Evaluating treatment protocols to prevent antibiotic resistance

Altering the proclivity towards daptomycin resistance in methicillin-resistant Staphylococcus aureus using combinations with other antibiotics

Daptomycin (DAP) is increasingly used as a part of combination therapy, particularly in complex methicillin-resistant Staphylococcus aureus (MRSA) infections. While multiple studies have reported the potential for synergy between DAP and adjunctive anti-infectives, few have examined the influence of adjunctive therapy on the emergence of DAP resistance. This study examined eight adjunctive antimicrobial combinations with DAP in vitro and the emergence of DAP resistance over time (up to 4 weeks) using clinical isolates of DAP-susceptible MRSA (MIC, 0.5 μg/ml) in which DAP resistance subsequently developed during patient therapy (MIC, 3 μg/ml). In addition to DAP susceptibility testing, selected strains were examined for phenotypic changes associated with DAP resistance, including changes to cell wall thickness (CWT) and cell membrane alterations. The addition of either oxacillin or clarithromycin in medium containing DAP significantly inhibited the development of DAP resistance through the entirety of the 4-week exposure (10- to 32-fold MIC reduction from that of DAP alone). Combinations with rifampin or fosfomycin were effective in delaying the emergence of DAP resistance through the end of week one only (week one MIC, 0.5 μg/ml; week four MIC, 24 μg/ml). Cell wall thickening was observed for all antibiotic combinations regardless of their effect on the DAP MIC (14 to 70% increase in CWT), while changes in cell membrane fluidity were variable and treatment dependent. DAP showed reduced activity against strains with DAP MICs of 1 to 12 μg/ml, but cell membrane integrity was still disrupted at concentrations achieved with doses greater than 10 mg/kg of body weight. The emergence of DAP resistance in MRSA is strongly influenced by the presence of subinhibitory concentrations of adjunctive antimicrobials. These data suggest that combining DAP with oxacillin or clarithromycin may delay the development of DAP resistance in cases requiring prolonged antibiotic therapy.

Modeling the effects of introducing a new antibiotic in a hospital setting: A case study

The increase in antibiotic resistance continues to pose a public health risk as very few new antibiotics are being produced, and bacteria resistant to currently prescribed antibiotics is growing. Within a typical hospital setting, one may find patients colonized with bacteria resistant to a single antibiotic, or, of a more emergent threat, patients may be colonized with bacteria resistant to multiple antibiotics. Precautions have been implemented to try to prevent the growth and spread of antimicrobial resistance such as a reduction in the distribution of antibiotics and increased hand washing and barrier preventions; however, the rise of this resistance is still evident. As a result, there is a new movement to try to re-examine the need for the development of new antibiotics. In this paper, we use mathematical models to study the possible benefits of implementing a new antibiotic in this setting; through these models, we examine the use of a new antibiotic that is distributed in various ways and how this could reduce total resistance in the hospital. We compare several different models in which patients colonized with both single and dual-resistant bacteria are present, including a model with no additional treatment protocols for the population colonized with dual-resistant bacteria as well as models including isolation and/or treatment with a new antibiotic. We examine the benefits and limitations of each scenario in the simulations presented.

Modeling methicillin-resistant Staphylococcus aureus in hospitals: transmission dynamics, antibiotic usage and its history

BACKGROUND

Methicillin-resistant Staphylococcus aureus (MRSA) is endemic in many hospital settings, posing substantial threats and economic burdens worldwide.

METHODS

We propose mathematical models to investigate the transmission dynamics of MRSA and determine factors that influence the prevalence of MRSA infection when antibiotics are given to patients to treat or prevent infections with either MRSA itself or other bacterial pathogens.

RESULTS

Our results suggest that: (i) MRSA always persists in the hospital when colonized and infected patients are admitted; (ii) the longer the duration of treatment of infected patients and the lower the probability of successful treatment will increase the prevalence of MRSA infection; (iii) the longer the duration of contamination of health care workers (HCWs) and the more their contacts with patients may increase the prevalence of MRSA infection; (iv) possible ways to control the prevalence of MRSA infection include treating patients with antibiotic history as quickly and efficiently as possible, screening and isolating colonized and infected patients at admission, and compliance with strict hand-washing rules by HCWs.

CONCLUSION

Our modeling studies offer an approach to investigating MRSA infection in hospital settings and the impact of antibiotic history on the incidence of infection. Our findings suggest important influences on the prevalence of MRSA infection which may be useful in designing control policies.

In-roads to the spread of antibiotic resistance: regional patterns of microbial transmission in northern coastal Ecuador

The evolution of antibiotic resistance (AR) increases treatment cost and probability of failure, threatening human health worldwide. The relative importance of individual antibiotic use, environmental transmission and rates of introduction of resistant bacteria in explaining community AR patterns is poorly understood. Evaluating their relative importance requires studying a region where they vary. The construction of a new road in a previously roadless area of northern coastal Ecuador provides a valuable natural experiment to study how changes in the social and natural environment affect the epidemiology of resistant Escherichia coli. We conducted seven bi-annual 15 day surveys of AR between 2003 and 2008 in 21 villages. Resistance to both ampicillin and sulphamethoxazole was the most frequently observed profile, based on antibiogram tests of seven antibiotics from 2210 samples. The prevalence of enteric bacteria with this resistance pair in the less remote communities was 80 per cent higher than in more remote communities (OR = 1.8 [1.3, 2.3]). This pattern could not be explained with data on individual antibiotic use. We used a transmission model to help explain this observed discrepancy. The model analysis suggests that both transmission and the rate of introduction of resistant bacteria into communities may contribute to the observed regional scale AR patterns, and that village-level antibiotic use rate determines which of these two factors predominate. While usually conceived as a main effect on individual risk, antibiotic use rate is revealed in this analysis as an effect modifier with regard to community-level risk of resistance.

