The pharmaco -, population and evolutionary dynamics of multi-drug therapy: experiments with S. aureus and E. coli and computer simulations

A mechanistic approach to optimize combination antibiotic therapy

Antimicrobial resistance is one of the most significant healthcare challenges of our times. Multidrug or combination therapies are sometimes required to treat severe infections; for example, the current protocols to treat pulmonary tuberculosis combine several antibiotics. However, combination therapy is usually based on lengthy empirical trials, and it is difficult to predict its efficacy. We propose a new tool to identify antibiotic synergy or antagonism and optimize combination therapies. Our model explicitly incorporates the mechanisms of individual drug action and estimates their combined effect using a mechanistic approach. By quantifying the impact on growth and death of a bacterial population, we can identify optimal combinations of multiple drugs. Our approach also allows for the investigation of the drugs' actions and the testing of theoretical hypotheses. We demonstrate the utility of this tool with in vitro Escherichia coli data using a combination of ampicillin and ciprofloxacin. In contrast to previous interpretations, our model finds a slight synergy between the antibiotics. Our mechanistic model allows investigating possible causes of the synergy.

Exploring antimicrobial interactions between metal ions and quaternary ammonium compounds toward synergistic metallo-antimicrobial formulations

Multi-target antimicrobial agents are considered a viable alternative to target-specific antibiotics, resistance to which emerged as a global threat. Used centuries before the discovery of conventional antibiotics, metal(loid)-based antimicrobials (MBAs), which target multiple biomolecules within the bacterial cell, are regaining research interest. However, there is a significant limiting factor-the balance between cost and efficiency. In this article, we utilize a checkerboard assay approach to explore antimicrobial combinations of MBAs with commonly used quaternary ammonium compound (QAC) antiseptics in order to discover novel combinations with more pronounced antimicrobial properties than would be expected from a simple sum of antimicrobial effects of initial components. This phenomenon, called synergy, was herein demonstrated for several mixtures of Al3+with cetyltrimethylammonium bromide (CTAB) and TeO32- with benzalkonium chloride (BAC) and didecyldimethylammonium bromide (DDAB) against planktonic and biofilm growth of Pseudomonas aeruginosa ATCC27853. Biofilm growth of Escherichia coli ATCC25922 was synergistically inhibited by the Cu2 +and benzalkonium chloride (BAC) mixture. Multiple additive mixtures were identified for both organisms. The current study observed unexpected species and growth state specificities for the synergistic combinations. The benefit of synergistic mixtures will be captured in economy/efficiency optimization for antimicrobial applications in which MBAs and QACs are presently used.

IMPORTANCE

We are entering the antimicrobial resistance era (AMR), where resistance to antibiotics is becoming more and more prevalent. In order to address this issue, various approaches are being explored. In this article, we explore for synergy between two very different antimicrobials, the antiseptic class of quaternary ammonium compounds and antimicrobial metals. These two antimicrobials have very different actions. Considering a OneHealth approach to the problem, finding synergistic mixtures allows for greater efficacy at lower concentrations, which would also address antimicrobial pollution issues.

The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs-those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence-Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a 'fair' comparison. Yet, exceptions exist for drugs with steep dose-response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio-even if not always optimal-is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.

Targeting Intracellular Bacteria with Dual Drug-loaded Lactoferrin Nanoparticles

Treatment of microbial infections is becoming daunting because of widespread antimicrobial resistance. The treatment challenge is further exacerbated by the fact that certain infectious bacteria invade and localize within host cells, protecting the bacteria from antimicrobial treatments and the host's immune response. To survive in the intracellular niche, such bacteria deploy surface receptors similar to host cell receptors to sequester iron, an essential nutrient for their virulence, from host iron-binding proteins, in particular lactoferrin and transferrin. In this context, we aimed to target lactoferrin receptors expressed by macrophages and bacteria; as such, we prepared and characterized lactoferrin nanoparticles (Lf-NPs) loaded with a dual drug combination of antimicrobial natural alkaloids, berberine or sanguinarine, with vancomycin or imipenem. We observed increased uptake of drug-loaded Lf-NPs by differentiated THP-1 cells with up to 90% proportion of fluorescent cells, which decreased to about 60% in the presence of free lactoferrin, demonstrating the targeting ability of Lf-NPs. The encapsulated antibiotic drug cocktail efficiently cleared intracellular Staphylococcus aureus (Newman strain) compared to the free drug combinations. However, the encapsulated drugs and the free drugs alike exhibited a bacteriostatic effect against the hard-to-treat Mycobacterium abscessus (smooth variant). In conclusion, the results of this study demonstrate the potential of lactoferrin nanoparticles for the targeted delivery of antibiotic drug cocktails for the treatment of intracellular bacteria.

