Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections. Proc Natl Acad Sci U S A. 2014;111:8331-8338. [PMID: 24843148 DOI: 10.1073/pnas.1400352111]

A Whole-Body Mathematical Model of Sepsis Progression and Treatment Designed in the BioGears Physiology Engine

Sepsis is a debilitating condition associated with a high mortality rate that greatly strains hospital resources. Though advances have been made in improving sepsis diagnosis and treatment, our understanding of the disease is far from complete. Mathematical modeling of sepsis has the potential to explore underlying biological mechanisms and patient phenotypes that contribute to variability in septic patient outcomes. We developed a comprehensive, whole-body mathematical model of sepsis pathophysiology using the BioGears Engine, a robust open-source virtual human modeling project. We describe the development of a sepsis model and the physiologic response within the BioGears framework. We then define and simulate scenarios that compare sepsis treatment regimens. As such, we demonstrate the utility of this model as a tool to augment sepsis research and as a training platform to educate medical staff.

PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions

OBJECTIVE

Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a novel recommendation system for antibiotic prescription (PARS).

METHOD

Our approach is based on the combination of semantic technologies with MCDA (Multiple Criteria Decision Aiding) that allowed us to build a two level decision support model. Given a specific domain, the approach assesses the adequacy of an alternative/action (prescription of antibiotic) for a specific subject (patient) with an issue (bacterial infection) in a given context (medical). The goal of the first level of the decision support model is to select the set of alternatives which have the potential to be suitable. Then the second level sorts the alternatives into categories according to their adequacy using an MCDA sorting method (MR-Sort with Veto) and a structured set of description logic queries.

RESULTS

We applied this approach in the domain of antibiotic prescriptions, working closely with the EpiCura Hospital Center (BE). Its performance was compared to the EpiCura recommendation guidelines which are currently in use. The results showed that the proposed system is more consistent in its recommendations when compared with the static EpiCura guidelines. Moreover, with PARS the antibiotic prescribing workflow becomes more flexible. PARS allows the user (physician) to update incrementally and dynamically a patient's profile with more information, or to input knowledge modifications that accommodate the decision context (like the introduction of new side effects and antibiotics, the development of germs that are resistant, etc). At the end of our evaluation, we detail a number of limitations of the current version of PARS and discuss future perspectives.

Lying in Wait: Modeling the Control of Bacterial Infections via Antibiotic-Induced Proviruses

Antibiotic resistance is a growing concern for management of common bacterial infections. Here, we show that antibiotics can be effective at subinhibitory levels when bacteria carry latent phage. Our findings suggest that specific treatment strategies based on the identification of latent viruses in individual bacterial strains may be an effective personalized medicine approach to antibiotic stewardship.

Most bacteria and archaea are infected by latent viruses that change their physiology and responses to environmental stress. We use a population model of the bacterium-phage relationship to examine the role that latent phage play in the bacterial population over time in response to antibiotic treatment. We demonstrate that the stress induced by antibiotic administration, even if bacteria are resistant to killing by antibiotics, is sufficient to control the infection under certain conditions. This work expands the breadth of understanding of phage-antibiotic synergy to include both temperate and chronic viruses persisting in their latent form in bacterial populations.

IMPORTANCE Antibiotic resistance is a growing concern for management of common bacterial infections. Here, we show that antibiotics can be effective at subinhibitory levels when bacteria carry latent phage. Our findings suggest that specific treatment strategies based on the identification of latent viruses in individual bacterial strains may be an effective personalized medicine approach to antibiotic stewardship.

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.

Identifying determinants of persistent MRSA bacteremia using mathematical modeling

Persistent bacteremia caused by Staphylococcus aureus (SA), especially methicillin-resistant SA (MRSA), is a significant cause of morbidity and mortality. Despite susceptibility phenotypes in vitro, persistent MRSA strains fail to clear with appropriate anti-MRSA therapy during bacteremia in vivo. Thus, identifying the factors that cause such MRSA persistence is of direct and urgent clinical relevance. To address the dynamics of MRSA persistence in the face of host immunity and typical antibiotic regimens, we developed a mathematical model based on the overarching assumption that phenotypic heterogeneity is a hallmark of MRSA persistence. First, we applied an ensemble modeling approach and obtained parameter sets that satisfied the condition of a minimum inoculum dose to establish infection. Second, by simulating with the selected parameter sets under vancomycin therapy which follows clinical practices, we distinguished the models resulting in resolving or persistent bacteremia, based on the total SA exceeding a detection limit after five days of treatment. Third, to find key determinants that discriminate resolving and persistent bacteremia, we applied a machine learning approach and found that the immune clearance rate of persister cells is a key feature. But, fourth, when relapsing bacteremia was considered, the growth rate of persister cells was also found to be a key feature. Finally, we explored pharmacological strategies for persistent and relapsing bacteremia and found that a persister killer, but not a persister formation inhibitor, could provide for an effective cure the persistent bacteremia. Thus, to develop better clinical solutions for MRSA persistence and relapse, our modeling results indicate that we need to better understand the pathogen-host interactions of persister MRSAs in vivo.

