Outbreak of Resistant Pseudomonas aeruginosa Infections during a Quarterly Cycling Antibiotic Regimen

Fast drug rotation reduces bacterial resistance evolution in a microcosm experiment

Drug rotation (cycling), in which multiple drugs are administrated alternatively, has the potential for limiting resistance evolution in pathogens. The frequency of drug alternation could be a major factor to determine the effectiveness of drug rotation. Drug rotation practices often have low frequency of drug alternation, with an expectation of resistance reversion. Here we, based on evolutionary rescue and compensatory evolution theories, suggest that fast drug rotation can limit resistance evolution in the first place. This is because fast drug rotation would give little time for the evolutionarily rescued populations to recover in population size and genetic diversity, and thus decrease the chance of future evolutionary rescue under alternate environmental stresses. We experimentally tested this hypothesis using the bacterium Pseudomonas fluorescens and two antibiotics (chloramphenicol and rifampin). Increasing drug rotation frequency reduced the chance of evolutionary rescue, and most of the finally surviving bacterial populations were resistant to both drugs. Drug resistance incurred significant fitness costs, which did not differ among the drug treatment histories. A link between population sizes during the early stages of drug treatment and the end-point fates of populations (extinction vs survival) suggested that population size recovery and compensatory evolution before drug shift increase the chance of population survival. Our results therefore advocate fast drug rotation as a promising approach to reduce bacterial resistance evolution, which in particular could be a substitute for drug combination when the latter has safety risks.

The Effect of Antibiotic-Cycling Strategy on Antibiotic-Resistant Bacterial Infections or Colonization in Intensive Care Units: A Systematic Review and Meta-Analysis

Background

Antibiotic‐resistant bacteria, especially multidrug‐resistant strains, play a key role in impeding critical patients from survival and recovery. The effectiveness of the empiric use of antibiotics in the circling manner in intensive care units (ICUs) has not been analyzed in detail and remains controversial. Therefore, this systematic review and meta‐analysis were conducted to evaluate antibiotic‐cycling effect on the incidence of antibiotic‐resistant bacteria.

Methods

We searched PubMed, Embase, the Cochrane Central Register of Controlled Trials, and Web of Science for studies focusing on whether a cycling strategy of empiric use of antibiotics could curb the prevalence of antibiotic‐resistant bacteria in ICUs. The major outcomes were risk ratios (RRs) of antibiotic‐resistant infections or colonization per 1,000 patient days before and after the implementation of antibiotic cycling. A random‐effects model was adopted to estimate results in consideration of clinical heterogeneity among studies. The registration number of the meta‐analysis is CRD42018094464.

Results

Twelve studies, involving 2,261 episodes of resistant infections or colonization and 160,129 patient days, were included in the final analysis. Based on the available evidence, the antibiotic‐cycling strategy did not reduce the overall incidence of infections or colonization with resistant bacteria (RR = 0.823, 95% CI 0.655–1.035, p = .095). In subgroup analyses, the cycling strategy cut down the incidence of resistant bacteria more significantly than baseline period (p = .028) but showed no difference in comparison with mixing strategy (p = .758).

Linking Evidence to Action

Although the cycling strategy performed better than relatively free usage of antibiotics in the baseline period on reducing resistant bacteria, the cycling strategy did not show advantage when compared with the mixing strategy in subgroup analyses. In addition, these viewpoints still need more evidence to confirm.

Longitudinal comparative trial of antibiotic cycling and mixing on emergence of gram negative bacterial resistance in a pediatric medical intensive care unit

PURPOSE

To compare antibiotic mixing vs. cycling with respect to acquisition of resistance and PICU mortality.

