Clinical management of resistance evolution in a bacterial infection: A case study

The evolution of antibiotic resistance in an incurable and ultimately fatal infection: A retrospective case study

Background and objectives

The processes by which pathogens evolve within a host dictate the efficacy of treatment strategies designed to slow antibiotic resistance evolution and influence population-wide resistance levels. The aim of this study is to describe the underlying genetic and phenotypic changes leading to antibiotic resistance within a patient who died as resistance evolved to available antibiotics. We assess whether robust patterns of collateral sensitivity and response to combinations existed that might have been leveraged to improve therapy.

Methodology

We used whole-genome sequencing of nine isolates taken from this patient over 279 days of a chronic infection with Enterobacter hormaechei, and systematically measured changes in resistance against five of the most relevant drugs considered for treatment.

Results

The entirety of the genetic change is consistent with de novo mutations and plasmid loss events, without acquisition of foreign genetic material via horizontal gene transfer. The nine isolates fall into three genetically distinct lineages, with early evolutionary trajectories being supplanted by previously unobserved multi-step evolutionary trajectories. Importantly, although the population evolved resistance to all the antibiotics used to treat the infection, no single isolate was resistant to all antibiotics. Evidence of collateral sensitivity and response to combinations therapy revealed inconsistent patterns across this diversifying population.

Conclusions

Translating antibiotic resistance management strategies from theoretical and laboratory data to clinical situations, such as this, will require managing diverse population with unpredictable resistance trajectories.

High potency of sequential therapy with only β-lactam antibiotics

Evolutionary adaptation is a major source of antibiotic resistance in bacterial pathogens. Evolution-informed therapy aims to constrain resistance by accounting for bacterial evolvability. Sequential treatments with antibiotics that target different bacterial processes were previously shown to limit adaptation through genetic resistance trade-offs and negative hysteresis. Treatment with homogeneous sets of antibiotics is generally viewed to be disadvantageous as it should rapidly lead to cross-resistance. We here challenged this assumption by determining the evolutionary response of Pseudomonas aeruginosa to experimental sequential treatments involving both heterogenous and homogeneous antibiotic sets. To our surprise, we found that fast switching between only β-lactam antibiotics resulted in increased extinction of bacterial populations. We demonstrate that extinction is favored by low rates of spontaneous resistance emergence and low levels of spontaneous cross-resistance among the antibiotics in sequence. The uncovered principles may help to guide the optimized use of available antibiotics in highly potent, evolution-informed treatment designs.

Overuse of antibiotic drugs is leading to the appearance of antibiotic-resistant bacteria; this is, bacteria with mutations that allow them to survive treatment with specific antibiotics. This has made some bacterial infections difficult or impossible to treat. Learning more about how bacteria evolve resistance to antibiotics could help scientists find ways to prevent it and develop more effective treatments.

Changing antibiotics frequently may be one way to prevent bacteria from evolving resistance. That way if a bacterium acquires mutations that allow it to escape one antibiotic, another antibiotic will kill it, stopping it from dividing and preventing the appearance of descendants with resistance to several antibiotics. In order to use this approach, testing is needed to find the best sequences of antibiotics to apply and the optimal timings of treatment.

To find out more, Batra, Roemhild et al. grew bacteria in the laboratory and exposed them to different sequences of antibiotics, switching antibiotics at different time intervals. This showed that sequential treatments with different antibiotics can limit bacterial evolution, especially when antibiotics are switched quickly. Unexpectedly, one of the most effective sequences used very similar antibiotics. This was surprising because using similar antibiotics should lead to the evolution of cross-resistance, which is when a drug causes changes that make the bacterium less sensitive to other treatments. However, in the tested case, cross-resistance did not evolve when antibiotics were switched quickly, thereby ensuring efficiency of treatment.

Batra et al. show that alternating sequences of antibiotics may be an effective strategy to prevent drug resistance. Because the experiments were done in a laboratory setting it will be important to verify the results in studies in animals and humans before the approach can be used in medical or veterinary settings. If the results are confirmed, it could reduce the need to develop new antibiotics, which is expensive and time consuming.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

Pseudomonas aeruginosa populations in the cystic fibrosis lung lose susceptibility to newly applied β-lactams within 3 days

BACKGROUND

Chronic pulmonary infections by Pseudomonas aeruginosa require frequent intravenous antibiotic treatment in cystic fibrosis (CF) patients. Emergence of antimicrobial resistance is common in these patients, which to date has been investigated at long-term intervals only.

OBJECTIVES

To investigate under close to real-time conditions the dynamics of the response by P. aeruginosa to a single course of antibiotic therapy and the potentially associated rapid spread of antimicrobial resistance, as well as the impact on the airway microbiome.