Staphylococcus aureus causes potentially lethal infections of the bloodstream and target organs when able to enter the body, often via skin trauma or catheterization. Methicillin-resistant Staphylococcus aureus (MRSA) resist common antibiotics, but are often successfully treated with vancomycin. However, in some MRSA patients, vancomycin is less effective. This results in persistent bacteremia, even though the isolates can be effectively killed in vitro. MRSA bacteria are thought to switch between two forms, normal and persister cells, that are genetically identical. Persisters, the minor subpopulation, are slow-growing and show lower susceptibility to vancomycin than normal MRSA. To understand the dynamic interplay between the two bacterial populations when challenged by host immunity and vancomycin treatment, we developed a mathematical model and analyzed it in simulations of clinically relevant scenarios. Our work suggests that the immune clearance rate of persister MRSA rather than the MRSA switch rate is a key determinant to establish persistent bacteremia. The model also suggests that increasing killing rate of persisters is a promising therapeutic strategy. Our findings emphasize the need to better understand the interactions of persister MRSAs with host cells and immune responses in vivo.

Transient negative effects of antibiotics on phages do not jeopardise the advantages of combination therapies

Phages, the viruses of bacteria, have been proposed as antibacterial agents to complement or replace antibiotics due to the growing problem of resistance. In nature and in the clinic, antibiotics are ubiquitous and may affect phages indirectly via impacts on bacterial hosts. Even if the synergistic association of phages and antibiotics has been shown in several studies, the focus is often on bacteria with little known about the impact on phages. Evolutionary studies have demonstrated that time scale is an important factor in understanding the consequences of antimicrobial strategies, but this perspective is generally overlooked in phage-antibiotic combination studies. Here, we explore the effects of antibiotics on phages targeting the opportunistic pathogen Pseudomonas aeruginosa. We go beyond previous studies by testing the interaction between several types of antibiotics and phages, and evaluate the effects on several important phage parameters during 8 days of experimental co-evolution with bacteria. Our study reveals that antibiotics had a negative effect on phage density and efficacy early on, but not in the later stages of the experiment. The results indicate that antibiotics can affect phage adaptation, but that phages can nevertheless contribute to managing antibiotic resistance levels.

Bacterial persistence promotes the evolution of antibiotic resistance by increasing survival and mutation rates

Persisters are transiently antibiotic-tolerant cells that complicate the treatment of bacterial infections. Both theory and experiments have suggested that persisters facilitate genetic resistance by constituting an evolutionary reservoir of viable cells. Here, we provide evidence for a strong positive correlation between persistence and the likelihood to become genetically resistant in natural and lab strains of E. coli. This correlation can be partly attributed to the increased availability of viable cells associated with persistence. However, our data additionally show that persistence is pleiotropically linked with mutation rates. Our theoretical model further demonstrates that increased survival and mutation rates jointly affect the likelihood of evolving clinical resistance. Overall, these results suggest that the battle against antibiotic resistance will benefit from incorporating anti-persister therapies.

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.

Biochanin a Enhances the Defense Against Salmonella enterica Infection Through AMPK/ULK1/mTOR-Mediated Autophagy and Extracellular Traps and Reversing SPI-1-Dependent Macrophage (MΦ) M2 Polarization

A novel treatment regimen for bacterial infections is the pharmacological enhancement of the host's immune defenses. We demonstrated that biochanin A (BCA), an isoflavone constituent in some plants, could enhance both intra- and extracellular bactericidal activity of host cells. First, BCA could induce a complete autophagic response in nonphagocytic cells (HeLa) or macrophages (MΦ) via the AMPK/ULK1/mTOR pathway and Beclin-1-dependent manner, and BCA enhanced the killing of invading Salmonella by autophagy through reinforcing ubiquitinated adapter protein (LRSAM1, NDP52 and p62)-mediated recognition of intracellular bacteria and through the formation of autophagolysosomes. Second, we demonstrated that BCA could enhance the release of MΦ extracellular traps (METs) to remove extracellular Salmonella also via the AMPK/ULK1/mTOR pathway, not through reactive oxygen species (ROS) pathway. Furtherly, in a Salmonella-infected mouse model, BCA treatment increased intra- and extracellular bactericidal activity through the strengthening autophagy and MET production, respectively, in peritoneal MΦ, liver and spleen tissue. Additionally, our findings showed that BCA downregulated SPI-1 (Salmonella pathogenicity island 1) expression during Salmonella infection in vitro and in vivo to reverse the MΦ M2 polarization, which was different from the MΦ M1 phenotype caused by most of bacteria infection. Together, these findings suggest that BCA has an immunomodulatory effect on Salmonella-infected host cells and enhances their bactericidal activity in vitro and in vivo through autophagy, extracellular traps and regulation of MΦ polarization.