MATERIALS AND METHODS

Children between >1 month to 12 years admitted to a medical PICU were enrolled over three phases (baseline, mixing and cycling) with washout interval of 3 months following each antibiotic strategy. Following a baseline phase, empiric gram negative antibiotic protocol for suspected HCAI, was sequentially subjected to mixing and cycling using Latin Square methodology. Surveillance cultures were taken at admission, 48 h, weekly thereafter and within 2 days of PICU discharge. Acquisition of resistance and PICU mortality were primary and secondary outcomes respectively.

RESULTS

778 children were enrolled; 99 baseline, 146 mixing, 362 cycling, and 171 during two washout phases. Proportion of children with acquired resistance at baseline (56.6%) was significantly higher than mixing (22.6%) and cycling (18.51%) (p < .0001). Adjusted hazards of acquired resistance (HR:0.82; 95% CI: 0.53-1.25, p = .352), and PICU mortality (RR1.07; 95% CI: 0.71-1.60, p = .72) were similar in cycling and mixing strategies.

CONCLUSIONS

Acquisition of resistance was significantly lower in both mixing and cycling as compared to baseline phase. Both were similar with respect to risk of antibiotic resistance as well as incidence of HCAI and PICU mortality.

Five rules for resistance management in the antibiotic apocalypse, a road map for integrated microbial management

Resistance to new antimicrobials can become widespread within 2-3 years. Resistance problems are particularly acute for bacteria that can experience selection as both harmless commensals and pathogenic hospital-acquired infections. New drugs, although welcome, cannot tackle the antimicrobial resistance crisis alone: new drugs must be partnered with more sustainable patterns of use. However, the broader experience of resistance management in other disciplines, and the assumptions on which resistance rests, is not widely appreciated in clinical and microbiological disciplines. Improved awareness of the field of resistance management could improve clinical outcomes and help shape novel solutions. Here, the aim is to develop a pragmatic approach to developing a sustainable integrated means of using antimicrobials, based on an interdisciplinary synthesis of best practice, recent theory and recent clinical data. This synthesis emphasizes the importance of pre-emptive action and the value of reducing the supply of genetic novelty to bacteria under selection. The weight of resistance management experience also cautions against strategies that over-rely on the fitness costs of resistance or low doses. The potential (and pitfalls) of shorter courses, antibiotic combinations and antibiotic mixing or cycling are discussed in depth. Importantly, some of variability in the success of clinical trials of mixing approaches can be explained by the number and diversity of drugs in a trial, as well as whether trials encompass single wards or the wider transmission network that is a hospital. Consideration of the importance of data, and of the initially low frequency of resistance, leads to a number of additional recommendations. Overall, reduction in selection pressure, interference with the transmission of problematic genotypes and multidrug approaches (combinations, mixing or cycling) are all likely to be required for sustainability and the protection of forthcoming drugs.

Modeling antibiotic treatment in hospitals: A systematic approach shows benefits of combination therapy over cycling, mixing, and mono-drug therapies

Multiple treatment strategies are available for empiric antibiotic therapy in hospitals, but neither clinical studies nor theoretical investigations have yielded a clear picture when which strategy is optimal and why. Extending earlier work of others and us, we present a mathematical model capturing treatment strategies using two drugs, i.e the multi-drug therapies referred to as cycling, mixing, and combination therapy, as well as monotherapy with either drug. We randomly sample a large parameter space to determine the conditions determining success or failure of these strategies. We find that combination therapy tends to outperform the other treatment strategies. By using linear discriminant analysis and particle swarm optimization, we find that the most important parameters determining success or failure of combination therapy relative to the other treatment strategies are the de novo rate of emergence of double resistance in patients infected with sensitive bacteria and the fitness costs associated with double resistance. The rate at which double resistance is imported into the hospital via patients admitted from the outside community has little influence, as all treatment strategies are affected equally. The parameter sets for which combination therapy fails tend to fall into areas with low biological plausibility as they are characterised by very high rates of de novo emergence of resistance to both drugs compared to a single drug, and the cost of double resistance is considerably smaller than the sum of the costs of single resistance.

Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?