METHODS

We investigated a cohort of adult CF patients that were treated with a single course of antimicrobial combination therapy. Using daily sampling during treatment, we quantified the expression of resistance by P. aeruginosa (median of six isolates per daily sample, 347 isolates in total), measured bacterial load by P. aeruginosa-specific quantitative PCR and characterized the airway microbiome with a 16S rRNA-based approach. WGS was performed to reconstruct intrapatient strain phylogenies.

RESULTS

In two patients, we found rapid and large increases in resistance to meropenem and ceftazidime. Phylogenetic reconstruction of strain relationships revealed that resistance shifts are probably due to de novo evolution and/or the selection of resistant subpopulations. We observed high interindividual variation in the reduction of bacterial load, microbiome composition and antibiotic resistance.

CONCLUSIONS

We show that CF-associated P. aeruginosa populations can quickly respond to antibiotic therapy and that responses are patient specific. Thus, resistance evolution can be a direct consequence of treatment, and drug efficacy can be lost much faster than usually assumed. The consideration of these patient-specific rapid resistance shifts can help to improve treatment of CF-associated infections, for example by deeper sampling of bacteria for diagnostics, repeated monitoring of pathogen susceptibility and switching between drugs.

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.

Molecular mechanisms of collateral sensitivity to the antibiotic nitrofurantoin

Antibiotic resistance increasingly limits the success of antibiotic treatments, and physicians require new ways to achieve efficient treatment despite resistance. Resistance mechanisms against a specific antibiotic class frequently confer increased susceptibility to other antibiotic classes, a phenomenon designated collateral sensitivity (CS). An informed switch of antibiotic may thus enable the efficient treatment of resistant strains. CS occurs in many pathogens, but the mechanisms that generate hypersusceptibility are largely unknown. We identified several molecular mechanisms of CS against the antibiotic nitrofurantoin (NIT). Mutants that are resistant against tigecycline (tetracycline), mecillinam (β-lactam), and protamine (antimicrobial peptide) all show CS against NIT. Their hypersusceptibility is explained by the overexpression of nitroreductase enzymes combined with increased drug uptake rates, or increased drug toxicity. Increased toxicity occurs through interference of the native drug-response system for NIT, the SOS response, with growth. A mechanistic understanding of CS will help to develop drug switches that combat resistance.

Resistance mechanisms against a specific antibiotic class frequently often confer negative cross-resistance to other antibiotic classes, a phenomenon known as collateral sensitivity. This study shows that collateral sensitivity in bacteria can be explained by a combination of several mechanisms that can be exploited to develop drug switches that combat resistance.

Evolution in the Anthropocene: Informing Governance and Policy

The Anthropocene biosphere constitutes an unprecedented phase in the evolution of life on Earth with one species, humans, exerting extensive control. The increasing intensity of anthropogenic forces in the twenty-first century has widespread implications for attempts to govern both human-dominated ecosystems and the last remaining wild ecosystems. Here, we review how evolutionary biology can inform governance and policies in the Anthropocene, focusing on five governance challenges that span biodiversity, environmental management, food and other biomass production, and human health. The five challenges are: ( a) evolutionary feedbacks, ( b) maintaining resilience, ( c) alleviating constraints, ( d) coevolutionary disruption, and ( e) biotechnology. Strategies for governing these dynamics will themselves have to be coevolutionary, as eco-evolutionary and social dynamics change in response to each other.

Modified Antibiotic Adjuvant Ratios Can Slow and Steer the Evolution of Resistance: Co-amoxiclav as a Case Study

As antibiotic resistance spreads, developing sustainable methods to restore the efficacy of existing antibiotics is increasingly important. One widespread method is to combine antibiotics with synergistically acting adjuvants that inhibit resistance mechanisms, allowing drug killing. Here we use co-amoxiclav (a clinically important combination of the β-lactam antibiotic amoxicillin and the β-lactamase inhibitor clavulanate) to ask whether treatment efficacy and resistance evolution can be decoupled via component dosing modifications. A simple mathematical model predicts that different ratios of these two drug components can produce distinct evolutionary responses irrespective of the initial efficacy. We test this hypothesis by selecting Escherichia coli with a plasmid-encoded β-lactamase (CTX-M-14), against different concentrations of amoxicillin and clavulanate. Consistent with our theory, we found that while resistance evolved under all conditions, the component ratio influenced both the rate and mechanism of resistance evolution. Specifically, we found that the current clinical practice of high amoxicillin-to-clavulanate ratios resulted in the most rapid adaptation to antibiotics via gene dosing responses. Increased plasmid copy number allowed E. coli to increase β-lactamase dosing and effectively titrate out low quantities of clavulanate, restoring amoxicillin resistance. In contrast, high clavulanate ratios were more robust-plasmid copy number did not increase, although porin or efflux resistance mechanisms were found, as for all drug ratios. Our results indicate that by changing the ratio of adjuvant to antibiotic we can slow and steer the path of resistance evolution. We therefore suggest using increased adjuvant dosing regimens to slow the rate of resistance evolution.IMPORTANCE As antibiotic resistance spreads, a promising approach is to restore the effectiveness of existing drugs via coadministration with adjuvants that inhibit resistance. However, as for monotherapy, antibiotic-adjuvant therapies can select for a variety of resistance mechanisms, so it is imperative that adjuvants be used in a sustainable manner. We test whether the rate of resistance evolution can be decoupled from treatment efficacy using co-amoxiclav, a clinically important combination of the β-lactam amoxicillin and β-lactamase inhibitor clavulanate. Using experimental evolution and a simple theoretical model, we show that the current co-amoxiclav formulation with a high proportion of amoxicillin rapidly selects for resistance via increased β-lactamase production. On the other hand, formulations with more clavulanate and less amoxicillin have similar efficacies yet prevent the selective benefit of increased β-lactamase. We suggest that by blocking common paths to resistance, treatment combinations with the adjuvant in excess can slow the evolution of resistance.