Mechanism-Based Disease Progression Model Describing Host-Pathogen Interactions During the Pathogenesis of Acinetobacter baumannii Pneumonia

The emergence of highly resistant bacteria is a serious threat to global public health. The host immune response is vital for clearing bacteria from the infected host; however, the current drug development paradigm does not take host‐pathogen interactions into consideration. Here, we used a systems‐based approach to develop a quantitative, mechanism‐based disease progression model to describe bacterial dynamics, host immune response, and lung injury in an immunocompetent rat pneumonia model. Previously, Long‐Evans rats were infected with Acinetobacter baumannii (A. baumannii) strain 307‐0294 at five different inocula and total lung bacteria, interleukin‐1beta (IL‐1β), tumor necrosis factor‐α (TNF‐α), cytokine‐induced neutrophil chemoattractant 1 (CINC‐1), neutrophil counts, and albumin were quantified. Model development was conducted in ADAPT5 version 5.0.54 using a pooled approach with maximum likelihood estimation; all data were co‐modeled. The final model characterized host‐pathogen interactions during the natural time course of bacterial pneumonia. Parameters were estimated with good precision. Our expandable model will integrate drug effects to aid in the design of optimized antibiotic regimens.

Estimating treatment prolongation for persistent infections

Treatment of infectious diseases is often long and requires patients to take drugs even after they have seemingly recovered. This is because of a phenomenon called persistence, which allows small fractions of the bacterial population to survive treatment despite being genetically susceptible. The surviving subpopulation is often below detection limit and therefore is empirically inaccessible but can cause treatment failure when treatment is terminated prematurely. Mathematical models could aid in predicting bacterial survival and thereby determine sufficient treatment length. However, the mechanisms of persistence are hotly debated, necessitating the development of multiple mechanistic models. Here we develop a generalized mathematical framework that can accommodate various persistence mechanisms from measurable heterogeneities in pathogen populations. It allows the estimation of the relative increase in treatment length necessary to eradicate persisters compared to the majority population. To simplify and generalize, we separate the model into two parts: the distribution of the molecular mechanism of persistence in the bacterial population (e.g. number of efflux pumps or target molecules, growth rates) and the elimination rate of single bacteria as a function of that phenotype. Thereby, we obtain an estimate of the required treatment length for each phenotypic subpopulation depending on its size and susceptibility.

Variation in fluoroquinolone pharmacodynamic parameter values among isolates of two bacterial pathogens of bovine respiratory disease

To design an antimicrobial treatment regimen for a bacterial disease, data on the drug pharmacodynamics (PD) against selected drug-susceptible strains of the pathogen are used. The regimen is applied across such strains in the field, assuming the PD parameter values remain the same. We used time-kill experiments and PD modeling to investigate the fluoroquinolone enrofloxacin PD against different isolates of two bovine respiratory disease pathogens: four Mannheimia haemolytica and three Pasteurella multocida isolates. The models were fitted as mixed-effects non-linear regression; the fixed-effects PD parameter values were estimated after accounting for random variation among experimental replicates. There was both inter- and intra- bacterial species variability in the PD parameters Hill-coefficient and E_max (maximal decline of bacterial growth rate), with more variable PD responses among M. haemolytica than among P. multocida isolates. Moreover, the Hill-coefficient was correlated to the isolate’s maximal population growth rate in the absence of antimicrobial exposure (a.k.a. specific growth rate; Spearman’s ρ = 0.98, p-value = 0.003, n = 6 isolates excluding one outlier). Thus, the strain’s properties such as growth potential may impact its PD responses. This variability can have clinical implications. Modifying the treatment regimen depending on phenotypic properties of the pathogen strain causing disease may be a precision medicine approach.

Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas

Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.

In Vivo Pharmacokinetic/Pharmacodynamic Profiles of Danofloxacin in Rabbits Infected With Salmonella typhimurium After Oral Administration

