Does High-Dose Antimicrobial Chemotherapy Prevent 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.

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.

Evolutionary emergence of infectious diseases in heterogeneous host populations

The emergence and re-emergence of pathogens remains a major public health concern. Unfortunately, when and where pathogens will (re-)emerge is notoriously difficult to predict, as the erratic nature of those events is reinforced by the stochastic nature of pathogen evolution during the early phase of an epidemic. For instance, mutations allowing pathogens to escape host resistance may boost pathogen spread and promote emergence. Yet, the ecological factors that govern such evolutionary emergence remain elusive because of the lack of ecological realism of current theoretical frameworks and the difficulty of experimentally testing their predictions. Here, we develop a theoretical model to explore the effects of the heterogeneity of the host population on the probability of pathogen emergence, with or without pathogen evolution. We show that evolutionary emergence and the spread of escape mutations in the pathogen population is more likely to occur when the host population contains an intermediate proportion of resistant hosts. We also show that the probability of pathogen emergence rapidly declines with the diversity of resistance in the host population. Experimental tests using lytic bacteriophages infecting their bacterial hosts containing Clustered Regularly Interspaced Short Palindromic Repeat and CRISPR-associated (CRISPR-Cas) immune defenses confirm these theoretical predictions. These results suggest effective strategies for cross-species spillover and for the management of emerging infectious diseases.

The probability that an epidemic will break out is highly dependent on the ability of the pathogen to acquire new adaptive mutations and to induce evolutionary emergence. Forecasting pathogen emergence thus requires a good understanding of the interplay between the epidemiology and evolution taking place at the onset of an outbreak. Here, we provide a comprehensive theoretical framework to analyze the impact of host population heterogeneity on the probability of pathogen evolutionary emergence. We use this model to predict the impact of the fraction of susceptible hosts, the inoculum size of the pathogen, and the diversity of host resistance on pathogen emergence. Our experiments using lytic bacteriophages and CRISPR-resistant bacteria support our theoretical predictions and demonstrate that manipulating the diversity of resistance alleles in a host population may be an effective way to limit the emergence of new pathogens.

Antibiotic-driven escape of host in a parasite-induced Red Queen dynamics

Winnerless coevolution of hosts and parasites could exhibit Red Queen dynamics, which is characterized by parasite-driven cyclic switching of expressed host phenotypes. We hypothesize that the application of antibiotics to suppress the reproduction of parasites can provide an opportunity for the hosts to escape such winnerless coevolution. Here, we formulate a minimal mathematical model of host-parasite interaction involving multiple host phenotypes that are targeted by adapting parasites. Our model predicts the levels of antibiotic effectiveness that can steer the parasite-driven cyclic switching of host phenotypes (oscillations) to a stable equilibrium of host survival. Our simulations show that uninterrupted application of antibiotic with high-level effectiveness (greater than 85%) is needed to escape the Red Queen dynamics. Interrupted and low level of antibiotic effectiveness are indeed useless to stop host-parasite coevolution. This study can be a guide in designing good practices and protocols to minimize the risk of further progression of parasitic infections.

A Single-Agent Dual-Specificity Targeting of FOLR1 and DR5 as an Effective Strategy for Ovarian Cancer

Therapeutic antibodies targeting ovarian cancer (OvCa)-enriched receptors have largely been disappointing due to limited tumor-specific antibody-dependent cellular cytotoxicity. Here we report a symbiotic approach that is highly selective and superior compared with investigational clinical antibodies. This bispecific-anchored cytotoxicity activator antibody is rationally designed to instigate "cis" and "trans" cytotoxicity by combining specificities against folate receptor alpha-1 (FOLR1) and death receptor 5 (DR5). Whereas the in vivo agonist DR5 signaling requires FcγRIIB interaction, the FOLR1 anchor functions as a primary clustering point to retain and maintain a high level of tumor-specific apoptosis. The presented proof of concept study strategically makes use of a tumor cell-enriched anchor receptor for agonist death receptor targeting to potentially generate a clinically viable strategy for OvCa.

