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

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Current alternative therapies for treating drug-resistant Neisseria gonorrhoeae causing ophthalmia neonatorum

Ophthalmia neonatorum is a microbial contraction, damaging eyesight, occurring largely among neonates. Infants are particularly vulnerable to bacterial infections acquired during birth from infected mothers, especially from Neisseria gonorrhoeae and Chlamydia trachomatis. Over the decades, N. gonorrhoeae is alarmingly developing a resistance to most antibiotics currently prescribed. To counter this challenge, it is imperative to find potent and cost-effective therapeutic agents for prophylaxis and treatment, to which the N. gonorrhoeae cannot easily develop resistance. This review showcases alternate therapies such as antimicrobial-fatty acids, -peptides, -nano-formulations etc., currently evident against N. gonorrhoeae-mediated ophthalmia neonatorum, which remains a major cause of ocular morbidity, blindness and even death among neonates in developing countries.

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.

Combined immunotherapeutic effect of Leishmania-derived recombinant aldolase and Ambisome against experimental visceral leishmaniasis

BACKGROUND

Available therapeutics for visceral leishmaniasis (VL), a deadly parasitic infection, are usually associated with inadequate efficacy and adverse aftereffects. Further, the primary site of Leishmania parasite are host macrophages resulting in compromised immunity; ensuing marked T-cell immunosuppression. Such settings emphasize the exploration of chemo-immunotherapeutic strategies for improvising the infected person's immune status with better resolution of infection.

METHODS

Present work employs the immunization of Leishmania-infected hamsters with Leishmania-derived recombinant aldolase (rLdAld) and enolase (rLdEno) proteins in consort with the sub-optimal dose of Ambisome (2.5 mg/kg). After the completion of immunization, hamsters were sacrificed on day 60 and 90 post infection and different organ samples were collected to perform immunological assay for evaluating the therapeutic efficacy and modulation in protective cellular immune responses.

RESULTS

Combining these proteins, particularly rLdAld with Ambisome (2.5 mg/kg), has significantly reduced the parasitic load (∼80%) with remarkable enhancement in DTH and lymphoproliferative responses compared to the infected control and only Ambisome treated groups. Moreover, cytokine levels at RNA and protein levels were noticed to be inclined towards Th-1 phenotype through up-regulation of IFN-γ and TNF-α with significant down-regulation in IL-10 and TGF-β expression, an indication towards the generation of protective immunity against experimental VL.

CONCLUSION

Our experimental findings demonstrated that the chemo-immunotherapeutic approach could be an effective way of controlling human VL infection at minimal dosages of antileishmanial with reduced side-effects and propensity of drug resistance emergence.

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.

Effects of Four Antibiotics on the Diversity of the Intestinal Microbiota

Oral antibiotics remain the therapy of choice for severe bacterial infections; however, antibiotic use disrupts the intestinal microbiota, increasing the risk of colonization by intestinal pathogens. Currently, our understanding of antibiotic-mediated disturbances of the microbiota remains at the level of bacterial families or specific species, and little is known about the effect of antibiotics on potentially beneficial and pathogenic bacteria under the conditions of gut microbiota dysbiosis. Additionally, the question of whether the effects of antibiotics on the gut microbiota are temporary or permanent is controversial. In this study, we used 16S rRNA gene sequencing to evaluate the short- and long-term effects of ampicillin, vancomycin, metronidazole, and neomycin on the murine intestinal microbiota. We found that the changes in the intestinal microbiota reflected the antibiotics' mechanisms of action and that dysbiosis of the intestinal microbiota led to competition between different bacterial communities. In particular, an increase in Enterococcus, which accompanies a decrease in probiotics-related genera such as Lactobacillus, is commonly seen across antibiotic treatments. In addition, we found that these oral antibiotics had long-term negative effects on the intestinal microbiota and promoted the development of antibiotic-resistant bacterial strains. These results indicate that ampicillin, vancomycin, metronidazole, and neomycin have long-term negative effects and can cause irreversible changes in the diversity of the intestinal microbiota, thereby increasing the risk of host disease. IMPORTANCE The intestinal microbiota is a dynamic community of hundreds of millions of microorganisms that play important roles in human health. However, treatment with antibiotics can disrupt the delicate balance of this community, leading to deleterious effects on the host such as inflammation and enhanced susceptibility to infection. To date, most studies of the effects of antibiotic treatment on the microbiota have focused on specific intestinal pathogens and bacterial families. However, few studies have investigated the effects of antibiotic treatment on the relative abundance of probiotic bacteria, pathogenic bacteria, and opportunistic bacterial pathogens in the gut.