Zoocin A and lauricidin in combination reduce Streptococcus mutans growth in a multispecies biofilm

Dental caries is the most prevalent human infection. It is a multifactorial disease in which the microbial composition of dental plaque plays a major role in the development of clinical symptoms. The bacteria most often implicated in the development of caries are that group of streptococci referred to as the mutans streptococci, in particular Streptococcus mutans and Streptococcus sobrinus. One approach to the prevention of caries is to reduce the numbers of mutans streptococci in plaque to a level insufficient to support demineralization of the tooth. In this study, zoocin A, a peptidoglycan hydrolase, combined with lauricidin, a cell membrane active lipid, was shown over a 72 h period to selectively suppress the growth of S. mutans in a triple species biofilm. Growth of the non-target species Streptococcus oralis and Actinomyces viscosus was not inhibited. In treated systems the amount of extracellular polysaccharide matrix produced was much reduced as determined by use of fluorescein isothiocyanate conjugated wheat germ agglutinin. The pH of treated biofilms remained above neutral as opposed to a value of 4.3 in untreated controls. We conclude that use of antimicrobial compounds that specifically target cariogenic bacteria should be further explored.

Phages limit the evolution of bacterial antibiotic resistance in experimental microcosms

The evolution of multi-antibiotic resistance in bacterial pathogens, often resulting from de novo mutations, is creating a public health crisis. Phages show promise for combating antibiotic-resistant bacteria, the efficacy of which, however, may also be limited by resistance evolution. Here, we suggest that phages may be used as supplements to antibiotics in treating initially sensitive bacteria to prevent resistance evolution, as phages are unaffected by most antibiotics and there should be little cross-resistance to antibiotics and phages. In vitro experiments using the bacterium Pseudomonas fluorescens, a lytic phage, and the antibiotic kanamycin supported this prediction: an antibiotic–phage combination dramatically decreased the chance of bacterial population survival that indicates resistance evolution, compared with antibiotic treatment alone, whereas the phage alone did not affect bacterial survival. This effect of the combined treatment in preventing resistance evolution was robust to immigration of bacteria from an untreated environment, but not to immigration from environment where the bacteria had coevolved with the phage. By contrast, an isogenic hypermutable strain constructed from the wild-type P. fluorescens evolved resistance to all treatments regardless of immigration, but typically suffered very large fitness costs. These results suggest that an antibiotic–phage combination may show promise as an antimicrobial strategy.

"One-size-fits-all"? Optimizing treatment duration for bacterial infections

Historically, antibiotic treatment guidelines have aimed to maximize treatment efficacy and minimize toxicity, but have not considered the evolution of antibiotic resistance. Optimizing the duration and dosing of treatment to minimize the duration of symptomatic infection and selection pressure for resistance simultaneously has the potential to extend the useful therapeutic life of these valuable life-saving drugs without compromising the interests of individual patients.Here, using mathematical models, we explore the theoretical basis for shorter durations of treatment courses, including a range of ecological dynamics of bacteria that cause infections or colonize hosts as commensals. We find that immunity is an important mediating factor in determining the need for long duration of treatment. When immunity to infection is expected, shorter durations that reduce the selection for resistance without interfering with successful clinical outcome are likely to be supported. Adjusting drug treatment strategies to account for the impact of the differences in the ecological niche occupied by commensal flora relative to invasive bacteria could be effective in delaying the spread of bacterial resistance.

The evolutionary epidemiology of multilocus drug resistance

The evolution of resistance to drugs is a major public health concern as it erodes the efficacy of our therapeutic arsenal against bacterial, viral, and fungal pathogens. Increasingly, it is recognized that the evolution of resistance involves genetic changes at more than one locus, both in cases where multiple changes are required to obtain high-level resistance, and where compensatory changes at secondary loci ameliorate the costs of resistance. Similarly, multiple loci are often involved in the evolution of multidrug resistance. There has been widespread interest recently in understanding the evolutionary consequences of multilocus resistance, with many empirical studies documenting extensive patterns of genetic interactions (i.e., epistasis) among the loci involved. Currently, however, there are few general theoretical results available that bridge the gap between classical multilocus population genetics and mathematical epidemiology. Here, such theory is developed to shed new light on these previous studies, and to provide further guidance on the type of data required to predict the evolution of pathogens in response to drug pressure. Our results reveal the importance of feedbacks between the epidemiological and evolutionary dynamics, and illustrate how these feedbacks can be exploited to control resistance. In particular, we show how interventions such as social distancing and isolation can influence rates of recombination, and how this then can slow the spread of multilocus resistance and increase the likelihood of reversion to drug sensitivity once drug therapy has ceased.

MODELING ANTIBIOTIC RESISTANCE IN PREGNANT WOMAN AND FETUS

Infection caused by antibiotic-resistant pathogens is one of global public health problems. Many factors contribute to the emergence and spread of these pathogens. A model which describes the transmission dynamics of susceptible and resistant bacteria in a pregnant woman and the fetus is presented. Detailed qualitative analysis about positivity, boundedness, global stability and uniform persistence of the model is carried out. Numerical simulation and sensitivity analysis show that antibiotic input has potential impact for neonatal drug resistance. Our results show that the resistant bacteria in baby mainly come from antibiotics which are wrongly-used during gestational period, or foods containing antibiotic residues.

Contribution of mathematical modeling to the fight against bacterial antibiotic resistance

PURPOSE OF REVIEW

Modeling of antibiotic resistance in pathogenic bacteria responsible for human disease has developed considerably over the last decade. Herein, we summarize the main published studies to illustrate the contribution of models for understanding both within-host and population-based phenomena. We then suggest possible topics for future studies.