Pharmacodynamic considerations of collateral sensitivity in design of antibiotic treatment regimen

INTRODUCTION

Antibiotics have greatly reduced the morbidity and mortality due to infectious diseases. Although antibiotic resistance is not a new problem, its breadth now constitutes a significant threat to human health. One strategy to help combat resistance is to find novel ways to use existing drugs, even those that display high rates of resistance. For the pathogens Escherichia coli and Pseudomonas aeruginosa, pairs of antibiotics have been identified for which evolution of resistance to drug A increases sensitivity to drug B and vice versa. These research groups have proposed cycling such pairs to treat infections, and similar treatment strategies are being investigated for various cancer forms as well. While an exciting treatment prospect, no cycling experiments have yet been performed with consideration of pharmacokinetics and pharmacodynamics. To test the plausibility of such schemes and optimize them, we create a mathematical model with explicit pharmacokinetic/pharmacodynamic considerations.

MATERIALS AND METHODS

We evaluate antibiotic cycling protocols using pairs of such antibiotics and investigate the speed of ascent of multiply resistant mutants.

RESULTS

Our analyses show that when using low concentrations of antibiotics, treatment failure will always occur due to the rapid ascent and fixation of resistant mutants. However, moderate to high concentrations of some combinations of bacteriostatic and bactericidal antibiotics with multiday cycling prevent resistance from developing and increase the likelihood of treatment success.

CONCLUSION

Our results call for guarded optimism in application and development of such treatment protocols.

The impact of within-host ecology on the fitness of a drug-resistant parasite

Background and objectives

The rate of evolution of drug resistance depends on the fitness of resistant pathogens. The fitness of resistant pathogens is reduced by competition with sensitive pathogens in untreated hosts and so enhanced by competitive release in drug-treated hosts. We set out to estimate the magnitude of those effects on a variety of fitness measures, hypothesizing that competitive suppression and competitive release would have larger impacts when resistance was rarer to begin with.

Methodology

We infected mice with varying densities of drug-resistant Plasmodium chabaudi malaria parasites in a fixed density of drug-sensitive parasites and followed infection dynamics using strain-specific quantitative PCR.

Results

Competition with susceptible parasites reduced the absolute fitness of resistant parasites by 50–100%. Drug treatment increased the absolute fitness from 2- to >10 000-fold. The ecological context and choice of fitness measure was responsible for the wide variation in those estimates. Initial population growth rates poorly predicted parasite abundance and transmission probabilities.

Conclusions and implications

(i) The sensitivity of estimates of pathogen fitness to ecological context and choice of fitness measure make it difficult to derive field-relevant estimates of the fitness costs and benefits of resistance from experimental settings. (ii) Competitive suppression can be a key force preventing resistance from emerging when it is rare, as it is when it first arises. (iii) Drug treatment profoundly affects the fitness of resistance. Resistance evolution could be slowed by developing drug use policies that consider in-host competition.

Phage Therapy Faces Evolutionary Challenges

Antibiotic resistance evolution in bacteria indicates that one of the challenges faced by phage therapy is that, sooner or later, bacteria will evolve resistance to phages. Evidently, this is the case of every known antimicrobial therapy, but here this is also part of a ubiquitous natural process of co-evolution between phages and bacteria. Fundamental evolutionary studies hold some clues that are crucial to limit the problematic process of bacterial resistance during phage applications. First, I discuss here the importance of defining evolutionary and ecological factors influencing bacterial resistance and phage counter-defense mechanisms. Then, I comment on the interest of determining the co-evolutionary dynamics between phages and bacteria that may allow for selecting the conditions that will increase the probability of therapeutic success. I go on to suggest the varied strategies that may ensure the long-term success of phage therapy, including analysis of internal phage parameters and personalized treatments. In practical terms, these types of approaches will define evolutionary criteria regarding how to develop, and when to apply, therapeutic phage cocktails. Integrating this perspective in antimicrobial treatments, such as phage therapy, is among the necessary steps to expand its use in the near future, and to ensure its durability and success.

