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

Immunoinhibitory effects of anti-tuberculosis therapy induce the host vulnerability to tuberculosis recurrence

The host immune responses play a pivotal role in the establishment of long-term memory responses, which effectively aids in infection clearance. However, the prevailing anti-tuberculosis therapy, while aiming to combat tuberculosis (TB), also debilitates innate and adaptive immune components of the host. In this study, we explored how the front-line anti-TB drugs impact the host immune cells by modulating multiple signaling pathways and subsequently leading to disease relapse. Administration of these drugs led to a reduction in innate immune activation and also the cytokines required to trigger protective T cell responses. Moreover, these drugs led to activation-induced cell death in the mycobacterial-specific T cell leading to a reduced killing capacity. Furthermore, these drugs stalled the T cell differentiation into memory subsets by modulating the activation of STAT3, STAT4, FOXO1, and NFκB transcription factors and hampering the Th1 and Th17-mediated long-term host protective memory responses. These findings suggest the urgent need to augment directly observed treatment, short-course (DOTS) therapy with immunomodulatory agents to mitigate the adverse effects linked to the treatment.IMPORTANCEAs a central component of TB eradication initiatives, directly observed treatment, short-course (DOTS) therapy imparts immune-dampening effects during the course of treatment. This approach undermines the host immune system by delaying the activation process and lowering the immune response. In our investigation, we have unveiled the impact of DOTS on specific immune cell populations. Notably, the signaling pathways involving STAT3 and STAT4 critical for memory responses and NFκβ associated with pro-inflammation were substantially declined due to the therapy. Consequently, these drugs exhibit limited effectiveness in preventing recurrence of the disease. These observations highlight the imperative integration of immunomodulators to manage TB infection.

Dynamics of antibiotic resistance genes in the sediments of a water-diversion lake and its human exposure risk behaviour

The dynamics and exposure risk behaviours of antibiotic resistance genes (ARGs) in the sediments of water-diversion lakes remain poorly understood. In this study, spatiotemporal investigations of ARG profiles in sediments targeting non-water (NWDP) and water diversion periods (WDP) were conducted in Luoma Lake, a typical water-diversion lake, and an innovative dynamics-based risk assessment framework was constructed to evaluate ARG exposure risks to local residents. ARGs in sediments were significantly more abundant in the WDP than in the NWDP, but there was no significant variation in their spatial distribution in either period. Moreover, the pattern of ARG dissemination in sediments was unchanged between the WDP and NWDP, with horizontal gene transfer (HGT) and vertical gene transfer (VGT) contributing to ARG dissemination in both periods. However, water diversion altered the pattern in lake water, with HGT and VGT in the NWDP but only HGT in the WDP, which were critical pathways for the dissemination of ARGs. The significantly lower ARG sediment-water partition coefficient in the WDP indicated that water diversion could shift the fate of ARGs and facilitate their aqueous partitioning. Risk assessment showed that all age groups faced a higher human exposure risk of ARGs (HERA) in the WDP than in the NWDP, with the 45-59 age group having the highest risk. Furthermore, HERA increased overall with the bacterial carrying capacity in the local environment and peaked when the carrying capacity reached three (NWDP) or four (WDP) orders of magnitude higher than the observed bacterial population. HGT and VGT promoted, whereas ODF covering gene mutation and loss mainly reduced HERA in the lake. As the carrying capacity increased, the relative contribution of ODF to HERA remained relatively stable, whereas the dominant mechanism of HERA development shifted from HGT to VGT.

Therapeutic developments for tuberculosis and nontuberculous mycobacterial lung disease

Tuberculosis (TB) drug discovery and development has undergone nothing short of a revolution over the past 20 years. Successful public-private partnerships and sustained funding have delivered a much-improved understanding of mycobacterial disease biology and pharmacology and a healthy pipeline that can tolerate inevitable attrition. Preclinical and clinical development has evolved from decade-old concepts to adaptive designs that permit rapid evaluation of regimens that might greatly shorten treatment duration over the next decade. But the past 20 years also saw the rise of a fatal and difficult-to-cure lung disease caused by nontuberculous mycobacteria (NTM), for which the drug development pipeline is nearly empty. Here, we discuss the similarities and differences between TB and NTM lung diseases, compare the preclinical and clinical advances, and identify major knowledge gaps and areas of cross-fertilization. We argue that applying paradigms and networks that have proved successful for TB, from basic research to clinical trials, will help to populate the pipeline and accelerate curative regimen development for NTM disease.

Mathematical modeling of fluid dynamics in in vitro gut fermentation systems: A new tool to improve the interpretation of microbial metabolism

In vitro systems are widely employed to assess the impact of dietary compounds on the gut microbiota and their conversion into beneficial bacterial metabolites. However, the complex fluid dynamics and multi-segmented nature of these systems can complicate the comprehensive analysis of dietary compound fate, potentially confounding physical dilution or washout with microbial catabolism. In this study, we developed fluid dynamics models based on sets of ordinary differential equations to simulate the behavior of an inert compound within two commonly used in vitro systems: the continuous two-stage PolyFermS system and the semi-continuous multi-segmented SHIME® system as well as into various declinations of those systems. The models were validated by investigating the fate of blue dextran, demonstrating excellent agreement between experimental and modeling data (with r2 values ranging from 0.996 to 0.86 for different approaches). As a proof of concept for the utility of fluid dynamics models in in vitro system, we applied generated models to interpret metabolomic data of procyanidin A2 (ProA2) generated from the addition of proanthocyanidin (PAC)-rich cranberry extract to both the PolyFermS and SHIME® systems. The results suggested ProA2 degradation by the gut microbiota when compared to the modeling of an inert compound. Models of fluid dynamics developed in this study provide a foundation for comprehensive analysis of gut metabolic data in commonly utilized in vitro PolyFermS and SHIME® bioreactor systems and can enable a more accurate understanding of the contribution of bacterial metabolism to the variability in the concentration of target metabolites.

Network meta-analysis of antibiotic resistance patterns in gram-negative bacterial infections: a comparative study of carbapenems, fluoroquinolones, and aminoglycosides

Introduction

Antimicrobial resistance poses a grave global threat, particularly with the emergence of multidrug-resistant gram-negative bacterial infections, which severely limit treatment options. The increasing global threat of antimicrobial resistance demands rigorous investigation, particularly concerning multidrug-resistant gram-negative bacterial infections that present limited therapeutic options. This study employed a network meta-analysis, a powerful tool for comparative effectiveness assessment of diverse antibiotics. The primary aim of this study was to comprehensively evaluate and compare resistance patterns among widely used antibiotic classes, namely carbapenems, fluoroquinolones, and aminoglycosides, for combating gram-negative pathogens.

Methods

We searched PubMed, Web of Sciences, Scopus, Scholarly, Medline, Embase, and Cochrane databases up to August 27, 2023. Studies showing antibiotic resistance in clinical isolates of Enterobacteriaceae, Pseudomonas aeruginosa, and Acinetobacter baumannii exposed to carbapenems, fluoroquinolones, and aminoglycosides were included. This study determined treatment-specific resistance percentages and ranked these treatments based on resistance using a random-effects network meta-analysis technique. To investigate the impact of the study and pathogen features, subgroup and meta-regression analyses were performed. Risk ratios and 95% confidence intervals (CIs) were calculated using a network meta-analysis (NMA) incorporating both direct and indirect evidence. Clinical improvement, cure, microbiological eradication, and death from any cause were the primary outcomes. Nephrotoxicity was a secondary result.