Can we exploit our burgeoning understanding of molecular evolution to slow the progress of drug resistance? One role of an infection clinician is exactly that: to foresee trajectories to resistance during antibiotic treatment and to hinder that evolutionary course. But can this be done at a hospital-wide scale? Clinicians and theoreticians tried to when they proposed two conflicting behavioral strategies that are expected to curb resistance evolution in the clinic, these are known as “antibiotic cycling” and “antibiotic mixing.” However, the accumulated data from clinical trials, now approaching 4 million patient days of treatment, is too variable for cycling or mixing to be deemed successful. The former implements the restriction and prioritization of different antibiotics at different times in hospitals in a manner said to “cycle” between them. In antibiotic mixing, appropriate antibiotics are allocated to patients but randomly. Mixing results in no correlation, in time or across patients, in the drugs used for treatment which is why theorists saw this as an optimal behavioral strategy. So while cycling and mixing were proposed as ways of controlling evolution, we show there is good reason why clinical datasets cannot choose between them: by re-examining the theoretical literature we show prior support for the theoretical optimality of mixing was misplaced. Our analysis is consistent with a pattern emerging in data: neither cycling or mixing is a priori better than the other at mitigating selection for antibiotic resistance in the clinic.

Key words: antibiotic cycling, antibiotic mixing, optimal control, stochastic models.

Strategies to enhance rational use of antibiotics in hospital: a guideline by the German Society for Infectious Diseases

Introduction

In the time of increasing resistance and paucity of new drug development there is a growing need for strategies to enhance rational use of antibiotics in German and Austrian hospitals. An evidence-based guideline on recommendations for implementation of antibiotic stewardship (ABS) programmes was developed by the German Society for Infectious Diseases in association with the following societies, associations and institutions: German Society of Hospital Pharmacists, German Society for Hygiene and Microbiology, Paul Ehrlich Society for Chemotherapy, The Austrian Association of Hospital Pharmacists, Austrian Society for Infectious Diseases and Tropical Medicine, Austrian Society for Antimicrobial Chemotherapy, Robert Koch Institute.

Materials and methods

A structured literature research was performed in the databases EMBASE, BIOSIS, MEDLINE and The Cochrane Library from January 2006 to November 2010 with an update to April 2012 (MEDLINE and The Cochrane Library). The grading of recommendations in relation to their evidence is according to the AWMF Guidance Manual and Rules for Guideline Development.

Conclusion

The guideline provides the grounds for rational use of antibiotics in hospital to counteract antimicrobial resistance and to improve the quality of care of patients with infections by maximising clinical outcomes while minimising toxicity. Requirements for a successful implementation of ABS programmes as well as core and supplemental ABS strategies are outlined. The German version of the guideline was published by the German Association of the Scientific Medical Societies (AWMF) in December 2013.

Evaluation of a Mixing versus a Cycling Strategy of Antibiotic Use in Critically-Ill Medical Patients: Impact on Acquisition of Resistant Microorganisms and Clinical Outcomes

Objective

To compare the effect of two strategies of antibiotic use (mixing vs. cycling) on the acquisition of resistant microorganisms, infections and other clinical outcomes.

Methods

Prospective cohort study in an 8-bed intensive care unit during 35- months in which a mixing-cycling policy of antipseudomonal beta-lactams (meropenem, ceftazidime/piperacillin-tazobactam) and fluoroquinolones was operative. Nasopharyngeal and rectal swabs and respiratory secretions were obtained within 48h of admission and thrice weekly thereafter. Target microorganisms included methicillin-resistant S. aureus, vancomycin-resistant enterococci, third-generation cephalosporin-resistant Enterobacteriaceae and non-fermenters.