Sexual Conflict: Mechanisms and Emerging Themes in Resistance Biology

Sexual conflict is acknowledged as pervasive, with the potential to generate and maintain genetic variation. Mechanistic studies of conflict have been important in providing direct evidence for the existence of sexual conflict. They have also led to the growing realization that there is a striking phenotypic diversity of adaptations whose evolution can be shaped by sexually antagonistic selection. The mechanisms involved range from the use of genital spines, claspers, songs, and smells to ejaculate molecules. In one well-studied example, sexual conflict can occur over the sexually antagonistic effects of seminal fluid proteins in Drosophila melanogaster. However, an important puzzle remains, namely, why seminal fluid proteins are so numerous and complex, hence whether all or some are involved in mediating sexual conflict. I hypothesize that this rich diversity and the complexity of traits subject to sexually antagonistic selection in general may arise, at least in part, due to the deployment of sexually antagonistic adaptations in males in a way that lessens the probability of broadscale, strong resistance evolution in females. In elaborating this hypothesis, I explore how research into the evolution of resistance to insecticides, antimicrobials, and vaccines might be used to provide insights into the evolution of female resistance to the effects of sexually antagonistic manipulative traits of males. In this manner, the manipulative traits of males can be resistance-proofed.

Comparison of the synergistic anticancer activity of AlPcS4 photodynamic therapy in combination with different low‑dose chemotherapeutic agents on gastric cancer cells

Limited cellular delivery and internalization efficiency of Al(III) phthalocyanine chloride tetrasulfonic acid (AlPcS₄) induce poor penetration ability in cells and a slight photodynamic therapy (PDT) effect on gastric cancer. The combination treatment of AlPcS₄/PDT with low-dose chemotherapeutic agents may provide a promising treatment strategy to increase the weak delivery efficiency of AlPcS₄, reducing the dose of chemical agents without reducing efficacy, and improving apoptosis-inducing abilities, thereby increasing the antitumor effects and decreasing the noxious side effects on gastric cancer. We investigated and compared the synergistic antitumor growth effect on gastric cancer cells by combining AlPcS₄/PDT treatment with different low-dose chemotherapeutic agents, namely, 5-fluorouracil (5-FU), doxorubicin (DOX), cisplatin (CDDP), mitomycin C (MMC), and vincristine (VCR). The inhibitory effect was increased in treatments that combined AlPcS₄/PDT with all the aforementioned low-dose chemotherapeutic agents, to a different extent. An evident synergistic effect was obtained in the combination treatment of AlPcS₄/PDT with low-dose 5-FU, DOX, and MMC by increasing AlPcS₄ intracellular uptake ability, improving apoptosis-inducing abilities, and prolonging apoptosis-inducing time. The low-dose chemotherapeutic agents prolonged the apoptosis-inducing period of AlPcS₄/PDT, and AlPcS₄/PDT quickly improved apoptosis-inducing abilities of chemotherapy even at low doses. Generally, the combination treatment of AlPcS₄/PDT with low-dose chemotherapeutic agents had significant antitumor growth effects in addition to a low dark-cytotoxicity effect on gastric cancer, thereby representing an effective and feasible therapy method for gastric cancer.

Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria

The stress-induced mutagenesis hypothesis postulates that in response to stress, bacteria increase their genome-wide mutation rate, in turn increasing the chances that a descendant is able to better withstand the stress. This has implications for antibiotic treatment: exposure to subinhibitory doses of antibiotics has been reported to increase bacterial mutation rates and thus probably the rate at which resistance mutations appear and lead to treatment failure. More generally, the hypothesis posits that stress increases evolvability (the ability of a population to generate adaptive genetic diversity) and thus accelerates evolution. Measuring mutation rates under stress, however, is problematic, because existing methods assume there is no death. Yet subinhibitory stress levels may induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size, thus providing more opportunities to acquire mutations. We show that ignoring death leads to a systematic overestimation of mutation rates under stress. We developed a system based on plasmid segregation that allows us to measure death and division rates simultaneously in bacterial populations. Using this system, we found that a substantial death rate occurs at the tested subinhibitory concentrations previously reported to increase mutation rate. Taking this death rate into account lowers and sometimes removes the signal for stress-induced mutagenesis. Moreover, even when antibiotics increase mutation rate, we show that subinhibitory treatments do not increase genetic diversity and evolvability, again because of effects of the antibiotics on population dynamics. We conclude that antibiotic-induced mutagenesis is overestimated because of death and that understanding evolvability under stress requires accounting for the effects of stress on population dynamics as much as on mutation rate. Our goal here is dual: we show that population dynamics and, in particular, the numbers of cell divisions are crucial but neglected parameters in the evolvability of a population, and we provide experimental and computational tools and methods to study evolvability under stress, leading to a reassessment of the magnitude and significance of the stress-induced mutagenesis paradigm.

The effect of environmental stress on bacterial mutagenesis has been a paradigm-shift discovery. Recent developments include evidence that various antibiotics increase mutation rates in bacteria when used at subinhibitory concentrations. It is therefore suggested that such treatments promote resistance evolution because they increase the generation of genetic variation on which natural selection can act. However, existing methods to compute mutation rate neglect the effect of stress on death and population dynamics. Developing new experimental and computational tools, we find that taking death into account significantly lowers the signal for stress-induced mutagenesis. Moreover, we show that treatments that increase mutation rate do not always lead to increased genetic diversity, which questions the standard paradigm of increased evolvability under stress.

Hypothesis: Cancer Is a Disease of Evolved Trade-Offs Between Neoplastic Virulence and Transmission

Virulence is defined as the ability of a pathogen to cause morbidity and/or mortality in infected hosts. The relationship between virulence and transmissibility is complex; natural selection may promote decreased virulence to enhance host mobility and increase the probability for transmission, or transmissibility may be enhanced by increased virulence, leading to higher pathogen load and, in some cases, superior evasion from host defenses. An evolutionary trade-off exists between the ability of pathogens to maintain opportunities for long-term transmission via suppressed virulence and increased short-term transmission via enhanced virulence. We propose an analogy between transmissibility and virulence in microbial pathogens and in cancer. Thus, in the latter case, the outcome of invasive growth and metastasis is analogous to transmissibility, and virulence is defined by high rates of proliferation, invasiveness and motility, potential for metastasis, and the extent to which the cancer contributes to patient morbidity and mortality. Horizontal and vertical transmission, associated with increased or decreased pathogen virulence respectively, can also be utilized to model the neoplastic process and factors that would increase or decrease tumor aggressiveness. Concepts of soft vs. hard selection and evolutionary game theory can optimize our understanding of carcinogenesis and therapeutic strategies. Therefore, the language of transmissibility, horizontal vs. vertical transmission, selection, and virulence can be used to inform approaches to inhibit tumorigenic progression, and, more generally, for cancer prevention and treatment.

Beyond dose: Pulsed antibiotic treatment schedules can maintain individual benefit while reducing resistance

The emergence of treatment-resistant microbes is a key challenge for disease treatment and a leading threat to human health and wellbeing. New drugs are always in development, but microbes regularly and rapidly acquire resistance. We must consider if altering how we administer drugs at the individual level could slow development of resistance. Here we use mathematical models to show that exposing microbes to drug pulses could greatly reduce resistance without increasing individual pathogen load. Our results stem from two key factors: the presence of antibiotics creates a selection pressure for antibiotic resistant microbes, and large populations of bacteria are more likely to harbor drug resistance than small populations. Drug pulsing targets these factors simultaneously. Short duration pulses minimize the time during which there is selection for resistance, and high drug concentrations minimize pathogen abundance. Our work provides a theoretical basis for the design of in vitro and in vivo experiments to test how drug pulsing might reduce the impact of drug resistant infections.