Results

The analysis included 202 publications and 365,782 gram-negative isolates. The NMA included data from 20 studies and 4,835 patients. Carbapenems had the lowest resistance rates throughout the pathogen spectrum, with resistance percentages of 17.1, 22.4, and 33.5% for Enterobacteriaceae, P. aeruginosa, and A. baumannii, respectively. For the same infections, aminoglycosides showed resistance rates of 28.2, 39.1, and 50.2%, respectively. Fluoroquinolones had the highest resistance rates at 43.1, 57.3, and 65.7%, respectively. Unexpectedly, resistance to all three antibiotic classes has increased over time, with multidrug resistance being the most prevalent.

Conclusion

This extensive network meta-analysis provides an overview of the patterns of resistance throughout the world and how they are changing. The most effective choice is still carbapenems, but the increasing resistance highlights the critical need for multimodal therapies to protect antibiotic effectiveness against these powerful gram-negative infections.

The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance

Approximately 10% of antimicrobials used by humans in low- and middle-income countries are estimated to be substandard or falsified. In addition to their negative impact on morbidity and mortality, they may also be important drivers of antimicrobial resistance. Despite such concerns, our understanding of this relationship remains rudimentary. Substandard and falsified medicines have the potential to either increase or decrease levels of resistance, and here we discuss a range of mechanisms that could drive these changes. Understanding these effects and their relative importance will require an improved understanding of how different drug exposures affect the emergence and spread of resistance and of how the percentage of active pharmaceutical ingredients in substandard and falsified medicines is temporally and spatially distributed.

Role of MAIT cells in gastrointestinal tract bacterial infections in humans: More than a gut feeling

Mucosa-associated invariant T (MAIT) cells are the largest population of unconventional T cells in humans. These antimicrobial T cells are poised with rapid effector responses following recognition of the cognate riboflavin (vitamin B2)-like metabolite antigens derived from microbial riboflavin biosynthetic pathway. Presentation of this unique class of small molecule metabolite antigens is mediated by the highly evolutionarily conserved major histocompatibility complex class I-related protein. In humans, MAIT cells are widely found along the upper and lower gastrointestinal tracts owing to their high expression of chemokine receptors and homing molecules directing them to these tissue sites. In this review, we discuss recent findings regarding the roles MAIT cells play in various gastrointestinal bacterial infections, and how their roles appear to differ depending on the etiological agents and the anatomical location. We further discuss the potential mechanisms by which MAIT cells contribute to pathogen control, orchestrate adaptive immunity, as well as their potential contribution to inflammation and tissue damage during gastrointestinal bacterial infections, and the ensuing tissue repair following resolution. Finally, we propose and discuss the use of the emerging three-dimensional organoid technology to test different hypotheses regarding the role of MAIT cells in gastrointestinal bacterial infections, inflammation, and immunity.

Azithromycin alters Colony Stimulating Factor-1R (CSF-1R) expression and functional output of murine bone marrow-derived macrophages: A novel report

Antibiotic treatment may lead to side effects that require mechanistic explanation. We investigated the effect of azithromycin (AZM) treatment on bone marrow-derived macrophage (Mφ) generation, their functional output, and the subsequent effect on bacterial clearance in a mouse model of S. flexneri infection. To our fascination, AZM increased PU.1, C/EBPβ, CSF-1R/pCSF-1R expressions leading to M2-skewed in vitro BMDM generation. Altered Mφ-functions like- phagocytosis, oxidative stress generation, inflammasome-activation, cytokine release, and phenotype (pro-inflammatory-M1, anti-inflammatory-M2) even in the presence of infection were observed with AZM treatment. AZM increased CD206, egr2, arg1 (M2-marker) expression and activity while reducing CD68, inducible nitric oxide (iNOS) expression, and activity (M1-marker) in Mφs during infection. Pro-inflammatory cytokines (TNF-α, IL-12, IL-1β) were reduced and anti-inflammatory IL-10 release was augmented by AZM-treated-iMφs (aiMφs) along with decreased asc, nlrp3, aim2, nlrp1a, caspase1 expressions, and caspase3 activity signifying that aMφs/aiMφs were primed towards an anti-inflammatory phenotype. Interestingly, CSF-1R blockade increased NO, IL-12, TNF-α, IL-1β, decreased TGF-β release, and CD206 expression in aiMφs. T-cell co-stimulatory molecule cd40, cd86, and cd80 expressions were decreased in ai/aM1-Mφs and co-cultured CD8+, CD4+ T-cells had decreased proliferation, t-bet, IFN-γ, IL-17, IL-2 but increased foxp3, TGF-β, IL-4 which were rescued with CSF-1R blockade. Thus AZM affected Mφ-functions and subsequent T-cell responses independent of its antibacterial actions. This was validated in the balb/c model of S. flexneri infection. We conclude that AZM skewed BMDM generation to anti-inflammatory M2-like via increased CSF-1R expression. This warrants further investigation of AZM-induced altered-Mφ-generation during intracellular infections.

What are the missing pieces needed to stop antibiotic resistance?

As recognized by several international agencies, antibiotic resistance is nowadays one of the most relevant problems for human health. While this problem was alleviated with the introduction of new antibiotics into the market in the golden age of antimicrobial discovery, nowadays few antibiotics are in the pipeline. Under these circumstances, a deep understanding on the mechanisms of emergence, evolution and transmission of antibiotic resistance, as well as on the consequences for the bacterial physiology of acquiring resistance is needed to implement novel strategies, beyond the development of new antibiotics or the restriction in the use of current ones, to more efficiently treat infections. There are still several aspects in the field of antibiotic resistance that are not fully understood. In the current article, we make a non-exhaustive critical review of some of them that we consider of special relevance, in the aim of presenting a snapshot of the studies that still need to be done to tackle antibiotic resistance.

The tale of antibiotics beyond antimicrobials: Expanding horizons

Antibiotics had proved to be a godsend for mankind since their discovery. They were once the magical solution to the vexing problem of infection-related deaths. German scientist Paul Ehrlich had termed salvarsan as the silver bullet to treatsyphilis.As time passed, the magic of newly discovered silver bullets got tarnished with raging antibiotic resistance among bacteria and associated side-effects. Still, antibiotics remain the primary line of treatment for bacterial infections. Our understanding of their chemical and biological activities has increased immensely with advancement in the research field. Non-antibacterial effects of antibiotics are studied extensively to optimise their safer, broad-range use. These non-antibacterial effects could be both useful and harmful to us. Various researchers across the globe including our lab are studying the direct/indirect effects and molecular mechanisms behind these non-antibacterial effects of antibiotics. So, it is interesting for us to sum up the available literature. In this review, we have briefed the possible reason behind the non-antibacterial effects of antibiotics, owing to the endosymbiotic origin of host mitochondria. We further discuss the physiological and immunomodulatory effects of antibiotics. We then extend the review to discuss molecular mechanisms behind the plausible use of antibiotics as anticancer agents.