Developing synergistic drug combinations to restore antibiotic sensitivity in drug-resistant Mycobacterium tuberculosis

Tuberculosis (TB) is a leading global cause of mortality owing to an infectious agent, accounting for almost one-third of antimicrobial resistance (AMR) deaths annually. We aimed to identify synergistic anti-TB drug combinations with the capacity to restore therapeutic efficacy against drug-resistant mutants of the causative agent, Mycobacterium tuberculosis We investigated combinations containing the known translational inhibitors, spectinomycin (SPT) and fusidic acid (FA), or the phenothiazine, chlorpromazine (CPZ), which disrupts mycobacterial energy metabolism. Potentiation of whole-cell drug efficacy was observed in SPT-CPZ combinations. This effect was lost against an M. tuberculosis mutant lacking the major facilitator superfamily (MFS) efflux pump, Rv1258c. Notably, the SPT-CPZ combination partially restored SPT efficacy against an SPT-resistant mutant carrying a g1379t point mutation in rrs, encoding the mycobacterial 16S ribosomal RNA. Combinations of SPT with FA, which targets the mycobacterial elongation factor G, exhibited potentiating activity against wild-type M. tuberculosis Moreover, this combination produced a modest potentiating effect against both FA-monoresistant and SPT-monoresistant mutants. Finally, combining SPT with the frontline anti-TB agents, rifampicin (RIF) and isoniazid, resulted in enhanced activity in vitro and ex vivo against both drug-susceptible M. tuberculosis and a RIF-monoresistant rpoB S531L mutant.These results support the utility of novel potentiating drug combinations in restoring antibiotic susceptibility of M. tuberculosis strains carrying genetic resistance to any one of the partner compounds.

Computational models, databases and tools for antibiotic combinations

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

Pharmacokinetic-pharmacodynamic models for time courses of antibiotic effects: VSI: Antimicrobial Pharmacometrics

Pharmacokinetic-pharmacodynamic (PKPD) models have emerged as valuable tools for the characterisation and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics like MIC and PKPD indices, PKPD models enable to describe the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. We conducted a literature review to identify PKPD models based on (i) antibiotic compounds, (ii) Gram-positive or Gram-negative pathogens, and (iii) in vitro or in vivo longitudinal colony forming unit data. We identified 132 publications released between 1963 and 2021, including models based on exposure with single antibiotics (n=92) and drug combinations (n=40), as well as different experimental settings (e.g., static/traditional dynamic/hollow-fibre/animal time-kill models, n=90/27/32/11). An interactive, fully searchable table summarises the details of each model, i.e. variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects, and interactions. Furthermore, the review highlights main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models shall assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen-antibiotic combinations over time and ultimately facilitate model-informed antibiotic translation, dosing and drug development.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

EFSA Panel on Biological Hazards (BIOHAZ), Koutsoumanis K, Allende A, Alvarez‐Ordóñez A, Bolton D, Bover‐Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson‐Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López‐Alonso M, Nielsen SS, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Innocenti ML, Liébana E, López‐Gálvez G, Manini P, Stella P, Peixe L. Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 1: Methodology, general data gaps and uncertainties