Results

A total of 409 (42%) patients were included in mixing and 560 (58%) in cycling. Exposure to ceftazidime/piperacillin-tazobactam and fluoroquinolones was significantly higher in mixing while exposure to meropenem was higher in cycling, although overall use of antipseudomonals was not significantly different (37.5/100 patient-days vs. 38.1/100 patient-days). There was a barely higher acquisition rate of microorganisms during mixing, but this difference lost its significance when the cases due to an exogenous Burkholderia cepacia outbreak were excluded (19.3% vs. 15.4%, OR 0.8, CI 0.5–1.1). Acquisition of Pseudomonas aeruginosa resistant to the intervention antibiotics or with multiple-drug resistance was similar. There were no significant differences between mixing and cycling in the proportion of patients acquiring any infection (16.6% vs. 14.5%, OR 0.9, CI 0.6–1.2), any infection due to target microorganisms (5.9% vs. 5.2%, OR 0.9, CI 0.5–1.5), length of stay (median 5 d for both groups) or mortality (13.9 vs. 14.3%, OR 1.03, CI 0.7–1.3).

Conclusions

A cycling strategy of antibiotic use with a 6-week cycle duration is similar to mixing in terms of acquisition of resistant microorganisms, infections, length of stay and mortality.

Fighting antibiotic resistance in the intensive care unit using antibiotics

Antibiotic resistance is a global and increasing problem that is not counterbalanced by the development of new therapeutic agents. The prevalence of antibiotic resistance is especially high in intensive care units with frequently reported outbreaks of multidrug-resistant organisms. In addition to classical infection prevention protocols and surveillance programs, counterintuitive interventions, such as selective decontamination with antibiotics and antibiotic rotation have been applied and investigated to control the emergence of antibiotic resistance. This review provides an overview of selective oropharyngeal and digestive tract decontamination, decolonization of methicillin-resistant Staphylococcus aureus and antibiotic rotation as strategies to modulate antibiotic resistance in the intensive care unit.

Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multidrug resistance

Infections with rapidly evolving pathogens are often treated using combinations of drugs with different mechanisms of action. One of the major goal of combination therapy is to reduce the risk of drug resistance emerging during a patient's treatment. Although this strategy generally has significant benefits over monotherapy, it may also select for multidrug-resistant strains, particularly during long-term treatment for chronic infections. Infections with these strains present an important clinical and public health problem. Complicating this issue, for many antimicrobial treatment regimes, individual drugs have imperfect penetration throughout the body, so there may be regions where only one drug reaches an effective concentration. Here we propose that mismatched drug coverage can greatly speed up the evolution of multidrug resistance by allowing mutations to accumulate in a stepwise fashion. We develop a mathematical model of within-host pathogen evolution under spatially heterogeneous drug coverage and demonstrate that even very small single-drug compartments lead to dramatically higher resistance risk. We find that it is often better to use drug combinations with matched penetration profiles, although there may be a trade-off between preventing eventual treatment failure due to resistance in this way and temporarily reducing pathogen levels systemically. Our results show that drugs with the most extensive distribution are likely to be the most vulnerable to resistance. We conclude that optimal combination treatments should be designed to prevent this spatial effective monotherapy. These results are widely applicable to diverse microbial infections including viruses, bacteria, and parasites.

Antibiotic rotation strategies to reduce antimicrobial resistance in Gram-negative bacteria in European intensive care units: study protocol for a cluster-randomized crossover controlled trial

Background

Intensive care units (ICU) are epicenters for the emergence of antibiotic-resistant Gram-negative bacteria (ARGNB) because of high rates of antibiotic usage, rapid patient turnover, immunological susceptibility of acutely ill patients, and frequent contact between healthcare workers and patients, facilitating cross-transmission.

Antibiotic stewardship programs are considered important to reduce antibiotic resistance, but the effectiveness of strategies such as, for instance, antibiotic rotation, have not been determined rigorously. Interpretation of available studies on antibiotic rotation is hampered by heterogeneity in implemented strategies and suboptimal study designs. In this cluster-randomized, crossover trial the effects of two antibiotic rotation strategies, antibiotic mixing and cycling, on the prevalence of ARGNB in ICUs are determined. Antibiotic mixing aims to create maximum antibiotic heterogeneity, and cycling aims to create maximum antibiotic homogeneity during consecutive periods.