Native Pig Neutrophil Products: Insights into Their Antimicrobial Activity

Cationic antimicrobial peptides are molecules with potential applications for treating infections due to their antimicrobial and immunomodulatory properties. The aim of this work was to explore the antimicrobial activity and mechanisms of action of a porcine neutrophil cathelicidin mixture (MPPN). Gram-positive and Gram-negative bacteria were used to determine the minimum inhibitory concentration (MIC) and experiments of both time-kill kinetics and effects on growth curves were performed. Planar black lipid bilayer conductance was measured to analyze the interaction of MPPN with lipid bilayers. Visualization of bacterial surfaces and membrane alterations was achieved using atomic force microscopy and transmission electron microscopy. The effects on the activity of efflux pumps (EPs) were studied with an intracellular accumulation of acridine orange (AO) assay. In E. coli, MPPN behaves as a bactericide at high concentrations and as a bacteriostatic at lower concentrations. The bacteriostatic effect was also observed for slightly shorter periods in S. enterica. The mixture was not active on S. aureus. The increase in AO accumulation in the presence of MPPN indicates that, at least in E. coli, the mixture causes inhibition of the EP function. Observed and detected variable conductance events demonstrate a strong MPPN effect on lipid bilayers. Damage to the structure of treated E. coli indicates that MPPN induces alterations in the bacterial surface. The use of AMPs capable of inhibiting EP can be seen as a good tool to combat antimicrobial resistance since they could be used alone or in combination with other conventional antibiotics to which bacteria have become resistant.

Removal of natural anti-αGal antibodies elicits protective immunity against Gram-negative bacterial infections

Antibody-dependent enhancement (ADE) of bacterial infections occurs when blocking or inhibitory antibodies facilitate the infectivity of pathogens. In humans, antibodies involved in ADE of bacterial infections may include those naturally produced against Galα1-3Galβ1-4GlcNAcβ (αGal). Here, we investigate whether eliminating circulating anti-αGal antibodies using a soluble αGal glycopolymer confers protection against Gram-negative bacterial infections. We demonstrated that the in vivo intra-corporeal removal of anti-αGal antibodies in α1,3-galactosyltransferase knockout (GalT-KO) mice was associated with protection against mortality from Gram-negative sepsis after cecal ligation and puncture (CLP). The improved survival of GalT-KO mice was associated with an increased killing capacity of serum against Escherichia coli isolated after CLP and reduced binding of IgG1 and IgG3 to the bacteria. Additionally, inhibition of anti-αGal antibodies from human serum in vitro increases the bactericidal killing of E. coli O86:B7 and multidrug-resistant Klebsiella pneumoniae and Pseudomonas aeruginosa. In the case of E. coli O86:B7, there was also an improvement in bacteria opsonophagocytosis by macrophages. Both lytic mechanisms were related to a decreased binding of IgG2 to the bacteria. Our results show that protective immunity against Gram-negative bacterial pathogens can be elicited, and infectious diseases caused by these bacteria can be prevented by removing natural anti-αGal antibodies.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

Clinical and Genomic Evolution of Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infections over Two Time Periods at a Tertiary Care Hospital in South India: A Prospective Cohort Study

INTRODUCTION

The objective of this study was to examine the evolution of carbapenem-resistant Klebsiella pneumoniae (CRKp) infections and their impact at a tertiary care hospital in South India.

METHODS

A comparative analysis of clinical data from two prospective cohorts of patients with CRKp bacteremia (C1, 2014-2015; C2, 2021-2022) was carried out. Antimicrobial susceptibilities and whole genome sequencing (WGS) data of selected isolates were also analyzed.

RESULTS

A total of 181 patients were enrolled in the study, 56 from C1 and 125 from C2. CRKp bacteremia shifted from critically ill patients with neutropenia to others (ICU stay: C1, 73%; C2, 54%; p = 0.02). The overall mortality rate was 50% and the introduction of ceftazidime-avibactam did not change mortality significantly (54% versus 48%; p = 0.49). Oxacillinases (OXA) 232 and 181 were the most common mechanisms of resistance. WGS showed the introduction of New Delhi metallo-β-lactamase-5 (NDM-5), higher genetic diversity, accessory genome content, and plasmid burden, as well as increased convergence of hypervirulence and carbapenem resistance in C2.

CONCLUSIONS

CRKp continues to pose a significant clinical threat, despite the introduction of new antibiotics. The study highlights the evolution of resistance and virulence in this pathogen and the impact on patient outcomes in South India, providing valuable information for clinicians and researchers.

Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization

Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections. IMPORTANCE Antibiotic resistance is commonly viewed as universally costly, regardless of which bacterial cells express resistance. Here, we derive an opposing logic, where resistance in commensal bacteria can lead to reductions in pathogen density and improved outcomes on both the patient and public health scales. We use a mathematical model of commensal-pathogen interactions to define the necessary and sufficient conditions for beneficial resistance, highlighting the importance of reciprocal ecological inhibition to maximize the benefits of resistance. More broadly, we argue that determining the benefits as well as the costs of resistances in human microbiomes can transform resistance management from a minimization to an optimization problem. We discuss applied contexts and close with a review of key resistance optimization dimensions, including the magnitude, spectrum, and mechanism of resistance.

A narrative review of the intermediate category of the antimicrobial susceptibility test: relation with dosing and possible impact on antimicrobial stewardship

The interpretation of 'susceptible (S)' or 'resistant (R)' results of antimicrobial susceptibility testing is easily understood, but the interpretation of the 'intermediate (I)' category can be confusing. This review critically discusses how this categorization (clinical breakpoints) comes into being with the emphasis on the use of pharmacokinetics and pharmacodynamic data. It discusses the differences between the 'I' according to the CLSI and the EUCAST. This review also discusses the recent EUCAST change of the 'I' definition, and the impact of this change from laboratory and clinical points of view.

Exogenous metabolite feeding on altering antibiotic susceptibility in Gram-negative bacteria through metabolic modulation: a review

BACKGROUND

The rise of antimicrobial resistance at an alarming rate is outpacing the development of new antibiotics. The worrisome trends of multidrug-resistant Gram-negative bacteria have enormously diminished existing antibiotic activity. Antibiotic treatments may inhibit bacterial growth or lead to induce bacterial cell death through disruption of bacterial metabolism directly or indirectly. In light of this, it is imperative to have a thorough understanding of the relationship of bacterial metabolism with antimicrobial activity and leverage the underlying principle towards development of novel and effective antimicrobial therapies.

OBJECTIVE

Herein, we explore studies on metabolic analyses of Gram-negative pathogens upon antibiotic treatment. Metabolomic studies revealed that antibiotic therapy caused changes of metabolites abundance and perturbed the bacterial metabolism. Following this line of thought, addition of exogenous metabolite has been employed in in vitro, in vivo and in silico studies to activate the bacterial metabolism and thus potentiate the antibiotic activity.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Exogenous metabolites were discovered to cause metabolic modulation through activation of central carbon metabolism and cellular respiration, stimulation of proton motive force, increase of membrane potential, improvement of host immune protection, alteration of gut microbiome, and eventually facilitating antibiotic killing. The use of metabolites as antimicrobial adjuvants may be a promising approach in the fight against multidrug-resistant pathogens.

Immunosuppression broadens evolutionary pathways to drug resistance and treatment failure during Acinetobacter baumannii pneumonia in mice

Acinetobacter baumannii is increasingly refractory to antibiotic treatment in healthcare settings. As is true of most human pathogens, the genetic path to antimicrobial resistance (AMR) and the role that the immune system plays in modulating AMR during disease are poorly understood. Here we reproduced several routes to fluoroquinolone resistance, performing evolution experiments using sequential lung infections in mice that are replete with or depleted of neutrophils, providing two key insights into the evolution of drug resistance. First, neutropenic hosts acted as reservoirs for the accumulation of drug resistance during drug treatment. Selection for variants with altered drug sensitivity profiles arose readily in the absence of neutrophils, while immunocompetent animals restricted the appearance of these variants. Secondly, antibiotic treatment failure in the immunocompromised host was shown to occur without clinically defined resistance, an unexpected result that provides a model for how antibiotic failure occurs clinically in the absence of AMR. The genetic mechanism underlying both these results is initiated by mutations activating the drug egress pump regulator AdeL, which drives persistence in the presence of antibiotic. Therefore, antibiotic persistence mutations present a two-pronged risk during disease, causing drug treatment failure in the immunocompromised host while simultaneously increasing the emergence of high-level AMR.