Salmonella typhimurium is a highly transmissible pathogen in rabbits that causes significant losses. Danofloxacin shows excellent efficacy against S. typhimurium infections. However, there are few reports of the pharmacokinetic/pharmacodynamic (PK/PD) modeling of danofloxacin against this pathogen. The aim of this study was to evaluate the in vivo PK/PD relationship of danofloxacin in rabbits infected with S. typhimurium. We used the reduction of bacterial burden in the blood, liver, spleen, and lung as the target PD endpoints, and determined the PK/PD indexes that best correlated with the efficacy and its corresponding magnitude. Danofloxacin was administrated orally to experimentally S. typhimurium-infected rabbits once daily for three successive days. The concentrations of danofloxacin in the serum and the bacterial burden in the blood, liver, spleen, and lung were determined. The PK/PD relationships of danofloxacin against S. typhimurium were evaluated using a Sigmoid E_max model. The results showed that the area under the concentration-time curve from 0 to 24 h/minimum inhibitory concentration (AUC_{24 h}/MIC) ratio correlated well with the in vivo antibacterial effectiveness in different organs, with an r² of 0.8971, 0.9186, 0.9581, and 0.8708 in the blood, liver, spleen, and lung, respectively. The AUC_{24 h}/MIC ratios for the bactericidal effect (3 × Log₁₀ colony forming units/mL reductions) were 121.30, 354.28, 216.64, and 228.66 in the blood, liver, spleen, and lung, respectively, indicating that the in vivo effectiveness of danofloxacin against S. typhimurium using bacterial reduction in different organs as PD endpoints was not identical. This study illustrated that the selection of the target organ for bacterial reduction determination had little effect on best PK/PD parameter determination, but is critical for parameter magnitude calculation in antimicrobial PK/PD modeling, and furthermore, has an impact on the rational dosage optimization process.

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.

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.

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.

Measuring and modelling the response of Klebsiella pneumoniae KPC prey to Bdellovibrio bacteriovorus predation, in human serum and defined buffer

In worldwide conditions of increasingly antibiotic-resistant hospital infections, it is important to research alternative therapies. Bdellovibrio bacteriovorus bacteria naturally prey on Gram-negative pathogens, including antibiotic-resistant strains and so B. bacteriovorus have been proposed as "living antibiotics" to combat antimicrobially-resistant pathogens. Predator-prey interactions are complex and can be altered by environmental components. To be effective B. bacteriovorus predation needs to work in human body fluids such as serum where predation dynamics may differ to that studied in laboratory media. Here we combine mathematical modelling and lab experimentation to investigate the predation of an important carbapenem-resistant human pathogen, Klebsiella pneumoniae, by B. bacteriovorus in human serum versus buffer. We show experimentally that B. bacteriovorus is able to reduce prey numbers in each environment, on different timescales. Our mathematical model captures the underlying dynamics of the experimentation, including an initial predation-delay at the predator-prey-serum interface. Our research shows differences between predation in buffer and serum and highlights both the potential and limitations of B. bacteriovorus acting therapeutically against K. pneumoniae in serum, informing future research into the medicinal behaviours and dosing of this living antibacterial.

Convection shapes the trade-off between antibiotic efficacy and the selection for resistance in spatial gradients

Since penicillin was discovered about 90 years ago, we have become used to using drugs to eradicate unwanted pathogenic cells. However, using drugs to kill bacteria, viruses or cancer cells has the serious side effect of selecting for mutant types that survive the drug attack. A crucial question therefore is how one could eradicate as many cells as possible for a given acceptable risk of drug resistance evolution. We address this general question in a model of drug resistance evolution in spatial drug gradients, which recent experiments and theories have suggested as key drivers of drug resistance. Importantly, our model takes into account the influence of convection, resulting for instance from blood flow. Using stochastic simulations, we study the fates of individual resistance mutations and quantify the trade-off between the killing of wild-type cells and the rise of resistance mutations: shallow gradients and convection into the antibiotic region promote wild-type death, at the cost of increasing the establishment probability of resistance mutations. We can explain these observed trends by modeling the adaptation process as a branching random walk. Our analysis reveals that the trade-off between death and adaptation depends on the relative length scales of the spatial drug gradient and random dispersal, and the strength of convection. Our results show that convection can have a momentous effect on the rate of establishment of new mutations, and may heavily impact the efficiency of antibiotic treatment.

The Immune Response against Acinetobacter baumannii, an Emerging Pathogen in Nosocomial Infections

Acinetobacter baumannii is the etiologic agent of a wide range of nosocomial infections, including pneumonia, bacteremia, and skin infections. Over the last 45 years, an alarming increase in the antibiotic resistance of this opportunistic microorganism has been reported, a situation that hinders effective treatments. In order to develop effective therapies against A. baumannii it is crucial to understand the basis of host–bacterium interactions, especially those concerning the immune response of the host. Different innate immune cells such as monocytes, macrophages, dendritic cells, and natural killer cells have been identified as important effectors in the defense against A. baumannii; among them, neutrophils represent a key immune cell indispensable for the control of the infection. Several immune strategies to combat A. baumannii have been identified such as recognition of the bacteria by immune cells through pattern recognition receptors, specifically toll-like receptors, which trigger bactericidal mechanisms including oxidative burst and cytokine and chemokine production to amplify the immune response against the pathogen. However, a complete picture of the protective immune strategies activated by this bacteria and its potential therapeutic use remains to be determined and explored.

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.