RECENT FINDINGS

Model building of bacterial resistance has involved epidemiologists, biologists and modelers with two different objectives. First, modeling has helped largely in identifying and understanding the factors and biological phenomena responsible for the emergence and spread of resistant strains. Second, these models have become important decision support tools for medicine and public health.

SUMMARY

Major improvements of models in the coming years should take into account specific pathogen characteristics (resistance mechanisms, multiple colonization phenomena, cooperation and competition among species) and better description of the contacts associated with transmission risk within populations.

Ecology and evolution as targets: the need for novel eco-evo drugs and strategies to fight antibiotic resistance

In recent years, the explosive spread of antibiotic resistance determinants among pathogenic, commensal, and environmental bacteria has reached a global dimension. Classical measures trying to contain or slow locally the progress of antibiotic resistance in patients on the basis of better antibiotic prescribing policies have clearly become insufficient at the global level. Urgent measures are needed to directly confront the processes influencing antibiotic resistance pollution in the microbiosphere. Recent interdisciplinary research indicates that new eco-evo drugs and strategies, which take ecology and evolution into account, have a promising role in resistance prevention, decontamination, and the eventual restoration of antibiotic susceptibility. This minireview summarizes what is known and what should be further investigated to find drugs and strategies aiming to counteract the "four P's," penetration, promiscuity, plasticity, and persistence of rapidly spreading bacterial clones, mobile genetic elements, or resistance genes. The term "drug" is used in this eco-evo perspective as a tool to fight resistance that is able to prevent, cure, or decrease potential damage caused by antibiotic resistance, not necessarily only at the individual level (the patient) but also at the ecological and evolutionary levels. This view offers a wealth of research opportunities for science and technology and also represents a large adaptive challenge for regulatory agencies and public health officers. Eco-evo drugs and interventions constitute a new avenue for research that might influence not only antibiotic resistance but the maintenance of a healthy interaction between humans and microbial systems in a rapidly changing biosphere.

Hierarchical Bayesian inference for HIV dynamic differential equation models incorporating multiple treatment factors

Studies on HIV dynamics in AIDS research are very important in understanding the pathogenesis of HIV-1 infection and also in assessing the effectiveness of antiretroviral (ARV) treatment. Viral dynamic models can be formulated through a system of nonlinear ordinary differential equations (ODE), but there has been only limited development of statistical methodologies for inference. This article, motivated by an AIDS clinical study, discusses a hierarchical Bayesian nonlinear mixed-effects modeling approach to dynamic ODE models without a closed-form solution. In this model, we fully integrate viral load, medication adherence, drug resistance, pharmacokinetics, baseline covariates and time-dependent drug efficacy into the data analysis for characterizing long-term virologic responses. Our method is implemented by a data set from an AIDS clinical study. The results suggest that modeling HIV dynamics and virologic responses with consideration of time-varying clinical factors as well as baseline characteristics may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to ARV treatment and to help the evaluation of clinical trial design in response to existing therapies.

Population biological principles of drug-resistance evolution in infectious diseases

The emergence of resistant pathogens in response to selection pressure by drugs and their possible disappearance when drug use is discontinued are evolutionary processes common to many pathogens. Population biological models have been used to study the dynamics of resistance in viruses, bacteria, and eukaryotic microparasites both at the level of the individual treated host and of the treated host population. Despite the existence of generic features that underlie such evolutionary dynamics, different conclusions have been reached about the key factors affecting the rate of resistance evolution and how to best use drugs to minimise the risk of generating high levels of resistance. Improved understanding of generic versus specific population biological aspects will help to translate results between different studies, and allow development of a more rational basis for sustainable drug use than exists at present.

On being the right size: the impact of population size and stochastic effects on the evolution of drug resistance in hospitals and the community

The evolution of drug resistant bacteria is a severe public health problem, both in hospitals and in the community. Currently, some countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. Emergent resistant strains often originate in health care facilities, but it is unknown to what extent hospital size affects resistance evolution and the resulting spillover of hospital-associated pathogens to the community. We used two published datasets from the US and Ireland to investigate the effects of hospital size and controlled for several confounders such as antimicrobial usage, sampling frequency, mortality, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in a hospital strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the increase of hospital-acquired infections over time. One interpretation of this pattern is that chance effects in small hospitals impede the spread of drug-resistance. To investigate to what extent the size distribution of hospitals can directly affect the prevalence of antibiotic resistance, we use a stochastic epidemiological model describing the spread of drug resistance in a hospital setting as well as the interaction between one or several hospitals and the community. We show that the level of drug resistance typically increases with population size: In small hospitals chance effects cause large fluctuations in pathogen population size or even extinctions, both of which impede the acquisition and spread of drug resistance. Finally, we show that indirect transmission via environmental reservoirs can reduce the effect of hospital size because the slow turnover in the environment can prevent extinction of resistant strains. This implies that reducing environmental transmission is especially important in small hospitals, because such a reduction not only reduces overall transmission but might also facilitate the extinction of resistant strains. Overall, our study shows that the distribution of hospital sizes is a crucial factor for the spread of drug resistance.

The increasing spread of bacteria, which are resistant to antibiotics, is a serious threat to clinical care. Currently, several countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. However, empirical studies have shown that resistance levels correlate with hospital size. To illustrate this correlation, we analyze two published datasets from the US and Ireland and controlled for antimicrobial usage, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in hospitals strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the temporal increase of hospital-acquired infections. To investigate to what extent hospital size can directly affect the prevalence of antibiotic resistance, we use mathematical models describing the epidemic spread of resistance in hospitals and the community. We find that small hospitals typically lead to considerably lower resistance levels than large hospitals. However, this beneficial effect of small hospital size may be reduced if bacteria are transmitted indirectly via the environment. Therefore, reducing environmental transmission might be particularly important in small hospitals. Overall, our findings suggest that the short-term benefits of larger hospitals may come at the price of increasing resistance in the long term.