Modelling the antimicrobial pharmacodynamics for bacterial strains with versus without acquired resistance to fluoroquinolones or cephalosporins

OBJECTIVES

Antimicrobial resistance threatens therapeutic options for human and animal bacterial diseases worldwide. Current antimicrobial treatment regimens were designed against bacterial strains that were fully susceptible to them. To expand the useable lifetime of existing antimicrobial drug classes by modifying treatment regimens, data are needed on the antimicrobial pharmacodynamics (PD) against strains with reduced susceptibility. In this study, we generated and mathematically modelled the PD of the fluoroquinolone ciprofloxacin and the cephalosporin ceftriaxone against non-typhoidal Salmonella enterica subsp. enterica strains with varying levels of acquired resistance.

METHODS

We included Salmonella strains across categories of reduced susceptibility to fluoroquinolones or cephalosporins reported to date, including isolates from human infections, food-animal products sold in retail, and food-animal production. We generated PD data for each drug and strain via time-kill assay. Mathematical models were compared in their fit to represent the PD. The best-fit model's parameter values across the strain susceptibility categories were compared.

RESULTS

The inhibitory baseline sigmoid I_max (or E_max) model was best fit for the PD of each antimicrobial against a majority of the strains. There were statistically significant differences in the PD parameter values across the strain susceptibility categories for each antimicrobial.

CONCLUSION

The results demonstrate predictable multiparameter changes in the PD of these first-line antimicrobials depending on the Salmonella strain's susceptibility phenotype and specific genes conferring reduced susceptibility. The generated PD parameter estimates could be used to optimise treatment regimens against infections by strains with reduced susceptibility.

Bicarbonate modulates delafloxacin activity against MDR Staphylococcus aureus and Pseudomonas aeruginosa

OBJECTIVES

To investigate the utility of recently approved delafloxacin and other fluoroquinolones against leading MDR bacterial pathogens under physiologically relevant conditions.

METHODS

MIC and MBC assays were conducted for MDR strains of Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae in the standard antibiotic susceptibility testing medium CAMHB, amended Roswell-Park Memorial Institute tissue culture medium (RPMI+) or 20% fresh human whole blood. In vivo correlation of in vitro findings was performed in a murine P. aeruginosa pneumonia model. Mechanistic bases for the findings were explored by altering media conditions and with established fluoroquinolone accumulation assays.

RESULTS

Fluoroquinolone MICs were increased in RPMI+ compared with CAMHB for all four MDR pathogens. Specifically, delafloxacin MICs were increased 32-fold versus MDR S. aureus and 8-fold versus MDR P. aeruginosa. MBC assays in 20% human whole blood and a murine MDR P. aeruginosa pneumonia model both confirmed that delafloxacin activity was reduced under physiological conditions. Bicarbonate (HCO3-), a key component of host physiology found in RPMI+ but absent from CAMHB, dictated delafloxacin susceptibility in CAMHB and RPMI+ by impairing its intracellular accumulation.

CONCLUSIONS

Standard in vitro antibiotic susceptibility testing conditions overpredicted the effectiveness of delafloxacin against MDR pathogens by failing to capture the role of the biological buffer HCO3- to impair delafloxacin accumulation. This work showcases limitations of our current antibiotic susceptibility testing paradigm and highlights the importance of understanding host microenvironmental conditions that impact true clinical efficacy.

Two-day versus seven-day course of levofloxacin in acute COPD exacerbation: a randomized controlled trial

INTRODUCTION

Duration of antibiotic treatment in acute exacerbation of COPD (AECOPD) is most commonly based on expert opinion. Typical administration periods range from 5 to 7 days. A 2-day course with levofloxacin was not previously assessed. We performed a randomized clinical trial to evaluate the efficacy of 2-day versus 7-day treatment with levofloxacin in patients with AECOPD.

METHODS AND ANALYSIS

Patients with AECOPD were randomized to receive levofloxacin for 2 days and 5 days placebo (n = 155) or levofloxacin for 7 days (n = 155). All patients received a common dose of intravenous prednisone daily for 5 days. The primary outcome measure was cure rate, and secondary outcomes included need for additional antibiotics, ICU admission rate, re-exacerbation rate, death rate, and exacerbation-free interval (EFI) within 1-year follow-up. The study protocol has been prepared in accordance with the revised Helsinki Declaration for Biomedical Research Involving Human Subjects and Guidelines for Good Clinical Practice. The study was approved by ethics committees of all participating centers prior to implementation (Monastir and Sousse Universities).

RESULTS

310 patients were randomized to receive 2-day course of levofloxacin (n = 155) or 7-day course (n = 155). Cure rate was 79.3% (n = 123) and 74.2% (n = 115), respectively, in 2-day and 7-day groups [OR 1.3; 95% CI 0.78-2.2 (p = 0.28)]. Need for additional antibiotics rate was 3.2% and 1.9% in the 2-day group and 7-day group, respectively; (p = 0.43). ICU admission rate was not significantly different between both groups. One-year re-exacerbation rate was 34.8% (n = 54) in 2-day group versus 29% (n = 45) in 7-day group (p = 0.19); the EFI was 121 days (interquartile range, 99-149) versus 110 days (interquartile range, 89-132) in 2-day and 7-day treatment groups, respectively; (p = 0.73). One-year death rate was not significantly different between the 2 groups, 5.2% versus 7.1% in the 2-day group and 7-day group, respectively; (p = 0.26). No difference in adverse effects was detected.

CONCLUSION

Levofloxacin once daily for 2 days is not inferior to 7 days with respect to cure rate, need for additional antibiotics and hospital readmission in AECOPD. Our findings would improve patient compliance and reduce the incidence of bacterial resistance and adverse effects.

Tissue compartmentalization enables Salmonella persistence during chemotherapy

Antimicrobial chemotherapy can fail to eradicate the pathogen, even in the absence of antimicrobial resistance. Persisting pathogens can subsequently cause relapsing diseases. In vitro studies suggest various mechanisms of antibiotic persistence, but their in vivo relevance remains unclear because of the difficulty of studying scarce pathogen survivors in complex host tissues. Here, we localized and characterized rare surviving Salmonella in mouse spleen using high-resolution whole-organ tomography. Chemotherapy cleared >99.5% of the Salmonella but was inefficient against a small Salmonella subset in the white pulp. Previous models could not explain these findings: drug exposure was adequate, Salmonella continued to replicate, and host stresses induced only limited Salmonella drug tolerance. Instead, antimicrobial clearance required support of Salmonella-killing neutrophils and monocytes, and the density of such cells was lower in the white pulp than in other spleen compartments containing higher Salmonella loads. Neutrophil densities declined further during treatment in response to receding Salmonella loads, resulting in insufficient support for Salmonella clearance from the white pulp and eradication failure. However, adjunctive therapies sustaining inflammatory support enabled effective clearance. These results identify uneven Salmonella tissue colonization and spatiotemporal inflammation dynamics as main causes of Salmonella persistence and establish a powerful approach to investigate scarce but impactful pathogen subsets in complex host environments.