Putting evolution in elimination: Winning our ongoing battle with evolving malaria mosquitoes and parasites

Since 2000, the world has made significant progress in reducing malaria morbidity and mortality, and several countries in Africa, South America and South-East Asia are working hard to eliminate the disease. These elimination efforts continue to rely heavily on antimalarial drugs and insecticide-based interventions, which remain the cornerstones of malaria treatment and prevention. However, resistance has emerged against nearly every antimalarial drug and insecticide that is available. In this review we discuss the evolutionary consequences of the way we currently implement antimalarial interventions, which is leading to resistance and may ultimately lead to control failure, but also how evolutionary principles can be applied to extend the lifespan of current and novel interventions. A greater understanding of the general evolutionary principles that are at the core of emerging resistance is urgently needed if we are to develop improved resistance management strategies with the ultimate goal to achieve a malaria-free world.

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 Rescue over a Fitness Landscape

Evolutionary rescue describes a situation where adaptive evolution prevents the extinction of a population facing a stressing environment. Models of evolutionary rescue could in principle be used to predict the level of stress beyond which extinction becomes likely for species of conservation concern, or, conversely, the treatment levels most likely to limit the emergence of resistant pests or pathogens. Stress levels are known to affect both the rate of population decline (demographic effect) and the speed of adaptation (evolutionary effect), but the latter aspect has received less attention. Here, we address this issue using Fisher's geometric model of adaptation. In this model, the fitness effects of mutations depend both on the genotype and the environment in which they arise. In particular, the model introduces a dependence between the level of stress, the proportion of rescue mutants, and their costs before the onset of stress. We obtain analytic results under a strong-selection-weak-mutation regime, which we compare to simulations. We show that the effect of the environment on evolutionary rescue can be summarized into a single composite parameter quantifying the effective stress level, which is amenable to empirical measurement. We describe a narrow characteristic stress window over which the rescue probability drops from very likely to very unlikely as the level of stress increases. This drop is sharper than in previous models, as a result of the decreasing proportion of stress-resistant mutations as stress increases. We discuss how to test these predictions with rescue experiments across gradients of stress.

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

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

Institution-wide and Within-Patient Evolution of Daptomycin Susceptibility in Vancomycin-Resistant Enterococcus faecium Bloodstream Infections

We report daptomycin minimum inhibitory concentrations (MICs) for vancomycin-resistant Enterococcus faecium isolated from bloodstream infections over a 4-year period. The daptomycin MIC increased over time hospital-wide for initial isolates and increased over time within patients, culminating in 40% of patients having daptomycin-nonsusceptible isolates in the final year of the study. Infect Control Hosp Epidemiol 2018;39:226-228.

Antibiotic resistance in the wild: an eco-evolutionary perspective

The legacy of the use and misuse of antibiotics in recent decades has left us with a global public health crisis: antibiotic-resistant bacteria are on the rise, making it harder to treat infections. At the same time, evolution of antibiotic resistance is probably the best-documented case of contemporary evolution. To date, research on antibiotic resistance has largely ignored the complexity of interactions that bacteria engage in. However, in natural populations, bacteria interact with other species; for example, competition and grazing are import interactions influencing bacterial population dynamics. Furthermore, antibiotic leakage to natural environments can radically alter bacterial communities. Overall, we argue that eco-evolutionary feedback loops in microbial communities can be modified by residual antibiotics and evolution of antibiotic resistance. The aim of this review is to connect some of the well-established key concepts in evolutionary biology and recent advances in the study of eco-evolutionary dynamics to research on antibiotic resistance. We also identify some key knowledge gaps related to eco-evolutionary dynamics of antibiotic resistance, and review some of the recent technical advantages in molecular microbiology that offer new opportunities for tackling these questions. Finally, we argue that using the full potential of evolutionary theory and active communication across the different fields is needed for solving this global crisis more efficiently.