Neutropenia is independently associated with sub-therapeutic serum concentration of vancomycin

BACKGROUND

We aimed to identify the impact of the presence of neutropenia on serum vancomycin concentration (SVC).

METHODS

A retrospective study was conducted from January 2005 to December 2015. The study population was comprised of adult patients who were performed serum concentration of vancomycin. Patients with renal failure or using non-conventional dosages of vancomycin were excluded.

RESULTS

A total of 1307 adult patients were included in this study, of whom 163 (12.4%) were neutropenic. Patients with neutropenia presented significantly lower SVCs than non-neutropenic patients (P<0.0001). Multiple linear regressions showed significant association between neutropenia and trough SVC (beta coefficients, -2.351; P=0.004). Multiple logistic regression analysis also revealed a significant association between sub-therapeutic vancomycin concentrations (trough SVC values<10mg/l) and neutropenia (odds ratio, 1.75, P=0.029) CONCLUSIONS: The presence of neutropenia is significantly associated with low SVC, even after adjusting for other variables. Therefore, neutropenic patients had a higher risk of sub-therapeutic SVC compared with non-neutropenic patients. We recommended that vancomycin therapy should be monitored with TDM-guided optimization of dosage and intervals, especially in neutropenic patients.

Limitations of MIC as sole metric of pharmacodynamic response across the range of antimicrobial susceptibilities within a single bacterial species

The minimum inhibitory concentration (MIC) of an antimicrobial drug for a bacterial pathogen is used as a measure of the bacterial susceptibility to the drug. However, relationships between the antimicrobial concentration, bacterial susceptibility, and the pharmacodynamic (PD) inhibitory effect on the bacterial population are more complex. The relationships can be captured by multi-parameter models such as the E_max model. In this study, time-kill experiments were conducted with a zoonotic pathogen Pasteurella multocida and the fluoroquinolone enrofloxacin. Pasteurella multocida isolates with enrofloxacin MIC of 0.01 μg/mL, 1.5 μg/mL, and 2.0 μg/mL were used. An additive inhibitory E_max model was fitted to the data on bacterial population growth inhibition at different enrofloxacin concentrations. The values of PD parameters such as maximal growth inhibition, concentration achieving a half of the maximal inhibition, and Hill coefficient that captures steepness of the relationships between the concentration and effect, varied between the isolate with low MIC and less susceptible isolates. While enrofloxacin PD against the isolate with low MIC exhibited the expected concentration-dependent characteristics, the PD against the less susceptible isolates demonstrated time-dependent characteristics. The results demonstrate that bacterial antimicrobial susceptibility may need to be described by a combination of parameters rather than a single parameter of the MIC.

Diverse drug-resistant subpopulations of Mycobacterium tuberculosis are sustained in continuous culture

Drug resistance to tuberculosis (TB) has become more widespread over the past decade. As such, understanding the emergence and fitness of antibiotic-resistant subpopulations is crucial for the development of new interventions. Here we use a simple mathematical model to explain the differences in the response to isoniazid (INH) of Mycobacterium tuberculosis cells cultured under two growth rates in a chemostat. We obtain posterior distributions of model parameters consistent with data using a Markov chain Monte Carlo (MCMC) method. We explore the dynamics of diverse INH-resistant subpopulations consistent with these data in a multi-population model. We find that the simple model captures the qualitative behaviour of the cultures under both dilution rates and also present testable predictions about how diversity is maintained in such cultures.

Mathematical Models for Immunology: Current State of the Art and Future Research Directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.

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

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

Ciprofloxacin and ceftriaxone alter cytokine responses, but not Toll-like receptors, to Salmonella infection in vitro

OBJECTIVES

Antibiotics that enhance host natural defences to infection offer an alternative approach to treating infections. However, mechanisms underlying such processes are poorly understood. The aim of this study was to investigate the effects of clinically relevant concentrations of two antibiotics on bacterial interactions with murine macrophages.

METHODS

Adhesion of Salmonella Typhimurium SL1344 to and invasion by Salmonella Typhimurium SL1344 of antibiotic-treated or untreated J774 murine macrophages were measured using a tissue culture infection model. Expression of genes central to the Toll-like receptor (TLR) signalling pathway of macrophages infected with Salmonella was analysed using the RT(2) Profiler PCR Array. Cytokine production was measured by ELISA.

RESULTS

Adhesion of Salmonella Typhimurium SL1344 to J774 macrophage monolayers was increased when macrophages were exposed to ciprofloxacin and ceftriaxone, while invasion was decreased by ciprofloxacin. Expression of IL-1β and TNF-α mRNA was greater in SL1344-infected macrophages that had been treated with ciprofloxacin or ceftriaxone than in macrophages exposed to antibiotics alone or SL1344 alone. TLR mRNA was down-regulated by SL1344 infection, a response that was not altered by antibiotic pretreatment.

