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

Linking within- and between-host scales for understanding the evolutionary dynamics of quantitative antimicrobial resistance

Understanding both the epidemiological and evolutionary dynamics of antimicrobial resistance is a major public health concern. In this paper, we propose a nested model, explicitly linking the within- and between-host scales, in which the level of resistance of the bacterial population is viewed as a continuous quantitative trait. The within-host dynamics is based on integro-differential equations structured by the resistance level, while the between-host scale is additionally structured by the time since infection. This model simultaneously captures the dynamics of the bacteria population, the evolutionary transient dynamics which lead to the emergence of resistance, and the epidemic dynamics of the host population. Moreover, we precisely analyze the model proposed by particularly performing the uniform persistence and global asymptotic results. Finally, we discuss the impact of the treatment rate of the host population in controlling both the epidemic outbreak and the average level of resistance, either if the within-host scale therapy is a success or failure. We also explore how transitions between infected populations (treated and untreated) can impact the average level of resistance, particularly in a scenario where the treatment is successful at the within-host scale.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

Extraordinary parasite multiplication rates in human malaria infections

For pathogenic organisms, faster rates of multiplication promote transmission success, the potential to harm hosts, and the evolution of drug resistance. Parasite multiplication rates (PMRs) are often quantified in malaria infections, given the relative ease of sampling. Using modern and historical human infection data, we show that established methods return extraordinarily - and implausibly - large PMRs. We illustrate how inflated PMRs arise from two facets of malaria biology that are far from unique: (i) some developmental ages are easier to sample than others; (ii) the distribution of developmental ages changes over the course of infection. The difficulty of accurately quantifying PMRs demonstrates a need for robust methods and a subsequent re-evaluation of what is known even in the well-studied system of malaria.

Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization

Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections. IMPORTANCE Antibiotic resistance is commonly viewed as universally costly, regardless of which bacterial cells express resistance. Here, we derive an opposing logic, where resistance in commensal bacteria can lead to reductions in pathogen density and improved outcomes on both the patient and public health scales. We use a mathematical model of commensal-pathogen interactions to define the necessary and sufficient conditions for beneficial resistance, highlighting the importance of reciprocal ecological inhibition to maximize the benefits of resistance. More broadly, we argue that determining the benefits as well as the costs of resistances in human microbiomes can transform resistance management from a minimization to an optimization problem. We discuss applied contexts and close with a review of key resistance optimization dimensions, including the magnitude, spectrum, and mechanism of resistance.

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.

A narrative review of the intermediate category of the antimicrobial susceptibility test: relation with dosing and possible impact on antimicrobial stewardship

The interpretation of 'susceptible (S)' or 'resistant (R)' results of antimicrobial susceptibility testing is easily understood, but the interpretation of the 'intermediate (I)' category can be confusing. This review critically discusses how this categorization (clinical breakpoints) comes into being with the emphasis on the use of pharmacokinetics and pharmacodynamic data. It discusses the differences between the 'I' according to the CLSI and the EUCAST. This review also discusses the recent EUCAST change of the 'I' definition, and the impact of this change from laboratory and clinical points of view.

Hygiene may attenuate selection for antibiotic resistance by changing microbial community structure

Good hygiene, in both health care and the community, is central to containing the rise of antibiotic resistance, as well as to infection control more generally. But despite the well-known importance, the ecological mechanisms by which hygiene (or other transmission control measures) affect the evolution of resistance remain to be elucidated. Using metacommunity ecology theory, we here propose that hygiene attenuates the effect of antibiotic selection pressure. Specifically, we predict that hygiene limits the scope for antibiotics to induce competitive release of resistant bacteria within treated hosts, and that this is due to an effect of hygiene on the distribution of resistant and sensitive strains in the host population. We show this in a mathematical model of bacterial metacommunity dynamics, and test the results against data on antibiotic resistance, antibiotic treatment, and the use of alcohol-based hand rub in long-term care facilities. The data are consistent with hand rub use attenuating the resistance promoting effect of antibiotic treatment. Our results underscore the importance of hygiene, and point to a concrete way to weaken the link between antibiotic use and increasing resistance.