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.

Evolutionary ecology meets the antibiotic crisis: Can we control pathogen adaptation through sequential therapy?

The spread of antibiotic resistance is a global challenge that is fueled by evolution and ecological processes. Therefore, the design of new sustainable therapy should take account of these underlying processes—as proposed within the field of evolutionary medicine, yet usually not receiving the necessary attention from national and international health agencies. We here put the spotlight on a currently neglected treatment strategy: sequential therapy. Changes among antibiotics generate fluctuating selection conditions that are in general difficult to counter by any organism. We argue that sequential treatment designs can be specifically optimized by exploiting evolutionary trade-offs, for example collateral sensitivity and/or inducible physiological constraints, such as negative hysteresis, where pre-exposure to one antibiotic induces temporary hyper-sensitivity to another antibiotic. Our commentary provides an overview of sequential treatment strategies and outlines steps towards their further optimization.

Why the evolution of vaccine resistance is less of a concern than the evolution of drug resistance

Vaccines and antimicrobial drugs both impose strong selection for resistance. Yet only drug resistance is a major challenge for 21st century medicine. Why is drug resistance ubiquitous and not vaccine resistance? Part of the answer is that vaccine resistance is far less likely to evolve than drug resistance. But what happens when vaccine resistance does evolve? We review six putative cases. We find that in contrast to drug resistance, vaccine resistance is harder to detect and harder to confirm and that the mechanistic basis is less well understood. Nevertheless, in the cases we examined, the pronounced health benefits associated with vaccination have largely been sustained. Thus, we contend that vaccine resistance is less of a concern than drug resistance because it is less likely to evolve and when it does, it is less harmful to human and animal health and well-being. Studies of pathogen strains that evolve the capacity to replicate and transmit from vaccinated hosts will enhance our ability to develop next-generation vaccines that minimize the risk of harmful pathogen evolution.

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.

Why does drug resistance readily evolve but vaccine resistance does not?

Why is drug resistance common and vaccine resistance rare? Drugs and vaccines both impose substantial pressure on pathogen populations to evolve resistance and indeed, drug resistance typically emerges soon after the introduction of a drug. But vaccine resistance has only rarely emerged. Using well-established principles of population genetics and evolutionary ecology, we argue that two key differences between vaccines and drugs explain why vaccines have so far proved more robust against evolution than drugs. First, vaccines tend to work prophylactically while drugs tend to work therapeutically. Second, vaccines tend to induce immune responses against multiple targets on a pathogen while drugs tend to target very few. Consequently, pathogen populations generate less variation for vaccine resistance than they do for drug resistance, and selection has fewer opportunities to act on that variation. When vaccine resistance has evolved, these generalities have been violated. With careful forethought, it may be possible to identify vaccines at risk of failure even before they are introduced.

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.

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.

Hybrid antibiotics - clinical progress and novel designs

INTRODUCTION

There is a growing need for new antibacterial agents, but success in development of antibiotics in recent years has been limited. This has led researchers to investigate novel approaches to finding compounds that are effective against multi-drug resistant bacteria, and that delay onset of resistance. One such strategy has been to link antibiotics to produce hybrids designed to overcome resistance mechanisms.

AREAS COVERED

The concept of dual-acting hybrid antibiotics was introduced and reviewed in this journal in 2010. In the present review the authors sought to discover how clinical candidates described had progressed, and to examine how the field has developed. In three sections the authors cover the clinical progress of hybrid antibiotics, novel agents produced from hybridisation of two or more small-molecule antibiotics, and novel agents produced from hybridisation of antibiotics with small-molecules that have complementary activity.

EXPERT OPINION

Many key questions regarding dual-acting hybrid antibiotics remain to be answered, and the proposed benefits of this approach are yet to be demonstrated. While Cadazolid in particular continues to progress in the clinic, suggesting that there is promise in hybridisation through covalent linkage, it may be that properties other than antibacterial activity are key when choosing a partner molecule.