Systematic analysis of drug combinations against Gram-positive bacteria

The effect of combining antibiotics on resistance: A systematic review and meta-analysis

Background

Under which conditions antibiotic combination therapy decelerates rather than accelerates resistance evolution is not well understood. We examined the effect of combining antibiotics on within-patient resistance development across various bacterial pathogens and antibiotics.

Methods

We searched CENTRAL, EMBASE, and PubMed for (quasi)-randomised controlled trials (RCTs) published from database inception to 24 November 2022. Trials comparing antibiotic treatments with different numbers of antibiotics were included. Patients were considered to have acquired resistance if, at the follow-up culture, a resistant bacterium (as defined by the study authors) was detected that had not been present in the baseline culture. We combined results using a random effects model and performed meta-regression and stratified analyses. The trials' risk of bias was assessed with the Cochrane tool.

Results

42 trials were eligible and 29, including 5054 patients, qualified for statistical analysis. In most trials, resistance development was not the primary outcome and studies lacked power. The combined odds ratio for the acquisition of resistance comparing the group with the higher number of antibiotics with the comparison group was 1.23 (95% CI 0.68-2.25), with substantial between-study heterogeneity (I2=77%). We identified tentative evidence for potential beneficial or detrimental effects of antibiotic combination therapy for specific pathogens or medical conditions.

Conclusions

The evidence for combining a higher number of antibiotics compared to fewer from RCTs is scarce and overall compatible with both benefit or harm. Trials powered to detect differences in resistance development or well-designed observational studies are required to clarify the impact of combination therapy on resistance.

Funding

Support from the Swiss National Science Foundation (grant 310030B_176401 (SB, BS, CW), grant 32FP30-174281 (ME), grant 324730_207957 (RDK)) and from the National Institute of Allergy and Infectious Diseases (NIAID, cooperative agreement AI069924 (ME)) is gratefully acknowledged.

Predicting the evolution of antibiotic resistance

Predicting the evolution of antibiotic resistance is critical for realizing precision antibiotic therapies. How exactly to achieve such predictions is a theoretical challenge. Insights from mathematical models that reflect future behavior of microbes under antibiotic stress can inform intervention protocols. However, this requires going beyond heuristic approaches by modeling ecological and evolutionary responses linked to metabolic pathways and cellular functions. Developing such models is now becoming possible due to increasing data availability from systematic experiments with microbial systems. Here, I review recent theoretical advances promising building blocks to piece together a predictive theory of antibiotic resistance evolution. I focus on the conceptual framework of eco-evolutionary response models grounded on quantitative laws of bacterial physiology. These forward-looking models can predict previously unknown behavior of bacteria upon antibiotic exposure. With current developments covering mostly the case of ribosome-targeting antibiotics, I write this Opinion piece as an invitation to generalize the principles discussed here to a broader range of drugs and context dependencies.

Tracing the Evolution: A Comprehensive Bibliometric Analysis of Drug Interaction Clinical Studies

This study aims to meticulously map the bibliometric landscape of drug-drug interactions (DDIs) in clinical research. This represents the first use of bibliometric analysis to comprehensively highlight the evolutionary trends and core themes in this critical field of pharmacology. An exhaustive bibliometric search was performed within the Web of Science Core Collection, aiming to comprehensively gather literature on DDIs in clinical settings. A combination of sophisticated analytical tools including DIKW, VOSviewer, and Citespace was utilized for an in-depth exploration of bibliometric patterns and trends. Of the 3421 initially identified articles, 2622 were considered relevant. The analysis revealed a marked escalation in DDIs publications, with a peak observed in 2020. Five principal thematic clusters emerged: Safety and Adverse Reactions, Drug Metabolism and Efficacy, Disease and Drug Treatment, Research Methods and Practices, and Special Populations and Combined Medication. Key insights included the escalating significance of drug metabolism in pharmacokinetics, heightened focus on cardiovascular and antiviral therapeutics, and the advancing frontier of personalized medicine. Additionally, the analysis underscored the necessity for strategic attention to vulnerable populations and innovative methodological approaches. This study calls for the global harmonization of research methods in DDIs clinical investigations, advocating for the integration of personalized medicine paradigms and the implementation of cutting-edge computational analytics. It highlights the imperative for inclusive and collaborative research approaches to adeptly address the intricate challenges of contemporary pharmacotherapy.