The European Commission requested EFSA to assess, in collaboration with EMA, the specific concentrations of antimicrobials resulting from cross-contamination in non-target feed for food-producing animals below which there would not be an effect on the emergence of, and/or selection for, resistance in microbial agents relevant for human and animal health, as well as the levels of the antimicrobials which could have a growth promotion/increase yield effect. The assessment was performed for 24 antimicrobial active substances, as specified in the mandate. This scientific opinion describes the methodology used, and the main associated data gaps and uncertainties. To estimate the antimicrobial levels in the non-target feed that would not result in emergence of, and/or selection for, resistance, a model was developed. This 'Feed Antimicrobial Resistance Selection Concentration' (FARSC) model is based on the minimal selective concentration (MSC), or the predicted MSC (PMSC) if MSC for the most susceptible bacterial species is unavailable, the fraction of antimicrobial dose available for exposure to microorganisms in the large intestine or rumen (considering pharmacokinetic parameters), the daily faecal output or rumen volume and the daily feed intake. Currently, lack of data prevents the establishment of PMSC and/or FARSC for several antimicrobials and animal species. To address growth promotion, data from an extensive literature search were used. Specific assessments of the different substances grouped by antimicrobial classes are addressed in separate scientific opinions. General conclusions and recommendations were made.

Interaction Tolerance Detection Test for Understanding the Killing Efficacy of Directional Antibiotic Combinations

Combination treatments are commonly prescribed for enhancing drug efficacy, as well as for preventing the evolution of resistance. The interaction between drugs is typically evaluated near the MIC, using growth rate as a measure of treatment efficacy. However, for infections in which the killing activity of the treatment is important, measurements far above the MIC are needed. In this regime, the killing rate often becomes weakly concentration dependent, and a different metric is needed to characterize drug interactions. We evaluate the interaction metric on killing using an easy visual assay, the interaction tolerance detection test (iTDtest), that estimates the survival of bacteria under antibiotic combinations. We identify antibiotic combinations that enable the eradication of tolerant bacteria. Furthermore, the visualization of the antibiotic interactions reveals directional drug interactions and enables predicting high-order combination outcomes, therefore facilitating the determination of optimal treatments. IMPORTANCE The killing efficacy of antibiotic combinations is rarely measured in the clinical setting. However, in cases where the treatment is required to kill the infecting organism and not merely arrest its growth, the information on the killing efficacy is important, especially when tolerant strains are implicated. Here, we report on an easy method for the determination of the killing efficacy of antibiotic combinations which enabled to reveal combinations effective against tolerant bacteria. The results could be generally used to guide antimicrobial therapy in life-threatening infections.

In Vitro Studies of Persister Cells

Many bacterial pathogens can permanently colonize their host and establish either chronic or recurrent infections that the immune system and antimicrobial therapies fail to eradicate. Antibiotic persisters (persister cells) are believed to be among the factors that make these infections challenging. Persisters are subpopulations of bacteria which survive treatment with bactericidal antibiotics in otherwise antibiotic-sensitive cultures and were extensively studied in a hope to discover the mechanisms that cause treatment failures in chronically infected patients; however, most of these studies were conducted in the test tube. Research into antibiotic persistence has uncovered large intrapopulation heterogeneity of bacterial growth and regrowth but has not identified essential, dedicated molecular mechanisms of antibiotic persistence. Diverse factors and stresses that inhibit bacterial growth reduce killing of the bulk population and may also increase the persister subpopulation, implying that an array of mechanisms are present. Hopefully, further studies under conditions that simulate the key aspects of persistent infections will lead to identifying target mechanisms for effective therapeutic solutions.

Nitrite modulates aminoglycoside tolerance by inhibiting cytochrome heme-copper oxidase in bacteria

As a bacteriostatic agent, nitrite has been used in food preservation for centuries. When used in combination with antibiotics, nitrite is reported to work either cooperatively or antagonistically. However, the mechanism underlying these effects remains largely unknown. Here we show that nitrite mediates tolerance to aminoglycosides in both Gram-negative and Gram-positive bacteria, but has little interaction with other types of antibiotics. Nitrite directly and mainly inhibits cytochrome heme-copper oxidases (HCOs), and by doing so, the membrane potential is compromised, blocking uptake of aminoglycosides. In contrast, reduced respiration (oxygen consumption rate) resulting from nitrite inhibition is not critical for aminoglycoside tolerance. While our data indicate that nitrite is a promising antimicrobial agent targeting HCOs, cautions should be taken when used with other antibiotics, aminoglycosides in particular.