β-Lactam Dosing in Critical Patients: A Narrative Review of Optimal Efficacy and the Prevention of Resistance and Toxicity

Antimicrobial prescription in critically ill patients represents a complex challenge due to the difficult balance between infection treatment and toxicity prevention. Underexposure to antibiotics and therapeutic failure or, conversely, drug overexposure and toxicity may both contribute to a worse prognosis. Moreover, changes in organ perfusion and dysfunction often lead to unpredictable pharmacokinetics. In critically ill patients, interindividual and intraindividual real-time β-lactam antibiotic dose adjustments according to the patient's condition are critical. The continuous infusion of β-lactams and the therapeutic monitoring of their concentration have both been proposed to improve their efficacy, but strong data to support their use are still lacking. The knowledge of the pharmacokinetic/pharmacodynamic targets is poor and is mostly based on observational data. In patients with renal or hepatic failure, selecting the right dose is even more tricky due to changes in drug clearance, distribution, and the use of extracorporeal circuits. Intermittent usage may further increase the dosing conundrum. Recent data have emerged linking overexposure to β-lactams to central nervous system toxicity, mitochondrial recovery delay, and microbiome changes. In addition, it is well recognized that β-lactam exposure facilitates resistance selection and that correct dosing can help to overcome it. In this review, we discuss recent data regarding real-time β-lactam antibiotic dose adjustment, options in special populations, and the impacts on mitochondria and the microbiome.

The risk of drug resistance during long-acting antimicrobial therapy

The emergence of drug resistance during antimicrobial therapy is a major global health problem, especially for chronic infections like human immunodeficiency virus, hepatitis B and C, and tuberculosis. Sub-optimal adherence to long-term treatment is an important contributor to resistance risk. New long-acting drugs are being developed for weekly, monthly or less frequent dosing to improve adherence, but may lead to long-term exposure to intermediate drug levels. In this study, we analyse the effect of dosing frequency on the risk of resistance evolving during time-varying drug levels. We find that long-acting therapies can increase, decrease or have little effect on resistance, depending on the source (pre-existing or de novo) and degree of resistance, and rates of drug absorption and clearance. Long-acting therapies with rapid drug absorption, slow clearance and strong wild-type inhibition tend to reduce resistance caused by partially resistant strains in the early stages of treatment even if they do not improve adherence. However, if subpopulations of microbes persist and can reactivate during sub-optimal treatment, longer-acting therapies may substantially increase the resistance risk. Our results show that drug kinetics affect selection for resistance in a complicated manner, and that pathogen-specific models are needed to evaluate the benefits of new long-acting therapies.

Prophylactic Cefazolin Dosing in Obesity-a Systematic Review

Currently, there is no consensus on whether a standard 2-g prophylactic cefazolin dose provides sufficient antimicrobial coverage in obese surgical patients. This systematic review analysed both outcome and pharmacokinetic studies, aiming to determine the appropriate cefazolin dose. A systematic search was conducted using 4 databases. In total, 3 outcome and 15 pharmacokinetic studies met the inclusion criteria. All 3 outcome studies concluded that there is no need for increased dose. Also, 9 pharmacokinetic studies reached this conclusion; however, 6 pharmacokinetic studies recommended that 2-g dose is insufficient to achieve adequate plasma or tissue concentrations. The stronger body of evidence supports that 2-g dose of cefazolin is sufficient for surgery lasting up to 4 h; however, large-scale outcome studies are needed to confirm this evidence.

Bacteriomimetic Liposomes Improve Antibiotic Activity of a Novel Energy-Coupling Factor Transporter Inhibitor

Liposomes have been studied for decades as nanoparticulate drug delivery systems for cytostatics, and more recently, for antibiotics. Such nanoantibiotics show improved antibacterial efficacy compared to the free drug and can be effective despite bacterial recalcitrance. In this work, we present a loading method of bacteriomimetic liposomes for a novel, hydrophobic compound (HIPS5031) inhibiting energy-coupling factor transporters (ECF transporters), an underexplored antimicrobial target. The liposomes were composed of DOPG (18:1 (Δ9-cis) phosphatidylglycerol) and CL (cardiolipin), resembling the cell membrane of Gram-positive Staphylococcus aureus and Streptococcus pneumoniae, and enriched with cholesterol (Chol). The size and polydispersity of the DOPG/CL/± Chol liposomes remained stable over 8 weeks when stored at 4 °C. Loading of the ECF transporter inhibitor was achieved by thin film hydration and led to a high encapsulation efficiency of 33.19% ± 9.5% into the DOPG/CL/Chol liposomes compared to the phosphatidylcholine liposomes (DMPC/DPPC). Bacterial growth inhibition assays on the model organism Bacillus subtilis revealed liposomal HIPS5031 as superior to the free drug, showing a 3.5-fold reduction in CFU/mL at a concentration of 9.64 µM. Liposomal HIPS5031 was also shown to reduce B. subtilis biofilm. Our findings present an explorative basis for bacteriomimetic liposomes as a strategy against drug-resistant pathogens by surpassing the drug-formulation barriers of innovative, yet unfavorably hydrophobic, antibiotics.