Efficacy of intravitreal povidone-iodine administration for the treatment of Candida albicans endophthalmitis in rabbits

This study aimed to investigate the efficacy of intravitreal povidone-iodine (PI) administration for the treatment of Candida albicans endophthalmitis. Forty New Zealand white rabbits were divided into four groups (n = 10 per group). After the induction of endophthalmitis using Candida albicans, groups A, B, and C received single intravitreal injections of 0.035 mg voriconazole, 0.3 mg PI, and their combination, respectively. Rabbits that were administered sham injections were in group D as controls. Fundus photography, vitreous culture, electroretinography (ERG), and histologic examinations of the retina were conducted on day 7. The anterior chamber flare (grade 0 to 4), severity of iritis (grade 0 to 4), and vitreous opacity (grade 0 to 3) were scored. Candida albicans was cultured in the vitreous sample. On day 7, the vitreous opacities were found in all groups. Compared to that in group D, groups A, B, and C showed a lower score for flare (p < 0.001) and iritis (p < 0.001) and less fungal growth in the vitreous culture (n = 2, 1, 1, and 10 in groups A, B, C, and D, respectively; p < 0.001). Furthermore, ERG and histologic findings demonstrated less affected a- and b-waves and damaged retinal tissues in groups A, B, and C. However, these findings were not different among groups A, B, and C. PI significantly improved Candida albicans endophthalmitis, and the effect was comparable that of the voriconazole, although some vitreous opacities remained. No synergistic effect of the combination of PI and voriconazole was observed. Intravitreal PI may be useful to treat Candida albicans endophthalmitis.

Impact of First-Line Antimicrobials on Chlamydia trachomatis-Induced Changes in Host Metabolism and Cytokine Production

Urogenital infections with Chlamydia trachomatis (C. trachomatis) are the most common bacterial sexually transmitted diseases worldwide. As an obligate intracellular bacterium, chlamydial replication and pathogenesis depends on the host metabolic activity. First-line antimicrobials such as doxycycline (DOX) and azithromycin (AZM) have been recommended for the treatment of C. trachomatis infection. However, accumulating evidence suggests that treatment with AZM causes higher rates of treatment failure than DOX. Here, we show that an inferior efficacy of AZM compared to DOX is associated with the metabolic status of host cells. Chlamydial metabolism and infectious progeny of C. trachomatis were suppressed by therapeutic relevant serum concentrations of DOX or AZM. However, treatment with AZM could not suppress host cell metabolic pathways, such as glycolysis and mitochondrial oxidative phosphorylation, which are manipulated by C. trachomatis. The host cell metabolic activity was associated with a significant reactivation of C. trachomatis after removal of AZM treatment, but not after DOX treatment. Furthermore, AZM insufficiently attenuated interleukin (IL)-8 expression upon C. trachomatis infection and higher concentrations of AZM above therapeutic serum concentration were required for effective suppression of IL-8. Our data highlight that AZM is not as efficient as DOX to revert host metabolism in C. trachomatis infection. Furthermore, insufficient treatment with AZM failed to inhibit chlamydial reactivation as well as C. trachomatis induced cytokine responses. Its functional relevance and the impact on disease progression have to be further elucidated in vivo.

Immuno-pharmacokinetics of Meglumine Antimoniate in Patients With Cutaneous Leishmaniasis Caused by Leishmania (Viannia)

BACKGROUND

Control of cutaneous leishmaniasis (CL) relies on chemotherapy, yet gaps in our understanding of the determinants of therapeutic outcome impede optimization of antileishmanial drug regimens. Pharmacodynamic (PD) parameters of antimicrobials are based on the relationship between drug concentrations/exposure and microbial kill. However, viable Leishmania persist in a high proportion of individuals despite clinical resolution, indicating that determinants other than parasite clearance are involved in drug efficacy.

METHODS

In this study, the profiles of expression of neutrophils, monocytes, Th1 and Th17 gene signatures were characterized in peripheral blood mononuclear cells (PBMCs) during treatment with meglumine antimoniate (MA) and clinical cure of human CL caused by Leishmania (Viannia). We explored relationships of immune gene expression with plasma and intracellular antimony (Sb) concentrations.

RESULTS

Our findings show a rapid and orchestrated modulation of gene expression networks upon exposure to MA. We report nonlinear pharmacokinetic/pharmacodynamic (PK/PD) relationships of Sb and gene expression dynamics in PBMCs , concurring with a time lag in the detection of intracellular drug concentrations and with PK evidence of intracellular Sb accumulation.

CONCLUSIONS

Our results quantitatively portray the immune dynamics of therapeutic healing, and provide the knowledge base for optimization of antimonial drug treatments, guiding the selection and/or design of targeted drug delivery systems and strategies for targeted immunomodulation.

Combining mutation and horizontal gene transfer in a within-host model of antibiotic resistance

Antibiotics are used extensively to control infections in humans and animals, usually by injection or a course of oral tablets. There are several methods by which bacteria can develop antimicrobial resistance (AMR), including mutation during DNA replication and plasmid mediated horizontal gene transfer (HGT). We present a model for the development of AMR within a single host animal. We derive criteria for a resistant mutant strain to replace the existing wild-type bacteria, and for co-existence of the wild-type and mutant. Where resistance develops through HGT via conjugation we derive criteria for the resistant strain to be excluded or co-exist with the wild-type. Our results are presented as bifurcation diagrams with thresholds determined by the relative fitness of the bacteria strains, expressed in terms of reproduction numbers. The results show that it is possible that applying and then relaxing antibiotic control may lead to the bacterial load returning to pre-control levels, but with an altered structure with regard to the variants that comprise the population. Removing antimicrobial selection pressure will not necessarily reduce AMR and, at a population level, other approaches to infection prevention and control are required, particularly when AMR is driven by both mutation and mobile genetic elements.

Rapid evolution and host immunity drive the rise and fall of carbapenem resistance during an acute Pseudomonas aeruginosa infection

It is well established that antibiotic treatment selects for resistance, but the dynamics of this process during infections are poorly understood. Here we map the responses of Pseudomonas aeruginosa to treatment in high definition during a lung infection of a single ICU patient. Host immunity and antibiotic therapy with meropenem suppressed P. aeruginosa, but a second wave of infection emerged due to the growth of oprD and wbpM meropenem resistant mutants that evolved in situ. Selection then led to a loss of resistance by decreasing the prevalence of low fitness oprD mutants, increasing the frequency of high fitness mutants lacking the MexAB-OprM efflux pump, and decreasing the copy number of a multidrug resistance plasmid. Ultimately, host immunity suppressed wbpM mutants with high meropenem resistance and fitness. Our study highlights how natural selection and host immunity interact to drive both the rapid rise, and fall, of resistance during infection.

Dosage strategies for delaying resistance emergence in heterogeneous tumors

Drug resistance in cancer treatments is a frequent problem that, when it arises, leads to failure in therapeutic efforts. Tumor heterogeneity is the primary reason for resistance emergence and a precise treatment design that takes heterogeneity into account is required to postpone the rise of resistant subpopulations in the tumor environment. In this paper, we present a mathematical framework involving clonal evolution modeling of drug-sensitive and drug-resistant clones. Using our framework, we examine delaying the rise of resistance in heterogeneous tumors during control phase of therapy in a containment treatment approach. We apply pharmacokinetic/pharmacodynamic (PKPD) modeling and show that dosage strategies can be designed to control the resistant subpopulation. Our results show that the drug dosage and schedule determine the relative dynamics of sensitive and resistant clones. We present an optimal control problem that finds the dosing strategy that maximizes the delay in resistance emergence for a given period of containment treatment.

The Antibiotic Dosage of Fastest Resistance Evolution: gene amplifications underpinning the inverted-U

To determine the dosage at which antibiotic resistance evolution is most rapid, we treated Escherichia coli in vitro, deploying the antibiotic erythromycin at dosages ranging from zero to high. Adaptation was fastest just below erythromycin’s minimal inhibitory concentration (MIC) and genotype-phenotype correlations determined from whole genome sequencing revealed the molecular basis: simultaneous selection for copy number variation in three resistance mechanisms which exhibited an “inverted-U” pattern of dose-dependence, as did several insertion sequences and an integron. Many genes did not conform to this pattern, however, reflecting changes in selection as dose increased: putative media adaptation polymorphisms at zero antibiotic dosage gave way to drug target (ribosomal RNA operon) amplification at mid dosages whereas prophage-mediated drug efflux amplifications dominated at the highest dosages. All treatments exhibited E. coli increases in the copy number of efflux operons acrAB and emrE at rates that correlated with increases in population density. For strains where the inverted-U was no longer observed following the genetic manipulation of acrAB, it could be recovered by prolonging the antibiotic treatment at subMIC dosages.

Within-host bacterial growth dynamics with both mutation and horizontal gene transfer

The evolution and emergence of antibiotic resistance is a major public health concern. The understanding of the within-host microbial dynamics combining mutational processes, horizontal gene transfer and resource consumption, is one of the keys to solving this problem. We analyze a generic model to rigorously describe interactions dynamics of four bacterial strains: one fully sensitive to the drug, one with mutational resistance only, one with plasmidic resistance only, and one with both resistances. By defining thresholds numbers (i.e. each strain's effective reproduction and each strain's transition threshold numbers), we first express conditions for the existence of non-trivial stationary states. We find that these thresholds mainly depend on bacteria quantitative traits such as nutrient consumption ability, growth conversion factor, death rate, mutation (forward or reverse), and segregational loss of plasmid probabilities (for plasmid-bearing strains). Next, concerning the order in the set of strain's effective reproduction thresholds numbers, we show that the qualitative dynamics of the model range from the extinction of all strains, coexistence of sensitive and mutational resistance strains, to the coexistence of all strains at equilibrium. Finally, we go through some applications of our general analysis depending on whether bacteria strains interact without or with drug action (either cytostatic or cytotoxic).

Emerging Role for MAIT Cells in Control of Antimicrobial Resistance

Antimicrobial resistance is a serious threat to global public health as antibiotics are losing effectiveness due to rapid development of resistance. The human immune system facilitates control and clearance of resistant bacterial populations during the course of antimicrobial therapy. Here we review current knowledge of mucosa-associated invariant T (MAIT) cells, an arm of the immune system on the border between innate and adaptive, and their critical place in human antibacterial immunity. We propose that MAIT cells play important roles against antimicrobial-resistant infections through their capacity to directly clear multidrug-resistant bacteria and overcome mechanisms of antimicrobial resistance. Finally, we discuss outstanding questions pertinent to the possible advancement of host-directed therapy as an alternative intervention strategy for antimicrobial-resistant bacterial infections.

Treatment timing shifts the benefits of short and long antibiotic treatment over infection

Antibiotics are the major tool for treating bacterial infections. Rising antibiotic resistance, however, calls for a better use of antibiotics. While classical recommendations favor long and aggressive treatments, more recent clinical trials advocate for moderate regimens. In this debate, two axes of 'aggression' have typically been conflated: treatment intensity (dose) and treatment duration. The third dimension of treatment timing along each individual's infection course has rarely been addressed. By using a generic mathematical model of bacterial infection controlled by immune response, we examine how the relative effectiveness of antibiotic treatment varies with its timing, duration and antibiotic kill rate. We show that short or long treatments may both be beneficial depending on treatment onset, the target criterion for success and on antibiotic efficacy. This results from the dynamic trade-off between immune response build-up and resistance risk in acute, self-limiting infections, and uncertainty relating symptoms to infection variables. We show that in our model early optimal treatments tend to be 'short and strong', while late optimal treatments tend to be 'mild and long'. This suggests a shift in the aggression axis depending on the timing of treatment. We find that any specific optimal treatment schedule may perform more poorly if evaluated by other criteria, or under different host-specific conditions. Our results suggest that major advances in antibiotic stewardship must come from a deeper empirical understanding of bacterial infection processes in individual hosts. To guide rational therapy, mathematical models need to be constrained by data, including a better quantification of personal disease trajectory in humans. Lay summary: Bacterial infections are becoming more difficult to treat worldwide because bacteria are becoming resistant to the antibiotics used. Addressing this problem requires a better understanding of how treatment along with other host factors impact antibiotic resistance. Until recently, most theoretical research has focused on the importance of antibiotic dosing on antibiotic resistance, however, duration and timing of treatment remain less explored. Here, we use a mathematical model of a generic bacterial infection to study three aspects of treatment: treatment dose/efficacy (defined by the antibiotic kill rate), duration, and timing, and their impact on several infection endpoints. We show that short and long treatment success strongly depends on when treatment begins (defined by the symptom threshold), the target criterion to optimize, and on antibiotic efficacy. We find that if administered early in an infection, "strong and short" therapy performs better, while if treatment begins at higher bacterial densities, a "mild and long" course of antibiotics is favored. In the model host immune defenses are key in preventing relapses, controlling antibiotic resistant bacteria and increasing the effectiveness of moderate intervention. In order to improve rational treatments of human infections, we call for a better quantification of individual disease trajectories in bacteria-immunity space.

Mathematical modeling of the 'inoculum effect': six applicable models and the MIC advancement point concept

Antimicrobial treatment regimens against bacterial pathogens are designed using the drug's minimum inhibitory concentration (MIC) measured at a bacterial density of 5.7 log₁₀(colony-forming units (CFU)/mL) in vitro. However, MIC changes with pathogen density, which varies among infectious diseases and during treatment. Incorporating this into treatment design requires realistic mathematical models of the relationships. We compared the MIC–density relationships for Gram-negative Escherichia coli and non-typhoidal Salmonella enterica subsp. enterica and Gram-positive Staphylococcus aureus and Streptococcus pneumonia (for n = 4 drug-susceptible strains per (sub)species and 1–8 log₁₀(CFU/mL) densities), for antimicrobial classes with bactericidal activity against the (sub)species: β-lactams (ceftriaxone and oxacillin), fluoroquinolones (ciprofloxacin), aminoglycosides (gentamicin), glycopeptides (vancomycin) and oxazolidinones (linezolid). Fitting six candidate mathematical models to the log₂(MIC) vs. log₁₀(CFU/mL) curves did not identify one model best capturing the relationships across the pathogen–antimicrobial combinations. Gompertz and logistic models (rather than a previously proposed Michaelis–Menten model) fitted best most often. Importantly, the bacterial density after which the MIC sharply increases (an MIC advancement-point density) and that density's intra-(sub)species range evidently depended on the antimicrobial mechanism of action. Capturing these dependencies for the disease–pathogen–antimicrobial combination could help determine the MICs for which bacterial densities are most informative for treatment regimen design.