A Review: Antimicrobial Therapy for Human Pythiosis

Human pythiosis is associated with poor prognosis with significant mortality caused by Pythium insidiosum. Antimicrobials’ in vitro and in vivo results against P. insidiosum are inconsistent. Although antimicrobials are clinically useful, they are not likely to achieve therapeutic success alone without surgery and immunotherapy. New therapeutic options are therefore needed. This non-exhaustive review discusses the rationale antimicrobial therapy, minimum inhibitory concentrations, and efficacy of antibacterial and antifungal agents against P. insidiosum. This review further provides insight into the immunomodulating effects of antimicrobials that can enhance the immune response to infections. Current data support using antimicrobial combination therapy for the pharmacotherapeutic management of human pythiosis. Also, the success or failure of antimicrobial treatment in human pythiosis might depend on the immunomodulatory effects of drugs. The repurposing of existing drugs is a safe strategy for anti-P. insidiosum drug discovery. To improve patient outcomes in pythiosis, we suggest further research and a deeper understanding of P. insidiosum virulence factors, host immune response, and host immune system modification by antimicrobials.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

The quest of the best - A SAR study of trithiolato-bridged dinuclear Ruthenium(II)-Arene compounds presenting antiparasitic properties

A structure activity relationship (SAR) study of a library of 56 compounds (54 ruthenium and 2 osmium derivatives) based on the trithiolato-bridged dinuclear ruthenium(II)-arene scaffold (general formula [(η⁶-arene)₂Ru₂(μ₂-SR)₃]⁺, symmetric and [(η⁶-arene)₂Ru₂(μ₂-SR¹)₂(μ₂-SR²)]⁺, mixed, respectively) is reported. The 56 compounds (of which 34 are newly designed drug candidates) were synthesized by introducing chemical modifications at the level of bridge thiols, and they were grouped into eight families according to their structural features. The selected fittings were guided by previous results and focused on a fine-tuning of the physico-chemical and steric properties. Newly synthesized complexes were characterized by NMR spectroscopy, mass spectrometry and elemental analysis, and four single-crystal X-ray structures were obtained. The in vitro biological assessment of the compounds was realized by applying a three-step screening cascade: (i) evaluation of the activity against Toxoplasma gondii RH strain tachyzoites expressing β-galactosidase (T. gondii-β-gal) grown in human foreskin fibroblast monolayers (HFF) and assessment of toxicity in non-infected HFF host cells; (ii) dose-response assays using selected compound, and (iii) studies on the effects in murine splenocytes. A primary screening was performed at 1 and 0.1 μM, and resulted in the selection of 39 compounds that inhibited parasite proliferation at 1 μM by more than 95% and reduced the viability of HFF by less than 49%. In the secondary screening, dose-response assays showed that the selected compounds exhibited half maximal inhibitory concentration (IC₅₀) values for T. gondii-β-gal between 0.01 μM and 0.45 μM, with 30 compounds displaying an IC₅₀ lower than 0.1 μM. When applied to non-infected HFF monolayers at 2.5 μM, 8 compounds caused more than 90% and 31 compounds more than 30% viability impairment. The tertiary screening included 14 compounds that did not cause HFF viability loss higher than 50% at 2.5 μM. These derivatives were assessed for potential immunosuppressive activities. First, splenocyte viability was assessed after treatment of cells with concanavalin A (ConA) and lipopolysaccharide (LPS) with compounds applied at 0.1 and 0.5 μM. Subsequently, the 5 compounds exhibiting the lowest splenocyte toxicity were further evaluated for their potential to inhibit B and T cell proliferation. Overall, compound 55 [(η⁶-p-MeC₆H₄Prⁱ)₂Ru₂(μ₂-SC₆H₄-o-CF₃)₂(μ₂-SC₆H₄-p-OH)]Cl exhibited the most favorable features, and will be investigated as a scaffold for further optimization in terms of anti-parasitic efficacy and drug-like properties.