Informed switching strongly decreases the prevalence of antibiotic resistance in hospital wards

Antibiotic resistant nosocomial infections are an important cause of mortality and morbidity in hospitals. Antibiotic cycling has been proposed to contain this spread by a coordinated use of different antibiotics. Theoretical work, however, suggests that often the random deployment of drugs ("mixing") might be the better strategy. We use an epidemiological model for a single hospital ward in order to assess the performance of cycling strategies which take into account the frequency of antibiotic resistance in the hospital ward. We assume that information on resistance frequencies stems from microbiological tests, which are performed in order to optimize individual therapy. Thus the strategy proposed here represents an optimization at population-level, which comes as a free byproduct of optimizing treatment at the individual level. We find that in most cases such an informed switching strategy outperforms both periodic cycling and mixing, despite the fact that information on the frequency of resistance is derived only from a small sub-population of patients. Furthermore we show that the success of this strategy is essentially a stochastic phenomenon taking advantage of the small population sizes in hospital wards. We find that the performance of an informed switching strategy can be improved substantially if information on resistance tests is integrated over a period of one to two weeks. Finally we argue that our findings are robust against a (moderate) preexistence of doubly resistant strains and against transmission via environmental reservoirs. Overall, our results suggest that switching between different antibiotics might be a valuable strategy in small patient populations, if the switching strategies take the frequencies of resistance alleles into account.

A DYNAMIC BAYESIAN NONLINEAR MIXED-EFFECTS MODEL OF HIV RESPONSE INCORPORATING MEDICATION ADHERENCE, DRUG RESISTANCE AND COVARIATES()

HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates. In particular, we intend to address the questions: (i) how to summarize the MEMS adherence data for efficient prediction of virologic response after accounting for potential confounding factors such as drug resistance and covariates, and (ii) how to evaluate treatment effect of baseline characteristics interacted with adherence and other clinical factors. The approach is applied to an AIDS clinical trial involving 31 patients who had available data as required for the proposed model. Results demonstrate that the appropriate determinants of MEMS adherence rates are important in order to more efficiently predict virologic response, and investigations of adherence to ARV treatment would benefit from measuring not only adherence rate but also its summary metric assessment. Our study also shows that the mechanism-based dynamic model is powerful and effective to establish a relationship of virologic responses with medication adherence, virus resistance to drug and baseline covariates.

Evolutionary principles and their practical application

Evolutionary principles are now routinely incorporated into medicine and agriculture. Examples include the design of treatments that slow the evolution of resistance by weeds, pests, and pathogens, and the design of breeding programs that maximize crop yield or quality. Evolutionary principles are also increasingly incorporated into conservation biology, natural resource management, and environmental science. Examples include the protection of small and isolated populations from inbreeding depression, the identification of key traits involved in adaptation to climate change, the design of harvesting regimes that minimize unwanted life-history evolution, and the setting of conservation priorities based on populations, species, or communities that harbor the greatest evolutionary diversity and potential. The adoption of evolutionary principles has proceeded somewhat independently in these different fields, even though the underlying fundamental concepts are the same. We explore these fundamental concepts under four main themes: variation, selection, connectivity, and eco-evolutionary dynamics. Within each theme, we present several key evolutionary principles and illustrate their use in addressing applied problems. We hope that the resulting primer of evolutionary concepts and their practical utility helps to advance a unified multidisciplinary field of applied evolutionary biology.

Hypermutability and compensatory adaptation in antibiotic-resistant bacteria

Hypermutable (mutator) bacteria have been associated with the emergence of antibiotic resistance. A simple yet untested prediction is that mutator bacteria are able to compensate more quickly for pleiotropic fitness costs often associated with resistance, resulting in the maintenance of resistance in the absence of antibiotic selection. By using experimental populations of a wild-type and a mutator genotype of the pathogenic bacterium Pseudomonas aeruginosa, we show that mutator bacteria can evolve resistance to antibiotics more rapidly than wild-type bacteria and, crucially, that mutators are better able to compensate for the fitness cost of resistance, to the extent that all costs of resistance were entirely compensated for in mutators. When competed against immigrant antibiotic-susceptible bacteria in the absence of antibiotics, antibiotic resistance remained at a high level in mutator populations but disappeared in wild-type populations. These results suggest that selection for mutations that offset the fitness cost associated with antibiotic resistance may help to explain the high frequency of mutator bacteria and antibiotic resistance observed in chronic infections.

Optimal antiviral treatment strategies and the effects of resistance

Recent pandemic planning has highlighted the importance of understanding the effect that widespread antiviral use will have on the emergence and spread of resistance. A number of recent studies have determined that if resistance to antiviral medication can evolve, then deploying treatment at a less than maximum rate often minimizes the outbreak size. This finding, however, involves the assumption that treatment levels remain constant during the entire outbreak. Using optimal control theory, we address the question of optimal antiviral use by considering a large class of time-varying treatment strategies. We prove that, contrary to previous results, it is always optimal to treat at the maximum rate provided that this treatment occurs at the right time. In general the optimal strategy is to wait some fixed amount of time and then to deploy treatment at the maximum rate for the remainder of the outbreak. We derive analytical conditions that characterize this optimal amount of delay. Our results show that it is optimal to start treatment immediately when one of the following conditions holds: (i) immediate treatment can prevent an outbreak, (ii) the initial pool of susceptibles is small, or (iii) when the maximum possible rate of treatment is low, such that there is little de novo emergence of resistant strains. Finally, we use numerical simulations to verify that the results also hold under more general conditions.