This article is part of the themed issue ‘Human influences on evolution, and the ecological and societal consequences'.

New opportunities for managing acute and chronic lung infections

Lung diseases caused by microbial infections affect hundreds of millions of children and adults throughout the world. In Western populations, the treatment of lung infections is a primary driver of antibiotic resistance. Traditional therapeutic strategies have been based on the premise that the healthy lung is sterile and that infections grow in a pristine environment. As a consequence, rapid advances in our understanding of the composition of the microbiota of the skin and bowel have not yet been matched by studies of the respiratory tree. The recognition that the lungs are as populated with microorganisms as other mucosal surfaces provides the opportunity to reconsider the mechanisms and management of lung infections. Molecular analyses of the lung microbiota are revealing profound adverse responses to widespread antibiotic use, urbanization and globalization. This Opinion article proposes how technologies and concepts flowing from the Human Microbiome Project can transform the diagnosis and treatment of common lung diseases.

Exploiting ecology in drug pulse sequences in favour of population reduction

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

Variability in fitness effects can preclude selection of the fittest

Evolutionary biologists often predict the outcome of natural selection on an allele by measuring its effects on lifetime survival and reproduction of individual carriers. However, alleles affecting traits like sex, evolvability, and cooperation can cause fitness effects that depend heavily on differences in the environmental, social, and genetic context of individuals carrying the allele. This variability makes it difficult to summarize the evolutionary fate of an allele based solely on its effects on any one individual. Attempts to average over this variability can sometimes salvage the concept of fitness. In other cases evolutionary outcomes can only be predicted by considering the entire genealogy of an allele, thus limiting the utility of individual fitness altogether. We describe a number of intriguing new evolutionary phenomena that have emerged in studies that explicitly model long-term lineage dynamics and discuss implications for the evolution of infectious diseases.

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.

The selfish germ

Curiosity about the sex life of a wasp led to a new way of thinking and a powerful demonstration that evolutionary science could be predictive. That same approach could help find ways to slow or prevent treatment failures in cancer and infectious diseases.

Global change, parasite transmission and disease control: lessons from ecology

Parasitic infections are ubiquitous in wildlife, livestock and human populations, and healthy ecosystems are often parasite rich. Yet, their negative impacts can be extreme. Understanding how both anticipated and cryptic changes in a system might affect parasite transmission at an individual, local and global level is critical for sustainable control in humans and livestock. Here we highlight and synthesize evidence regarding potential effects of 'system changes' (both climatic and anthropogenic) on parasite transmission from wild host-parasite systems. Such information could inform more efficient and sustainable parasite control programmes in domestic animals or humans. Many examples from diverse terrestrial and aquatic natural systems show how abiotic and biotic factors affected by system changes can interact additively, multiplicatively or antagonistically to influence parasite transmission, including through altered habitat structure, biodiversity, host demographics and evolution. Despite this, few studies of managed systems explicitly consider these higher-order interactions, or the subsequent effects of parasite evolution, which can conceal or exaggerate measured impacts of control actions. We call for a more integrated approach to investigating transmission dynamics, which recognizes these complexities and makes use of new technologies for data capture and monitoring, and to support robust predictions of altered parasite dynamics in a rapidly changing world.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.