Methods/Design

This is an open cluster-randomized crossover study of mixing and cycling of antibiotics in eight ICUs in five European countries. During cycling (9 months) third- or fourth-generation cephalosporins, piperacillin-tazobactam and carbapenems will be rotated during consecutive 6-week periods as the primary empiric treatment in patients suspected of infection caused by Gram-negative bacteria. During mixing (9 months), the same antibiotics will be rotated for each consecutive antibiotic course. Both intervention periods will be preceded by a baseline period of 4 months. ICUs will be randomized to consecutively implement either the mixing and then cycling strategy, or vice versa. The primary outcome is the ICU prevalence of ARGNB, determined through monthly point-prevalence screening of oropharynx and perineum. Secondary outcomes are rates of acquisition of ARGNB, bacteremia and appropriateness of therapy, length of stay in the ICU and ICU mortality. Results will be adjusted for intracluster correlation, and patient- and ICU-level variables of case-mix and infection-prevention measures using advanced regression modeling.

Discussion

This trial will determine the effects of antibiotic mixing and cycling on the unit-wide prevalence of ARGNB in ICUs.

Trial registration

ClinicalTrials.gov NCT01293071 December 2010.

Cycling empirical antibiotic therapy in hospitals: meta-analysis and models

The rise of resistance together with the shortage of new broad-spectrum antibiotics underlines the urgency of optimizing the use of available drugs to minimize disease burden. Theoretical studies suggest that coordinating empirical usage of antibiotics in a hospital ward can contain the spread of resistance. However, theoretical and clinical studies came to different conclusions regarding the usefulness of rotating first-line therapy (cycling). Here, we performed a quantitative pathogen-specific meta-analysis of clinical studies comparing cycling to standard practice. We searched PubMed and Google Scholar and identified 46 clinical studies addressing the effect of cycling on nosocomial infections, of which 11 met our selection criteria. We employed a method for multivariate meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate/1000 patient days of both total infections by 4.95 [9.43–0.48] and resistant infections by 7.2 [14.00–0.44]. This positive effect was observed in most pathogens despite a large variance between individual species. Our findings remain robust in uni- and multivariate metaregressions. We used theoretical models that reflect various infections and hospital settings to compare cycling to random assignment to different drugs (mixing). We make the realistic assumption that therapy is changed when first line treatment is ineffective, which we call “adjustable cycling/mixing”. In concordance with earlier theoretical studies, we find that in strict regimens, cycling is detrimental. However, in adjustable regimens single resistance is suppressed and cycling is successful in most settings. Both a meta-regression and our theoretical model indicate that “adjustable cycling” is especially useful to suppress emergence of multiple resistance. While our model predicts that cycling periods of one month perform well, we expect that too long cycling periods are detrimental. Our results suggest that “adjustable cycling” suppresses multiple resistance and warrants further investigations that allow comparing various diseases and hospital settings.

The rise of antibiotic resistance is a major concern for public health. In hospitals, frequent usage of antibiotics leads to high resistance levels; at the same time the patients are especially vulnerable. We therefore urgently need treatment strategies that limit resistance without compromising patient care. Here, we investigate two strategies that coordinate the usage of different antibiotics in a hospital ward: “cycling”, i.e. scheduled changes in antibiotic treatment for all patients, and “mixing”, i.e. random assignment of patients to antibiotics. Previously, theoretical and clinical studies came to different conclusions regarding the usefulness of these strategies. We combine meta-analyses of clinical studies and epidemiological modeling to address this question. Our meta-analyses suggest that cycling is beneficial in reducing the total incidence rate of hospital-acquired infections as well as the incidence rate of resistant infections, and that this is most pronounced at low baseline levels of resistance. We corroborate our findings with theoretical epidemiological models. When incorporating treatment adjustment upon deterioration of a patient's condition (“adjustable cycling”), we find that our theoretical model is in excellent accordance with the clinical data. With this combined approach we present substantial evidence that adjustable cycling can be beneficial for suppressing the emergence of multiple resistance.