Antibiotic control of antibiotic resistance in hospitals: a simulation study

BACKGROUND

Using mathematical deterministic models of the epidemiology of hospital-acquired infections and antibiotic resistance, it has been shown that the rates of hospital-acquired bacterial infection and frequency of antibiotic infections can be reduced by (i) restricting the admission of patients colonized with resistant bacteria, (ii) increasing the rate of turnover of patients, (iii) reducing transmission by infection control measures, and (iv) the use of second-line drugs for which there is no resistance. In an effort to explore the generality and robustness of the predictions of these deterministic models to the real world of hospitals, where there is variation in all of the factors contributing to the incidence of infection, we developed and used a stochastic model of the epidemiology of hospital-acquired infections and resistance. In our analysis of the properties of this model we give particular consideration different regimes of using second-line drugs in this process.

METHODS

We developed a simple model that describes the transmission of drug-sensitive and drug-resistant bacteria in a small hospital. Colonized patients may be treated with a standard drug, for which there is some resistance, and with a second-line drug, for which there is no resistance. We then ran deterministic and stochastic simulation programs, based on this model, to predict the effectiveness of various treatment strategies.

RESULTS

The results of the analysis using our stochastic model support the predictions of the deterministic models; not only will the implementation of any of the above listed measures substantially reduce the incidences of hospital-acquired infections and the frequency of resistance, the effects of their implementation should be seen in months rather than the years or decades anticipated to control resistance in open communities. How effectively and how rapidly the application of second-line drugs will contribute to the decline in the frequency of resistance to the first-line drugs depends on how these drugs are administered. The earlier the switch to second-line drugs, the more effective this protocol will be. Switching to second-line drugs at random is more effective than switching after a defined period or only after there is direct evidence that the patient is colonized with bacteria resistant to the first antibiotic.

CONCLUSIONS

The incidence of hospital-acquired bacterial infections and frequencies of antibiotic resistant bacteria can be markedly and rapidly reduced by different readily implemented procedures. The efficacy using second line drugs to achieve these ends depends on the protocol used for their administration.

Prospective strategies to delay the evolution of anti-malarial drug resistance: weighing the uncertainty

Background

The evolution of drug resistance in malaria parasites highlights a need to identify and evaluate strategies that could extend the useful therapeutic life of anti-malarial drugs. Such strategies are deployed to best effect before resistance has emerged, under conditions of great uncertainty.

Methods

Here, the emergence and spread of resistance was modelled using a hybrid framework to evaluate prospective strategies, estimate the time to drug failure, and weigh uncertainty. The waiting time to appearance was estimated as the product of low mutation rates, drug pressure, and parasite population sizes during treatment. Stochastic persistence and the waiting time to establishment were simulated as an evolving branching process. The subsequent spread of resistance was simulated in simple epidemiological models.

Results

Using this framework, the waiting time to the failure of artemisinin combination therapy (ACT) for malaria was estimated, and a policy of multiple first-line therapies (MFTs) was evaluated. The models quantify the effects of reducing drug pressure in delaying appearance, reducing the chances of establishment, and slowing spread. By using two first-line therapies in a population, it is possible to reduce drug pressure while still treating the full complement of cases.

Conclusions

At a global scale, because of uncertainty about the time to the emergence of ACT resistance, there was a strong case for MFTs to guard against early failure. Our study recommends developing operationally feasible strategies for implementing MFTs, such as distributing different ACTs at the clinic and for home-based care, or formulating different ACTs for children and adults.

Qualitative analysis of models with different treatment protocols to prevent antibiotic resistance

This paper is concerned with the qualitative analysis of two models [S. Bonhoeffer, M. Lipsitch, B.R. Levin, Evaluating treatment protocols to prevent antibiotic resistance, Proc. Natl. Acad. Sci. USA 94 (1997) 12106] for different treatment protocols to prevent antibiotic resistance. Detailed qualitative analysis about the local or global stability of the equilibria of both models is carried out in term of the basic reproduction number R(0). For the model with a single antibiotic therapy, we show that if R(0)<1, then the disease-free equilibrium is globally asymptotically stable; if R(0)>1, then the disease-endemic equilibrium is globally asymptotically stable. For the model with multiple antibiotic therapies, stabilities of various equilibria are analyzed and combining treatment is shown better than cycling treatment. Numerical simulations are performed to show that the dynamical properties depend intimately upon the parameters.

A European survey of antibiotic management of methicillin-resistant Staphylococcus aureus infection: current clinical opinion and practice

Although the epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) varies across Europe, healthcare-associated MRSA infections are common in many countries. Despite several national guidelines, the approach to treatment of MRSA infections varies across the continent, and there are multiple areas of management uncertainty for which there is little clinical evidence to guide practice. A faculty, convened to explore some of these areas, devised a survey that was used to compare the perspectives of infection specialists from across Europe on the management of MRSA infections with those of the faculty specialists. The survey instrument, a web-based questionnaire, was sent to 3840 registered delegates of the 19th European Congress of Clinical Microbiology and Infectious Diseases, held in April 2009. Of the 501 (13%) respondents to the survey, 84% were infection/microbiology specialists and 80% were from Europe. This article reports the survey results from European respondents, and shows a broad range of opinion and practice on a variety of issues pertaining to the management of minor and serious MRSA infections, such as pneumonia, bacteraemia, and skin and soft tissue infections. The issues include changing epidemiology, when and when not to treat, choice of treatment, and duration and route of treatment. The survey identified areas where practice can be improved and where further research is needed, and also identified areas of pan-European consensus of opinion that could be applied to European guidelines for the management of MRSA infection.

Community-associated methicillin-resistant Staphylococcus aureus in outpatients, United States, 1999-2006

Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has become a major problem in US hospitals already dealing with high levels of hospital-associated MRSA (HA-MRSA). Using antimicrobial drug susceptibility data for 1999-2006 from The Surveillance Network, we characterized the relationship between outpatient and inpatient levels of CA-MRSA nationally. In outpatients, the frequency of CA-MRSA isolates has increased >7 x during 1999-2006, which suggests that outpatients have become a major reservoir for CA-MRSA. However, contrary to results in other reports, although CA-MRSA increases are associated with decreases in the frequency of HA-MRSA in hospitals, the decreases are only modest. This finding suggests that instead of replacing HA-MRSA in the hospital, CA-MRSA is adding to the overall presence of MRSA already found within the hospital population.

Compensation of fitness costs and reversibility of antibiotic resistance mutations

Strains of bacterial pathogens that have acquired mutations conferring antibiotic resistance often have a lower growth rate and are less invasive or transmissible initially than their susceptible counterparts. However, fitness costs of resistance mutations can be ameliorated by secondary site mutations. These so-called compensatory mutations may restore fitness in the absence and/or presence of antimicrobials. We review literature data and show that the fitness gains in the absence and presence of antibiotic treatment need not be correlated. The aim of this study is to gain a better conceptual grasp of how compensatory mutations with different fitness gains affect evolutionary trajectories, in particular reversibility. To this end, we developed a theoretical model with which we consider both a resistance and a compensation locus. We propose an intuitively understandable parameterization for the fitness values of the four resulting genotypes (wild type, resistance mutation only, compensatory mutation only, and both mutations) in the absence and presence of treatment. The differential fitness gains, together with the turnover rate and the mutation rate, strongly affected the success of antibacterial treatment, reversibility, and long-term abundance of resistant strains. We therefore propose that experimental studies of compensatory mutations should include fitness measurements of all possible genotypes in both the absence and presence of an antibiotic.

The use of new and better antibiotics for bacterial infections in patients with leukemia

Bacterial infection is the most common complication of chemotherapy-induced neutropenia particularly in patients with hematologic malignancies. Bacterial infections predominate during the initial phases of neutropenic episodes. The spectrum of bacterial infection continues to evolve globally and locally at the institutional level, as do patterns of antimicrobial susceptibility/resistance. These trends are often associated with local treatment practices (eg, use of antimicrobial prophylaxis, open versus restricted formularies, clinical pathways and/or guidelines) and have a significant effect on the nature of empiric antimicrobial therapy. Increasing rates of resistance among gram-positive and gram-negative bacteria are posing new therapeutic challenges. These challenges can to some extent be overcome by new drug development. Many novel agents for the treatment of resistant gram-positive infections have been developed and are being evaluated in clinical trials. Newer agents for the treatment of Clostridium difficile associated diarrhea are also in the pipeline. Far fewer options to treat multi-drug resistant gram-negative infections exist, and new drug development is lagging behind. Consequently, the judicious use of currently available agents is essential. This is best achieved by the development of multidisciplinary antibiotic stewardship teams that gather baseline data, make recommendations for appropriate antimicrobial usage, and provide monitoring and feedback services to clinical care providers. Along with strict adherence to infection control policies, antimicrobial stewardship provides the best strategies for the management of infectious complications in patients with hematologic malignancies and other high-risk settings.

Monopoly pricing of an antibiotic subject to bacterial resistance

We develop a dynamic bio-economic model of bacterial resistance and disease transmission in which we characterize the pricing policy of a monopolist who is protected by a patent. After expiration, the monopolist behaves competitively in a generic industry having open access to the common pool of antibiotic efficacy and infection. The monopolist manages endogenously the levels of antibiotic efficacy as well as the infected population, which represent quality and market size respectively and achieves, at least temporarily, higher such levels than a hypothetically myopic monopolist who does not take into account the dynamic externalities. The pricing policy and the biological system is characterized by the turnpike property. Before the patent vanishes, the monopolist behaves more and more myopically, leading to a continuous decrease in the price of the antibiotic. Once the generic industry takes over, a discontinuous fall in price occurs. Whether a prolongation of the patent is socially desirable depends on the relative levels of antibiotic efficacy and infection.

Evolution in health and medicine Sackler colloquium: a public choice framework for controlling transmissible and evolving diseases

Control measures used to limit the spread of infectious disease often generate externalities. Vaccination for transmissible diseases can reduce the incidence of disease even among the unvaccinated, whereas antimicrobial chemotherapy can lead to the evolution of antimicrobial resistance and thereby limit its own effectiveness over time. We integrate the economic theory of public choice with mathematical models of infectious disease to provide a quantitative framework for making allocation decisions in the presence of these externalities. To illustrate, we present a series of examples: vaccination for tetanus, vaccination for measles, antibiotic treatment of otitis media, and antiviral treatment of pandemic influenza.

The dynamics of mutations associated with anti-malarial drug resistance in Plasmodium falciparum

The evolution of resistance in Plasmodium falciparum against safe and affordable drugs such as chloroquine (CQ) and sulfadoxine-pyrimethamine (SP) is a major global health threat. Investigating the dynamics of resistance against these antimalarial drugs will lead to approaches for addressing the problem of resistance in malarial parasites that are solidly based in evolutionary genetics and population biology. In this article, we discuss current developments in population biology modeling and evolutionary genetics. Despite great advancements achieved in the past decade, understanding the complex dynamics of mutations conferring drug resistance in P. falciparum requires approaches that consider the parasite population structure among other demographic processes.

Effects of daptomycin in combination with other antimicrobial agents: a review of in vitro and animal model studies

This review summarizes the in vitro and animal model data available on antibiotic combinations with daptomycin. The majority of studies focus on the clinically relevant combinations of daptomycin with rifampicin or with gentamicin. These studies demonstrate that daptomycin does not adversely affect the activity of other antimicrobial agents that may be administered concomitantly. Overall, additive or indifferent effects with daptomycin combinations were observed; however, synergy was observed for certain isolates of vancomycin-resistant enterococci when exposed to daptomycin and rifampicin. Unexpected synergy was demonstrated against methicillin-resistant Staphylococcus aureus by daptomycin and beta-lactams. Most importantly, no in vitro antagonism of daptomycin with any other agent tested was confirmed in these studies. The most striking in vivo effects were noted in two different complicated infection models; i.e. osteomyelitis and implant infections, where rifampicin combinations with daptomycin increased efficacy and reduced the incidence of rifampicin resistance.

The effect of physician-patient collaboration on patient adherence in non-psychiatric medicine

OBJECTIVE

Factors contributing to treatment adherence are poorly understood but the physician-patient interaction is one factor that is known to affect patient adherence.

METHODS

This meta-analysis systematically reviewed the published literature to determine the magnitude of the relationships between physician-patient collaboration and patient adherence.

RESULTS

A statistically significant weighted mean effect size of M(d)=0.145 from 48 published studies indicated better physician-patient collaboration is associated with better patient adherence. The relationship between collaboration and adherence was sustained for pediatric and adult populations, chronic and acute conditions, and primary physician and specialists.

CONCLUSION

These results emphasize the need for physician-patient collaboration within the medical consultation.

PRACTICE IMPLICATIONS

The inclusion of the patient's perspective during the consultation is essential to obtaining cooperation once the patient has left the physician's office.

Positive epistasis drives the acquisition of multidrug resistance

The evolution of multiple antibiotic resistance is an increasing global problem. Resistance mutations are known to impair fitness, and the evolution of resistance to multiple drugs depends both on their costs individually and on how they interact—epistasis. Information on the level of epistasis between antibiotic resistance mutations is of key importance to understanding epistasis amongst deleterious alleles, a key theoretical question, and to improving public health measures. Here we show that in an antibiotic-free environment the cost of multiple resistance is smaller than expected, a signature of pervasive positive epistasis among alleles that confer resistance to antibiotics. Competition assays reveal that the cost of resistance to a given antibiotic is dependent on the presence of resistance alleles for other antibiotics. Surprisingly we find that a significant fraction of resistant mutations can be beneficial in certain resistant genetic backgrounds, that some double resistances entail no measurable cost, and that some allelic combinations are hotspots for rapid compensation. These results provide additional insight as to why multi-resistant bacteria are so prevalent and reveal an extra layer of complexity on epistatic patterns previously unrecognized, since it is hidden in genome-wide studies of genetic interactions using gene knockouts.

Understanding the nature of genetic interactions, known as epistasis, is crucial in biology. The strength and type of epistasis is relevant for the evolution of sex, buffering of genetic variation, speciation, and the topography of fitness landscapes. While epistasis between gene deletions has been the recent focus of research, interactions between randomly selected alleles, which are of the greatest evolutionary interest, have not. We have studied the strength and type of epistasis amongst alleles that confer antibiotic resistance and have found that: in an antibiotic-free environment, the cost of multiple resistance is smaller than expected—a signature of pervasive positive epistasis amongst alleles that confer resistance to antibiotics; epistatic interactions are allele specific; a significant fraction of resistant mutations can be beneficial in certain resistant genetic backgrounds; some double resistances entail no measurable cost; and some allelic combinations are hotspots for rapid compensation. Overall, our findings provide added reasoning as to why multi-resistance is so difficult to eradicate. Importantly, our results of allelic-specific epistasis reveal an extra layer of complexity on epistatic patterns previously unrecognized.

The in vitro fitness cost of antimicrobial resistance in Escherichia coli varies with the growth conditions

The objective of this study was to investigate the influence of stressful growth conditions on the fitness cost of antimicrobial resistance in Escherichia coli BJ4 caused by chromosomal mutations and plasmid acquisition. The fitness cost of chromosomal streptomycin resistance increased significantly when the bacteria were grown under all stress conditions tested, while the cost in 1/3 Luria-Bertani was not significantly changed in a streptomycin+rifampicin mutant. The increase in the fitness cost depended in a nonregular manner on the strain/stress combination. The fitness cost of plasmid-encoded resistance on R751 did not differ significantly, and was generally less under stressful growth conditions than in rich media. The fitness cost associated with R751 with the multiple drug resistance cassette from Salmonella Typhimurium DT104 increased significantly only under stressful conditions at low pH and at high-salt concentrations. Strains with an impaired rpoS demonstrated a reduced fitness only during growth in a high-salt concentration. In conclusion, it was demonstrated that bacterial fitness cost in association with antimicrobial resistance generally increases under stressful growth conditions. However, the growth potential of bacteria with antimicrobial resistances did not increase in a straightforward manner in these in vitro experiments and is therefore probably even more difficult to predict in vivo.

Evolutionary epidemiology of drug-resistance in space

How can we optimize the use of drugs against parasites to limit the evolution of drug resistance? This question has been addressed by many theoretical studies focusing either on the mixing of various treatments, or their temporal alternation. Here we consider a different treatment strategy where the use of the drug may vary in space to prevent the rise of drug-resistance. We analyze epidemiological models where drug-resistant and drug-sensitive parasites compete in a one-dimensional spatially heterogeneous environment. Two different parasite life-cycles are considered: (i) direct transmission between hosts, and (ii) vector-borne transmission. In both cases we find a critical size of the treated area, under which the drug-resistant strain cannot persist. This critical size depends on the basic reproductive ratios of each strain in each environment, on the ranges of dispersal, and on the duration of an infection with drug-resistant parasites. We discuss optimal treatment strategies that limit disease prevalence and the evolution of drug-resistance.