Emergence of Resistance to Fungicides: The Role of Fungicide Dose

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

Mathematical Modelling to Guide Drug Development for Malaria Elimination

Mathematical models of the dynamics of a drug within the host are now frequently used to guide drug development. These generally focus on assessing the efficacy and duration of response to guide patient therapy. Increasingly, antimalarial drugs are used at the population level, to clear infections, provide chemoprevention, and to reduce onward transmission of infection. However, there is less clarity on the extent to which different drug properties are important for these different uses. In addition, the emergence of drug resistance poses new threats to longer-term use and highlights the need for rational drug development. Here, we argue that integrating within-host pharmacokinetic and pharmacodynamic (PK/PD) models with mathematical models for the population-level transmission of malaria is key to guiding optimal drug design to aid malaria elimination.

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.

Population dynamics of epidemic and endemic states of drug-resistance emergence in infectious diseases

The emergence and spread of drug-resistance during treatment of many infectious diseases continue to degrade our ability to control and mitigate infection outcomes using therapeutic measures. While the coverage and efficacy of treatment remain key factors in the population dynamics of resistance, the timing for the start of the treatment in infectious individuals can significantly influence such dynamics. We developed a between-host disease transmission model to investigate the short-term (epidemic) and long-term (endemic) states of infections caused by two competing pathogen subtypes, namely the wild-type and resistant-type, when the probability of developing resistance is a function of delay in start of the treatment. We characterize the behaviour of disease equilibria and obtain a condition to minimize the fraction of population infectious at the endemic state in terms of probability of developing resistance and its transmission fitness. For the short-term epidemic dynamics, we illustrate that depending on the likelihood of resistance development at the time of treatment initiation, the same epidemic size may be achieved with different delays in start of the treatment, which may correspond to significantly different treatment coverages. Our results demonstrate that early initiation of treatment may not necessarily be the optimal strategy for curtailing the incidence of resistance or the overall disease burden. The risk of developing drug-resistance in-host remains an important factor in the management of resistance in the population.

Antibiotic stress selects against cooperation in the pathogenic bacterium Pseudomonas aeruginosa

Cheats are a pervasive threat to public goods production in natural and human communities, as they benefit from the commons without contributing to it. Although ecological antagonisms such as predation, parasitism, competition, and abiotic environmental stress play key roles in shaping population biology, it is unknown how such stresses generally affect the ability of cheats to undermine cooperation. We used theory and experiments to address this question in the pathogenic bacterium, Pseudomonas aeruginosa Although public goods producers were selected against in all populations, our competition experiments showed that antibiotics significantly increased the advantage of nonproducers. Moreover, the dominance of nonproducers in mixed cultures was associated with higher resistance to antibiotics than in either monoculture. Mathematical modeling indicates that accentuated costs to producer phenotypes underlie the observed patterns. Mathematical analysis further shows how these patterns should generalize to other taxa with public goods behaviors. Our findings suggest that explaining the maintenance of cooperative public goods behaviors in certain natural systems will be more challenging than previously thought. Our results also have specific implications for the control of pathogenic bacteria using antibiotics and for understanding natural bacterial ecosystems, where subinhibitory concentrations of antimicrobials frequently occur.

Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections

The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.

Towards the just and sustainable use of antibiotics

The emergence and spread of antibiotic resistant pathogens poses a big challenge to policy-makers, who need to oversee the transformation of health systems that evolved to provide easy access to these drugs into ones that encourage appropriate use of antimicrobials, whilst reducing the risk of resistance. This is a particular challenge for low and middle-income countries with pluralistic health systems where antibiotics are available in a number of different markets. This review paper considers access and use of antibiotics in these countries from a complex adaptive system perspective. It highlights the main areas of intervention that could provide the key to addressing the sustainable long term use and availability of antibiotics. A focus on the synergies between interventions addressing access strategies, antibiotic quality, diagnostics for low-resource settings, measures to encourage just and sustainable decision making and help seeking optimal therapeutic and dosing strategies are key levers for the sustainable future of antibiotic use. Successful integration of such strategies will be dependent on effective governance mechanisms, effective partnerships and coalition building and accurate evaluation systems at national, regional and global levels.

Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases

Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting.