Antibiotic Restriction Might Facilitate the Emergence of Multi-drug Resistance

An agent-based model on antimicrobial de-escalation in intensive care units: Implications on clinical trial design

Antimicrobial de-escalation refers to reducing the spectrum of antibiotics used in treating bacterial infections. This strategy is widely recommended in many antimicrobial stewardship programs and is believed to reduce patients' exposure to broad-spectrum antibiotics and prevent resistance. However, the ecological benefits of de-escalation have not been universally observed in clinical studies. This paper conducts computer simulations to assess the ecological effects of de-escalation on the resistance prevalence of Pseudomonas aeruginosa-a frequent pathogen causing nosocomial infections. Synthetic data produced by the models are then used to estimate the sample size and study period needed to observe the predicted effects in clinical trials. Our results show that de-escalation can reduce colonization and infections caused by bacterial strains resistant to the empiric antibiotic, limit the use of broad-spectrum antibiotics, and avoid inappropriate empiric therapies. Further, we show that de-escalation could reduce the overall super-infection incidence, and this benefit becomes more evident under good compliance with hand hygiene protocols among health care workers. Finally, we find that any clinical study aiming to observe the essential effects of de-escalation should involve at least ten arms and last for four years-a size never attained in prior studies. This study explains the controversial findings of de-escalation in previous clinical studies and illustrates how mathematical models can inform outcome expectations and guide the design of clinical studies.

The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component

Multidrug antibiotic resistance is an urgent public health concern. Multiple strategies have been suggested to alleviate this problem, including the use of antibiotic combinations and cyclic therapies. We examine how adaptation to (1) combinations of drugs affects resistance to individual drugs, and to (2) individual drugs alters responses to drug combinations. To evaluate this, we evolved multiple strains of drug resistant Staphylococcus epidermidis in the lab. We show that evolving resistance to four highly synergistic combinations does not result in cross-resistance to all of their components. Likewise, prior resistance to one antibiotic in a combination does not guarantee survival when exposed to the combination. We also identify four 3-step and four 2-step treatments that inhibit bacterial growth and confer collateral sensitivity with each step, impeding the development of multidrug resistance. This study highlights the importance of considering higher-order drug combinations in sequential therapies and how antibiotic interactions can influence the evolutionary trajectory of bacterial populations.

Resistance-minimising strategies for introducing a novel antibiotic for gonorrhoea treatment: a mathematical modelling study

BACKGROUND

Gonorrhoea is a highly prevalent sexually transmitted infection and an urgent public health concern because of increasing antibiotic resistance in Neisseria gonorrhoeae. Only ceftriaxone remains as the recommended treatment in the USA. With the prospect of new anti-gonococcal antibiotics being approved, we aimed to evaluate how to deploy a new drug to maximise its clinically useful lifespan.

METHODS

We used a compartmental model of gonorrhoea transmission in a US population of men who have sex with men (MSM) to compare strategies for introducing a new antibiotic for gonorrhoea treatment. The MSM population was stratified into three sexual activity groups (low, intermediate, and high) characterised by annual rates of partner change. The four introduction strategies tested were: (1) random 50-50 allocation, where each treatment-seeking infected individual had a 50% probability of receiving either drug A (current drug; a ceftriaxone-like antibiotic) or drug B (a new antibiotic), effective at time 0; (2) combination therapy of both the current drug and the new antibiotic; (3) reserve strategy, by which the new antibiotic was held in reserve until the current therapy reached a 5% threshold prevalence of resistance; and (4) gradual switch, or the gradual introduction of the new drug until random 50-50 allocation was reached. The primary outcome of interest was the time until 5% prevalence of resistance to each of the drugs (the new drug and the current ceftriaxone-like antibiotic); sensitivity of the primary outcome to the properties of the new antibiotic, specifically the probability of resistance emergence after treatment and the fitness costs of resistance, was explored. Secondary outcomes included the time to a 1% resistance threshold for each drug, as well as population-level prevalence, mean and range annual incidence, and the cumulative number of incident gonococcal infections.

FINDINGS

Under baseline model conditions, a 5% prevalence of resistance to each of drugs A and B was reached within 13·9 years with the reserve strategy, 18·2 years with the gradual switch strategy, 19·2 years with the random 50-50 allocation strategy, and 19·9 years with the combination therapy strategy. The reserve strategy was consistently inferior for mitigating antibiotic resistance under the parameter space explored and was increasingly outperformed by the other strategies as the probability of de novo resistance emergence decreased and as the fitness costs associated with resistance increased. Combination therapy tended to prolong the development of antibiotic resistance and minimise the number of annual gonococcal infections (under baseline model conditions, mean number of incident infections per year 178 641 [range 177 998-181 731] with combination therapy, 180 084 [178 011-184 405] with the reserve strategy).

INTERPRETATION

Our study argues for rapid introduction of new anti-gonococcal antibiotics, recognising that the feasibility of each strategy must incorporate cost, safety, and other practical concerns. The analyses should be revisited once robust estimates of key parameters-ie, the likelihood of emergence of resistance and fitness costs of resistance for the new antibiotic-are available.

FUNDING

US Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases.

Resistance-minimizing strategies for introducing a novel antibiotic for gonorrhea treatment: a mathematical modeling study

Background

Gonorrhea is a highly prevalent sexually transmitted infection and an urgent public health concern due to increasing antibiotic resistance. Only ceftriaxone remains as the recommended treatment in the U.S. The prospect of approval of new anti-gonococcal antibiotics raises the question of how to deploy a new drug to maximize its clinically useful lifespan.

Methods

We used a compartmental model of gonorrhea transmission in the U.S. population of men who have sex with men to compare strategies for introducing a new antibiotic for gonorrhea treatment. The strategies tested included holding the new antibiotic in reserve until the current therapy reached a threshold prevalence of resistance; using either drug, considering immediate and gradual introduction of the new drug; and combination therapy. The primary outcome of interest was the time until 5% prevalence of resistance to both the novel drug and to the current first-line drug (ceftriaxone).

Findings

The reserve strategy was consistently inferior for mitigating antibiotic resistance under the parameter space explored. The reserve strategy was increasingly outperformed by the other strategies as the probability of de novo resistance emergence decreased and as the fitness costs associated with resistance increased. Combination therapy tended to prolong the development of antibiotic resistance and minimize the number of annual gonococcal infections.

Interpretation

Our study argues for rapid introduction of new anti-gonococcal antibiotics, recognizing that the feasibility of each strategy must incorporate cost, safety, and other practical concerns. The analyses should be revisited once robust estimates of key parameters-likelihood of emergence of resistance and fitness costs of resistance for the new antibiotic-are available.

Funding

U.S. Centers for Disease Control and Prevention (CDC), National Institute of Allergy and Infectious Diseases.

Association between Tulathromycin Treatment for Bovine Respiratory Disease and Antimicrobial Resistance Profiles among Gut Commensals and Foodborne Bacterial Pathogens Isolated from Feces of Beef Steers

ABSTRACT

This study was conducted to evaluate the association between a therapeutic dose of tulathromycin for bovine respiratory disease in beef steers and the antimicrobial and multidrug resistance profiles of the gastrointestinal tract commensals Escherichia coli and Enterococcus spp. and the foodborne pathogens Salmonella enterica and Campylobacter spp. isolated from fecal samples. Individual fecal samples were collected on days 0, 14, and 28 from 70 beef steers that were housed in a single pen and had been treated or not treated with tulathromycin. Samples were cultured for bacterial isolation, and isolates were tested for antimicrobial susceptibility with the broth microdilution method to determine the MICs of clinically relevant antimicrobials used in both human and veterinary medicine. Generalized linear mixed effects models were fitted to estimate the prevalence of the bacterial species and the prevalence of resistant isolates over time and between treated and nontreated cattle and of multidrug-resistant isolates. Model-adjusted mean prevalences of E. coli, Enterococcus spp., S. enterica, and Campylobacter spp. were 99.5, 85.9, 1.5, and 17.7%, respectively. The prevalence of erythromycin-resistant Enterococcus spp. was significantly higher on day 14 (59.7%) than on day 28 (22.2%). A higher prevalence of erythromycin-resistant Enterococcus spp. was found in samples from treated (59.3%) than in samples from nontreated (27.6%) animals. Multidrug resistance (three or more antimicrobial classes) was observed in 8.4% of E. coli isolates and 62.7% of Enterococcus isolates. The administration of tulathromycin was significantly associated with an increased prevalence of erythromycin-resistant Enterococcus spp. isolates.

HIGHLIGHTS

The impact of public health interventions on the future prevalence of ESBL-producing Klebsiella pneumoniae: a population based mathematical modelling study

Background

Future prevalence of colonization with extended-spectrum betalactamase (ESBL-) producing K. pneumoniae in humans and the potential of public health interventions against the spread of these resistant bacteria remain uncertain.

Methods

Based on antimicrobial consumption and susceptibility data recorded during > 13 years in a Swiss region, we developed a mathematical model to assess the comparative effect of different interventions on the prevalence of colonization.

Results

Simulated prevalence stabilized in the near future when rates of antimicrobial consumption and in-hospital transmission were assumed to remain stable (2025 prevalence: 6.8% (95CI%:5.4–8.8%) in hospitals, 3.5% (2.5–5.0%) in the community versus 6.1% (5.0–7.5%) and 3.2% (2.3–4.2%) in 2019, respectively). When overall antimicrobial consumption was set to decrease by 50%, 2025 prevalence declined by 75% in hospitals and by 64% in the community. A 50% decline in in-hospital transmission rate led to a reduction in 2025 prevalence of 31% in hospitals and no reduction in the community. The best model fit estimated that 49% (6–100%) of observed colonizations could be attributable to sources other than human-to-human transmission within the geographical setting.

Conclusions

Projections suggests that overall antimicrobial consumption will be, by far, the most powerful driver of prevalence and that a large fraction of colonizations could be attributed to non-local transmissions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07441-z.

Oregano Oil and Harmless Blue Light to Synergistically Inactivate Multidrug-Resistant Pseudomonas aeruginosa

Blue light (BL) at 405 nm and oregano essential oil (OEO) have shown bactericidal activity by its own. Here, we demonstrated that the two synergistically killed multidrug-resistant (MDR) Pseudomonas aeruginosa (Pa). Pa ATCC19660 and HS0065 planktonic cells and mature biofilms were reduced by more than 7 log₁₀ after treatment by BL combined with OEO, in sharp contrast to no significant bacterial reduction with the monotreatment. The duo also sufficiently eliminated acute or biofilm-associated infection of open burn wounds in murine without incurring any harmful events in the skin. The synergic bactericide was attributed mainly to the ability of OEO to magnify cytotoxic reactive oxygen species (ROS) production initiated by BL that excited endogenous tetrapyrrole macrocycles in bacteria while completely sparing the surrounding tissues from the phototoxic action. OEO ingredient analysis in combination with microbial assays identified carvacrol and its isomer thymol to be the major phytochemicals that cooperated with BL executing synergic killing. The finding argues persuasively for valuable references of carvacrol and thymol in assessing and standardizing the bactericidal potential of various OEO products.

Comparing optimization criteria in antibiotic allocation protocols

Clinicians prescribing antibiotics in a hospital context follow one of several possible 'treatment protocols'-heuristic rules designed to balance the immediate needs of patients against the long-term threat posed by the evolution of antibiotic resistance and multi-resistant bacteria. Several criteria have been proposed for assessing these protocols; unfortunately, these criteria frequently conflict with one another, each providing a different recommendation as to which treatment protocol is best. Here, we review and compare these optimization criteria. We are able to demonstrate that criteria focused primarily on slowing evolution of resistance are directly antagonistic to patient health both in the short and long term. We provide a new optimization criteria of our own, intended to more meaningfully balance the needs of the future and present. Asymptotic methods allow us to evaluate this criteria and provide insights not readily available through the numerical methods used previously in the literature. When cycling antibiotics, we find an antibiotic switching time which proves close to optimal across a wide range of modelling assumptions.

Candidates for Repurposing as Anti-Virulence Agents Based on the Structural Profile Analysis of Microbial Collagenase Inhibitors

The pharmacological inhibition of the bacterial collagenases (BC) enzymes is considered a promising strategy to block the virulence of the bacteria without targeting the selection mechanism leading to drug resistance. The chemical structures of the Clostridium perfringens collagenase A (ColA) inhibitors were analyzed using Bemis-Murcko skeletons, Murcko frameworks, the type of plain rings, and docking studies. The inhibitors were classified based on their structural architecture and various scoring methods were implemented to predict the probability of new compounds to inhibit ColA and other BC. The analyses indicated that all compounds contain at least one aromatic ring, which is often a nitrobenzene fragment. 2-Nitrobenzene based compounds are, on average, more potent BC inhibitors compared to those derived from 4-nitrobenzene. The molecular descriptors MDEO-11, AATS0s, ASP-0, and MAXDN were determined as filters to identify new BC inhibitors and highlighted the necessity for a compound to contain at least three primary oxygen atoms. The DrugBank database was virtually screened using the developed methods. A total of 100 compounds were identified as potential BC inhibitors, of which, 10 are human approved drugs. Benzthiazide, entacapone, and lodoxamide were chosen as the best candidates for in vitro testing based on their pharmaco-toxicological profile.

Antibiotic treatment protocols revisited: the challenges of a conclusive assessment by mathematical modelling

Hospital-acquired bacterial infections lead to prolonged hospital stays and increased mortality. The problem is exacerbated by antibiotic-resistant strains that delay or impede effective treatment. To ensure successful therapy and to manage antibiotic resistance, treatment protocols that draw on several different antibiotics might be used. This includes the administration of drug cocktails to individual patients (combination therapy) but also the random assignment of drugs to different patients (mixing) and a regular switch in the default drug used in the hospital from drug A to drug B and back (cycling). For more than 20 years, mathematical models have been used to assess the prospects of antibiotic combination therapy, mixing and cycling. But while tendencies in their ranking across studies have emerged, the picture remains surprisingly inconclusive and incomplete. In this article, we review existing modelling studies and demonstrate by means of examples how methodological factors complicate the emergence of a consistent picture. These factors include the choice of the criterion by which the effects of the protocols are compared, the model implementation and its analysis. We thereafter discuss how progress can be made and suggest future modelling directions.

Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting

The rapid rise of antibiotic resistance, combined with the increasing cost and difficulties to develop new antibiotics, calls for treatment strategies that enable more sustainable antibiotic use. The development of such strategies, however, is impeded by the lack of suitable experimental approaches that allow testing their effects under realistic epidemiological conditions. Here, we present an approach to compare the effect of alternative multidrug treatment strategies in vitro using a robotic liquid-handling platform. We use this framework to study resistance evolution and spread implementing epidemiological population dynamics for treatment, transmission, and patient admission and discharge, as may be observed in hospitals. We perform massively parallel experimental evolution over up to 40 d and complement this with a computational model to infer the underlying population-dynamical parameters. We find that in our study, combination therapy outperforms monotherapies, as well as cycling and mixing, in minimizing resistance evolution and maximizing uninfecteds, as long as there is no influx of double resistance into the focal treated community.

A game theoretic approach reveals that discretizing clinical information can reduce antibiotic misuse

The overuse of antibiotics is exacerbating the antibiotic resistance crisis. Since this problem is a classic common-goods dilemma, it naturally lends itself to a game-theoretic analysis. Hence, we designed a model wherein physicians weigh whether antibiotics should be prescribed, given that antibiotic usage depletes its future effectiveness. The physicians' decisions rely on the probability of a bacterial infection before definitive laboratory results are available. We show that the physicians' equilibrium decision rule of antibiotic prescription is not socially optimal. However, we prove that discretizing the information provided to physicians can mitigate the gap between their equilibrium decisions and the social optimum of antibiotic prescription. Despite this problem's complexity, the effectiveness of the discretization solely depends on the type of information available to the physician to determine the nature of infection. This is demonstrated on theoretic distributions and a clinical dataset. Our results provide a game-theory based guide for optimal output of current and future decision support systems of antibiotic prescription.

Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling

OBJECTIVES

Microbial resistance exhibits dependency patterns between different antibiotics, termed cross-resistance and collateral sensitivity. These patterns differ between experimental and clinical settings. It is unclear whether the differences result from biological reasons or from confounding, biasing results found in clinical settings. We set out to elucidate the underlying dependency patterns between resistance to different antibiotics from clinical data, while accounting for patient characteristics and previous antibiotic usage.

METHODS

Additive Bayesian network modelling was employed to simultaneously estimate relationships between variables in a dataset of bacterial cultures derived from hospitalized patients and tested for resistance to multiple antibiotics. Data contained resistance results, patient demographics and previous antibiotic usage, for five bacterial species: Escherichia coli (n = 1054), Klebsiella pneumoniae (n = 664), Pseudomonas aeruginosa (n = 571), CoNS (n = 495) and Proteus mirabilis (n = 415).

RESULTS

All links between resistance to the various antibiotics were positive. Multiple direct links between resistance of antibiotics from different classes were observed across bacterial species. For example, resistance to gentamicin in E. coli was directly linked with resistance to ciprofloxacin (OR = 8.39, 95% credible interval 5.58-13.30) and sulfamethoxazole/trimethoprim (OR = 2.95, 95% credible interval 1.97-4.51). In addition, resistance to various antibiotics was directly linked with previous antibiotic usage.

CONCLUSIONS

Robust relationships among resistance to antibiotics belonging to different classes, as well as resistance being linked to having taken antibiotics of a different class, exist even when taking into account multiple covariate dependencies. These relationships could help inform choices of antibiotic treatment in clinical settings.

Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records

BACKGROUND

Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning algorithms. However, they are scarcely used for empiric antibiotic therapy. Here we accurately predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using machine learning algorithms applied to patients' electronic medical records (EMR).

METHODS

The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their electronic medical records. Five antibiotics were examined: Ceftazidime (n=2942), Gentamicin (n=4360), Imipenem (n=2235), Ofloxacin (n=3117) and Sulfamethoxazole-Trimethoprim (n=3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble combining all three algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis.

RESULTS

The ensemble model outperformed the separate models and produced accurate predictions on a test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble model yielded area under the receiver-operating-characteristic (auROC) scores of 0.73-0.79, for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. The effects of different variables on the predictions were assessed and found consistent with previously identified risk factors for antibiotic resistance.

CONCLUSIONS

Our study demonstrates the potential of machine learning models to accurately predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapid information regarding the infecting bacterial species can improve predictions substantially. The implementation of such systems should be seriously considered by clinicians to aid correct empiric therapy and to potentially reduce antibiotic misuse.

Informed and uninformed empirical therapy policies

We argue that a proper distinction must be made between informed and uninformed decision making when setting empirical therapy policies, as this allows one to estimate the value of gathering more information about the pathogens and their transmission and thus to set research priorities. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in a health care facility and the emergence and spread of resistance to two drugs. We focus on information and uncertainty regarding the parameters of this model. We consider a family of adaptive empirical therapy policies. In the uninformed setting, the best adaptive policy allowsone to reduce the average cumulative infected patient days over 2 years by 39.3% (95% confidence interval (CI), 30.3-48.1%) compared to the combination therapy. Choosing empirical therapy policies while knowing the exact parameter values allows one to further decrease the cumulative infected patient days by 3.9% (95% CI, 2.1-5.8%) on average. In our setting, the benefit of perfect information might be offset by increased drug consumption.

Kouzani A, Mosadegh B, Gharaie S. Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Advancements in materials science and fabrication techniques have contributed to the significant growing attention to a wide variety of sensors for digital healthcare. While the progress in this area is tremendously impressive, few wearable sensors with the capability of real-time blood pressure monitoring are approved for clinical use. One of the key obstacles in the further development of wearable sensors for medical applications is the lack of comprehensive technical evaluation of sensor materials against the expected clinical performance. Here, we present an extensive review and critical analysis of various materials applied in the design and fabrication of wearable sensors. In our unique transdisciplinary approach, we studied the fundamentals of blood pressure and examined its measuring modalities while focusing on their clinical use and sensing principles to identify material functionalities. Then, we carefully reviewed various categories of functional materials utilized in sensor building blocks allowing for comparative analysis of the performance of a wide range of materials throughout the sensor operational-life cycle. Not only this provides essential data to enhance the materials' properties and optimize their performance, but also, it highlights new perspectives and provides suggestions to develop the next generation pressure sensors for clinical use.

Evolution of antibiotic cross-resistance and collateral sensitivity in Staphylococcus epidermidis using the mutant prevention concentration and the mutant selection window

In bacteria, evolution of resistance to one antibiotic is frequently associated with increased resistance (cross-resistance) or increased susceptibility (collateral sensitivity) to other antibiotics. Cross-resistance and collateral sensitivity are typically evaluated at the minimum inhibitory concentration (MIC). However, these susceptibility changes are not well characterized with respect to the mutant prevention concentration (MPC), the antibiotic concentration that prevents a single-step mutation from occurring. We measured the MIC and the MPC for Staphylococcus epidermidis and 14 single-drug resistant strains against seven antibiotics. We found that the MIC and the MPC were positively correlated but that this correlation weakened if cross-resistance did not evolve. If any type of resistance did evolve, the range of concentrations between the MIC and the MPC tended to shift right and widen. Similar patterns of cross-resistance and collateral sensitivity were observed at the MIC and MPC levels, though more symmetry was observed at the MIC level. Whole-genome sequencing revealed mutations in both known-target and nontarget genes. Moving forward, examining both the MIC and the MPC may lead to better predictions of evolutionary trajectories in antibiotic-resistant bacteria.

Antimicrobial stewardship programs; a two-part narrative review of step-wise design and issues of controversy. Part II: Ten questions reflecting knowledge gaps and issues of controversy in the field of antimicrobial stewardship

Regardless of one's opinion on antimicrobial stewardship programs (ASPs), it is hardly possible to work in hospital care and not be exposed to the term or its practical effects. Despite the term being relatively new, the number of publications in the field is vast, including several excellent reviews of general and specific aspects. Work in antimicrobial stewardship is complex, and include aspects not only of infectious disease and microbiology, but also of epidemiology, genetics, behavioural psychology, systems science, economics and ethics, to name but a few. This review aims to take several of these aspects and the scientific evidence from antimicrobial stewardship studies and merge them into two questions: How should we design ASPs based on what we know today? and Which are the most essential unanswered questions regarding antimicrobial stewardship on a broader scale? This narrative review is written in two separate parts aiming to provide answers to the two questions. The first part, published separately, is written as a step-wise approach to designing a stewardship intervention based on the pillars of unmet need, feasibility, scientific evidence and necessary core elements. It is written mainly as a guide to someone new to the field. It is sorted into five distinct steps; (a) focusing on designing aims; (b) assessing performance and local barriers to rational antimicrobial use; (c) deciding on intervention technique; (d) practical, tailored design including core element inclusion; and (e) evaluation and sustainability. This second part formulates 10 critical questions on controversies in the field of antimicrobial stewardship. It is aimed at clinicians and researchers with stewardship experience and strives to promote discussion, not to provide answers.

Modeling Antibiotic Use Strategies in Intensive Care Units: Comparing De-escalation and Continuation

Antimicrobial de-escalation refers to the treatment mechanism of switching from empiric antibiotics with good coverage to alternatives based on laboratory susceptibility test results, with the aim of avoiding unnecessary use of broad-spectrum antibiotics. In a previous study, we have developed multi-strain and multi-drug models in an intensive care unit setting, to evaluate the benefits and trade-offs of de-escalation in comparison with the conventional strategy called antimicrobial continuation. Our simulation results indicated that for a large portion of credible parameter combinations, de-escalation reduces the use of the empiric antibiotic but increases the probabilities of colonization and infections. In this paper, we first simplify the previous models to compare the long-term dynamical behaviors between de-escalation and continuation systems under a two-strain scenario. The analytical results coincide with our previous findings in the complex models, indicating the benefits and unintended consequences of de-escalation strategy result from the nature of this treatment mechanism, not from the complexity of the high-dimensional systems. By extending the models to three-strain scenarios, we find that de-escalation is superior than continuation in preventing outbreaks of invading strains that are resistant to empiric antibiotics. Thus decisions on antibiotic use strategies should be made specifically according to ICU conditions and intervention objectives.

Defining and combating antibiotic resistance from One Health and Global Health perspectives

Several interconnected human, animal and environmental habitats can contribute to the emergence, evolution and spread of antibiotic resistance, and the health of these contiguous habitats (the focus of the One Health approach) may represent a risk to human health. Additionally, the expansion of resistant clones and antibiotic resistance determinants among human-associated, animal-associated and environmental microbiomes have the potential to alter bacterial population genetics at local and global levels, thereby modifying the structure, and eventually the productivity, of microbiomes where antibiotic-resistant bacteria can expand. Conversely, any change in these habitats (including pollution by antibiotics or by antibiotic-resistant organisms) may influence the structures of their associated bacterial populations, which might affect the spread of antibiotic resistance to, and among, the above-mentioned microbiomes. Besides local transmission among connected habitats-the focus of studies under the One Health concept-the transmission of resistant microorganisms might occur on a broader (even worldwide) scale, requiring coordinated Global Health actions. This Review provides updated information on the elements involved in the evolution and spread of antibiotic resistance at local and global levels, and proposes studies to be performed and strategies to be followed that may help reduce the burden of antibiotic resistance as well as its impact on human and planetary health.

Wound closure and follow-up after total knee arthroplasty - Do they affect the rate of antibiotic prescription?

BACKGROUND

This study aimed to evaluate risk factors for oral antibiotic prescription in the first six weeks after primary TKA, particularly whether the wound closure method (staples or sutures) and two-week follow-up clinician (surgeon or general practitioner (GP)) altered antibiotic use.

METHODS

Four thousand eight hundred forty-six TKAs from January 2013 to December 2016 at three tertiary hospitals in Auckland, New Zealand were analysed by manual review of patient electronic records and a national prescription database. Surgeon preference dictates the method of wound closure and whether wound review is followed up by the operating surgeon or by the patient's GP. Univariate and multivariate analysis was carried out to identify significant patient and surgical risk factors for oral antibiotic prescribing.

RESULTS

Oral antibiotics were prescribed in 24% of patients following primary TKA. Twenty-six percent of patients closed with staples were prescribed oral antibiotics versus 19% with sutures (adjusted OR = 1.4, p < 0.004). Excluding re-presentations and readmissions, GPs prescribed oral antibiotics in 22% of patients compared to seven percent of patients seen by surgeons (adjusted OR = 2.8, p < 0.001). Other risk factors for antibiotic prescription included increasing age, BMI and ASA score.

CONCLUSION

Oral antibiotic prescribing rates are higher if the wound was closed with staples and if a GP performed the two-week follow-up. Improved communication between surgeons and GPs are required to ensure adequate follow-up following TKA and appropriate oral antibiotic use.

Variation in Mutant Prevention Concentrations

Objectives:Understanding how phenotypic traits vary has been a longstanding goal of evolutionary biologists. When examining antibiotic-resistance in bacteria, it is generally understood that the minimum inhibitory concentration (MIC) has minimal variation specific to each bacterial strain-antibiotic combination. However, there is a less studied resistance trait, the mutant prevention concentration (MPC), which measures the MIC of the most resistant sub-population. Whether and how MPC varies has been poorly understood. Here, we ask a simple, yet important question: How much does the MPC vary, within a single strain-antibiotic association? Using a Staphylococcus species and five antibiotics from five different antibiotic classes—ciprofloxacin, doxycycline, gentamicin, nitrofurantoin, and oxacillin—we examined the frequency of resistance for a wide range of concentrations per antibiotic, and measured the repeatability of the MPC, the lowest amount of antibiotic that would ensure no surviving cells in a 10¹⁰ population of bacteria.

Results: We found a wide variation within the MPC and distributions that were rarely normal. When antibiotic resistance evolved, the distribution of the MPC changed, with all distributions becoming wider and some multi-modal.

Conclusion: Unlike the MIC, there is high variability in the MPC for a given bacterial strain-antibiotic combination.

Characterization of Two New Multidrug-Resistant Strains of Mycobacterium smegmatis: Tools for Routine In Vitro Screening of Novel Anti-Mycobacterial Agents

Mycobacterium tuberculosis is a pathogen of global public health concern. This threat is exacerbated by the emergence of multidrug-resistant and extremely-drug-resistant strains of the pathogen. We have obtained two distinct clones of multidrug-resistant Mycobacterium smegmatis after gradual exposure of Mycobacterium smegmatis mc² 155 to increasing concentrations of erythromycin. The resulting resistant strains of Mycobacterium smegmatis exhibited robust viability in the presence of high concentrations of erythromycin and were found to be resistant to a wide range of other antimicrobials. They also displayed a unique growth phenotype in comparison to the parental drug-susceptible Mycobacterium smegmatis mc² 155, and a distinct colony morphology in the presence of cholesterol. We propose that these two multidrug-resistant clones of Mycobacterium smegmatis could be used as model organisms at the inceptive phase of routine in vitro screening of novel antimicrobial agents targeted against multidrug-resistant Mycobacterial tuberculosis.

Variability in the Adaptive Response of Antibiotic-Resistant Salmonella Typhimurium to Environmental Stresses

This study was designed to evaluate the resistance phenotype and genotype of wild type (WT)-, cefotaxime (CET)-, and ciprofloxacin (CIP)-induced Salmonella Typhimurium ATCC 19585, CIP-resistant Salmonella Typhimurium ATCC 19585, Salmonella Typhimurium CCARM 8009, and Salmonella Typhimurium KCCM 40253 before and after exposure to pH 4.5, 4% NaCl, and heat at 42°C. The susceptibilities of WT Salmonella Typhimurium ATCC 19585 and WT Salmonella Typhimurium KCCM 40253 to all antibiotics tested in this study were decreased after CET and CIP induction with the exception with kanamycin, meropenem, and polymyxin B. The highest β-lactamase activities were 2.8 and 3.3 nmol/(min·mL), respectively, at the WT- and CET-induced Salmonella Typhimurium CCARM 8009. FT-IR spectra were found to be dominant at the region from 1,700 to 1,500 cm^-1 corresponding to proteins such as amides I, II, and III. The relative expression levels of efflux pump-related genes (acrA, acrB, and TolC), porin-related gene (ompC), virulence-related gene (stn), adhesion-related gene (fimA), and stress-induced alternative sigma factor (rpoS) varied in the antibiotic resistance and stress exposure. This study provides useful information for understanding the antibiotic resistance profile, physicochemical property, and gene expression pattern in Salmonella Typhimurium in association with the induction of antibiotic resistance and exposure to environmental stresses.

Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review

Selective pressure exerted by the widespread use of antibacterial drugs is accelerating the development of resistant bacterial populations. The purpose of this scoping review was to summarise the range of studies that use dynamic models to analyse the problem of bacterial resistance in relation to antibacterial use in human and animal populations. A comprehensive search of the peer-reviewed literature was performed and non-duplicate articles (n = 1486) were screened in several stages. Charting questions were used to extract information from the articles included in the final subset (n = 81). Most studies (86%) represent the system of interest with an aggregate model; individual-based models are constructed in only seven articles. There are few examples of inter-host models outside of human healthcare (41%) and community settings (38%). Resistance is modelled for a non-specific bacterial organism and/or antibiotic in 40% and 74% of the included articles, respectively. Interventions with implications for antibacterial use were investigated in 67 articles and included changes to total antibiotic consumption, strategies for drug management and shifts in category/class use. The quality of documentation related to model assumptions and uncertainty varies considerably across this subset of articles. There is substantial room to improve the transparency of reporting in the antibacterial resistance modelling literature as is recommended by best practice guidelines.

Vaccination can drive an increase in frequencies of antibiotic resistance among nonvaccine serotypes of Streptococcus pneumoniae

The bacterial pathogen Streptococcus pneumoniae is a major public health concern, being responsible for more than 1.5 million deaths annually through pneumonia, meningitis, and septicemia. Available vaccines target only a subset of serotypes, so vaccination is often accompanied by a rise in the frequency of nonvaccine serotypes. Epidemiological studies suggest that such a change in serotype frequencies is often coupled with an increase of antibiotic resistance among nonvaccine serotypes. Building on previous multilocus models for bacterial pathogen population structure, we have developed a theoretical framework incorporating variation of serotype and antibiotic resistance to examine how their associations may be affected by vaccination. Using this framework, we find that vaccination can result in a rapid increase in the frequency of preexisting resistant variants of nonvaccine serotypes due to the removal of competition from vaccine serotypes.

Use of mathematical modelling to assess the impact of vaccines on antibiotic resistance

Antibiotic resistance is a major global threat to the provision of safe and effective health care. To control antibiotic resistance, vaccines have been proposed as an essential intervention, complementing improvements in diagnostic testing, antibiotic stewardship, and drug pipelines. The decision to introduce or amend vaccination programmes is routinely based on mathematical modelling. However, few mathematical models address the impact of vaccination on antibiotic resistance. We reviewed the literature using PubMed to identify all studies that used an original mathematical model to quantify the impact of a vaccine on antibiotic resistance transmission within a human population. We reviewed the models from the resulting studies in the context of a new framework to elucidate the pathways through which vaccination might impact antibiotic resistance. We identified eight mathematical modelling studies; the state of the literature highlighted important gaps in our understanding. Notably, studies are limited in the range of pathways represented, their geographical scope, and the vaccine-pathogen combinations assessed. Furthermore, to translate model predictions into public health decision making, more work is needed to understand how model structure and parameterisation affects model predictions and how to embed these predictions within economic frameworks.

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.

With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing

Background

Natural selection favors changes that lead to genotypes possessing high fitness. A conflict arises when several mutations are required for adaptation, but each mutation is separately deleterious. The process of a population evolving from a genotype encoding for a local fitness maximum to a higher fitness genotype is termed an adaptive peak shift.

Results

Here we suggest cooperative behavior as a factor that can facilitate adaptive peak shifts. We model cooperation in a public goods scenario, wherein each individual contributes resources that are later equally redistributed among all cooperating individuals. We use mathematical modeling and stochastic simulations to study the effect of cooperation on peak shifts in both panmictic and structured populations. Our results show that cooperation can substantially affect the rate of complex adaptation. Furthermore, we show that cooperation increases the population diversity throughout the peak shift process, thus increasing the robustness of the population to sudden environmental changes.

Conclusions

We provide a new explanation to adaptive valley crossing in natural populations and suggest that the long term evolution of a species depends on its social behavior.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-017-0983-2) contains supplementary material, which is available to authorized users.

Benefits and unintended consequences of antimicrobial de-escalation: Implications for stewardship programs

Sequential antimicrobial de-escalation aims to minimize resistance to high-value broad-spectrum empiric antimicrobials by switching to alternative drugs when testing confirms susceptibility. Though widely practiced, the effects de-escalation are not well understood. Definitions of interventions and outcomes differ among studies. We use mathematical models of the transmission and evolution of Pseudomonas aeruginosa in an intensive care unit to assess the effect of de-escalation on a broad range of outcomes, and clarify expectations. In these models, de-escalation reduces the use of high-value drugs and preserves the effectiveness of empiric therapy, while also selecting for multidrug-resistant strains and leaving patients vulnerable to colonization and superinfection. The net effect of de-escalation in our models is to increase infection prevalence while also increasing the probability of effective treatment. Changes in mortality are small, and can be either positive or negative. The clinical significance of small changes in outcomes such as infection prevalence and death may exceed more easily detectable changes in drug use and resistance. Integrating harms and benefits into ranked outcomes for each patient may provide a way forward in the analysis of these tradeoffs. Our models provide a conceptual framework for the collection and interpretation of evidence needed to inform antimicrobial stewardship.

Antibiotic cross-resistance in the lab and resistance co-occurrence in the clinic: Discrepancies and implications in E.coli

INTRODUCTION

Antibiotic resistance is an important public health issue, and vast resources are invested in researching new ways to fight it. Recent experimental works have shown that resistance to some antibiotics can result in increased susceptibility to others, namely induce cross-sensitivity. This phenomenon could be utilized to increase efficiency of antibiotic treatment strategies that minimize resistance. However, as conditions in experimental settings and in the clinic may differ substantially, the implications of cross-sensitivity for clinical settings are not guaranteed and should be examined.

METHODS

In this work we analyzed data of Escherichia coli isolates from patients' blood, sampled in Rabin Medical Center, Israel, to examine co-occurrence of resistance to antibiotics in the clinic. We compared the co-occurrence patterns with cross-sensitivity patterns observed in the lab.

RESULTS

Our data showed only positively associated occurrence of resistance, even with antibiotics that were shown to induce cross-sensitivity in laboratory conditions. We used a mathematical model to examine the potential effects of cross-sensitivity versus co-occurrence on the spread of drug resistance.

CONCLUSIONS

We conclude that resistance frequencies in the clinic can have a substantial effect on the success of treatment strategies, and should be considered alongside experimental evidence of cross-sensitivity.

pH dependent supramolecular recognition of dapoxyl sodium sulfonate with 2-hydroxypropyl β-cyclodextrin: an application towards food-additive formulation

pH dependent host–guest complexation of dapoxyl sodium sulfonate (DSS), an intramolecular charge transfer dye, with 2-hydroxypropyl beta-cyclodextrin (HP-β-CD) has been investigated.