CONCLUSIONS

Clinically relevant concentrations of two antibiotics differentially enhanced the response of immune cells and their interaction with bacteria, increasing bacterial adhesion to macrophages and increasing cytokine production. As increased expression of IL-1β fosters apoptosis of Salmonella-infected macrophages and clearance by neutrophils, the immunomodulatory potential of these antibiotics may explain, in part, why these two drugs continue to be used to treat salmonellosis successfully.

ClassTR: Classifying Within-Host Heterogeneity Based on Tandem Repeats with Application to Mycobacterium tuberculosis Infections

Genomic tools have revealed genetically diverse pathogens within some hosts. Within-host pathogen diversity, which we refer to as "complex infection", is increasingly recognized as a determinant of treatment outcome for infections like tuberculosis. Complex infection arises through two mechanisms: within-host mutation (which results in clonal heterogeneity) and reinfection (which results in mixed infections). Estimates of the frequency of within-host mutation and reinfection in populations are critical for understanding the natural history of disease. These estimates influence projections of disease trends and effects of interventions. The genotyping technique MLVA (multiple loci variable-number tandem repeats analysis) can identify complex infections, but the current method to distinguish clonal heterogeneity from mixed infections is based on a rather simple rule. Here we describe ClassTR, a method which leverages MLVA information from isolates collected in a population to distinguish mixed infections from clonal heterogeneity. We formulate the resolution of complex infections into their constituent strains as an optimization problem, and show its NP-completeness. We solve it efficiently by using mixed integer linear programming and graph decomposition. Once the complex infections are resolved into their constituent strains, ClassTR probabilistically classifies isolates as clonally heterogeneous or mixed by using a model of tandem repeat evolution. We first compare ClassTR with the standard rule-based classification on 100 simulated datasets. ClassTR outperforms the standard method, improving classification accuracy from 48% to 80%. We then apply ClassTR to a sample of 436 strains collected from tuberculosis patients in a South African community, of which 92 had complex infections. We find that ClassTR assigns an alternate classification to 18 of the 92 complex infections, suggesting important differences in practice. By explicitly modeling tandem repeat evolution, ClassTR helps to improve our understanding of the mechanisms driving within-host diversity of pathogens like Mycobacterium tuberculosis.

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

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

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

Fosfomycin enhances phagocyte-mediated killing of Staphylococcus aureus by extracellular traps and reactive oxygen species

The successful treatment of bacterial infections is the achievement of a synergy between the host's immune defences and antibiotics. Here, we examined whether fosfomycin (FOM) could improve the bactericidal effect of phagocytes, and investigated the potential mechanisms. FOM enhanced the phagocytosis and extra- or intracellular killing of S. aureus by phagocytes. And FOM enhanced the extracellular killing of S. aureus in macrophage (MФ) and in neutrophils mediated by extracellular traps (ETs). ET production was related to NADPH oxidase-dependent reactive oxygen species (ROS). Additionally, FOM increased the intracellular killing of S. aureus in phagocytes, which was mediated by ROS through the oxidative burst process. Our results also showed that FOM alone induced S. aureus producing hydroxyl radicals in order to kill the bacterial cells in vitro. In a mouse peritonitis model, FOM treatment increased the bactericidal extra- and intracellular activity in vivo, and FOM strengthened ROS and ET production from peritoneal lavage fluid ex vivo. An IVIS imaging system assay further verified the observed in vivo bactericidal effect of the FOM treatment. This work may provide a deeper understanding of the role of the host's immune defences and antibiotic interactions in microbial infections.

How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance

Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population.

DOI:http://dx.doi.org/10.7554/eLife.10559.001

Antibiotics are chemical compounds used to treat bacterial infections. The discovery of antibiotics, starting with penicillin in 1929, revolutionized medicine, making it possible to cure or prevent life-threatening infections such as tetanus and pneumonia. However, many bacteria have become resistant to one or more antibiotics and so can no longer be killed by these drugs.

The emergence of antibiotic resistance reflects an evolutionary process that occurs during antibiotic treatment. While the antibiotic will kill most bacteria, some bacteria may naturally have a feature or genetic mutation that allows them to survive in the presence of the antibiotic. These bacteria then reproduce and pass on their resistant traits, eventually leading to the emergence of a new antibiotic-resistant strain of bacteria. Once a resistant strain exists it may be able to spread from one person to another.

There is conflicting evidence about how best to prevent antibiotic-resistant bacteria from evolving and spreading. The results of some experiments suggest that treating bacteria with large doses of antibiotics early in an infection is the most effective way to optimize treatment and minimize the risk of an antibiotic-resistant strain developing. However, other studies suggest that exposing bacteria to high levels of antibiotics more efficiently selects for resistance; in this case a more moderate approach should be used when treating bacterial infections.