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

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

Nontoxic Carbon Quantum Dots/g-C3 N4 for Efficient Photocatalytic Inactivation of Staphylococcus aureus under Visible Light

The widespread use of antibiotics has caused the rapid emergence of antibiotic-resistant bacterial strains and antibiotic resistance genes in the past few decades. Photocatalytic inactivation, a promising approach for the killing of pathogens, efficiently avoids the problems induced by antimicrobial drugs. However, traditional photocatalysts usually have some disadvantages, such as high costs of raw materials, ultraviolet ray excitation, and potential leaching of toxic metals. Here, a metal-free heterojunction photocatalyst, denoted as CQDs/g-C₃ N₄ , is synthesized through incorporating carbon quantum dots (CQDs) on graphitic carbon nitride (g-C₃ N₄ ), which significantly enhances photocatalytic inactivation of Staphylococcus aureus (S. aureus) compared with pure g-C₃ N₄ in vitro. CQDs/g-C₃ N₄ causes a rapid increase of intracellular reactive oxygen species levels and destruction of cell membranes under visible light, eventually leading to death of bacteria. The efficacy of CQDs/g-C₃ N₄ is further examined by a mouse cutaneous infection model of S. aureus. CQDs/g-C₃ N₄ markedly reduces the bacterial loads and prompts lesion recovery in mice, as compared with g-C₃ N₄ -treated group. In vivo and in vitro toxicity analyses show that the side effects of CQDs/g-C₃ N₄ are negligible. Considering the efficient photocatalytic inactivation and nontoxicity of CQDs/g-C₃ N₄ , this visible-light-driven photocatalyst paves a brand new avenue for the treatment of S. aureus infection.

Collateral sensitivity constrains resistance evolution of the CTX-M-15 β-lactamase

Antibiotic resistance is a major challenge to global public health. Discovery of new antibiotics is slow and to ensure proper treatment of bacterial infections new strategies are needed. One way to curb the development of antibiotic resistance is to design drug combinations where the development of resistance against one drug leads to collateral sensitivity to the other drug. Here we study collateral sensitivity patterns of the globally distributed extended-spectrum β-lactamase CTX-M-15, and find three non-synonymous mutations with increased resistance against mecillinam or piperacillin-tazobactam that simultaneously confer full susceptibility to several cephalosporin drugs. We show in vitro and in mice that a combination of mecillinam and cefotaxime eliminates both wild-type and resistant CTX-M-15. Our results indicate that mecillinam and cefotaxime in combination constrain resistance evolution of CTX-M-15, and illustrate how drug combinations can be rationally designed to limit the resistance evolution of horizontally transferred genes by exploiting collateral sensitivity patterns.

Variation in fluoroquinolone pharmacodynamic parameter values among isolates of two bacterial pathogens of bovine respiratory disease

To design an antimicrobial treatment regimen for a bacterial disease, data on the drug pharmacodynamics (PD) against selected drug-susceptible strains of the pathogen are used. The regimen is applied across such strains in the field, assuming the PD parameter values remain the same. We used time-kill experiments and PD modeling to investigate the fluoroquinolone enrofloxacin PD against different isolates of two bovine respiratory disease pathogens: four Mannheimia haemolytica and three Pasteurella multocida isolates. The models were fitted as mixed-effects non-linear regression; the fixed-effects PD parameter values were estimated after accounting for random variation among experimental replicates. There was both inter- and intra- bacterial species variability in the PD parameters Hill-coefficient and E_max (maximal decline of bacterial growth rate), with more variable PD responses among M. haemolytica than among P. multocida isolates. Moreover, the Hill-coefficient was correlated to the isolate’s maximal population growth rate in the absence of antimicrobial exposure (a.k.a. specific growth rate; Spearman’s ρ = 0.98, p-value = 0.003, n = 6 isolates excluding one outlier). Thus, the strain’s properties such as growth potential may impact its PD responses. This variability can have clinical implications. Modifying the treatment regimen depending on phenotypic properties of the pathogen strain causing disease may be a precision medicine approach.

Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration

Environmental antibiotic risk management requires an understanding of how subinhibitory antibiotic concentrations contribute to the spread of resistance. We develop a simple model of competition between sensitive and resistant bacterial strains to predict the minimum selection concentration (MSC), the lowest level of antibiotic at which resistant bacteria are selected. We present an analytical solution for the MSC based on the routinely measured MIC, the selection coefficient (sc) that expresses fitness differences between strains, the intrinsic net growth rate, and the shape of the bacterial growth dose-response curve with antibiotic or metal exposure (the Hill coefficient [κ]). We calibrated the model by optimizing the Hill coefficient to fit previously reported experimental growth rate difference data. The model fit varied among nine compound-taxon combinations examined but predicted the experimentally observed MSC/MIC ratio well (R² ≥ 0.95). The shape of the antibiotic response curve varied among compounds (0.7 ≤ κ ≤ 10.5), with the steepest curve being found for the aminoglycosides streptomycin and kanamycin. The model was sensitive to this antibiotic response curve shape and to the sc, indicating the importance of fitness differences between strains for determining the MSC. The MSC can be >1 order of magnitude lower than the MIC, typically by the factor sc^κ This study provides an initial quantitative depiction and a framework for a research agenda to examine the growing evidence of selection for resistant bacterial communities at low environmental antibiotic concentrations.

Suppressive drug combinations and their potential to combat antibiotic resistance

Antibiotic effectiveness often changes when two or more such drugs are administered simultaneously and unearthing antibiotic combinations with enhanced efficacy (synergy) has been a longstanding clinical goal. However, antibiotic resistance, which undermines individual drugs, threatens such combined treatments. Remarkably, it has emerged that antibiotic combinations whose combined effect is lower than that of at least one of the individual drugs can slow or even reverse the evolution of resistance. We synthesize and review studies of such so-called 'suppressive interactions' in the literature. We examine why these interactions have been largely disregarded in the past, the strategies used to identify them, their mechanistic basis, demonstrations of their potential to reverse the evolution of resistance and arguments for and against using them in clinical treatment. We suggest future directions for research on these interactions, aiming to expand the basic body of knowledge on suppression and to determine the applicability of suppressive interactions in the clinic.

Antibiotic tolerance facilitates the evolution of resistance

Controlled experimental evolution during antibiotic treatment can help to explain the processes leading to antibiotic resistance in bacteria. Recently, intermittent antibiotic exposures have been shown to lead rapidly to the evolution of tolerance—that is, the ability to survive under treatment without developing resistance. However, whether tolerance delays or promotes the eventual emergence of resistance is unclear. Here we used in vitro evolution experiments to explore this question. We found that in all cases, tolerance preceded resistance. A mathematical population-genetics model showed how tolerance boosts the chances for resistance mutations to spread in the population. Thus, tolerance mutations pave the way for the rapid subsequent evolution of resistance. Preventing the evolution of tolerance may offer a new strategy for delaying the emergence of resistance.

Limitations of MIC as sole metric of pharmacodynamic response across the range of antimicrobial susceptibilities within a single bacterial species

The minimum inhibitory concentration (MIC) of an antimicrobial drug for a bacterial pathogen is used as a measure of the bacterial susceptibility to the drug. However, relationships between the antimicrobial concentration, bacterial susceptibility, and the pharmacodynamic (PD) inhibitory effect on the bacterial population are more complex. The relationships can be captured by multi-parameter models such as the E_max model. In this study, time-kill experiments were conducted with a zoonotic pathogen Pasteurella multocida and the fluoroquinolone enrofloxacin. Pasteurella multocida isolates with enrofloxacin MIC of 0.01 μg/mL, 1.5 μg/mL, and 2.0 μg/mL were used. An additive inhibitory E_max model was fitted to the data on bacterial population growth inhibition at different enrofloxacin concentrations. The values of PD parameters such as maximal growth inhibition, concentration achieving a half of the maximal inhibition, and Hill coefficient that captures steepness of the relationships between the concentration and effect, varied between the isolate with low MIC and less susceptible isolates. While enrofloxacin PD against the isolate with low MIC exhibited the expected concentration-dependent characteristics, the PD against the less susceptible isolates demonstrated time-dependent characteristics. The results demonstrate that bacterial antimicrobial susceptibility may need to be described by a combination of parameters rather than a single parameter of the MIC.

A Randomized Control Trial Comparing 2 Levofloxacin-Containing Second-Line Therapies for Helicobacter pylori Eradication

Summary of Trial Design.Lengthy exposure to quinolone-containing triple therapy in Helicobacter pylori eradication leads to the development of drug resistance. Sequential therapy with a quinolone and metronidazole -containing regimen appears to be an effective treatment option. This randomized controlled trial aimed to compare the efficacy of 5-plus 5 days' levofloxacin and metronidazole-containing sequential therapy (EALM) with that of 10-day levofloxacin-containing triple therapy (EAL) in second-line H pylori eradication treatment.One hundred and sixty-four patients who had failed the H pylori eradication attempts using the standard triple therapy (proton pump inhibitor bid, clarithromycin 500 mg bid, amoxicillin 1 g bid × 7 days) were randomly assigned to either an EALM therapy group (n = 82; esomeprazole 40 mg bid and amoxicillin 1 g bid for 5 days, followed by esomeprazole 40 mg bid, levofloxacin 500 mg qd, and metronidazole 500 mg tid, for 5 days) or a 10-day EAL therapy group (n = 82; levofloxacin 500 mg qd, amoxicillin 1 g bid, and esomeprazole 40 mg bid). One patient was lost to follow-up in each group. Follow-up for H pylori status was performed 4 to 8 weeks later.Eradication rates for the EALM and EAL groups were 90.2% (74/82, 95% confidence interval [CI] = 83.7%-96.8%) and 80.5% (66/82, 95% CI = 71.7%-89.2%, P = 0.077) in the intention-to-treat analysis; and 91.4% (74/81, 95% CI = 85.1%-97.6%) and 81.5% (66/81, 95% CI = 72.8%-90.1%, P = 0.067) in the per-protocol analysis. The adverse events for the EALM and EAL groups were 23.5% versus 11.1%, P = 0.038 but were all very mild and were well tolerated except for 1 patient with poor compliance. The compliances were 98.8% and 100%, respectively, between the 2 groups. An antibiotic resistance to levofloxacin was the clinical factor influencing the efficacy of H. pylori eradication therapy in the EAL group, and dual resistance to levofloxacin and metronidazole in the EALM group.Levofloxacin and metronidazole-containing sequential therapy achieved a >90% eradication rate as a second-line H pylori therapy. Dual antibiotic resistance to levofloxacin and metronidazole was the clinical factor influencing the efficacy of H pylori eradication therapy in the sequential therapy (ClinicalTrials.gov number: NCT02596620).

Combination Effects of Antimicrobial Peptides

Antimicrobial peptides (AMPs) are ancient and conserved across the tree of life. Their efficacy over evolutionary time has been largely attributed to their mechanisms of killing. Yet, the understanding of their pharmacodynamics both in vivo and in vitro is very limited. This is, however, crucial for applications of AMPs as drugs and also informs the understanding of the action of AMPs in natural immune systems. Here, we selected six different AMPs from different organisms to test their individual and combined effects in vitro. We analyzed their pharmacodynamics based on the Hill function and evaluated the interaction of combinations of two and three AMPs. Interactions of AMPs in our study were mostly synergistic, and three-AMP combinations displayed stronger synergism than two-AMP combinations. This suggests synergism to be a common phenomenon in AMP interaction. Additionally, AMPs displayed a sharp increase in killing within a narrow dose range, contrasting with those of antibiotics. We suggest that our results could lead a way toward better evaluation of AMP application in practice and shed some light on the evolutionary consequences of antimicrobial peptide interactions within the immune system of organisms.