Resource competition may lead to effective treatment of antibiotic resistant infections

Drug resistance is a common problem in the fight against infectious diseases. Recent studies have shown conditions (which we call antiR) that select against resistant strains. However, no specific drug administration strategies based on this property exist yet. Here, we mathematically compare growth of resistant versus sensitive strains under different treatments (no drugs, antibiotic, and antiR), and show how a precisely timed combination of treatments may help defeat resistant strains. Our analysis is based on a previously developed model of infection and immunity in which a costly plasmid confers antibiotic resistance. As expected, antibiotic treatment increases the frequency of the resistant strain, while the plasmid cost causes a reduction of resistance in the absence of antibiotic selection. Our analysis suggests that this reduction occurs under competition for limited resources. Based on this model, we estimate treatment schedules that would lead to a complete elimination of both sensitive and resistant strains. In particular, we derive an analytical expression for the rate of resistance loss, and hence for the time necessary to turn a resistant infection into sensitive (tclear). This time depends on the experimentally measurable rates of pathogen division, growth and plasmid loss. Finally, we estimated tclear for a specific case, using available empirical data, and found that resistance may be lost up to 15 times faster under antiR treatment when compared to a no treatment regime. This strategy may be particularly suitable to treat chronic infection. Finally, our analysis suggests that accounting explicitly for a resistance-decaying rate may drastically change predicted outcomes in host-population models.

Overcoming drug resistance in multi-drug resistant cancers and microorganisms: a conceptual framework

Resistance development against multiple drugs is a common feature among many pathogens--including bacteria such as Pseudomonas aeruginosa, viruses, and parasites--and also among cancers. The reasons are two-fold. Most commonly-used rationally-designed small molecule drugs or monoclonal antibodies, as well as antibiotics, strongly inhibit a key single step in the growth and proliferation of the pathogen or cancer cells. The disease agents quickly change or switch off this single target, or activate the efflux mechanisms to pump out the drug, thereby becoming resistant to the drug. A second problem is the way drugs are designed. The pharmaceutical industry chooses to use, by high-throughput screening, compounds that are maximally inhibitory to the key single step in the growth of the pathogen or cancer, thereby promoting selective pressure. An ideal drug would be one that inhibits multiple steps in the disease progression pathways with less stringency in these steps. Low levels of inhibition at multiple steps provide cumulative strong inhibitory effect, but little incentives or ability on the part of the pathogen/cancer to develop resistance. Such intelligent drug design involving multiple less stringent inhibitory steps is beyond the scope of the drug industry and requires evolutionary wisdom commonly possessed by bacteria. This review surveys assessments of the current clinical situation with regard to drug resistance in P. aeruginosa, and examines tools currently employed to limit this trend. We then provide a conceptual framework in which we explore the similarities between multi-drug resistance in pathogens and in cancers. We summarize promising work on anti-cancer drugs derived from the evolutionary wisdom of bacteria such as P. aeruginosa, and how such strategies can be the basis for how to look for candidate protein/peptide antibiotic drugs from bioengineered bugs. Such multi-domain proteins, unlike diffusible antibiotics, are not diffusible because of their large size and are often released only on contact with the perceived competitor. Thus, multi-domain proteins are missed during traditional methods of looking for growth zone inhibition of susceptible bacteria as demonstrated by antibiotics, but may represent the weapons of the future in the fights against both drug-resistant cancers and pathogens such as P. aeruginosa.