Ticagrelor alters the membrane of Staphylococcus aureus and enhances the activity of vancomycin and daptomycin without eliciting cross-resistance

Infections with multidrug-resistant bacteria pose a major healthcare problem which urges the need for novel treatment options. Besides its potent antiplatelet properties, ticagrelor has antibacterial activity against Gram-positive bacteria, including methicillin- and vancomycin-resistant Staphylococcus aureus (MRSA and VRSA). Several retrospective studies in cardiovascular patients support an antibacterial effect of this drug which is not related to its antiplatelet activity. We investigated the mechanism of action of ticagrelor in Staphylococcus aureus and model Bacillus subtilis, and assessed cross-resistance with two conventional anti-MRSA antibiotics, vancomycin and daptomycin. Bacillus subtilis bioreporter strains revealed ticagrelor-induced cell envelope-related stress responses. Sub-inhibitory drug concentrations caused membrane depolarization, impaired positioning of both the peripheral membrane protein MinD and the peptidoglycan precursor lipid II, and it affected cell shape. At the MIC, ticagrelor destroyed membrane integrity, indicated by the influx of membrane impermeable dyes, and lipid aggregate formation. Whole-genome sequencing of in vitro-generated ticagrelor-resistant MRSA clones revealed mutations in genes encoding ClpP, ClpX, and YjbH. Lipidomic analysis of resistant clones displayed changes in levels of the most abundant lipids of the Staphylococcus aureus membrane, for example, cardiolipins, phosphatidylglycerols, and diacylglycerols. Exogeneous cardiolipin, phosphatidylglycerol, or diacylglycerol antagonized the antibacterial properties of ticagrelor. Ticagrelor enhanced MRSA growth inhibition and killing by vancomycin and daptomycin in both exponential and stationary phases. Finally, no cross-resistance was observed between ticagrelor and daptomycin, or vancomycin. Our study demonstrates that ticagrelor targets multiple lipids in the cytoplasmic membrane of Gram-positive bacteria, thereby retaining activity against multidrug-resistant staphylococci including daptomycin- and vancomycin-resistant strains.IMPORTANCEInfections with multidrug-resistant bacteria pose a major healthcare problem with an urgent need for novel treatment options. The antiplatelet drug ticagrelor possesses antibacterial activity against Gram-positive bacteria including methicillin-resistant and vancomycin-resistant Staphylococcus aureus strains. We report a unique, dose-dependent, antibacterial mechanism of action of ticagrelor, which alters the properties and integrity of the bacterial cytoplasmic membrane. Ticagrelor retains activity against multidrug-resistant staphylococci, including isolates carrying the most common in vivo selected daptomycin resistance mutations and vancomycin-intermediate Staphylococcus aureus. Our data support the use of ticagrelor as adjunct therapy against multidrug-resistant strains. Because of the presence of multiple non-protein targets of this drug within the bacterial membrane, resistance development is expected to be slow. All these findings corroborate the accumulating observational clinical evidence for a beneficial anti-bacterial effect of ticagrelor in cardiovascular patients in need of such treatment.

Balancing act: counteracting adverse drug effects on the microbiome

The human gut microbiome, a community of microbes that plays a crucial role in our wellbeing, is highly adaptable but also vulnerable to drug treatments. This vulnerability can have serious consequences for the host, for example, increasing susceptibility to infections, immune, metabolic, and cognitive disorders. However, the microbiome's adaptability also provides opportunities to prevent, protect, or even reverse drug-induced damage. Recently, several innovative approaches have emerged aimed at minimizing the collateral damage of drugs on the microbiome. Here, we outline these approaches, discuss their applicability in different treatment scenarios, highlight current challenges, and suggest avenues that may lead to an effective protection of the microbiome.