Enrofloxacin Alters Fecal Microbiota and Resistome Irrespective of Its Dose in Calves

Enrofloxacin is a fluoroquinolone drug used to prevent and control bovine respiratory disease (BRD) complex in multiple or single doses, ranging from 7.5 to 12.5 mg/kg body weight. Here, we examined the effects of high and low doses of a single subcutaneously injected enrofloxacin on gut microbiota and resistome in calves. Thirty-five calves sourced for this study were divided into five groups: control (n = 7), two low dose groups (n = 14, 7.5 mg/kg), and two high dose groups (n = 14, 12.5 mg/kg). One group in the low and high dose groups was challenged with Mannheimia haemolytica to induce BRD. Both alpha and beta diversities were significantly different between pre- and post-treatment microbial communities (q < 0.05). The high dose caused a shift in a larger number of genera than the low dose. Using metagenomic ProxiMeta Hi-C, 32 unique antimicrobial resistance genes (ARGs) conferring resistance to six antibiotic classes were detected with their reservoirs, and the high dose favored clonal expansion of ARG-carrying bacterial hosts. In conclusion, enrofloxacin treatment can alter fecal microbiota and resistome irrespective of its dose. Hi-C sequencing provides significant benefits for unlocking new insights into the ARG ecology of complex samples; however, limitations in sample size and sequencing depth suggest that further work is required to validate the findings.

Herbicide resistance management strategies: how do they compare with those for insecticides, fungicides and antibiotics?

Herbicides are the largest category of pesticides used in global agriculture, which is reflected in the rate of increase in the number of unique cases of herbicide-resistant weed biotypes since the late 1950s. Recommended herbicide resistance management strategies and tactics have evolved over the past 50 years through cumulative research and experience and have been regularly reviewed. Nevertheless, new perspectives may be gained by viewing current recommended strategies through the lens of insecticide, fungicide, and antibiotic resistance management. What commonalities exist and what is the basis for disparate strategies? Although pesticide and antibiotic mixtures (or combinations) are generally more effective than rotations (or alternations) in mitigating or managing resistance, the latter strategy is often employed because of greater ease of implementation and other reasons. We conclude that there are more common than different strategies for mitigating or managing pesticide and antibiotic resistance. Overall, a reduction in selection pressure for resistance evolution through diverse multi-tactic management programmes, and disruption or mitigation of the dispersal or transmission of problematic genotypes are needed to sustain the longevity of current and future mode-of-action products for crop and human health protection. © 2021 Society of Chemical Industry. © 2021 Society of Chemical Industry.

Combining mutation and horizontal gene transfer in a within-host model of antibiotic resistance

Antibiotics are used extensively to control infections in humans and animals, usually by injection or a course of oral tablets. There are several methods by which bacteria can develop antimicrobial resistance (AMR), including mutation during DNA replication and plasmid mediated horizontal gene transfer (HGT). We present a model for the development of AMR within a single host animal. We derive criteria for a resistant mutant strain to replace the existing wild-type bacteria, and for co-existence of the wild-type and mutant. Where resistance develops through HGT via conjugation we derive criteria for the resistant strain to be excluded or co-exist with the wild-type. Our results are presented as bifurcation diagrams with thresholds determined by the relative fitness of the bacteria strains, expressed in terms of reproduction numbers. The results show that it is possible that applying and then relaxing antibiotic control may lead to the bacterial load returning to pre-control levels, but with an altered structure with regard to the variants that comprise the population. Removing antimicrobial selection pressure will not necessarily reduce AMR and, at a population level, other approaches to infection prevention and control are required, particularly when AMR is driven by both mutation and mobile genetic elements.

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.