Here, Colijn and Cohen present a mathematical model that suggests that the natural competition between the antibiotic-resistant and antibiotic-sensitive strains of bacteria influence which treatment strategy should be taken. Strains were modeled both within individual hosts and spreading in a community of individuals. In the models, aggressive antibiotic treatment is most effective (in that it minimizes antibiotic resistance) when the antibiotic-resistant strain either does not experience strong competition from the non-resistant strains of bacteria or is not fit enough to be a good competitor. However, a more moderate treatment is appropriate when the two strains are competing and the antibiotic-resistant strain is a fit competitor. Competition may mean that moderate treatment is best to avoid resistance at the community level, even in situations when aggressive treatment is likely best for individuals.

Two important future challenges are to better understand the diversity of strains in bacterial infections, and to develop tools to measure to what extent strains are effectively competing with each other.

DOI:http://dx.doi.org/10.7554/eLife.10559.002

Addressing Concerns about Changing the Route of Antimicrobial Administration from Intravenous to Oral in Adult Inpatients

BACKGROUND

Many health care institutions are in the process of establishing antimicrobial stewardship programs. Changing the route of administration of antimicrobial agents from intravenous to oral (IV to PO) is a simple, well-recognized intervention that is often part of an antimicrobial stewardship program. However, the attending health care team may have concerns about making this switch.

OBJECTIVES

To provide insights into common concerns related to IV to PO conversion, with the aim of helping antimicrobial stewardship teams to address them.

DATA SOURCES

Published clinical trials and reviews were identified from a literature search of Ovid MEDLINE with the keywords (step down or switch or conversion or transition or sequential) and (antibiotics or antibacterial agents or antimicrobial or anti-infective agents).

DATA SYNTHESIS

The following issues are addressed in this review: benefits of the oral route, serum concentrations yielded by the oral formulation, source of pharmacokinetic data, clinical outcomes, provision of care in the intensive care unit, fear of therapeutic failure, and administration of antimicrobials via feeding tube.

CONCLUSIONS

When considering a change to oral therapy, it is important to have a thorough understanding of key aspects of the antimicrobial agent, the patient, and the disease being treated. The antimicrobial stewardship team has an important role in facilitating IV to PO conversion, educating prescribers, and addressing any concerns or reservations that may interfere with timely transition from IV to PO administration.

Pharmacokinetic and pharmacodynamic considerations of antimicrobial drug therapy in cancer patients with kidney dysfunction

Patients with cancer have a high inherent risk of infectious complications. In addition, the incidence of acute and chronic kidney dysfunction rises in this population. Anti-infective drugs often require dosing modifications based on an estimate of kidney function, usually the glomerular filtration rate (GFR). However, there is still no preferential GFR formula to be used, and in acute kidney injury there is always a considerable time delay between true kidney function and estimated GFR. In most cases, the anti-infective therapy should start with an immediate and high loading dose. Pharmacokinetic as well as pharmacodynamic principles must be applied for further dose adjustment. Anti-infective drugs with time-dependent action should be given with the target of high trough concentrations (e.g., beta lactam antibiotics, penems, vancomycin, antiviral drugs). Anti-infective drugs with concentration-dependent action should be given with the target of high peak concentrations (e.g., aminoglycosides, daptomycin, colistin, quinolones). Our group created a pharmacokinetic database, called NEPharm, hat serves as a reference to obtain reliable dosing regimens of anti-infective drugs in kidney dysfunction as well as renal replacement therapy. To avoid the risk of either too low or too infrequent peak concentrations, we prefer the eliminated fraction rule for dose adjustment calculations.

Tackling Drug Resistant Infection Outbreaks of Global Pandemic Escherichia coli ST131 Using Evolutionary and Epidemiological Genomics

High-throughput molecular screening is required to investigate the origin and diffusion of antimicrobial resistance in pathogen outbreaks. The most frequent cause of human infection is Escherichia coli, which is dominated by sequence type 131 (ST131)-a set of rapidly radiating pandemic clones. The highly infectious clades of ST131 originated firstly by a mutation enhancing conjugation and adhesion. Secondly, single-nucleotide polymorphisms occurred enabling fluoroquinolone-resistance, which is near-fixed in all ST131. Thirdly, broader resistance through beta-lactamases has been gained and lost frequently, symptomatic of conflicting environmental selective effects. This flexible approach to gene exchange is worrying and supports the proposition that ST131 will develop an even wider range of plasmid and chromosomal elements promoting antimicrobial resistance. To stop ST131, deep genome sequencing is required to understand the origin, evolution and spread of antimicrobial resistance genes. Phylogenetic methods that decipher past events can predict future patterns of virulence and transmission based on genetic signatures of adaptation and gene exchange. Both the effect of partial antimicrobial exposure and cell dormancy caused by variation in gene expression may accelerate the development of resistance. High-throughput sequencing can decode measurable evolution of cell populations within patients associated with systems-wide changes in gene expression during treatments. A multi-faceted approach can enhance assessment of antimicrobial resistance in E. coli ST131 by examining transmission dynamics between hosts to achieve a goal of pre-empting resistance before it emerges by optimising antimicrobial treatment protocols.