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)

While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are widely recognized in the fields of quantitative and evolutionary genetics, "environment-byenvironment interaction" (ExE) is a term used less often. In this study, we find that environmentby-environment interactions are a meaningful driver of phenotypes, and moreover, that they differ across different genotypes (suggestive of ExExG). To support this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. Our findings reveal that the effectiveness of a drug combination, relative to single drugs, often differs across drug resistant mutants. Remarkably, even mutants that differ by only a single nucleotide change can have dramatically different drug × drug (ExE) interactions. We also introduce a new framework that more accurately predicts the direction and magnitude of ExE interactions for some mutants. Understanding how ExE interactions change across genotypes (ExExG) is crucial not only for modeling the evolution of pathogenic microbes, but also for enhancing our knowledge of the underlying cell biology and the sources of phenotypic variance within populations. While the significance of ExExG interactions has been overlooked in evolutionary and population genetics, these fields and others stand to benefit from understanding how these interactions shape the complex behavior of living systems.

Viral-bacterial co-infections screen in vitro reveals molecular processes affecting pathogen proliferation and host cell viability

The broadening of accessible methodologies has enabled mechanistic insights into single-pathogen infections, yet the molecular mechanisms underlying co-infections remain largely elusive, despite their clinical frequency and relevance, generally exacerbating symptom severity and fatality. Here, we describe an unbiased in vitro screening of pairwise co-infections in a murine macrophage model, quantifying pathogen proliferation and host cell death in parallel over time. The screen revealed that the majority of interactions are antagonistic for both metrics, highlighting general patterns depending on the pathogen virulence strategy. We subsequently decipher two distinct molecular interaction points: Firstly, murine Adenovirus 3 modifies ASC-dependent inflammasome responses in murine macrophages, altering host cell death and cytokine production, thereby impacting secondary Salmonella infection. Secondly, murine Adenovirus 2 infection triggers upregulation of Mprip, a crucial mediator of phagocytosis, which in turn causes increased Yersinia uptake, specifically in virus pre-infected bone-marrow-derived macrophages. This work therefore encompasses both a first-of-its-kind systematic assessment of host-pathogen-pathogen interactions, and mechanistic insight into molecular mediators during co-infection.

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

β-lactamase expression induces collateral sensitivity in Escherichia coli

Major antibiotic groups are losing effectiveness due to the uncontrollable spread of antimicrobial resistance (AMR) genes. Among these, β-lactam resistance genes -encoding β-lactamases- stand as the most common resistance mechanism in Enterobacterales due to their frequent association with mobile genetic elements. In this context, novel approaches that counter mobile AMR are urgently needed. Collateral sensitivity (CS) occurs when the acquisition of resistance to one antibiotic increases susceptibility to another antibiotic and can be exploited to eliminate AMR selectively. However, most CS networks described so far emerge as a consequence of chromosomal mutations and cannot be leveraged to tackle mobile AMR. Here, we dissect the CS response elicited by the acquisition of a prevalent antibiotic resistance plasmid to reveal that the expression of the β-lactamase gene blaOXA-48 induces CS to colistin and azithromycin. We next show that other clinically relevant mobile β-lactamases produce similar CS responses in multiple, phylogenetically unrelated E. coli strains. Finally, by combining experiments with surveillance data comprising thousands of antibiotic susceptibility tests, we show that β-lactamase-induced CS is pervasive within Enterobacterales. These results highlight that the physiological side-effects of β-lactamases can be leveraged therapeutically, paving the way for the rational design of specific therapies to block mobile AMR or at least counteract their effects.

Data-driven learning of structure augments quantitative prediction of biological responses

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design.

Nε-lysine acetylation of the histone-like protein HBsu influences antibiotic survival and persistence in Bacillus subtilis