Impact of Baseline Characteristics on Future Episodes of Bloodstream Infections: Multistate Model in Septic Patients With Bloodstream Infections

BACKGROUND

Looking only at the index infection, studies have described risk factors for infections caused by resistant bacteria. We hypothesized that septic patients with bloodstream infections may transition across states characterized by different microbiology and that their trajectory is not uniform. We also hypothesized that baseline risk factors may influence subsequent blood culture results.

METHODS

All adult septic patients with positive blood cultures over a 7-year period were included in the study. Baseline risk factors were recorded. We followed all survivors longitudinally and recorded subsequent blood culture results. We separated states into bacteremia caused by gram-positive cocci, susceptible gram-negative bacilli (sGNB), resistant GNB (rGNB), and Candida spp. Detrimental transitions were considered when transitioning to a culture with a higher mortality risk (rGNB and Candida spp.). A multistate Markov-like model was used to determine risk factors associated with detrimental transitions.

RESULTS

A total of 990 patients survived and experienced at least 1 transition, with a total of 4282 transitions. Inappropriate antibiotics, previous antibiotic exposure, and index bloodstream infection caused by either rGNB or Candida spp. were associated with detrimental transitions. Double antibiotic therapy (beta-lactam plus either an aminoglycoside or a fluoroquinolone) protected against detrimental transitions.

CONCLUSION

Baseline characteristics that include prescribed antibiotics can identify patients at risk for subsequent bloodstream infections caused by resistant bacteria. By altering the initial treatment, we could potentially influence future bacteremic states.

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.

The Antibiotic Dosage of Fastest Resistance Evolution: gene amplifications underpinning the inverted-U

To determine the dosage at which antibiotic resistance evolution is most rapid, we treated Escherichia coli in vitro, deploying the antibiotic erythromycin at dosages ranging from zero to high. Adaptation was fastest just below erythromycin’s minimal inhibitory concentration (MIC) and genotype-phenotype correlations determined from whole genome sequencing revealed the molecular basis: simultaneous selection for copy number variation in three resistance mechanisms which exhibited an “inverted-U” pattern of dose-dependence, as did several insertion sequences and an integron. Many genes did not conform to this pattern, however, reflecting changes in selection as dose increased: putative media adaptation polymorphisms at zero antibiotic dosage gave way to drug target (ribosomal RNA operon) amplification at mid dosages whereas prophage-mediated drug efflux amplifications dominated at the highest dosages. All treatments exhibited E. coli increases in the copy number of efflux operons acrAB and emrE at rates that correlated with increases in population density. For strains where the inverted-U was no longer observed following the genetic manipulation of acrAB, it could be recovered by prolonging the antibiotic treatment at subMIC dosages.

Idiosyncratic variation in the fitness costs of tetracycline-resistance mutations in Escherichia coli

A bacterium's fitness relative to its competitors, both in the presence and absence of antibiotics, plays a key role in its ecological success and clinical impact. In this study, we examine whether tetracycline-resistant mutants are less fit in the absence of the drug than their sensitive parents, and whether the fitness cost of resistance is constant or variable across independently derived lines. Tetracycline-resistant lines suffered, on average, a reduction in fitness of almost 8%. There was substantial among-line variation in the fitness cost. This variation was not associated with the level of resistance conferred by the mutations, nor did it vary significantly across several genetic backgrounds. The two resistant lines with the most extreme fitness costs involved functionally unrelated mutations on different genetic backgrounds. However, there was also significant variation in the fitness costs for mutations affecting the same pathway and even different alleles of the same gene. Our findings demonstrate that the fitness costs of antibiotic resistance do not always correlate with the phenotypic level of resistance or the underlying genetic changes. Instead, these costs reflect the idiosyncratic effects of particular resistance mutations and the genetic backgrounds in which they occur.

Fighting microbial pathogens by integrating host ecosystem interactions and evolution

Successful therapies to combat microbial diseases and cancers require incorporating ecological and evolutionary principles. Drawing upon the fields of ecology and evolutionary biology, we present a systems-based approach in which host and disease-causing factors are considered as part of a complex network of interactions, analogous to studies of "classical" ecosystems. Centering this approach around empirical examples of disease treatment, we present evidence that successful therapies invariably engage multiple interactions with other components of the host ecosystem. Many of these factors interact nonlinearly to yield synergistic benefits and curative outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged to improve the study of pathogenesis in situ and to develop more effective therapies. An eco-evolutionary systems perspective has surprising and important consequences, and we use it to articulate areas of high research priority for improving treatment strategies.

Daptomycin treatment impacts resistance in off-target populations of vancomycin-resistant Enterococcus faecium

The antimicrobial resistance crisis has persisted despite broad attempts at intervention. It has been proposed that an important driver of resistance is selection imposed on bacterial populations that are not the intended target of antimicrobial therapy. But to date, there has been limited quantitative measure of the mean and variance of resistance following antibiotic exposure. Here we focus on the important nosocomial pathogen Enterococcus faecium in a hospital system where resistance to daptomycin is evolving despite standard interventions. We hypothesized that the intravenous use of daptomycin generates off-target selection for resistance in transmissible gastrointestinal (carriage) populations of E. faecium. We performed a cohort study in which the daptomycin resistance of E. faecium isolated from rectal swabs from daptomycin-exposed patients was compared to a control group of patients exposed to linezolid, a drug with similar indications. In the daptomycin-exposed group, daptomycin resistance of E. faecium from the off-target population was on average 50% higher than resistance in the control group (n = 428 clones from 22 patients). There was also greater phenotypic diversity in daptomycin resistance within daptomycin-exposed patients. In patients where multiple samples over time were available, a wide variability in temporal dynamics were observed, from long-term maintenance of resistance to rapid return to sensitivity after daptomycin treatment stopped. Sequencing of isolates from a subset of patients supports the argument that selection occurs within patients. Our results demonstrate that off-target gastrointestinal populations rapidly respond to intravenous antibiotic exposure. Focusing on the off-target evolutionary dynamics may offer novel avenues to slow the spread of antibiotic resistance.

Modifying Adaptive Therapy to Enhance Competitive Suppression

Adaptive therapy is a promising new approach to cancer treatment. It is designed to leverage competition between drug-sensitive and drug-resistant cells in order to suppress resistance and maintain tumor control for longer. Prompted by encouraging results from a recent pilot clinical trial, we evaluate the design of this initial test of adaptive therapy and identify three simple modifications that should improve performance. These modifications are designed to increase competition and are easy to implement. Using the mathematical model that supported the recent adaptive therapy trial, we show that the suggested modifications further delay time to tumor progression and also increase the range of patients who can benefit from adaptive therapy.

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.

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.

Why is preventing antibiotic resistance so hard? Analysis of failed resistance management

We describe the case of a patient with pancreatitis followed by intra-abdominal infection in which source control was not achieved. Antimicrobial therapy led to the emergence of resistance in multiple organisms through multiple population dynamics processes. While the initial insult was not due to infection, subsequent infections with resistant organisms contributed to a poor outcome for the patient. Though resistance evolution was a known risk, it was difficult to predict the next organism that would arise in the setting of antibiotic pressure and its resistance profile. This case illustrates the clinical challenge of antibiotic resistance that current approaches cannot readily prevent.

LAY SUMMARY

Why is antibiotic resistance management so complex? Distinct evolutionary processes unfold when antibiotic treatment is initiated that lead, separately and together, to the undesired outcome of antibiotic resistance. This clinical case exemplifies some of those processes and highlights the dire need for evolutionary risk assessments to be incorporated into clinical decision making.

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

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.

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.

Acute-on-chronic liver failure alters meropenem pharmacokinetics in critically ill patients with continuous hemodialysis: an observational study

BACKGROUND

Infection and sepsis are a main cause of acute-on-chronic liver failure (ACLF). Adequate dosing of antimicrobial therapy is of central importance to improve outcome. Liver failure may alter antibiotic drug concentrations via changes of drug distribution and elimination. We studied the pharmacokinetics of meropenem in critically ill patients with ACLF during continuous veno-venous hemodialysis (CVVHD) and compared it to critically ill patients without concomitant liver failure (NLF).

METHODS

In this prospective cohort study, patients received meropenem 1 g tid short-term infusion (SI). Meropenem serum samples were analyzed by high-performance liquid chromatography. A population pharmacokinetic analysis was performed followed by Monte Carlo simulations of (A) meropenem 1 g tid SI, (B) 2 g loading plus 1 g prolonged infusion tid (C) 2 g tid SI, and (D) 2 g loading and continuous infusion of 3 g/day on days 1 and 7. Probability of target attainment (PTA) was assessed for 4× the epidemiological cut-off values for Enterobacterales (4 × 0.25 mg/L) and Pseudomonas spp. (4 × 2 mg/L).

RESULTS

Nineteen patients were included in this study. Of these, 8 patients suffered from ACLF. A two-compartment model with linear clearance from the central compartment described meropenem pharmacokinetics. The peripheral volume of distribution (V₂) was significantly higher in ACLF compared to NLF (38.6L versus 19.7L, p = .05). PTA for Enterobacterales was achieved in 100% for all dosing regimens. PTA for Pseudomonas spp. in ACLF on day 1/7 was: A: 18%/80%, B: 94%/88%, C: 85%/98% D: 100%/100% and NLF: A: 48%/65%, B: 91%/83%, C: 91%/93%, D: 100%/100%.

CONCLUSION

ALCF patients receiving CVVHD had a higher V₂ and may require a higher loading dose of meropenem. For Pseudomonas, high doses or continuous infusion are required to reach PTA in ACLF patients.

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.

Bystander Selection for Antimicrobial Resistance: Implications for Patient Health

Antimicrobial therapy promotes resistance emergence in target infections and in off-target microbiota. Off-target resistance emergence threatens patient health when off-target populations are a source of future infections, as they are for many important drug-resistant pathogens. However, the health risks of antimicrobial exposure in off-target populations remain largely unquantified, making rational antibiotic stewardship challenging. Here, we discuss the contribution of bystander antimicrobial exposure to the resistance crisis, the implications for antimicrobial stewardship, and some novel opportunities to limit resistance evolution while treating target pathogens.

Personalizing the Management of Pneumonia

Pneumonia is a highly prevalent disease with considerable morbidity and mortality. However, diagnosis and therapy still rely on antiquated methods, leading to the vast overuse of antimicrobials, which carries risks for both society and the individual. Furthermore, outcomes in severe pneumonia remain poor. Genomic techniques have the potential to transform the management of pneumonia through deep characterization of pathogens as well as the host response to infection. This characterization will enable the delivery of selective antimicrobials and immunomodulatory therapy that will help to offset the disorder associated with overexuberant immune responses.

Selection and co-selection of antibiotic resistances among Escherichia coli by antibiotic use in primary care: An ecological analysis

Background

The majority of studies that link antibiotic usage and resistance focus on simple associations between the resistance against a specific antibiotic and the use of that specific antibiotic. However, the relationship between antibiotic use and resistance is more complex. Here we evaluate selection and co-selection by assessing which antibiotics, including those mainly prescribed for respiratory tract infections, are associated with increased resistance to various antibiotics among Escherichia coli isolated from urinary samples.

Methods

Monthly primary care prescribing data were obtained from National Health Service (NHS) Digital. Positive E. coli records from urine samples in English primary care (n = 888,207) between April 2014 and January 2016 were obtained from the Second Generation Surveillance System. Elastic net regularization was used to evaluate associations between prescribing of different antibiotic groups and resistance against amoxicillin, cephalexin, ciprofloxacin, co-amoxiclav and nitrofurantoin at the clinical commissioning group (CCG) level. England is divided into 209 CCGs, with each NHS practice prolonging to one CCG.

Results

Amoxicillin prescribing (measured in DDD/ 1000 inhabitants / day) was positively associated with amoxicillin (RR 1.03, 95% CI 1.01–1.04) and ciprofloxacin (RR 1.09, 95% CI 1.04–1.17) resistance. In contrast, nitrofurantoin prescribing was associated with lower levels of resistance to amoxicillin (RR 0.92, 95% CI 0.84–0.97). CCGs with higher levels of trimethoprim prescribing also had higher levels of ciprofloxacin resistance (RR 1.34, 95% CI 1.10–1.59).

Conclusion

Amoxicillin, which is mainly (and often unnecessarily) prescribed for respiratory tract infections is associated with increased resistance against various antibiotics among E. coli causing urinary tract infections. Our findings suggest that when predicting the potential impact of interventions on antibiotic resistances it is important to account for use of other antibiotics, including those typically used for other indications.

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.

Microbial evolutionary medicine: from theory to clinical practice

Medicine and clinical microbiology have traditionally attempted to identify and eliminate the agents that cause disease. However, this traditional approach is becoming inadequate for dealing with a changing disease landscape. Major challenges to human health are non-communicable chronic diseases, often driven by altered immunity and inflammation, and communicable infections from agents which harbour antibiotic resistance. This Review focuses on the so-called evolutionary medicine framework, to study how microbial communities influence human health. The evolutionary medicine framework aims to predict and manipulate microbial effects on human health by integrating ecology, evolutionary biology, microbiology, bioinformatics, and clinical expertise. We focus on the potential of evolutionary medicine to address three key challenges: detecting microbial transmission, predicting antimicrobial resistance, and understanding microbe-microbe and human-microbe interactions in health and disease, in the context of the microbiome.