Impact of a Multimodal Antimicrobial Stewardship Program on Pseudomonas aeruginosa Susceptibility and Antimicrobial Use in the Intensive Care Unit Setting

Objective. To study the impact of our multimodal antibiotic stewardship program on Pseudomonas aeruginosa susceptibility and antibiotic use in the intensive care unit (ICU) setting. Methods. Our stewardship program employed the key tenants of published antimicrobial stewardship guidelines. These included prospective audits with intervention and feedback, formulary restriction with preauthorization, educational conferences, guidelines for use, antimicrobial cycling, and de-escalation of therapy. ICU antibiotic use was measured and expressed as defined daily doses (DDD) per 1,000 patient-days. Results. Certain temporal relationships between antibiotic use and ICU resistance patterns appeared to be affected by our antibiotic stewardship program. In particular, the ICU use of intravenous ciprofloxacin and ceftazidime declined from 148 and 62.5 DDD/1,000 patient-days to 40.0 and 24.5, respectively, during 2004 to 2007. An increase in the use of these agents and resistance to these agents was witnessed during 2008–2010. Despite variability in antibiotic usage from the stewardship efforts, we were overall unable to show statistical relationships with P. aeruginosa resistance rate. Conclusion. Antibiotic resistance in the ICU setting is complex. Multimodal stewardship efforts attempt to prevent resistance, but such programs clearly have their limits.

Conserving antibiotics for the future: new ways to use old and new drugs from a pharmacokinetic and pharmacodynamic perspective

There is a growing need to optimize the use of old and new antibiotics to treat serious as well as less serious infections. The topic of how to use pharmacokinetic and pharmacodynamic (PK/PD) knowledge to conserve antibiotics for the future was elaborated on in a workshop of the conference (The conference "The Global Need for Effective Antibiotics - moving towards concerted action", ReAct, Uppsala, Sweden, 2010). The optimization of dosing regimens is accomplished by choosing the dose and schedule that results in the antimicrobial exposure that will achieve the microbiological and clinical outcome desired while simultaneously suppressing emergence of resistance. PK/PD of antimicrobial agents describe how the therapeutic drug effect is dependent on the potency of a drug against a microorganism and the exposure (the concentration of antimicrobial available for effect over time). The description and modeling of these relationships quantitatively then allow for a rational approach to dose optimization and several strategies to that purpose are described. These strategies include not only the dosing regimen itself but also the duration of therapy, preventing collateral damage through inappropriate use and the application of PK/PD in drug development. Furthermore, PK/PD relationships of older antibiotics need to be urgently established. The need for global harmonization of breakpoints is also suggested and would add efficacy to antibiotic therapy. For each of the strategies, a number of priority actions are provided.

Characterization of clonal strains of Pseudomonas aeruginosa isolated from cystic fibrosis patients in Ontario, Canada

Pseudomonas aeruginosa is an opportunistic pathogen that can form biofilms in the lungs and airways of cystic fibrosis (CF) patients, resulting in chronic endobronchial infection. Two clonal strains of P. aeruginosa, named type A and type B, have recently been identified and have been found to infect more than 20% of CF patients in Ontario, Canada. In this study, 4 type A and 4 type B isolates retrieved from 8 CF patients in Ontario, Canada, were characterized. All 8 isolates grew well in rich medium and formed biofilms in vitro. Antibiotic resistance profiles of bacteria grown in biofilms and planktonic culture were studied via minimal bactericidal concentration assays for tobramycin, gentamicin, and ciprofloxacin. Compared to laboratory strains of P. aeruginosa, all 8 isolates showed increased resistance to all antibiotics studied in both biofilm and planktonic assays. Gene expression analysis of mexX, representing the MexXY-OprM efflux pump, and mexA, representing MexAB-OprM, revealed that these genes were up-regulated in the 8 clinical isolates. These results suggest clonal type A and type B isolates of P. aeruginosa isolated from CF patients in Ontario, Canada, show a multidrug resistance pattern that can be partially explained as being due to the increased expression of common antibiotic efflux systems.