Modeling the Effect of Memory in the Adaptive Immune Response

It is well understood that there are key differences between a primary immune response and subsequent responses. Specifically, memory T cells that remain after a primary response drive the clearance of antigen in later encounters. While the existence of memory T cells is widely accepted, the specific mechanisms that govern their function are generally debated. In this paper, we develop a mathematical model of the immune response. This model follows the creation, activation, and regulation of memory T cells, which allows us to explore the differences between the primary and secondary immune responses. Through the incorporation of memory T cells, we demonstrate how the immune system can mount a faster and more effective secondary response. This mathematical model provides a quantitative framework for studying chronic infections and auto-immune diseases.

The social network: Impact of host and microbial interactions on bacterial antibiotic tolerance and persistence

Antibiotics have vastly improved our quality of life since their discovery and introduction into modern medicine. Yet, widespread use and misuse have compromised the efficacy of these compounds and put our ability to cure infectious diseases in jeopardy. To defend themselves against antibiotics, bacteria have evolved an arsenal of survival strategies. In addition to acquiring mutations and genetic determinants that confer antibiotic resistance, bacteria can respond to environmental cues and adopt reversible phenotypic changes that transiently enhance their ability to survive adverse conditions, including those brought on by antibiotics. These antibiotic tolerant and persistent bacteria, which are prevalent in biofilms and can survive antimicrobial therapy without inheriting resistance, are thought to underlie treatment failure and infection relapse. At infection sites, bacteria encounter a range of signals originating from host immunity and the local microbiota that can induce transcriptomic and metabolic reprogramming. In this review, we will focus on the impact of host factors and microbial interactions on antibiotic tolerance and persistence. We will also outline current efforts in leveraging the knowledge of host-microbe and microbe-microbe interactions in designing therapies that potentiate antibiotic activity and reduce the burden caused by recurrent infections.

Polymyxin-Induced Cell Death of Human Macrophage-Like THP-1 and Neutrophil-Like HL-60 Cells Associated with the Activation of Apoptotic Pathways

Innate immunity is crucial for the host to defend against infections, and understanding the effect of polymyxins on innate immunity is important for optimizing their clinical use. In this study, we investigated the potential toxicity of polymyxins on human macrophage-like THP-1 and neutrophil-like HL-60 cells. Differentiated THP-1 human macrophages (THP-1-dMs) and HL-60 human neutrophils (HL-60-dNs) were employed. Flow cytometry was used to measure the concentration-dependent effects (100 to 2,500 μM for THP-1-dMs and 5 to 2,500 μM for HL-60-dNs) and time-dependent effects (1,000 μM for THP-1-dMs and 300 μM for HL-60-dNs) of polymyxin B over 24 h. Effects of polymyxin B on mitochondrial activity, activation of caspase-3, caspase-8, and caspase-9, and Fas ligand (FasL) expression in both cell lines were examined using fluorescence imaging, colorimetric, and fluorometric assays. In both cell lines, polymyxin B induced concentration- and time-dependent loss of viability at 24 h with 50% effective concentration (EC₅₀) values of 751.8 μM (95% confidence interval [CI], 692.1 to 816.6 μM; Hill slope, 3.09 to 5.64) for THP-1-dM cells and 175.4 μM (95% CI, 154.8 to 198.7 μM; Hill slope, 1.42 to 2.21) for HL-60-dN cells. A concentration-dependent loss of mitochondrial membrane potential and generation of mitochondrial superoxide was also observed. Polymyxin B-induced apoptosis was associated with concentration-dependent activation of all three tested caspases. The death receptor apoptotic pathway activation was demonstrated by a concentration-dependent increase of FasL expression. For the first time, our results reveal that polymyxin B induced concentration- and time-dependent cell death in human macrophage-like THP-1 and neutrophil-like HL-60 cells associated with mitochondrial and death receptor apoptotic pathways.

Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times

Mass production and use of antibiotics has led to the rise of resistant bacteria, a problem possibly exacerbated by inappropriate and non-optimal application. Antibiotic treatment often follows fixed-dose regimens, with a standard dose of antibiotic administered equally spaced in time. But are such fixed-dose regimens optimal or can alternative regimens be designed to increase efficacy? Yet, few mathematical models have aimed to identify optimal treatments based on biological data of infections inside a living host. In addition, assumptions to make the mathematical models analytically tractable limit the search space of possible treatment regimens (e.g. to fixed-dose treatments). Here, we aimed to address these limitations by using experiments in a Galleria mellonella (insect) model of bacterial infection to create a fully parametrised mathematical model of a systemic Vibrio infection. We successfully validated this model with biological experiments, including treatments unseen by the mathematical model. Then, by applying artificial intelligence, this model was used to determine optimal antibiotic dosage regimens to treat the host to maximise survival while minimising total antibiotic used. As expected, host survival increased as total quantity of antibiotic applied during the course of treatment increased. However, many of the optimal regimens tended to follow a large initial ‘loading’ dose followed by doses of incremental reductions in antibiotic quantity (dose ‘tapering’). Moreover, application of the entire antibiotic in a single dose at the start of treatment was never optimal, except when the total quantity of antibiotic was very low. Importantly, the range of optimal regimens identified was broad enough to allow the antibiotic prescriber to choose a regimen based on additional criteria or preferences. Our findings demonstrate the utility of an insect host to model antibiotic therapies in vivo and the approach lays a foundation for future regimen optimisation for patient and societal benefits.

Research into optimal antibiotic use to improve efficacy is far behind that of cancer care, where personalised treatment is common. The integration of mathematical models with biological observations offers hope to optimise antibiotic use, and in this present study an in vivo insect model of systemic Vibrio infection was used for the first time to determine critical model parameters for optimal antibiotic treatment. By this approach, the optimal regimens tended to result from a large initial ‘loading’ dose followed by subsequent doses of incremental reductions in antibiotic quantity (dose ‘tapering’). The approach and findings of this study opens a new avenue towards optimal application of our precious antibiotic arsenal and may lead to more effective treatment regimens for patients, thus reducing the health and economic burdens associated with bacterial infections. Importantly, it can be argued that until we understand how to use a single antibiotic optimally, it is unlikely we will identify optimal ways to use multiple antibiotics simultaneously.

Human antimicrobial peptide, LL-37, induces non-inheritable reduced susceptibility to vancomycin in Staphylococcus aureus

Antimicrobial peptides (AMPs) are central components of the innate immune system providing protection against pathogens. Yet, serum and tissue concentrations vary between individuals and with disease conditions. We demonstrate that the human AMP LL-37 lowers the susceptibility to vancomycin in the community-associated methicillin-resistant S. aureus (CA-MRSA) strain FPR3757 (USA300). Vancomycin is used to treat serious MRSA infections, but treatment failures occur despite MRSA strains being tested susceptible according to standard susceptibility methods. Exposure to physiologically relevant concentrations of LL-37 increased the minimum inhibitory concentration (MIC) of S. aureus towards vancomycin by 75%, and resulted in shortened lag-phase and increased colony formation at sub-inhibitory concentrations of vancomycin. Computer simulations using a mathematical antibiotic treatment model indicated that a small increase in MIC might decrease the efficacy of vancomycin in clearing a S. aureus infection. This prediction was supported in a Galleria mellonella infection model, where exposure of S. aureus to LL-37 abolished the antimicrobial effect of vancomycin. Thus, physiological relevant concentrations of LL-37 reduce susceptibility to vancomycin, indicating that tissue and host specific variations in LL-37 concentrations may influence vancomycin susceptibility in vivo.