A multi-scale approach to designing therapeutics for tuberculosis

Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and

Is selection relevant in the evolutionary emergence of drug resistance?

The emergence of drug-resistant pathogens is often considered a canonical case of evolution by natural selection. Here we argue that the strength of selection can be a poor predictor of the rate of resistance emergence. It is possible for a resistant strain to be under negative selection and still emerge in an infection or spread in a population. Measuring the right parameters is a necessary first step toward the development of evidence-based resistance-management strategies. We argue that it is the absolute fitness of the resistant strains that matters most and that a primary determinant of the absolute fitness of a resistant strain is the ecological context in which it finds itself.

Antibiotic resistance: a primer and call to action

During the past century, discoveries of microorganisms as causes of infections and antibiotics as effective therapeutic agents have contributed to significant gains in public health in many parts of the world. Health agencies worldwide are galvanizing attention toward antibiotic resistance, which is a major threat to public health (Centers for Disease Control and Prevention, 2013; World Health Organization, 2014). Some life scientists believe that we are approaching the post-antibiotic age (Davies & Davies, 2010). The growing threat of antimicrobial resistance is fueled by complex factors with biological, behavioral, and societal aspects. This primer provides an overview of antibiotic resistance and its growing burden on public health, the biological and behavioral mechanisms that increase antibiotic resistance, and examples of where health communication scholars can contribute to efforts to make our current antibiotic drugs last as long as possible. In addition, we identify compelling challenges for current communication theories and practices.

Public health evolutionary biology of antimicrobial resistance: priorities for intervention

The three main processes shaping the evolutionary ecology of antibiotic resistance (AbR) involve the emergence, invasion and occupation by antibiotic-resistant genes of significant environments for human health. The process of emergence in complex bacterial populations is a high-frequency, continuous swarming of ephemeral combinatory genetic and epigenetic explorations inside cells and among cells, populations and communities, expanding in different environments (migration), creating the stochastic variation required for evolutionary progress. Invasion refers to the process by which AbR significantly increases in frequency in a given (invaded) environment, led by external invaders local multiplication and spread, or by endogenous conversion. Conversion occurs because of the spread of AbR genes from an exogenous resistant clone into an established (endogenous) bacterial clone(s) colonizing the environment; and/or because of dissemination of particular resistant genetic variants that emerged within an endogenous clonal population. Occupation of a given environment by a resistant variant means a permanent establishment of this organism in this environment, even in the absence of antibiotic selection. Specific interventions on emergence influence invasion, those acting on invasion also influence occupation and interventions on occupation determine emergence. Such interventions should be simultaneously applied, as they are not simple solutions to the complex problem of AbR.

Evolutionary rescue: linking theory for conservation and medicine

Evolutionary responses that rescue populations from extinction when drastic environmental changes occur can be friend or foe. The field of conservation biology is concerned with the survival of species in deteriorating global habitats. In medicine, in contrast, infected patients are treated with chemotherapeutic interventions, but drug resistance can compromise eradication of pathogens. These contrasting biological systems and goals have created two quite separate research communities, despite addressing the same central question of whether populations will decline to extinction or be rescued through evolution. We argue that closer integration of the two fields, especially of theoretical understanding, would yield new insights and accelerate progress on these applied problems. Here, we overview and link mathematical modelling approaches in these fields, suggest specific areas with potential for fruitful exchange, and discuss common ideas and issues for empirical testing and prediction.

Persistence: a copacetic and parsimonious hypothesis for the existence of non-inherited resistance to antibiotics

We postulate that phenotypic resistance to antibiotics, persistence, is not an evolved (selected-for) character but rather like mutation, an inadvertent product of different kinds of errors and glitches. The rate of generation of these errors is augmented by exposure to these drugs. The genes that have been identified as contributing to the production of persisters are analogous to the so-called mutator genes; they modulate the rate at which these errors occur and/or are corrected. In theory, these phenotypically resistant bacteria can retard the rate of microbiological cure by antibiotic treatment.

A model-guided analysis and perspective on the evolution and epidemiology of antibiotic resistance and its future

A simple epidemiological model is used as a framework to explore the potential efficacy of measures to control antibiotic resistance in community-based self-limiting human infections. The analysis of the properties of this model predict that resistance can be maintained at manageable levels if: first, the rates at which specific antibiotics are used declines with the frequency of resistance to these drugs; second, resistance rarely emerges during therapy; and third, external sources rarely contribute to the entry of resistant bacteria into the community. We discuss the feasibility and limitations of these measures to control the rates of antibiotic resistance and the potential of advances in diagnostic procedures to facilitate this endeavor.