrane integrity studies revealed a substantial increase in membrane depolarization and membrane permeability in the surviving cells. Against FADDI-PA107, planktonic and biofilm bacteria were completely eradicated. In summary, the combination of polymyxin B and meropenem demonstrated synergistic bacterial killing while reinstating the efficacy of two previously ineffective antibiotics against difficult-to-treat polymyxin-resistant MDR P. aeruginosa.

Using ecological coexistence theory to understand antibiotic resistance and microbial competition

Tackling antibiotic resistance necessitates deep understanding of how resource competition within and between species modulates the fitness of resistant microbes. Recent advances in ecological coexistence theory offer a powerful framework to probe the mechanisms regulating intra- and interspecific competition, but the significance of this body of theory to the problem of antibiotic resistance has been largely overlooked. In this Perspective, we draw on emerging ecological theory to illustrate how changes in resource niche overlap can be equally important as changes in competitive ability for understanding costs of resistance and the persistence of resistant pathogens in microbial communities. We then show how different temporal patterns of resource and antibiotic supply, alongside trade-offs in competitive ability at high and low resource concentrations, can have diametrically opposing consequences for the coexistence and exclusion of resistant and susceptible strains. These insights highlight numerous opportunities for innovative experimental and theoretical research into the ecological dimensions of antibiotic resistance.

Dosage strategies for delaying resistance emergence in heterogeneous tumors

Drug resistance in cancer treatments is a frequent problem that, when it arises, leads to failure in therapeutic efforts. Tumor heterogeneity is the primary reason for resistance emergence and a precise treatment design that takes heterogeneity into account is required to postpone the rise of resistant subpopulations in the tumor environment. In this paper, we present a mathematical framework involving clonal evolution modeling of drug-sensitive and drug-resistant clones. Using our framework, we examine delaying the rise of resistance in heterogeneous tumors during control phase of therapy in a containment treatment approach. We apply pharmacokinetic/pharmacodynamic (PKPD) modeling and show that dosage strategies can be designed to control the resistant subpopulation. Our results show that the drug dosage and schedule determine the relative dynamics of sensitive and resistant clones. We present an optimal control problem that finds the dosing strategy that maximizes the delay in resistance emergence for a given period of containment treatment.

Treatment timing shifts the benefits of short and long antibiotic treatment over infection

Antibiotics are the major tool for treating bacterial infections. Rising antibiotic resistance, however, calls for a better use of antibiotics. While classical recommendations favor long and aggressive treatments, more recent clinical trials advocate for moderate regimens. In this debate, two axes of 'aggression' have typically been conflated: treatment intensity (dose) and treatment duration. The third dimension of treatment timing along each individual's infection course has rarely been addressed. By using a generic mathematical model of bacterial infection controlled by immune response, we examine how the relative effectiveness of antibiotic treatment varies with its timing, duration and antibiotic kill rate. We show that short or long treatments may both be beneficial depending on treatment onset, the target criterion for success and on antibiotic efficacy. This results from the dynamic trade-off between immune response build-up and resistance risk in acute, self-limiting infections, and uncertainty relating symptoms to infection variables. We show that in our model early optimal treatments tend to be 'short and strong', while late optimal treatments tend to be 'mild and long'. This suggests a shift in the aggression axis depending on the timing of treatment. We find that any specific optimal treatment schedule may perform more poorly if evaluated by other criteria, or under different host-specific conditions. Our results suggest that major advances in antibiotic stewardship must come from a deeper empirical understanding of bacterial infection processes in individual hosts. To guide rational therapy, mathematical models need to be constrained by data, including a better quantification of personal disease trajectory in humans. Lay summary: Bacterial infections are becoming more difficult to treat worldwide because bacteria are becoming resistant to the antibiotics used. Addressing this problem requires a better understanding of how treatment along with other host factors impact antibiotic resistance. Until recently, most theoretical research has focused on the importance of antibiotic dosing on antibiotic resistance, however, duration and timing of treatment remain less explored. Here, we use a mathematical model of a generic bacterial infection to study three aspects of treatment: treatment dose/efficacy (defined by the antibiotic kill rate), duration, and timing, and their impact on several infection endpoints. We show that short and long treatment success strongly depends on when treatment begins (defined by the symptom threshold), the target criterion to optimize, and on antibiotic efficacy. We find that if administered early in an infection, "strong and short" therapy performs better, while if treatment begins at higher bacterial densities, a "mild and long" course of antibiotics is favored. In the model host immune defenses are key in preventing relapses, controlling antibiotic resistant bacteria and increasing the effectiveness of moderate intervention. In order to improve rational treatments of human infections, we call for a better quantification of individual disease trajectories in bacteria-immunity space.

Adapt or Perish: Evolutionary Rescue in a Gradually Deteriorating Environment

We investigate the evolutionary rescue of a microbial population in a gradually deteriorating environment, through a combination of analytical calculations and stochastic simulations. We consider a population destined for extinction in the absence of mutants, which can survive only if mutants sufficiently adapted to the new environment arise and fix. We show that mutants that appear later during the environment deterioration have a higher probability to fix. The rescue probability of the population increases with a sigmoidal shape when the product of the carrying capacity and of the mutation probability increases. Furthermore, we find that rescue becomes more likely for smaller population sizes and/or mutation probabilities if the environment degradation is slower, which illustrates the key impact of the rapidity of environment degradation on the fate of a population. We also show that our main conclusions are robust across various types of adaptive mutants, including specialist and generalist ones, as well as mutants modeling antimicrobial resistance evolution. We further express the average time of appearance of the mutants that do rescue the population and the average extinction time of those that do not. Our methods can be applied to other situations with continuously variable fitnesses and population sizes, and our analytical predictions are valid in the weak-to-moderate mutation regime.

The Effects of Succinate Dehydrogenase Inhibitor Fungicide Dose and Mixture on Development of Resistance in Venturia inaequalis

Understanding how fungicide application practices affect selection for fungicide resistance is imperative for continued sustainable agriculture. Here, we examined the effect of field applications of the succinate dehydrogenase inhibitor (SDHI) fluxapyroxad at different doses and mixtures on the SDHI sensitivity of Venturia inaequalis, the apple scab pathogen. Fungicide applications were part of selection programs involving different doses (high or low) and mixtures (with a second single-site fungicide or a multisite fungicide). These programs were tested in two apple orchards over 4 years to determine potential cumulative selection effects on resistance. Each year after program applications, apple scab lesions were collected, and relative growth assays were conducted to understand shifts in fluxapyroxad sensitivity. After 4 years, there was a trend toward a reduction in sensitivity to fluxapyroxad for most selection programs in comparison to that in the non-selective-pressure control. In most years, the selection program plots treated with low-dose fluxapyroxad applications resulted in a larger number of isolates with reduced sensitivity, supporting the use of higher doses for disease management. Few significant differences (P < 0.05) in fungicide sensitivity were observed between isolates collected from plots where fungicide mixtures were applied compared to that in untreated plots, supporting the use of multiple modes of action in field applications. In all, appropriate doses and mixtures may contribute to increased longevity of SDHI fungicides used on perennial crops like apples.IMPORTANCE Of much debate is the effect of fungicide application dose on resistance development, as fungicide resistance is a critical barrier to effective disease management in agricultural systems. Our field study in apples investigated the effect of fungicide application dose and mixture on the selection of succinate dehydrogenase inhibitor resistance in Venturia inaequalis, a fungal pathogen that causes the economically important disease apple scab. Understanding how to best delay the development of resistance can result in increased efficacy, fewer applications, and sustainable fungicide use. Results from this study may have relevance to other perennial crops that require multiple fungicide applications and that are impacted by the development of resistance.

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones

Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.

Effect of drug dose and timing of treatment on the emergence of drug resistance in vivo in a malaria model

BACKGROUND AND OBJECTIVES

There is a significant interest in identifying clinically effective drug treatment regimens that minimize the de novo evolution of antimicrobial resistance in pathogen populations. However, in vivo studies that vary treatment regimens and directly measure drug resistance evolution are rare. Here, we experimentally investigate the role of drug dose and treatment timing on resistance evolution in an animal model.

METHODOLOGY

In a series of experiments, we measured the emergence of atovaquone-resistant mutants of Plasmodium chabaudi in laboratory mice, as a function of dose or timing of treatment (day post-infection) with the antimalarial drug atovaquone.

RESULTS

The likelihood of high-level resistance emergence increased with atovaquone dose. When varying the timing of treatment, treating either very early or late in infection reduced the risk of resistance. When we varied starting inoculum, resistance was more likely at intermediate inoculum sizes, which correlated with the largest population sizes at time of treatment.

CONCLUSIONS AND IMPLICATIONS

(i) Higher doses do not always minimize resistance emergence and can promote the emergence of high-level resistance. (ii) Altering treatment timing affects the risk of resistance emergence, likely due to the size of the population at the time of treatment, although we did not test the effect of immunity whose influence may have been important in the case of late treatment. (iii) Finding the 'right' dose and 'right' time to maximize clinical gains and limit resistance emergence can vary depending on biological context and was non-trivial even in our simplified experiments.

LAY SUMMARY

In a mouse model of malaria, higher drug doses led to increases in drug resistance. The timing of drug treatment also impacted resistance emergence, likely due to the size of the population at the time of treatment.

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

Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, a containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here, we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant Escherichia coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.

Penicillin V four times daily for five days versus three times daily for 10 days in patients with pharyngotonsillitis caused by group A streptococci: randomised controlled, open label, non-inferiority study

OBJECTIVE

To determine whether total exposure to penicillin V can be reduced while maintaining adequate clinical efficacy when treating pharyngotonsillitis caused by group A streptococci.

DESIGN

Open label, randomised controlled non-inferiority study.

SETTING

17 primary healthcare centres in Sweden between September 2015 and February 2018.

PARTICIPANTS

Patients aged 6 years and over with pharyngotonsillitis caused by group A streptococci and three or four Centor criteria (fever ≥38.5°C, tender lymph nodes, coatings of the tonsils, and absence of cough).

INTERVENTIONS

Penicillin V 800 mg four times daily for five days (total 16 g) compared with the current recommended dose of 1000 mg three times daily for 10 days (total 30 g).

MAIN OUTCOME MEASURES

Primary outcome was clinical cure five to seven days after the end of antibiotic treatment. The non-inferiority margin was prespecified to 10 percentage points. Secondary outcomes were bacteriological eradication, time to relief of symptoms, frequency of relapses, complications and new tonsillitis, and patterns of adverse events.

RESULTS

Patients (n=433) were randomly allocated to the five day (n=215) or 10 day (n=218) regimen. Clinical cure in the per protocol population was 89.6% (n=181/202) in the five day group and 93.3% (n=182/195) in the 10 day group (95% confidence interval -9.7 to 2.2). Bacteriological eradication was 80.4% (n=156/194) in the five day group and 90.7% (n=165/182) in the 10 day group. Eight and seven patients had relapses, no patients and four patients had complications, and six and 13 patients had new tonsillitis in the five day and 10 day groups, respectively. Time to relief of symptoms was shorter in the five day group. Adverse events were mainly diarrhoea, nausea, and vulvovaginal disorders; the 10 day group had higher incidence and longer duration of adverse events.

CONCLUSIONS

Penicillin V four times daily for five days was non-inferior in clinical outcome to penicillin V three times daily for 10 days in patients with pharyngotonsillitis caused by group A streptococci. The number of relapses and complications did not differ between the two intervention groups. Five day treatment with penicillin V four times daily might be an alternative to the currently recommended 10 day regimen.

TRIAL REGISTRATION

EudraCT 2015-001752-30; ClinicalTrials.gov NCT02712307.

Diffusion-driven enhancement of the antibiotic resistance selection window

The current crisis of antimicrobial resistance in clinically relevant pathogens has highlighted our limited understanding of the ecological and evolutionary forces that drive drug resistance adaptation. For instance, although human tissues are highly heterogeneous, most of our mechanistic understanding about antibiotic resistance evolution is based on constant and well-mixed environmental conditions. A consequence of considering spatial heterogeneity is that, even if antibiotics are prescribed at high dosages, the penetration of drug molecules through tissues inevitably produces antibiotic gradients, exposing bacterial populations to a range of selective pressures and generating a dynamic fitness landscape that changes in space and time. In this paper, we will use a combination of mathematical modelling and computer simulations to study the population dynamics of susceptible and resistant strains competing for resources in a network of micro-environments with varying degrees of connectivity. Our main result is that highly connected environments increase diffusion of drug molecules, enabling resistant phenotypes to colonize a larger number of spatial locations. We validated this theoretical result by culturing fluorescently labelled Escherichia coli in 3D-printed devices that allow us to control the rate of diffusion of antibiotics between neighbouring compartments and quantify the spatio-temporal distribution of resistant and susceptible bacterial cells.

Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels

Antimicrobial resistance is one of the major public health threats of the 21st century. There is a pressing need to adopt more efficient treatment strategies in order to prevent the emergence and spread of resistant strains. The common approach is to treat patients with high drug doses, both to clear the infection quickly and to reduce the risk of de novo resistance. Recently, several studies have argued that, at least in some cases, low-dose treatments could be more suitable to reduce the within-host emergence of antimicrobial resistance. However, the choice of a drug dose may have consequences at the population level, which has received little attention so far. Here, we study the influence of the drug dose on resistance and disease management at the host and population levels. We develop a nested two-strain model and unravel trade-offs in treatment benefits between an individual and the community. We use several measures to evaluate the benefits of any dose choice. Two measures focus on the emergence of resistance, at the host level and at the population level. The other two focus on the overall treatment success: the outbreak probability and the disease burden. We find that different measures can suggest different dosing strategies. In particular, we identify situations where low doses minimize the risk of emergence of resistance at the individual level, while high or intermediate doses prove most beneficial to improve the treatment efficiency or even to reduce the risk of resistance in the population.

Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa

The scale-up of antiretroviral therapy (ART) in South Africa substantially reduced AIDS-related deaths and new HIV infections. However, its success is threatened by the emergence of resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI). The MARISA (Modelling Antiretroviral drug Resistance In South Africa) model presented here aims at investigating the time trends and factors driving NNRTI resistance in South Africa. MARISA is a compartmental model that includes the key aspects of the local HIV epidemic: continuum of care, disease progression, and gender. The dynamics of NNRTI resistance emergence and transmission are then added to this framework. Model parameters are informed using data from HIV cohorts participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) and literature estimates, or fitted to UNAIDS estimates. Using this novel approach of triangulating clinical and resistance data from various sources, MARISA reproduces the time trends of HIV in South Africa in 2005–2016, with a decrease in new infections, undiagnosed individuals, and AIDS-related deaths. MARISA captures the dynamics of the spread of NNRTI resistance: high levels of acquired drug resistance (ADR, in 83% of first-line treatment failures in 2016), and increasing transmitted drug resistance (TDR, in 8.1% of ART initiators in 2016). Simulation of counter-factual scenarios reflecting alternative public health policies shows that increasing treatment coverage would have resulted in fewer new infections and deaths, at the cost of higher TDR (11.6% in 2016 for doubling the treatment rate). Conversely, improving switching to second-line treatment would have led to lower TDR (6.5% in 2016 for doubling the switching rate) and fewer new infections and deaths. Implementing drug resistance testing would have had little impact. The rapid ART scale-up and inadequate switching to second-line treatment were the key drivers of the spread of NNRTI resistance in South Africa. However, even though some interventions could have substantially reduced the level of NNRTI resistance, no policy including NNRTI-based first line regimens could have prevented this spread. Thus, by combining epidemiological data on HIV in South Africa with biological data on resistance evolution, our modelling approach identified key factors driving NNRTI resistance, highlighting the need of alternative first-line regimens.

Resistance to non-nucleoside reverse transcriptase inhibitors (NNRTI) threatens the long-term success of antiretroviral therapy (ART) roll-out in South Africa. We developed a compartmental model integrating the local HIV epidemiology with biological mechanisms of drug resistance. A first dimension of the model accounts for the continuum of care: infection, diagnosis, first-line treatment with suppression or failure, and second-line treatment. Other dimensions include: disease progression (CD4 counts), gender, and acquisition and transmission of NNRTI resistance. Whenever possible, we informed the parameters using the data available from local cohorts. Other parameters were informed using literature or UNAIDS estimates. The model captured the rise of NNRTI resistance during the period. We assessed the impact of counter-factual scenarios reflecting alternative countrywide policies during the period 2005 to 2016, considering either increasing ART coverage, improving management of treatment failure, broadening ART eligibility, or implementing drug resistance testing before ART initiation. We identified key drivers of the NNRTI resistance epidemic: large-scale ART roll-out and insufficient monitoring of first-line treatment failure. The model also suggested that no policy including NNRTI-based first line regimens could have prevented the spread of NNRTI resistance.

Suppression of bacteriocin resistance using live, heterospecific competitors

Rapidly spreading antibiotic resistance has led to the need for novel alternatives and sustainable strategies for antimicrobial use. Bacteriocins are a class of proteinaceous anticompetitor toxins under consideration as novel therapeutic agents. However, bacteriocins, like other antimicrobial agents, are susceptible to resistance evolution and will require the development of sustainable strategies to prevent or decelerate the evolution of resistance. Here, we conduct proof-of-concept experiments to test whether introducing a live, heterospecific competitor along with a bacteriocin dose can effectively suppress the emergence of bacteriocin resistance in vitro. Previous work with conventional chemotherapeutic agents suggests that competition between conspecific sensitive and resistant pathogenic cells can effectively suppress the emergence of resistance in pathogenic populations. However, the threshold of sensitive cells required for such competitive suppression of resistance may often be too high to maintain host health. Therefore, here we aim to ask whether the principle of competitive suppression can be effective if a heterospecific competitor is used. Our results show that a live competitor introduced in conjunction with low bacteriocin dose can effectively control resistance and suppress sensitive cells. Further, this efficacy can be matched by using a bacteriocin-producing competitor without any additional bacteriocin. These results provide strong proof of concept for the effectiveness of competitive suppression using live, heterospecific competitors. Currently used probiotic strains or commensals may provide promising candidates for the therapeutic use of bacteriocin-mediated competitive suppression.

Five rules for resistance management in the antibiotic apocalypse, a road map for integrated microbial management

Resistance to new antimicrobials can become widespread within 2-3 years. Resistance problems are particularly acute for bacteria that can experience selection as both harmless commensals and pathogenic hospital-acquired infections. New drugs, although welcome, cannot tackle the antimicrobial resistance crisis alone: new drugs must be partnered with more sustainable patterns of use. However, the broader experience of resistance management in other disciplines, and the assumptions on which resistance rests, is not widely appreciated in clinical and microbiological disciplines. Improved awareness of the field of resistance management could improve clinical outcomes and help shape novel solutions. Here, the aim is to develop a pragmatic approach to developing a sustainable integrated means of using antimicrobials, based on an interdisciplinary synthesis of best practice, recent theory and recent clinical data. This synthesis emphasizes the importance of pre-emptive action and the value of reducing the supply of genetic novelty to bacteria under selection. The weight of resistance management experience also cautions against strategies that over-rely on the fitness costs of resistance or low doses. The potential (and pitfalls) of shorter courses, antibiotic combinations and antibiotic mixing or cycling are discussed in depth. Importantly, some of variability in the success of clinical trials of mixing approaches can be explained by the number and diversity of drugs in a trial, as well as whether trials encompass single wards or the wider transmission network that is a hospital. Consideration of the importance of data, and of the initially low frequency of resistance, leads to a number of additional recommendations. Overall, reduction in selection pressure, interference with the transmission of problematic genotypes and multidrug approaches (combinations, mixing or cycling) are all likely to be required for sustainability and the protection of forthcoming drugs.

How to improve antibiotic awareness campaigns: findings of a WHO global survey

INTRODUCTION

We aimed to examine the characteristics of antibiotic awareness campaigns (AAC) conducted on a national or regional level since 2010.

METHODS

In October 2016, the WHO invited stakeholders involved in the planning or conduct of AACs to answer a web questionnaire. We solicited general information about the characteristics of the AAC, with a particular focus on key messages supporting optimal use of antibiotics.

RESULTS

Stakeholders in 93 countries were contacted and 55 countries responded. Overall, 60 AACs from 16 low/middle-income countries (LMIC) and 31 high-income countries were identified. Forty-five campaigns (75%) were conducted on a national level and most of them (47/60; 78%) were organised by public health authorities and publicly funded. There were no major differences between LMICs and high-income countries in the types of key messages. The scientifically questionable 'Finish your prescription' slogan was used by 31 AACs (52%). A One Health approach was mentioned in 13/60 AACs (22%). Most messages were universally applicable; adaptation to locally prevalent public misconceptions was not systematic. The evaluation of the impact of campaigns was still incomplete, as only 18 AACs (30%) assessed their impact on antibiotic use.

CONCLUSION

For future AACs, it seems essential to base messages more rigorously on scientific evidence, context specificities and behavioural change theory. A new generation of messages that encourage first-choice use of narrow spectrum antibiotics is needed, reflecting international efforts to preserve broad spectrum antibiotic classes. Evaluation of the impact of AACs remains suboptimal.

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

Membrane computing is a bio-inspired computing paradigm whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested "membrane-surrounded entities" able to divide, propagate, and die; to be transferred into other membranes; to exchange informative material according to flexible rules; and to mutate and be selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multilevel evolutionary biology of antibiotic resistance. We examined a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and vice versa, the cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, and the antibiotics and dosing found in the opening spaces in the microbiota where resistant phenotypes multiply. We also evaluated the selective strengths of some drugs and the influence of the time 0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multilevel analysis of complex microbial landscapes.IMPORTANCE The work that we present here represents the culmination of many years of investigation in looking for a suitable methodology to simulate the multihierarchical processes involved in antibiotic resistance. Everything started with our early appreciation of the different independent but embedded biological units that shape the biology, ecology, and evolution of antibiotic-resistant microorganisms. Genes, plasmids carrying these genes, cells hosting plasmids, populations of cells, microbial communities, and host's populations constitute a complex system where changes in one component might influence the other ones. How would it be possible to simulate such a complexity of antibiotic resistance as it occurs in the real world? Can the process be predicted, at least at the local level? A few years ago, and because of their structural resemblance to biological systems, we realized that membrane computing procedures could provide a suitable frame to approach these questions. Our manuscript describes the first application of this modeling methodology to the field of antibiotic resistance and offers a bunch of examples-just a limited number of them in comparison with the possible ones to illustrate its unprecedented explanatory power.

An ecosystem framework for understanding and treating disease

Pathogens and cancers are pervasive health risks in the human population. I argue that if we are to better understand disease and its treatment, then we need to take an ecological perspective of disease itself. I generalize and extend an emerging framework that views disease as an ecosystem and many of its components as interacting in a community. I develop the framework for biological etiological agents (BEAs) that multiply within humans—focusing on bacterial pathogens and cancers—but the framework could be extended to include other host and parasite species. I begin by describing why we need an ecosystem framework to understand disease, and the main components and interactions in bacterial and cancer disease ecosystems. Focus is then given to the BEA and how it may proceed through characteristic states, including emergence, growth, spread and regression. The framework is then applied to therapeutic interventions. Central to success is preventing BEA evasion, the best known being antibiotic resistance and chemotherapeutic resistance in cancers. With risks of evasion in mind, I propose six measures that either introduce new components into the disease ecosystem or manipulate existing ones. An ecosystem framework promises to enhance our understanding of disease, BEA and host (co)evolution, and how we can improve therapeutic outcomes.

Repeated sublethal freshwater exposures reduce the amoebic gill disease parasite, Neoparamoeba perurans, on Atlantic salmon

Freshwater bathing is one of the main treatment options available against amoebic gill disease (AGD) affecting multiple fish hosts in mariculture systems. Prevailing freshwater treatments are designed to be long enough to kill Neoparamoeba perurans, the ectoparasite causing AGD, which may select for freshwater tolerance. Here, we tested whether using shorter, sublethal freshwater treatment durations are a viable alternative to lethal ones for N. perurans (2-4 hr). Under in vitro conditions, gill-isolated N. perurans attached to plastic substrate in sea water lifted off after ≥2 min in freshwater, but survival was not impacted until 60 min. In an in vivo experiment, AGD-affected Atlantic salmon Salmo salar subjected daily to 30 min (sublethal to N. perurans) and 120 min (lethal to N. perurans) freshwater treatments for 6 days consistently reduced N. perurans cell numbers on gills (based on qPCR analysis) compared to daily 3 min freshwater or seawater treatments for 6 days. Our results suggest that targeting cell detachment rather than cell death with repeated freshwater treatments of shorter duration than typical baths could be used in AGD management. However, the consequences of modifying the intensity of freshwater treatment regimes on freshwater tolerance evolution in N. perurans populations require careful consideration.

Community interactions and spatial structure shape selection on antibiotic resistant lineages

Polymicrobial interactions play an important role in shaping the outcome of antibiotic treatment, yet how multispecies communities respond to antibiotic assault is still little understood. Here we use an individual-based simulation model of microbial biofilms to investigate how competitive and mutualistic interactions between an antibiotic-resistant and a susceptible strain (or species) influence the two-lineage community response to antibiotic exposure. Our model predicts that while increasing competition and antibiotics leads to increasing competitive release of the antibiotic-resistant strain, hitting a mutualistic community of cross-feeding species with antibiotics leads to a mutualistic suppression effect where both susceptible and resistant species are harmed. We next show that the impact of antibiotics is further governed by emergent spatial feedbacks within communities. Mutualistic cross-feeding communities can rescue susceptible members by subsidizing their growth inside the biofilm despite lack of access to the nutrient-rich and high-antibiotic growing front. Moreover, we show that antibiotic detoxification by resistant cells can protect nearby susceptible cells, but such cross-protection is more effective in mutualistic communities because mutualism drives mixing of resistant and susceptible cells. In contrast, competition leads to segregation, which ultimately prevents susceptible cells to profit from detoxification. Understanding how the interplay between microbial metabolic interactions and community spatial structuring shapes the outcome of antibiotic treatment can be key to effectively leverage the power of antibiotics and promote microbiome health.

Pathogens -microorganisms that make us sick- often live within dynamic and complex multispecies communities, where they may not only compete for limiting resources but also exchange beneficial resources or services with other resident species. While antibiotics are commonly used to get rid of such harmful microbes, the community-wide effects of antibiotic treatment and its consequences for antibiotic resistance are still not well understood. How do competitive or mutually beneficial interactions between antibiotic resistant and susceptible species influence community resistance to antibiotics? Here we investigate this question using a computational model. We find that antibiotic exposure favours the resistant lineage when resistant and susceptible strains are competitors but harms both types when they are mutualists. With antibiotic-detoxifying resistant cells, cross-protection of susceptible cells is more effective in mutualistic communities because mutualism drives mixing of susceptible and resistant cells. In contrast, competition leads to their segregation, precluding susceptible cells to profit from their competitor’s local detoxification. Our findings highlight that knowing not only what species are present but also how they interact with each other and arrange themselves in space is central to understanding antibiotic resistance and to informing the development of strategies that promote microbiome health.

Host population structure and treatment frequency maintain balancing selection on drug resistance

It is a truism that antimicrobial drugs select for resistance, but explaining pathogen- and population-specific variation in patterns of resistance remains an open problem. Like other common commensals, Streptococcus pneumoniae has demonstrated persistent coexistence of drug-sensitive and drug-resistant strains. Theoretically, this outcome is unlikely. We modelled the dynamics of competing strains of S. pneumoniae to investigate the impact of transmission dynamics and treatment-induced selective pressures on the probability of stable coexistence. We find that the outcome of competition is extremely sensitive to structure in the host population, although coexistence can arise from age-assortative transmission models with age-varying rates of antibiotic use. Moreover, we find that the selective pressure from antibiotics arises not so much from the rate of antibiotic use per se but from the frequency of treatment: frequent antibiotic therapy disproportionately impacts the fitness of sensitive strains. This same phenomenon explains why serotypes with longer durations of carriage tend to be more resistant. These dynamics may apply to other potentially pathogenic, microbial commensals and highlight how population structure, which is often omitted from models, can have a large impact.

Cooperation, competition and antibiotic resistance in bacterial colonies

Bacteria commonly live in dense and genetically diverse communities associated with surfaces. In these communities, competition for resources and space is intense, and yet we understand little of how this affects the spread of antibiotic-resistant strains. Here, we study interactions between antibiotic-resistant and susceptible strains using in vitro competition experiments in the opportunistic pathogen Pseudomonas aeruginosa and in silico simulations. Selection for intracellular resistance to streptomycin is very strong in colonies, such that resistance is favoured at very low antibiotic doses. In contrast, selection for extracellular resistance to carbenicillin is weak in colonies, and high doses of antibiotic are required to select for resistance. Manipulating the density and spatial structure of colonies reveals that this difference is partly explained by the fact that the local degradation of carbenicillin by β-lactamase-secreting cells protects neighbouring sensitive cells from carbenicillin. In addition, we discover a second unexpected effect: the inducible elongation of cells in response to carbenicillin allows sensitive cells to better compete for the rapidly growing colony edge. These combined effects mean that antibiotic treatment can select against antibiotic-resistant strains, raising the possibility of treatment regimes that suppress sensitive strains while limiting the rise of antibiotic resistance. We argue that the detailed study of bacterial interactions will be fundamental to understanding and overcoming antibiotic resistance.

The importance of Evolutionary Medicine in developing countries: A case for Pakistan's medical schools

Evolutionary Medicine (EM) is a fundamental science exploring why our bodies are plagued with disease and hindered by limitations. EM views the body as an assortment of benefits, mistakes, and compromises molded over millennia. It highlights the role of evolution in numerous diseases encountered in community and family medicine clinics of developing countries. It enables us to ask informed questions and develop novel responses to global health problems. An understanding of the field is thus crucial for budding doctors, but its study is currently limited to a handful of medical schools in high-income countries. For the developing world, Pakistan's medical schools may be excellent starting posts as the country is beset with communicable and non-communicable diseases that are shaped by evolution. Remarkably, Pakistani medical students are open to studying and incorporating EM into their training. Understanding the principles of EM could empower them to tackle growing health problems in the country. Additionally, some difficulties that western medical schools face in integrating EM into their curriculum may not be a hindrance in Pakistan. We propose solutions for the remaining challenges, including obstinate religious sentiments. Herein, we make the case that incorporating EM is particularly important in developing countries such as Pakistan and that it is achievable in its medical student body.

Viable adhered Staphylococcus aureus highly reduced on novel antimicrobial sutures using chlorhexidine and octenidine to avoid surgical site infection (SSI)

BACKGROUND

Surgical sutures can promote migration of bacteria and thus start infections. Antiseptic coating of sutures may inhibit proliferation of adhered bacteria and avoid such complications.

OBJECTIVES

This study investigated the inhibition of viable adhering bacteria on novel antimicrobially coated surgical sutures using chlorhexidine or octenidine, a critical factor for proliferation at the onset of local infections. The medical need, a rapid eradication of bacteria in wounds, can be fulfilled by a high antimicrobial efficacy during the first days after wound closure.

METHODS

As a pretesting on antibacterial efficacy against relevant bacterial pathogens a zone of inhibition assay was conducted with middle ranged concentrated suture coatings (22 μg/cm). For further investigation of adhering bacteria in detail the most clinically relevant Staphylococcus aureus (ATCC®49230™) was used. Absorbable braided sutures were coated with chlorhexidine-laurate, chlorhexidine-palmitate, octenidine-laurate, and octenidine-palmitate. Each coating type resulted in 11, 22, or 33 μg/cm drug content on sutures. Scanning electron microscopy (SEM) was performed once to inspect the coating quality and twice to investigate if bacteria have colonized on sutures. Adhesion experiments were assessed by exposing coated sutures to S. aureus suspensions for 3 h at 37°C. Subsequently, sutures were sonicated and the number of viable bacteria released from the suture surface was determined. Furthermore, the number of viable planktonic bacteria was measured in suspensions containing antimicrobial sutures. Commercially available sutures without drugs (Vicryl®, PGA Resorba®, and Gunze PGA), as well as triclosan-containing Vicryl® Plus were used as control groups.

RESULTS

Zone of inhibition assay documented a multispecies efficacy of novel coated sutures against tested bacterial strains, comparable to most relevant S. aureus over 48 hours. SEM pictures demonstrated uniform layers on coated sutures with higher roughness for palmitate coatings and sustaining integrity of coated sutures. Adherent S. aureus were found via SEM on all types of investigated sutures. The novel antimicrobial sutures showed significantly less viable adhered S. aureus bacteria (up to 6.1 log) compared to Vicryl® Plus (0.5 log). Within 11 μg/cm drug-containing sutures, octenidine-palmitate (OL11) showed the highest number of viable adhered S. aureus (0.5 log), similar to Vicryl® Plus. Chlorhexidine-laurate (CL11) showed the lowest number of S. aureus on sutures (1.7 log), a 1.2 log greater reduction. In addition, planktonic S. aureus in suspensions were highly inhibited by CL11 (0.9 log) represents a 0.6 log greater reduction compared to Vicryl® Plus (0.3 log).

CONCLUSIONS

Novel antimicrobial sutures can potentially limit surgical site infections caused by multiple pathogenic bacterial species. Therefore, a potential inhibition of multispecies biofilm formation is assumed. In detail tested with S. aureus, the chlorhexidine-laurate coating (CL11) best meets the medical requirements for a fast bacterial eradication. This suture coating shows the lowest survival rate of adhering as well as planktonic bacteria, a high drug release during the first-clinically most relevant- 48 hours, as well as biocompatibility. Thus, CL11 coatings should be recommended for prophylactic antimicrobial sutures as an optimal surgical supplement to reduce wound infections. However, animal and clinical investigations are important to prove safety and efficacy for future applications.

Exploiting ecology in drug pulse sequences in favour of population reduction

A deterministic population dynamics model involving birth and death for a two-species system, comprising a wild-type and more resistant species competing via logistic growth, is subjected to two distinct stress environments designed to mimic those that would typically be induced by temporal variation in the concentration of a drug (antibiotic or chemotherapeutic) as it permeates through the population and is progressively degraded. Different treatment regimes, involving single or periodical doses, are evaluated in terms of the minimal population size (a measure of the extinction probability), and the population composition (a measure of the selection pressure for resistance or tolerance during the treatment). We show that there exist timescales over which the low-stress regime is as effective as the high-stress regime, due to the competition between the two species. For multiple periodic treatments, competition can ensure that the minimal population size is attained during the first pulse when the high-stress regime is short, which implies that a single short pulse can be more effective than a more protracted regime. Our results suggest that when the duration of the high-stress environment is restricted, a treatment with one or multiple shorter pulses can produce better outcomes than a single long treatment. If ecological competition is to be exploited for treatments, it is crucial to determine these timescales, and estimate for the minimal population threshold that suffices for extinction. These parameters can be quantified by experiment.

Implementing Precision Antimicrobial Therapy for the Treatment of Bovine Respiratory Disease: Current Limitations and Perspectives

The therapeutic efficacy of an early treatment protocol with an infection-stage adjusted fluoroquinolone regimen was evaluated in a field study on young bulls (YBs) presenting signs of bovine respiratory disease (BRD). A total of 195 YB (Charolais, Limousin, and Rouge-des-Prés breeds) from 6 farms implementing or not prophylactic antimicrobial treatments (PROPHY or absence) were randomly assigned to 1 of 2 experiment groups based on time of detection of BRD and first-line marbofloxacin regimen, early adjusted dose [Early 2 (E2)] or late standard dose [Late 10 (L10)]. Each YB was administered orally a reticulo-rumen bolus, allowing continuous monitoring of ruminal temperature. In the E2 group, YB presenting early signs of BRD, i.e., an increase in ruminal temperature over 40.2°C and persisting more than 12 h, confirmed by a clinical examination showing no or mild signs of BRD, were given 2 mg/kg of marbofloxacin. In the L10 group, YBs presenting moderate or severe signs of BRD at visual inspection, confirmed at clinical examination, were given 10 mg/kg of marbofloxacin. If needed, YBs were given a relapse treatment. The YBs were followed for 30 days. The proportions of first and relapse treatments were calculated, as well as the therapeutic efficacy at day 10. In the E2 group, the first-line treatments’ proportion was significantly higher (P < 0.05), while the relapse treatments’ proportion tended to be higher (P = 0.08), than in the L10 group. Evolution of clinical scores (CSs) of diseased YB was followed for 10 days. In both groups, CS and rectal temperature decreased significantly 24 h after treatment (P < 0.05). Treatment incidences (TI) representing antimicrobial consumption assessed on used daily doses (UDD) were calculated. Antimicrobial consumption of marbofloxacin and relapse treatments were not significantly different between the groups. These values were strongly influenced by the recourse to a prophylactic antimicrobial treatment, accounting for more than 90% of the antimicrobial amount in the herds implementing prophylaxis. The higher number of treatments in the groups treated on the basis of ruminal temperature monitoring, the accuracy of the detection method, and the necessary conditions to implement precision antimicrobial therapy in the field are discussed in this article.

Phagocytes, Antibiotics, and Self-Limiting Bacterial Infections

Most antibiotic use in humans is to reduce the magnitude and term of morbidity of acute, community-acquired infections in immune competent patients, rather than to save lives. Thanks to phagocytic leucocytes and other host defenses, the vast majority of these infections are self-limiting. Nevertheless, there has been a negligible amount of consideration of the contribution of phagocytosis and other host defenses in the research for, and the design of, antibiotic treatment regimens, which hyper-emphasizes antibiotics as if they were the sole mechanism responsible for the clearance of infections. Here, we critically review this approach and its limitations. With the aid of a heuristic mathematical model, we postulate that if the rate of phagocytosis is great enough, for acute, normally self-limiting infections, then (i) antibiotics with different pharmacodynamic properties would be similarly effective, (ii) low doses of antibiotics can be as effective as high doses, and (iii) neither phenotypic nor inherited antibiotic resistance generated during therapy are likely to lead to treatment failure.

Interventions for the prevention of recurrent erysipelas and cellulitis

BACKGROUND

Erysipelas and cellulitis (hereafter referred to as 'cellulitis') are common bacterial skin infections usually affecting the lower extremities. Despite their burden of morbidity, the evidence for different prevention strategies is unclear.

OBJECTIVES

To assess the beneficial and adverse effects of antibiotic prophylaxis or other prophylactic interventions for the prevention of recurrent episodes of cellulitis in adults aged over 16.

SEARCH METHODS

We searched the following databases up to June 2016: the Cochrane Skin Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and LILACS. We also searched five trials registry databases, and checked reference lists of included studies and reviews for further references to relevant randomised controlled trials (RCTs). We searched two sets of dermatology conference proceedings, and BIOSIS Previews.

SELECTION CRITERIA

Randomised controlled trials evaluating any therapy for the prevention of recurrent cellulitis.

DATA COLLECTION AND ANALYSIS

Two authors independently carried out study selection, data extraction, assessment of risks of bias, and analyses. Our primary prespecified outcome was recurrence of cellulitis when on treatment and after treatment. Our secondary outcomes included incidence rate, time to next episode, hospitalisation, quality of life, development of resistance to antibiotics, adverse reactions and mortality.

MAIN RESULTS

We included six trials, with a total of 573 evaluable participants, who were aged on average between 50 and 70. There were few previous episodes of cellulitis in those recruited to the trials, ranging between one and four episodes per study.Five of the six included trials assessed prevention with antibiotics in participants with cellulitis of the legs, and one assessed selenium in participants with cellulitis of the arms. Among the studies assessing antibiotics, one study evaluated oral erythromycin (n = 32) and four studies assessed penicillin (n = 481). Treatment duration varied from six to 18 months, and two studies continued to follow up participants after discontinuation of prophylaxis, with a follow-up period of up to one and a half to two years. Four studies were single-centre, and two were multicentre; they were conducted in five countries: the UK, Sweden, Tunisia, Israel, and Austria.Based on five trials, antibiotic prophylaxis (at the end of the treatment phase ('on prophylaxis')) decreased the risk of cellulitis recurrence by 69%, compared to no treatment or placebo (risk ratio (RR) 0.31, 95% confidence interval (CI) 0.13 to 0.72; n = 513; P = 0.007), number needed to treat for an additional beneficial outcome (NNTB) six, (95% CI 5 to 15), and we rated the certainty of evidence for this outcome as moderate.Under prophylactic treatment and compared to no treatment or placebo, antibiotic prophylaxis reduced the incidence rate of cellulitis by 56% (RR 0.44, 95% CI 0.22 to 0.89; four studies; n = 473; P value = 0.02; moderate-certainty evidence) and significantly decreased the rate until the next episode of cellulitis (hazard ratio (HR) 0.51, 95% CI 0.34 to 0.78; three studies; n = 437; P = 0.002; moderate-certainty evidence).The protective effects of antibiotic did not last after prophylaxis had been stopped ('post-prophylaxis') for risk of cellulitis recurrence (RR 0.88, 95% CI 0.59 to 1.31; two studies; n = 287; P = 0.52), incidence rate of cellulitis (RR 0.94, 95% CI 0.65 to 1.36; two studies; n = 287; P = 0.74), and rate until next episode of cellulitis (HR 0.78, 95% CI 0.39 to 1.56; two studies; n = 287). Evidence was of low certainty.Effects are relevant mainly for people after at least two episodes of leg cellulitis occurring within a period up to three years.We found no significant differences in adverse effects or hospitalisation between antibiotic and no treatment or placebo; for adverse effects: RR 0.87, 95% CI 0.58 to 1.30; four studies; n = 469; P = 0.48; for hospitalisation: RR 0.77, 95% CI 0.37 to 1.57; three studies; n = 429; P = 0.47, with certainty of evidence rated low for these outcomes. The existing data did not allow us to fully explore its impact on length of hospital stay.The common adverse reactions were gastrointestinal symptoms, mainly nausea and diarrhoea; rash (severe cutaneous adverse reactions were not reported); and thrush. Three studies reported adverse effects that led to discontinuation of the assigned therapy. In one study (erythromycin), three participants reported abdominal pain and nausea, so their treatment was changed to penicillin. In another study, two participants treated with penicillin withdrew from treatment due to diarrhoea or nausea. In one study, around 10% of participants stopped treatment due to pain at the injection site (the active treatment group was given intramuscular injections of benzathine penicillin).None of the included studies assessed the development of antimicrobial resistance or quality-of-life measures.With regard to the risks of bias, two included studies were at low risk of bias and we judged three others as being at high risk of bias, mainly due to lack of blinding.

AUTHORS' CONCLUSIONS

In terms of recurrence, incidence, and time to next episode, antibiotic is probably an effective preventive treatment for recurrent cellulitis of the lower limbs in those under prophylactic treatment, compared with placebo or no treatment (moderate-certainty evidence). However, these preventive effects of antibiotics appear to diminish after they are discontinued (low-certainty evidence). Treatment with antibiotic does not trigger any serious adverse events, and those associated are minor, such as nausea and rash (low-certainty evidence). The evidence is limited to people with at least two past episodes of leg cellulitis within a time frame of up to three years, and none of the studies investigated other common interventions such as lymphoedema reduction methods or proper skin care. Larger, high-quality studies are warranted, including long-term follow-up and other prophylactic measures.

Emergence of Resistance to Fungicides: The Role of Fungicide Dose

Resistance to antimicrobial drugs allows pathogens to survive drug treatment. The time taken for a new resistant mutant to reach a population size that is unlikely to die out by chance is called "emergence time." Prolonging emergence time would delay loss of control. We investigate the effect of fungicide dose on the emergence time in fungal plant pathogens. A population dynamical model is combined with dose-response data for Zymoseptoria tritici, an important wheat pathogen. Fungicides suppress sensitive pathogen population. This has two effects. First, the rate of appearance of resistant mutants is reduced, hence the emergence takes longer. Second, more healthy host tissue becomes available for resistant mutants, increasing their chances to invade and accelerates emergence. In theory, the two competing effects may lead to a non-monotonic dependence of the emergence time on fungicide dose that exhibits a minimum. But according to field data, fungicides are unable to reduce the fungicide-sensitive population strongly enough even at high doses. Hence, for full resistance over realistic ranges of pathogen's life history and fungicide dose-response parameters, emergence time decreases monotonically with increasing dose. For partial resistance, there can be cases within a limited parameter range, when emergence decelerates at higher doses.

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

When resistance to anticancer or antimicrobial drugs evolves in a patient, highly effective chemotherapy can fail, threatening patient health and lifespan. Standard practice is to treat aggressively, effectively eliminating drug-sensitive target cells as quickly as possible. This prevents sensitive cells from acquiring resistance de novo but also eliminates populations that can competitively suppress resistant populations. Here we analyse that evolutionary trade-off and consider recent suggestions that treatment regimens aimed at containing rather than eliminating tumours or infections might more effectively delay the emergence of resistance. Our general mathematical analysis shows that there are situations in which regimens aimed at containment will outperform standard practice even if there is no fitness cost of resistance, and, in those cases, the time to treatment failure can be more than doubled. But, there are also situations in which containment will make a bad prognosis worse. Our analysis identifies thresholds that define these situations and thus can guide treatment decisions. The analysis also suggests a variety of interventions that could be used in conjunction with cytotoxic drugs to inhibit the emergence of resistance. Fundamental principles determine, across a wide range of disease settings, the circumstances under which standard practice best delays resistance emergence-and when it can be bettered.

Benefits and unintended consequences of antimicrobial de-escalation: Implications for stewardship programs

Sequential antimicrobial de-escalation aims to minimize resistance to high-value broad-spectrum empiric antimicrobials by switching to alternative drugs when testing confirms susceptibility. Though widely practiced, the effects de-escalation are not well understood. Definitions of interventions and outcomes differ among studies. We use mathematical models of the transmission and evolution of Pseudomonas aeruginosa in an intensive care unit to assess the effect of de-escalation on a broad range of outcomes, and clarify expectations. In these models, de-escalation reduces the use of high-value drugs and preserves the effectiveness of empiric therapy, while also selecting for multidrug-resistant strains and leaving patients vulnerable to colonization and superinfection. The net effect of de-escalation in our models is to increase infection prevalence while also increasing the probability of effective treatment. Changes in mortality are small, and can be either positive or negative. The clinical significance of small changes in outcomes such as infection prevalence and death may exceed more easily detectable changes in drug use and resistance. Integrating harms and benefits into ranked outcomes for each patient may provide a way forward in the analysis of these tradeoffs. Our models provide a conceptual framework for the collection and interpretation of evidence needed to inform antimicrobial stewardship.

A randomized controlled study of 5 and 10 days treatment with phenoxymethylpenicillin for pharyngotonsillitis caused by streptococcus group A - a protocol study

Background

In 2014 the Swedish government assigned to The Public Health Agency of Sweden to conduct studies to evaluate optimal use of existing antibiotic agents. The aim is to optimize drug use and dosing regimens to improve the clinical efficacy. The present study was selected following a structured prioritizing process by independent experts.

Methods

This phase IV study is a randomized, open-label, multicenter study with non-inferiority design regarding the therapeutic use of penicillin V with two parallel groups. The overall aim is to study if the total exposure with penicillin V can be reduced from 1000 mg three times daily for 10 days to 800 mg four times daily for 5 days when treating Streptococcus pyogenes (Lancefield group A) pharyngotonsillitis. Patients will be recruited from 17 primary health care centers in Sweden. Adult men and women, youth and children ≥6 years of age who consult for sore throat and is judged to have a pharyngotonsillitis, with 3–4 Centor criteria and a positive rapid test for group A streptococci, will be included in the study. The primary outcome is clinical cure 5–7 days after discontinuation of antibiotic treatment. Follow-up controls will be done by telephone after 1 and 3 months. Throat symptoms, potential relapses and complications will be monitored, as well as adverse events. Patients (n = 432) will be included during 2 years.

Discussion

In the era of increasing antimicrobial resistance and the shortage of new antimicrobial agents it is necessary to revisit optimal usage of old antibiotics. Old antimicrobial drugs are often associated with inadequate knowledge on pharmacokinetics and pharmacodynamics and lack of optimized dosing regimens based on randomized controlled clinical trials. If a shorter and more potent treatment regimen is shown to be equivalent with the normal 10 day regimen this can imply great advantages for both patients (adherence, adverse events, resistance) and the community (resistance, drug costs).

Trial registration

EudraCT number 2015-001752-30. Protocol FoHM/Tonsillit2015 date 22 June 2015, version 2. Approved by MPA of Sweden 3 July 2015, Approved by Regional Ethical Review Board in Lund, 25 June 2015.

Modelling concentrations of antimicrobial drugs: comparative pharmacokinetics of cephalosporin antimicrobials and accuracy of allometric scaling in food-producing and companion animals

Background

To optimize antimicrobial dosing in different animal species, pharmacokinetic information is necessary. Due to the plethora of cephalosporin antimicrobials and animal species in which they are used, assessment of pharmacokinetics in all species is unfeasible. In this study we aimed to describe pharmacokinetic data of cephalosporins by reviewing the available literature for food producing and companion animal species. We assessed the accuracy of interspecies extrapolation using allometric scaling techniques to determine pharmacokinetic characteristics of cephalosporins in animal species for which literature data is unavailable. We assessed the accuracy of allometric scaling by comparing the predicted and the published pharmacokinetic value in an animal species/humans not included in the allometric modelling.