Antibiotics and Innate Immunity: A Cooperative Effort Toward the Successful Treatment of Infections

Despite the common ancestry of antimicrobial and immunological science, a divergence driven by artificially construed paradigms in microbiology has placed limits on how we understand the mechanisms of antibiotics in vivo. We summarize recent updates on data that shed light on how antibiotics interact with components of innate immunity.

Human MAIT cell cytolytic effector proteins synergize to overcome carbapenem resistance in Escherichia coli

Mucosa-associated invariant T (MAIT) cells are abundant antimicrobial T cells in humans and recognize antigens derived from the microbial riboflavin biosynthetic pathway presented by the MHC-Ib-related protein (MR1). However, the mechanisms responsible for MAIT cell antimicrobial activity are not fully understood, and the efficacy of these mechanisms against antibiotic resistant bacteria has not been explored. Here, we show that MAIT cells mediate MR1-restricted antimicrobial activity against Escherichia coli clinical strains in a manner dependent on the activity of cytolytic proteins but independent of production of pro-inflammatory cytokines or induction of apoptosis in infected cells. The combined action of the pore-forming antimicrobial protein granulysin and the serine protease granzyme B released in response to T cell receptor (TCR)-mediated recognition of MR1-presented antigen is essential to mediate control against both cell-associated and free-living, extracellular forms of E. coli. Furthermore, MAIT cell-mediated bacterial control extends to multidrug-resistant E. coli primary clinical isolates additionally resistant to carbapenems, a class of last resort antibiotics. Notably, high levels of granulysin and granzyme B in the MAIT cell secretomes directly damage bacterial cells by increasing their permeability, rendering initially resistant E. coli susceptible to the bactericidal activity of carbapenems. These findings define the role of cytolytic effector proteins in MAIT cell-mediated antimicrobial activity and indicate that granulysin and granzyme B synergize to restore carbapenem bactericidal activity and overcome carbapenem resistance in E. coli.

Mucosa-associated invariant T (MAIT) cells are abundant antimicrobial T cells in humans that recognize bacterial metabolites. This study shows that MAIT cells exert potent antimicrobial activity against both cell-associated and extracellular forms of Escherichia coli, including strains that are resistant to the last resort antibiotics carbapenems.

A survey of within-host and between-hosts modelling for antibiotic resistance

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.

Effect of drug dose and timing of treatment on the emergence of drug resistance in vivo in a malaria model

BACKGROUND AND OBJECTIVES

There is a significant interest in identifying clinically effective drug treatment regimens that minimize the de novo evolution of antimicrobial resistance in pathogen populations. However, in vivo studies that vary treatment regimens and directly measure drug resistance evolution are rare. Here, we experimentally investigate the role of drug dose and treatment timing on resistance evolution in an animal model.

METHODOLOGY

In a series of experiments, we measured the emergence of atovaquone-resistant mutants of Plasmodium chabaudi in laboratory mice, as a function of dose or timing of treatment (day post-infection) with the antimalarial drug atovaquone.

RESULTS

The likelihood of high-level resistance emergence increased with atovaquone dose. When varying the timing of treatment, treating either very early or late in infection reduced the risk of resistance. When we varied starting inoculum, resistance was more likely at intermediate inoculum sizes, which correlated with the largest population sizes at time of treatment.

CONCLUSIONS AND IMPLICATIONS

(i) Higher doses do not always minimize resistance emergence and can promote the emergence of high-level resistance. (ii) Altering treatment timing affects the risk of resistance emergence, likely due to the size of the population at the time of treatment, although we did not test the effect of immunity whose influence may have been important in the case of late treatment. (iii) Finding the 'right' dose and 'right' time to maximize clinical gains and limit resistance emergence can vary depending on biological context and was non-trivial even in our simplified experiments.

LAY SUMMARY

In a mouse model of malaria, higher drug doses led to increases in drug resistance. The timing of drug treatment also impacted resistance emergence, likely due to the size of the population at the time of treatment.

Influence of cerebrospinal fluid drainage and other variables on the plasma vancomycin trough levels in postoperative neurosurgical patients

BACKGROUND

Therapeutic drug monitoring (TDM) aims to minimise the clinical impact of vancomycin (VCM) pharmacokinetic variability. However, TDM data are limited among specific patient populations, including postoperative neurosurgical populations. The objective of this study was to retrospectively investigate the influence of cerebrospinal fluid (CSF) drainage and other factors on the serum trough concentrations of VCM.

METHODS

We analysed 154 patients who had been hospitalised in the neurosurgical ward and received intravenous infusions of VCM. We compared the daily doses of VCM, serum VCM concentrations, and serum concentration/dose ratio (C/D ratio) between patients who underwent CSF drainage (drainage group) and controls (nondrainage group). In addition, we also elucidated other factors affecting the attainment of target concentrations.

RESULTS

The patients in the drainage group showed a significantly lower trough concentration of VCM (6.2 ± 4.2 µg/mL) than that shown by the nondrainage group (8.5 ± 6.6 µg/mL, p = 0.03). Furthermore, the patients in the drainage group showed a significantly different trough C/D ratio (3.1 ± 2.1) than that shown by the nondrainage group (4.3 ± 3.4, p = 0.014). The Mann-Whitney U test demonstrated significantly lower VCM trough levels with concomitant use of diuretic than without (p = 0.004). Multivariable logistic regression demonstrated that coadministered diuretic independently predicted subtherapeutic trough levels of <10 µg/mL (p = 0.04). The concomitant use of albumin and other variables exerted no effects on VCM trough levels.

CONCLUSIONS

These data suggest that CSF drainage and diuretics have different effects, but it seems that both lower the VCM concentration in postoperative neurosurgical patients. Our findings strongly suggest that a high dose of VCM is required to maintain optimal serum concentrations of VCM in patients managed with CSF drainage or concomitant use of diuretic.

Simulation modelling for immunologists

The immune system is inordinately complex with many interacting components determining overall outcomes. Mathematical and computational modelling provides a useful way in which the various contributions of different immunological components can be probed in an integrated manner. Here, we provide an introductory overview and review of mechanistic simulation models. We start out by briefly defining these types of models and contrasting them to other model types that are relevant to the field of immunology. We follow with a few specific examples and then review the different ways one can use such models to answer immunological questions. While our examples focus on immune responses to infection, the overall ideas and descriptions of model uses can be applied to any area of immunology.

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

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

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

OBJECTIVE

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

METHOD

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

RESULTS

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

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

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

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

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

Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels

Antimicrobial resistance is one of the major public health threats of the 21st century. There is a pressing need to adopt more efficient treatment strategies in order to prevent the emergence and spread of resistant strains. The common approach is to treat patients with high drug doses, both to clear the infection quickly and to reduce the risk of de novo resistance. Recently, several studies have argued that, at least in some cases, low-dose treatments could be more suitable to reduce the within-host emergence of antimicrobial resistance. However, the choice of a drug dose may have consequences at the population level, which has received little attention so far. Here, we study the influence of the drug dose on resistance and disease management at the host and population levels. We develop a nested two-strain model and unravel trade-offs in treatment benefits between an individual and the community. We use several measures to evaluate the benefits of any dose choice. Two measures focus on the emergence of resistance, at the host level and at the population level. The other two focus on the overall treatment success: the outbreak probability and the disease burden. We find that different measures can suggest different dosing strategies. In particular, we identify situations where low doses minimize the risk of emergence of resistance at the individual level, while high or intermediate doses prove most beneficial to improve the treatment efficiency or even to reduce the risk of resistance in the population.