The challenge of antimicrobial resistance: What economics can contribute

As antibiotic consumption grows, bacteria are becoming increasingly resistant to treatment. Antibiotic resistance undermines much of modern health care, which relies on access to effective antibiotics to prevent and treat infections associated with routine medical procedures. The resulting challenges have much in common with those posed by climate change, which economists have responded to with research that has informed and shaped public policy. Drawing on economic concepts such as externalities and the principal–agent relationship, we suggest how economics can help to solve the challenges arising from increasing resistance to antibiotics. We discuss solutions to the key economic issues, from incentivizing the development of effective new antibiotics to improving antibiotic stewardship through financial mechanisms and regulation.

Individualising Therapy to Minimize Bacterial Multidrug Resistance

The scourge of antibiotic resistance threatens modern healthcare delivery. A contributing factor to this significant issue may be antibiotic dosing, whereby standard antibiotic regimens are unable to suppress the emergence of antibiotic resistance. This article aims to review the role of pharmacokinetic and pharmacodynamic (PK/PD) measures for optimising antibiotic therapy to minimise resistance emergence. It also seeks to describe the utility of combination antibiotic therapy for suppression of resistance and summarise the role of biomarkers in individualising antibiotic therapy. Scientific journals indexed in PubMed and Web of Science were searched to identify relevant articles and summarise existing evidence. Studies suggest that optimising antibiotic dosing to attain defined PK/PD ratios may limit the emergence of resistance. A maximum aminoglycoside concentration to minimum inhibitory concentration (MIC) ratio of > 20, a fluoroquinolone area under the concentration-time curve to MIC ratio of > 285 and a β-lactam trough concentration of > 6 × MIC are likely required for resistance suppression. In vitro studies demonstrate a clear advantage for some antibiotic combinations. However, clinical evidence is limited, suggesting that the use of combination regimens should be assessed on an individual patient basis. Biomarkers, such as procalcitonin, may help to individualise and reduce the duration of antibiotic treatment, which may minimise antibiotic resistance emergence during therapy. Future studies should translate laboratory-based studies into clinical trials and validate the appropriate clinical PK/PD predictors required for resistance suppression in vivo. Other adjunct strategies, such as biomarker-guided therapy or the use of antibiotic combinations require further investigation.

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.

Cooperation among cancer cells: applying game theory to cancer

Cell cooperation promotes many of the hallmarks of cancer via the secretion of diffusible factors that can affect cancer cells or stromal cells in the tumour microenvironment. This cooperation cannot be explained simply as the collective action of cells for the benefit of the tumour because non-cooperative subclones can constantly invade and free-ride on the diffusible factors produced by the cooperative cells. A full understanding of cooperation among the cells of a tumour requires methods and concepts from evolutionary game theory, which has been used successfully in other areas of biology to understand similar problems but has been underutilized in cancer research. Game theory can provide insights into the stability of cooperation among cells in a tumour and into the design of potentially evolution-proof therapies that disrupt this cooperation.

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.

Predicting drug resistance evolution: insights from antimicrobial peptides and antibiotics

Antibiotic resistance constitutes one of the most pressing public health concerns. Antimicrobial peptides (AMPs) of multicellular organisms are considered part of a solution to this problem, and AMPs produced by bacteria such as colistin are last-resort drugs. Importantly, AMPs differ from many antibiotics in their pharmacodynamic characteristics. Here we implement these differences within a theoretical framework to predict the evolution of resistance against AMPs and compare it to antibiotic resistance. Our analysis of resistance evolution finds that pharmacodynamic differences all combine to produce a much lower probability that resistance will evolve against AMPs. The finding can be generalized to all drugs with pharmacodynamics similar to AMPs. Pharmacodynamic concepts are familiar to most practitioners of medical microbiology, and data can be easily obtained for any drug or drug combination. Our theoretical and conceptual framework is, therefore, widely applicable and can help avoid resistance evolution if implemented in antibiotic stewardship schemes or the rational choice of new drug candidates.