The spread of drug-resistant parasites erodes the efficacy of therapeutic treatments against many infectious diseases and is a major threat of the 21st century. The evolution of drug-resistance depends, among other things, on how the treatments are administered at the population level. “Resistance management” consists of finding optimal treatment strategies that both reduce the consequence of an infection at the individual host level, and limit the spread of drug-resistance in the pathogen population. Several studies have focused on the effect of mixing different treatments, or of alternating them in time. Here, we analyze another strategy, where the use of the drug varies spatially: there are places where no one receives any treatment. We find that such a spatial heterogeneity can totally prevent the rise of drug-resistance, provided that the size of treated patches is below a critical threshold. The range of parasite dispersal, the relative costs and benefits of being drug-resistant compared to being drug-sensitive, and the duration of an infection with drug-resistant parasites are the main factors determining the value of this threshold. Our analysis thus provides some general guidance regarding the optimal spatial use of drugs to prevent or limit the evolution of drug-resistance.

Engineered bacteriophage targeting gene networks as adjuvants for antibiotic therapy

Antimicrobial drug development is increasingly lagging behind the evolution of antibiotic resistance, and as a result, there is a pressing need for new antibacterial therapies that can be readily designed and implemented. In this work, we engineered bacteriophage to overexpress proteins and attack gene networks that are not directly targeted by antibiotics. We show that suppressing the SOS network in Escherichia coli with engineered bacteriophage enhances killing by quinolones by several orders of magnitude in vitro and significantly increases survival of infected mice in vivo. In addition, we demonstrate that engineered bacteriophage can enhance the killing of antibiotic-resistant bacteria, persister cells, and biofilm cells, reduce the number of antibiotic-resistant bacteria that arise from an antibiotic-treated population, and act as a strong adjuvant for other bactericidal antibiotics (e.g., aminoglycosides and beta-lactams). Furthermore, we show that engineering bacteriophage to target non-SOS gene networks and to overexpress multiple factors also can produce effective antibiotic adjuvants. This work establishes a synthetic biology platform for the rapid translation and integration of identified targets into effective antibiotic adjuvants.

Modelling binary mixtures of herbicides in populations resistant to one of the components: evaluation for resistance management

BACKGROUND

Herbicide mixtures are commonly proposed to delay the selection of herbicide resistance in susceptible populations (called the SM strategy). However, in practice, herbicide mixtures are often used when resistance to one of the two active ingredients has already been detected in the targeted population (called the RM strategy). It is doubtful whether such a practice can select against resistance, as the corresponding selection pressure is still exerted. As a consequence, the effect of mixtures on the evolution of an already detected resistance to one of the herbicides in the combination remains largely unexplored. In the present work, a simple model was developed to explore further the necessary and sufficient conditions under which a binary RM strategy might stabilise or even reduce resistance frequency.

RESULTS

Covering the hypothetical largest range of parameters, 39% of 9000 random simulations attest that the RM strategy might theoretically reduce resistance frequency. When strong enough, high genetic cost of resistance, negative cross-resistance between the herbicides associated in the mixture and reduced selection differential between resistant and susceptible plants can counterbalance the resistance advantage to one of the two applied herbicides. However, the required conditions for an RM strategy to ensure resistance containment in natural conditions seldom overlap with experimental parameter estimates given in the literature.

CONCLUSION

It is concluded that the sufficient conditions for an RM strategy to be effective would rarely be encountered. As a consequence, the strategy of formulating mixtures with herbicides for which resistance has already been detected should be avoided.

The impact of different antibiotic regimens on the emergence of antimicrobial-resistant bacteria

Backgroud

The emergence and ongoing spread of antimicrobial-resistant bacteria is a major public health threat. Infections caused by antimicrobial-resistant bacteria are associated with substantially higher rates of morbidity and mortality compared to infections caused by antimicrobial-susceptible bacteria. The emergence and spread of these bacteria is complex and requires incorporating numerous interrelated factors which clinical studies cannot adequately address.

Methods/Principal Findings

A model is created which incorporates several key factors contributing to the emergence and spread of resistant bacteria including the effects of the immune system, acquisition of resistance genes and antimicrobial exposure. The model identifies key strategies which would limit the emergence of antimicrobial-resistant bacterial strains. Specifically, the simulations show that early initiation of antimicrobial therapy and combination therapy with two antibiotics prevents the emergence of resistant bacteria, whereas shorter courses of therapy and sequential administration of antibiotics promote the emergence of resistant strains.

Conclusions/Significance

The principal findings suggest that (i) shorter lengths of antibiotic therapy and early interruption of antibiotic therapy provide an advantage for the resistant strains, (ii) combination therapy with two antibiotics prevents the emergence of resistance strains in contrast to sequential antibiotic therapy, and (iii) early initiation of antibiotics is among the most important factors preventing the emergence of resistant strains. These findings provide new insights into strategies aimed at optimizing the administration of antimicrobials for the treatment of infections and the prevention of the emergence of antimicrobial resistance.

Impact of antimicrobial usage on the transmission dynamics of antimicrobial resistant bacteria among pigs

There is increasing evidence showing that antimicrobial consumption provides a powerful selective force that promotes the emergence of resistance in pathogenic, commensal as well as zoonotic bacteria in animals. The main aim of this study was to develop a modeling framework that can be used to assess the impact of antimicrobial usage in pigs on the emergence and transmission of resistant bacteria within a finisher pig farm. The transmission dynamics of drug-sensitive and drug-resistant bacteria among pigs in the herd were characterized by studying the local and global stability properties of steady state solutions of the system. Numerical simulations demonstrating the influence of factors such as initial prevalence of infection, presence of pre-existing antimicrobial resistant mutants, and frequency of treatment on predicted prevalence were performed. Sensitivity analysis revealed that two parameters had a huge influence on the predicted proportion of pigs carrying resistant bacteria: (a) the transmission coefficient between uninfected pigs and those infected with drug-resistant bacteria during treatment (beta(2)) and after treatment stops (beta(3)), and (b) the spontaneous clear-out rate of drug-resistant bacteria during treatment (gamma(2)) and immediately after treatment stops (gamma(3)). Control measures should therefore be geared towards reducing the magnitudes of beta(2) and beta(3) or increasing those of gamma(2) and gamma(3).