The Effect of Colistin Treatment on the Selection of Colistin-Resistant Escherichia coli in Weaner Pigs

The treatment of diarrhea in the postweaning period is a common reason for the use of antimicrobials in pig production, and Escherichia coli is the single most important causative agent for this condition. Colistin has recently been classified as a critically important antimicrobial for human health, as it is a last-resort drug against certain multi-drug-resistant Gram-negative bacteria. Therefore, the use of colistin has been significantly reduced in some countries, including Denmark. Despite this, the drug is still commonly used to treat diarrhea in pigs in many countries, and there is a need to understand the risks associated with this practice. We performed a prospective cohort study to investigate the effect of colistin treatment on the changes in the average minimum inhibitory concentration (MIC) in commensal E. coli in a pig herd where no colistin-resistant bacteria were detectable before treatment. One group of pigs was batch treated with colistin after the clinical observation of diarrhea, one group was batch treated with colistin approximately 10 days before the expected onset of diarrhea, and a control group was not treated with colistin but provided with nonantimicrobial antidiarrheal feed supplement. Treatment with colistin in the dose and time combinations used did not result in a significant increase in the average colistin MIC values in E. coli. Moreover, no E. coli strains showed a MIC above the breakpoint of >2 mg/L against colistin. Co-selection of resistance to other antimicrobials was not observed.

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).

Fighting microbial pathogens by integrating host ecosystem interactions and evolution

Successful therapies to combat microbial diseases and cancers require incorporating ecological and evolutionary principles. Drawing upon the fields of ecology and evolutionary biology, we present a systems-based approach in which host and disease-causing factors are considered as part of a complex network of interactions, analogous to studies of "classical" ecosystems. Centering this approach around empirical examples of disease treatment, we present evidence that successful therapies invariably engage multiple interactions with other components of the host ecosystem. Many of these factors interact nonlinearly to yield synergistic benefits and curative outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged to improve the study of pathogenesis in situ and to develop more effective therapies. An eco-evolutionary systems perspective has surprising and important consequences, and we use it to articulate areas of high research priority for improving treatment strategies.

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.

Modifying Adaptive Therapy to Enhance Competitive Suppression

Adaptive therapy is a promising new approach to cancer treatment. It is designed to leverage competition between drug-sensitive and drug-resistant cells in order to suppress resistance and maintain tumor control for longer. Prompted by encouraging results from a recent pilot clinical trial, we evaluate the design of this initial test of adaptive therapy and identify three simple modifications that should improve performance. These modifications are designed to increase competition and are easy to implement. Using the mathematical model that supported the recent adaptive therapy trial, we show that the suggested modifications further delay time to tumor progression and also increase the range of patients who can benefit from adaptive therapy.

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.

The safety profile of Bald's eyesalve for the treatment of bacterial infections

The rise in antimicrobial resistance has prompted the development of alternatives to combat bacterial infections. Bald's eyesalve, a remedy used in the Early Medieval period, has previously been shown to have efficacy against Staphylococcus aureus in in vitro and in vivo models of chronic wounds. However, the safety profile of Bald's eyesalve has not yet been demonstrated, and this is vital before testing in humans. Here, we determined the safety potential of Bald's eyesalve using in vitro, ex vivo, and in vivo models representative of skin or eye infections. We also confirmed that Bald's eyesalve is active against an important eye pathogen, Neisseria gonorrhoeae. Low levels of cytotoxicity were observed in eyesalve-treated cell lines representative of skin and immune cells. Results from a bovine corneal opacity and permeability test demonstrated slight irritation to the cornea that resolved within 10 min. The slug mucosal irritation assay revealed that a low level of mucus was secreted by slugs indicating moderate mucosal irritation. We obtained promising results from mouse wound closure experiments; no visible signs of irritation or inflammation were observed. Our results suggest that Bald's eyesalve could be tested further on human volunteers to assess safety for topical application against bacterial infections.

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.

Within-host spatiotemporal dynamic of systemic salmonellosis: Ways to track infection, reaction to vaccination and antimicrobial treatment

During the last two decades our understanding of the complex in vivo host-pathogen interactions has increased due to technical improvements and new research tools. The rapid advancement of molecular biology, flow cytometry and microscopy techniques, combined with mathematical modelling, have empowered in-depth studies of systemic bacterial infections across scales from single molecules, to cells, to organs and systems to reach the whole organism level. By tracking subpopulations of bacteria in vivo using molecular or fluorescent tags, it has been possible to reconstruct the spread of infection within and between organs, allowing unprecedented quantification of the effects of antimicrobial treatment and vaccination. This review illustrates recent advances in the study of heterogeneous traits of the infection process and illustrate approaches to investigate the reciprocal interactions between antimicrobial treatments, bacterial growth/death as well as inter- and intra-organ spread. We also discuss how vaccines impact the in vivo behaviour of bacteria and how these findings can guide vaccine design and rational antimicrobial treatment.

A data-based mathematical modelling study to quantify the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica during treatment and relapse

Antibiotic therapy has drastically reduced the mortality and sequelae of bacterial infections. From naturally occurring to chemically synthesized, different classes of antibiotics have been successfully used without detailed knowledge of how they affect bacterial dynamics in vivo. However, a proportion of patients receiving antimicrobial therapy develop recrudescent infections post-treatment. Relapsing infections are attributable to incomplete clearance of bacterial populations following antibiotic administration; the metabolic profile of this antibiotic-recalcitrant bacterial subpopulation, the spatio-temporal context of its emergence and the variance of antibiotic-bacterial interactions in vivo remain unclear. Here, we develop and apply a mechanistic mathematical model to data from a study comparing the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica serovar Typhimurium in murine infections. Using the inferential capacity of our model, we show that the antibiotic-recalcitrant bacteria following ampicillin, but not ciprofloxacin, treatment belong to a non-replicating phenotype. Aligning with previous studies, we independently estimate that the lymphoid tissues and spleen are important reservoirs of non-replicating bacteria. Finally, we predict that post-treatment, the progenitors of the non-growing and growing bacterial populations replicate and die at different rates. Ultimately, the liver, spleen and mesenteric lymph nodes are all repopulated by progenitors of the previously non-growing phenotype in ampicillin-treated mice.

Integrating mathematical models with experimental data to investigate the within-host dynamics of bacterial infections

Bacterial infections still constitute a major cause of mortality and morbidity worldwide. The unavailability of therapeutics, antimicrobial resistance and the chronicity of infections due to incomplete clearance contribute to this phenomenon. Despite the progress in antimicrobial and vaccine development, knowledge about the effect that therapeutics have on the host–bacteria interactions remains incomplete. Insights into the characteristics of bacterial colonization and migration between tissues and the relationship between replication and host- or therapeutically induced killing can enable efficient design of treatment approaches. Recently, innovative experimental techniques have generated data enabling the qualitative characterization of aspects of bacterial dynamics. Here, we argue that mathematical modeling as an adjunct to experimental data can enrich the biological insight that these data provide. However, due to limited interdisciplinary training, efforts to combine the two remain limited. To promote this dialogue, we provide a categorization of modeling approaches highlighting their relationship to data generated by a range of experimental techniques in the area of in vivo bacterial dynamics. We outline common biological themes explored using mathematical models with case studies across all pathogen classes. Finally, this review advocates multidisciplinary integration to improve our mechanistic understanding of bacterial infections and guide the use of existing or new therapies.

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.

Infection, antibiotic exposure and development of atopic dermatitis: A nationwide case-control study

Previous studies suggest an association between atopic dermatitis (AD) and exposure to microorganisms and antibiotics. However, these studies have limitations, and the sole influence on the development of AD was elusive. We performed a nationwide population-based case-control study in a Korean population to investigate the association between AD and early-life infection or antibiotic exposure. A total of 244 805 children with AD from the 2 283 601 children born between January 2010 and December 2014 and an equal number of sex- and age-matched healthy children were enrolled. A conditional logistic regression analysis showed that the episode of infection and antibiotic exposure were associated with an increased risk of AD (odds ratio [OR], 1.60; 95% confidence interval [CI], 1.58-1.63 for infection; and OR, 1.11; 95% CI, 1.09-1.13 for antibiotic exposure). A dose-dependent relationship was observed between risk for AD, the number of infection episodes and antibiotic cycles and the duration of antibiotic exposure. On further analysis using a conditional logistic model, the risk of AD was less when the antibiotics were used during the infection episode than that without the use of antibiotics, especially if the duration of the infection was short. Although our study could not consider the effect of cause or severity of infection, class of antibiotics and genetic or environmental factors of enrolled subjects, our results suggested that infection and antibiotic exposure were associated with an increased risk of AD. In addition, the results also implied that the use of antibiotics during an infection episode can decrease the risk of AD induced by the infection and that appropriate management of infections can minimize the risk of AD induced by infection or antibiotics.

Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, a containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here, we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant Escherichia coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

The molecular underpinnings of antibiotic resistance are increasingly understood, but less is known about how these molecular events influence microbial dynamics on the population scale. Here, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where increasing population size or delaying drug exposure can promote population collapse. Specifically, we demonstrate how density-dependent feedback loops couple population growth and antibiotic efficacy when communities include drug-resistant subpopulations, leading to a wide range of behavior, including population survival, collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Finally, we demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx collapse.

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and drug resistance profiles. For example, selection in a dual β-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and β-lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations are nearly growth-optimized along a contour connecting profiles selected by the component drugs. Our results highlight trade-offs between drug interactions and resistance profiles during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.

Synthesis, Biological Assessment, and Structure Activity Relationship Studies of New Flavanones Embodying Chromene Moieties

Novel flavanones that incorporate chromene motifs are synthesized via a one-step multicomponent reaction. The structures of the new chromenes are elucidated by using IR, ¹H-NMR, ¹³C-NMR, ¹H-¹H COSY, HSQC, HMBC, and elemental analysis. The new compounds are screened for their in vitro antimicrobial and cytotoxic activities. The antimicrobial properties are investigated and established against seven human pathogens, employing the agar well diffusion method and the minimum inhibitory concentrations. A majority of the assessed derivatives are found to exhibit significant antimicrobial activities against most bacterial strains, in comparison to standard reference drugs. Moreover, their cytotoxicity is appraised against four different human carcinoma cell lines: human colon carcinoma (HCT-116), human hepatocellular carcinoma (HepG-2), human breast adenocarcinoma (MCF-7), and adenocarcinoma human alveolar basal epithelial cell (A-549). All the desired compounds are subjected to in-silico studies, forecasting their drug likeness, bioactivity, and the absorption, distribution, metabolism, and excretion (ADME) properties prior to their synthetic assembly. The in-silico molecular docking evaluation of all the targeted derivatives is undertaken on gyrase B and the cyclin-dependent kinase. The in-silico predicted outcomes were endorsed by the in vitro studies.

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.

Spontaneous Clearance of Pharyngeal Gonococcal Infections: A Retrospective Study in Patients of the Sexually Transmitted Infections Clinic; Amsterdam, the Netherlands; 2012 to 2015

INTRODUCTION

Pharyngeal Neisseria gonorrhoeae infections are mostly asymptomatic, yet sustain ongoing gonococcal transmission. We assessed the proportion of pharyngeal gonorrhea that spontaneously clears and identified determinants of clearance.

METHODS

At the sexually transmitted infections clinic Amsterdam, at-risk women and men who have sex with men were routinely screened for pharyngeal N. gonorrhoeae using an RNA-based nucleic acid amplification test (NAAT; Aptima Combo 2).We retrospectively examined medical records of pharyngeal gonorrhea patients (January 2012-August 2015). We included patients who returned for antibiotic treatment and had a new sample taken for NAAT before treatment. Spontaneous clearance was defined as a negative NAAT result at the follow-up visit.

RESULTS

During the study period, 1266 cases with a pharyngeal gonorrhea were not treated at the first consultation and returned for a follow-up visit. Median (interquartile range) time between the first consultation and follow-up was 10 (7-14) days. Spontaneous clearance was found in 139 cases (11.0%) and was associated with age at least 45 years (vs. 16-24 years; adjusted odds ratio, 2.02 [95% confidence interval, 1.09-3.75]) and with time from the first consultation to follow-up (adjusted odds ratio, 1.08 [1.06-1.10], per extra day).

CONCLUSIONS

Eleven percent of pharyngeal gonorrhea cases cleared spontaneously. Spontaneous clearance of pharyngeal gonorrhea was more often seen among older patients.

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.

Susceptibility of microbial cells to the modified PIP2-binding sequence of gelsolin anchored on the surface of magnetic nanoparticles

BACKGROUND

Magnetic nanoparticles (MNPs) are characterized by unique physicochemical and biological properties that allow their employment as highly biocompatible drug carriers. Gelsolin (GSN) is a multifunctional actin-binding protein involved in cytoskeleton remodeling and free circulating actin sequestering. It was reported that a gelsolin derived phosphoinositide binding domain GSN 160-169, (PBP10 peptide) coupled with rhodamine B, exerts strong bactericidal activity.

RESULTS

In this study, we synthesized a new antibacterial and antifungal nanosystem composed of MNPs and a PBP10 peptide attached to the surface. The physicochemical properties of these nanosystems were analyzed by spectroscopy, calorimetry, electron microscopy, and X-ray studies. Using luminescence based techniques and a standard killing assay against representative strains of Gram-positive (Staphylococcus aureus MRSA Xen 30) and Gram-negative (Pseudomonas aeruginosa Xen 5) bacteria and against fungal cells (Candida spp.) we demonstrated that magnetic nanoparticles significantly enhance the effect of PBP10 peptides through a membrane-based mode of action, involving attachment and interaction with cell wall components, disruption of microbial membrane and increased uptake of peptide. Our results also indicate that treatment of both planktonic and biofilm forms of pathogens by PBP10-based nanosystems is more effective than therapy with either of these agents alone.

CONCLUSIONS

The results show that magnetic nanoparticles enhance the antimicrobial activity of the phosphoinositide-binding domain of gelsolin, modulate its mode of action and strengthen the idea of its employment for developing the new treatment methods of infections.

Using SimulATe to model the effects of antibiotic selective pressure on the dynamics of pathogenic bacterial populations

Antibiotics are notable weapons in fighting bacteria. Nowadays, however, the effectiveness of antibiotics is severely hindered by the increasing levels of antibiotic resistances in pathogenic bacterial populations, which can persist due to the selective pressure caused by antibiotic exposure. Arguably, the main cause of antibiotic resistances endurance in nature is antibiotic misuse, such as via overusing, inappropriate prescribing as well as the uncontrolled use in agriculture and livestock. There is also a lack of knowledge on appropriate antibiotic usage by the general public. Public scientific literacy and more research on therapeutic practices are fundamental to tackle this problem. Here, we present SimulATe a software which allows the simulation of the effects of antibiotic therapies on bacterial populations during human infections. This software can be used to develop students’ scientific literacy, using infections and antibiotic treatments as context to engage students in scientific practices, and discussions on antibiotic treatment onset and duration or on its use in immunosuppressed or critically ill individuals. SimulATe’s features also allow it to be used for research purposes allowing the simulation of real scenarios and exploration of their outcomes across the parameters’ landscape.

Antibiotics as both friends and foes of the human gut microbiome: The microbial community approach

The exposure of the human gut to antibiotics can have a great impact on human health. Antibiotics pertain to the preservation of human health and are useful tools for fighting bacterial infections. They can be used for curing infections and can play a critical role in immunocompromised or chronic patients, or in fighting childhood severe malnutrition. Yet, the genomic and phylogenetic diversity of the human gut changes under antibiotic exposure. Antibiotics can also have severe side effects on human gut health, due to the spreading of potential antibiotic resistance genetic traits and to their correlation with virulence of some bacterial pathogens. They can shape, and even disrupt, the composition and functioning diversity of the human gut microbiome. Traditionally bacterial antibiotic resistances have been evaluated at clone or population level. However, the understanding of these two apparently disparate perspectives as both friends and foes may come from the study of microbiomes as a whole and from the evaluation of both positive and negative effects of antibiotics on microbial community dynamics and diversity. In this review we present some metagenomic tools and databases that enable the studying of antibiotic resistance in human gut metagenomes, promoting the development of personalized medicine strategies, new antimicrobial therapy protocols and patient follow-up.

An ecosystem framework for understanding and treating disease

Pathogens and cancers are pervasive health risks in the human population. I argue that if we are to better understand disease and its treatment, then we need to take an ecological perspective of disease itself. I generalize and extend an emerging framework that views disease as an ecosystem and many of its components as interacting in a community. I develop the framework for biological etiological agents (BEAs) that multiply within humans—focusing on bacterial pathogens and cancers—but the framework could be extended to include other host and parasite species. I begin by describing why we need an ecosystem framework to understand disease, and the main components and interactions in bacterial and cancer disease ecosystems. Focus is then given to the BEA and how it may proceed through characteristic states, including emergence, growth, spread and regression. The framework is then applied to therapeutic interventions. Central to success is preventing BEA evasion, the best known being antibiotic resistance and chemotherapeutic resistance in cancers. With risks of evasion in mind, I propose six measures that either introduce new components into the disease ecosystem or manipulate existing ones. An ecosystem framework promises to enhance our understanding of disease, BEA and host (co)evolution, and how we can improve therapeutic outcomes.

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

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

Pharmacodynamic considerations of collateral sensitivity in design of antibiotic treatment regimen

INTRODUCTION

Antibiotics have greatly reduced the morbidity and mortality due to infectious diseases. Although antibiotic resistance is not a new problem, its breadth now constitutes a significant threat to human health. One strategy to help combat resistance is to find novel ways to use existing drugs, even those that display high rates of resistance. For the pathogens Escherichia coli and Pseudomonas aeruginosa, pairs of antibiotics have been identified for which evolution of resistance to drug A increases sensitivity to drug B and vice versa. These research groups have proposed cycling such pairs to treat infections, and similar treatment strategies are being investigated for various cancer forms as well. While an exciting treatment prospect, no cycling experiments have yet been performed with consideration of pharmacokinetics and pharmacodynamics. To test the plausibility of such schemes and optimize them, we create a mathematical model with explicit pharmacokinetic/pharmacodynamic considerations.

MATERIALS AND METHODS

We evaluate antibiotic cycling protocols using pairs of such antibiotics and investigate the speed of ascent of multiply resistant mutants.

RESULTS

Our analyses show that when using low concentrations of antibiotics, treatment failure will always occur due to the rapid ascent and fixation of resistant mutants. However, moderate to high concentrations of some combinations of bacteriostatic and bactericidal antibiotics with multiday cycling prevent resistance from developing and increase the likelihood of treatment success.

CONCLUSION

Our results call for guarded optimism in application and development of such treatment protocols.

Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas

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

Computer simulations of the signalling network in FLT3 +-acute myeloid leukaemia - indications for an optimal dosage of inhibitors against FLT3 and CDK6

Background

Mutations in the FMS-like tyrosine kinase 3 (FLT3) are associated with uncontrolled cellular functions that contribute to the development of acute myeloid leukaemia (AML). We performed computer simulations of the FLT3-dependent signalling network in order to study the pathways that are involved in AML development and resistance to targeted therapies.

Results

Analysis of the simulations revealed the presence of alternative pathways through phosphoinositide 3 kinase (PI3K) and SH2-containing sequence proteins (SHC), that could overcome inhibition of FLT3. Inhibition of cyclin dependent kinase 6 (CDK6), a related molecular target, was also tested in the simulation but was not found to yield sufficient benefits alone.

Conclusions

The PI3K pathway provided a basis for resistance to treatments. Alternative signalling pathways could not, however, restore cancer growth signals (proliferation and loss of apoptosis) to the same levels as prior to treatment, which may explain why FLT3 resistance mutations are the most common resistance mechanism. Finally, sensitivity analysis suggested the existence of optimal doses of FLT3 and CDK6 inhibitors in terms of efficacy and toxicity.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2145-y) contains supplementary material, which is available to authorized users.

Phagocytes, Antibiotics, and Self-Limiting Bacterial Infections

Most antibiotic use in humans is to reduce the magnitude and term of morbidity of acute, community-acquired infections in immune competent patients, rather than to save lives. Thanks to phagocytic leucocytes and other host defenses, the vast majority of these infections are self-limiting. Nevertheless, there has been a negligible amount of consideration of the contribution of phagocytosis and other host defenses in the research for, and the design of, antibiotic treatment regimens, which hyper-emphasizes antibiotics as if they were the sole mechanism responsible for the clearance of infections. Here, we critically review this approach and its limitations. With the aid of a heuristic mathematical model, we postulate that if the rate of phagocytosis is great enough, for acute, normally self-limiting infections, then (i) antibiotics with different pharmacodynamic properties would be similarly effective, (ii) low doses of antibiotics can be as effective as high doses, and (iii) neither phenotypic nor inherited antibiotic resistance generated during therapy are likely to lead to treatment failure.

How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient

When resistance to anticancer or antimicrobial drugs evolves in a patient, highly effective chemotherapy can fail, threatening patient health and lifespan. Standard practice is to treat aggressively, effectively eliminating drug-sensitive target cells as quickly as possible. This prevents sensitive cells from acquiring resistance de novo but also eliminates populations that can competitively suppress resistant populations. Here we analyse that evolutionary trade-off and consider recent suggestions that treatment regimens aimed at containing rather than eliminating tumours or infections might more effectively delay the emergence of resistance. Our general mathematical analysis shows that there are situations in which regimens aimed at containment will outperform standard practice even if there is no fitness cost of resistance, and, in those cases, the time to treatment failure can be more than doubled. But, there are also situations in which containment will make a bad prognosis worse. Our analysis identifies thresholds that define these situations and thus can guide treatment decisions. The analysis also suggests a variety of interventions that could be used in conjunction with cytotoxic drugs to inhibit the emergence of resistance. Fundamental principles determine, across a wide range of disease settings, the circumstances under which standard practice best delays resistance emergence-and when it can be bettered.

Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections

The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.

Towards the just and sustainable use of antibiotics

The emergence and spread of antibiotic resistant pathogens poses a big challenge to policy-makers, who need to oversee the transformation of health systems that evolved to provide easy access to these drugs into ones that encourage appropriate use of antimicrobials, whilst reducing the risk of resistance. This is a particular challenge for low and middle-income countries with pluralistic health systems where antibiotics are available in a number of different markets. This review paper considers access and use of antibiotics in these countries from a complex adaptive system perspective. It highlights the main areas of intervention that could provide the key to addressing the sustainable long term use and availability of antibiotics. A focus on the synergies between interventions addressing access strategies, antibiotic quality, diagnostics for low-resource settings, measures to encourage just and sustainable decision making and help seeking optimal therapeutic and dosing strategies are key levers for the sustainable future of antibiotic use. Successful integration of such strategies will be dependent on effective governance mechanisms, effective partnerships and coalition building and accurate evaluation systems at national, regional and global levels.