Nε-lysine acetylation is recognized as a prevalent post-translational modification (PTM) that regulates proteins across all three domains of life. In Bacillus subtilis, the histone-like protein HBsu is acetylated at seven sites, which regulates DNA compaction and the process of sporulation. In Mycobacteria, DNA compaction is a survival strategy in response antibiotic exposure. Acetylation of the HBsu ortholog HupB decondenses the chromosome to escape this drug-induced, non-growing state, and in addition, regulates the formation of drug-tolerant subpopulations by altering gene expression. We hypothesized that the acetylation of HBsu plays similar regulatory roles. First, we measured nucleoid area by fluorescence microscopy and in agreement, we found that wild-type cells compacted their nucleoids upon kanamycin exposure, but not exposure to tetracycline. We analyzed a collection of HBsu mutants that contain lysine substitutions that mimic the acetylated (glutamine) or unacetylated (arginine) forms of the protein. Our findings indicate that some level of acetylation is required at K3 for a proper response and K75 must be deacetylated. Next, we performed time-kill assays of wild-type and mutant strains in the presence of different antibiotics and found that interfering with HBsu acetylation led to faster killing rates. Finally, we examined the persistent subpopulation and found that altering the acetylation status of HBsu led to an increase in persister cell formation. In addition, we found that most of the deacetylation-mimic mutants, which have compacted nucleoids, were delayed in resuming growth following removal of the antibiotic, suggesting that acetylation is required to escape the persistent state. Together, this data adds an additional regulatory role for HBsu acetylation and further supports the existence of a histone-like code in bacteria.

Stability-based approaches in chemoproteomics

Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.

Within-species variability of antibiotic interactions in Gram-negative bacteria

Treatments with antibiotic combinations are becoming increasingly important even though the supposed clinical benefits of combinations are, in many cases, unclear. Here, we systematically examined how several clinically used antibiotics interact and affect the antimicrobial efficacy against five especially problematic Gram-negative pathogens. A total of 232 bacterial isolates were tested against different pairwise antibiotic combinations spanning five classes, and the ability of all combinations in inhibiting growth was quantified. Descriptive statistics, principal component analysis (PCA), and Spearman's rank correlation matrix were used to determine the correlations between the different combinations on interaction outcome. Several important conclusions can be drawn from the 696 examined interactions. Firstly, within a species, the interactions are in general conserved but can be isolate-specific for a given antibiotic combination and can range from antagonistic to synergistic. Secondly, additive and antagonistic interactions are the most common observed across species and antibiotics, with 87.1% of isolate-antibiotic combinations being additive, 11.6% antagonistic, and only 0.3% showing synergy. These findings suggest that to achieve the highest precision and efficacy of combination therapy, not only isolate-specific interaction profiling ought to be routinely performed, in particular to avoid using drug combinations that show antagonistic interaction and an expected associated reduction in efficacy, but also discovering rare and potentially valuable synergistic interactions.IMPORTANCEAntibiotic combinations are often used to treat bacterial infections, which aim to increase treatment efficacy and reduce resistance evolution. Typically, it is assumed that one specific antibiotic combination has the same effect on different isolates of the same species, i.e., the interaction is conserved. Here, we tested this idea by examining how several clinically used antibiotics interact and affect the antimicrobial efficacy against several bacterial pathogens. Our results show that, even though within a species the interactions are often conserved, there are also isolate-specific differences for a given antibiotic combination that can range from antagonistic to synergistic. These findings suggest that isolate-specific interaction profiling ought to be performed in clinical microbiology routine to avoid using antagonistic drug combinations that might reduce treatment efficacy.

Antibiotic failure: Beyond antimicrobial resistance

Despite significant progress in antibiotic discovery, millions of lives are lost annually to infections. Surprisingly, the failure of antimicrobial treatments to effectively eliminate pathogens frequently cannot be attributed to genetically-encoded antibiotic resistance. This review aims to shed light on the fundamental mechanisms contributing to clinical scenarios where antimicrobial therapies are ineffective (i.e., antibiotic failure), emphasizing critical factors impacting this under-recognized issue. Explored aspects include biofilm formation and sepsis, as well as the underlying microbiome. Therapeutic strategies beyond antibiotics, are examined to address the dimensions and resolution of antibiotic failure, actively contributing to this persistent but escalating crisis. We discuss the clinical relevance of antibiotic failure beyond resistance, limited availability of therapies, potential of new antibiotics to be ineffective, and the urgent need for novel anti-infectives or host-directed therapies directly addressing antibiotic failure. Particularly noteworthy is multidrug adaptive resistance in biofilms that represent 65 % of infections, due to the lack of approved therapies. Sepsis, responsible for 19.7 % of all deaths (as well as severe COVID-19 deaths), is a further manifestation of this issue, since antibiotics are the primary frontline therapy, and yet 23 % of patients succumb to this condition.