Results

In general, excretion of cephalosporins takes place mainly through renal mechanisms in the unchanged form and volume of distribution is limited in all animal species. Differences in plasma protein binding capacity and elimination half-life are observed but available information was limited. Using allometric scaling, correlations between body weight (BW) and volume of distribution (Vd) and clearance (Cl) were R² > 0.97 and R² > 0.95 respectively for ceftazidime, ceftiofur, cefquinome and cefepime but not ceftriaxone. The allometric exponent ranged from 0.80 to 1.31 for Vd and 0.83 to 1.24 for Cl. Correlations on half-life ranged from R² 0.07–0.655 (literature) and R² 0.102–0.876 (calculated).

Conclusions

Allometric scaling can be applied for interspecies extrapolation of cephalosporin pharmacokinetic parameters Vd and Cl, but not elimination half-life. We hypothesize that the accuracy could be improved by using more refined scaling techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/s12917-016-0817-2) contains supplementary material, which is available to authorized users.

Integrating Antimicrobial Therapy with Host Immunity to Fight Drug-Resistant Infections: Classical vs. Adaptive Treatment

Antimicrobial resistance of infectious agents is a growing problem worldwide. To prevent the continuing selection and spread of drug resistance, rational design of antibiotic treatment is needed, and the question of aggressive vs. moderate therapies is currently heatedly debated. Host immunity is an important, but often-overlooked factor in the clearance of drug-resistant infections. In this work, we compare aggressive and moderate antibiotic treatment, accounting for host immunity effects. We use mathematical modelling of within-host infection dynamics to study the interplay between pathogen-dependent host immune responses and antibiotic treatment. We compare classical (fixed dose and duration) and adaptive (coupled to pathogen load) treatment regimes, exploring systematically infection outcomes such as time to clearance, immunopathology, host immunization, and selection of resistant bacteria. Our analysis and simulations uncover effective treatment strategies that promote synergy between the host immune system and the antimicrobial drug in clearing infection. Both in classical and adaptive treatment, we quantify how treatment timing and the strength of the immune response determine the success of moderate therapies. We explain key parameters and dimensions, where an adaptive regime differs from classical treatment, bringing new insight into the ongoing debate of resistance management. Emphasizing the sensitivity of treatment outcomes to the balance between external antibiotic intervention and endogenous natural defenses, our study calls for more empirical attention to host immunity processes.

Long-term effects of single and combined introductions of antibiotics and bacteriophages on populations of Pseudomonas aeruginosa

With escalating resistance to antibiotics, there is an urgent need to develop alternative therapies against bacterial pathogens and pests. One of the most promising is the employment of bacteriophages (phages), which may be highly specific and evolve to counter antiphage resistance. Despite an increased understanding of how phages interact with bacteria, we know very little about how their interactions may be modified in antibiotic environments and, reciprocally, how phage may affect the evolution of antibiotic resistance. We experimentally evaluated the impacts of single and combined applications of antibiotics (different doses and different types) and phages on in vitro evolving populations of the opportunistic pathogen Pseudomonas aeruginosa PAO1. We also assessed the effects of past treatments on bacterial virulence in vivo, employing larvae of Galleria mellonella to survey the treatment consequences for the pathogen. We find a strong synergistic effect of combining antibiotics and phages on bacterial population density and in limiting their recovery rate. Our long-term study establishes that antibiotic dose is important, but that effects are relatively insensitive to antibiotic type. From an applied perspective, our results indicate that phages can contribute to managing antibiotic resistance levels, with limited consequences for the evolution of bacterial virulence.

Within-host competition and drug resistance in the human malaria parasite Plasmodium falciparum

Infections with the malaria parasite Plasmodium falciparum typically comprise multiple strains, especially in high-transmission areas where infectious mosquito bites occur frequently. However, little is known about the dynamics of mixed-strain infections, particularly whether strains sharing a host compete or grow independently. Competition between drug-sensitive and drug-resistant strains, if it occurs, could be a crucial determinant of the spread of resistance. We analysed 1341 P. falciparum infections in children from Angola, Ghana and Tanzania and found compelling evidence for competition in mixed-strain infections: overall parasite density did not increase with additional strains, and densities of individual chloroquine-sensitive (CQS) and chloroquine-resistant (CQR) strains were reduced in the presence of competitors. We also found that CQR strains exhibited low densities compared with CQS strains (in the absence of chloroquine), which may underlie observed declines of chloroquine resistance in many countries following retirement of chloroquine as a first-line therapy. Our observations support a key role for within-host competition in the evolution of drug-resistant malaria. Malaria control and resistance-management efforts in high-transmission regions may be significantly aided or hindered by the effects of competition in mixed-strain infections. Consideration of within-host dynamics may spur development of novel strategies to minimize resistance while maximizing the benefits of control measures.

How could preventive therapy affect the prevalence of drug resistance? Causes and consequences

Various forms of preventive and prophylactic antimicrobial therapies have been proposed to combat HIV (e.g. pre-exposure prophylaxis), tuberculosis (e.g. isoniazid preventive therapy) and malaria (e.g. intermittent preventive treatment). However, the potential population-level effects of preventative therapy (PT) on the prevalence of drug resistance are not well understood. PT can directly affect the rate at which resistance is acquired among those receiving PT. It can also indirectly affect resistance by altering the rate at which resistance is acquired through treatment for active disease and by modifying the level of competition between transmission of drug-resistant and drug-sensitive pathogens. We propose a general mathematical model to explore the ways in which PT can affect the long-term prevalence of drug resistance. Depending on the relative contributions of these three mechanisms, we find that increasing the level of coverage of PT may result in increases, decreases or non-monotonic changes in the overall prevalence of drug resistance. These results demonstrate the complexity of the relationship between PT and drug resistance in the population. Care should be taken when predicting population-level changes in drug resistance from small pilot studies of PT or estimates based solely on its direct effects.

Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance?

High-dose chemotherapy has long been advocated as a means of controlling drug resistance in infectious diseases but recent empirical studies have begun to challenge this view. We develop a very general framework for modeling and understanding resistance emergence based on principles from evolutionary biology. We use this framework to show how high-dose chemotherapy engenders opposing evolutionary processes involving the mutational input of resistant strains and their release from ecological competition. Whether such therapy provides the best approach for controlling resistance therefore depends on the relative strengths of these processes. These opposing processes typically lead to a unimodal relationship between drug pressure and resistance emergence. As a result, the optimal drug dose lies at either end of the therapeutic window of clinically acceptable concentrations. We illustrate our findings with a simple model that shows how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best. A review of the available empirical evidence provides broad support for these general conclusions. Our analysis opens up treatment options not currently considered as resistance management strategies, and it also simplifies the experiments required to determine the drug doses which best retard resistance emergence in patients.

The evolution of antimicrobial resistant pathogens threatens much of modern medicine. For over one hundred years, the advice has been to ‘hit hard’, in the belief that high doses of antimicrobials best contain resistance evolution. We argue that nothing in evolutionary theory supports this as a good rule of thumb in the situations that challenge medicine. We show instead that the only generality is to either use the highest tolerable drug dose or the lowest clinically effective dose; that is, one of the two edges of the therapeutic window. This approach suggests treatment options not currently considered, and simplifies the experiments required to identify the dose that best retards resistance evolution.