Synergistic, collaterally sensitive β-lactam combinations suppress resistance in MRSA

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most prevalent multidrug-resistant pathogens worldwide, exhibiting increasing resistance to the latest antibiotic therapies. Here we show that the triple β-lactam combination meropenem-piperacillin-tazobactam (ME/PI/TZ) acts synergistically and is bactericidal against MRSA subspecies N315 and 72 other clinical MRSA isolates in vitro and clears MRSA N315 infection in a mouse model. ME/PI/TZ suppresses evolution of resistance in MRSA via reciprocal collateral sensitivity of its constituents. We demonstrate that these activities also extend to other carbapenem-penicillin-β-lactamase inhibitor combinations. ME/PI/TZ circumvents the tight regulation of the mec and bla operons in MRSA, the basis for inducible resistance to β-lactam antibiotics. Furthermore, ME/PI/TZ subverts the function of penicillin-binding protein-2a (PBP2a) via allostery, which we propose as the mechanism for both synergy and collateral sensitivity. Showing in vivo activity similar to that of linezolid, ME/PI/TZ demonstrates that combinations of older β-lactam antibiotics could be effective against MRSA infections in humans.

Prediction of resistance development against drug combinations by collateral responses to component drugs

Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability to do so. Thus, predictive models are needed to rationally design resistance-limiting therapeutic regimens. Using adaptive evolution, we studied the resistance response of the common pathogen Escherichia coli to 5 different single antibiotics and all 10 different antibiotic drug pairs. By analyzing the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance evolution.

Antibiotic efficacy is linked to bacterial cellular respiration

Bacteriostatic and bactericidal antibiotic treatments result in two fundamentally different phenotypic outcomes--the inhibition of bacterial growth or, alternatively, cell death. Most antibiotics inhibit processes that are major consumers of cellular energy output, suggesting that antibiotic treatment may have important downstream consequences on bacterial metabolism. We hypothesized that the specific metabolic effects of bacteriostatic and bactericidal antibiotics contribute to their overall efficacy. We leveraged the opposing phenotypes of bacteriostatic and bactericidal drugs in combination to investigate their activity. Growth inhibition from bacteriostatic antibiotics was associated with suppressed cellular respiration whereas cell death from most bactericidal antibiotics was associated with accelerated respiration. In combination, suppression of cellular respiration by the bacteriostatic antibiotic was the dominant effect, blocking bactericidal killing. Global metabolic profiling of bacteriostatic antibiotic treatment revealed that accumulation of metabolites involved in specific drug target activity was linked to the buildup of energy metabolites that feed the electron transport chain. Inhibition of cellular respiration by knockout of the cytochrome oxidases was sufficient to attenuate bactericidal lethality whereas acceleration of basal respiration by genetically uncoupling ATP synthesis from electron transport resulted in potentiation of the killing effect of bactericidal antibiotics. This work identifies a link between antibiotic-induced cellular respiration and bactericidal lethality and demonstrates that bactericidal activity can be arrested by attenuated respiration and potentiated by accelerated respiration. Our data collectively show that antibiotics perturb the metabolic state of bacteria and that the metabolic state of bacteria impacts antibiotic efficacy.

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

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

Evolutionary rescue: linking theory for conservation and medicine

Evolutionary responses that rescue populations from extinction when drastic environmental changes occur can be friend or foe. The field of conservation biology is concerned with the survival of species in deteriorating global habitats. In medicine, in contrast, infected patients are treated with chemotherapeutic interventions, but drug resistance can compromise eradication of pathogens. These contrasting biological systems and goals have created two quite separate research communities, despite addressing the same central question of whether populations will decline to extinction or be rescued through evolution. We argue that closer integration of the two fields, especially of theoretical understanding, would yield new insights and accelerate progress on these applied problems. Here, we overview and link mathematical modelling approaches in these fields, suggest specific areas with potential for fruitful exchange, and discuss common ideas and issues for empirical testing and prediction.

What should be considered in the treatment of bacterial infections by multi-drug therapies: a mathematical perspective?

Bacterial infections are a global health concern with high levels of mortality and morbidity associated. The resistance of pathogens to drugs is one leading cause of this problem, being common the administration of multiple drugs to improve the therapeutic effects. This review critically explores diverse aspects involved in the treatment of bacterial infections through multi-drug therapies, from a mathematical and within-host perspectives. Five recent models were selected and are reviewed. These models fall into the following question: which drugs to select, the respective dose, the administration period to effectively eradicate the infection in the shortest period of time and with reduced side effects? In this analysis, three groups of variables were considered: pharmacokinetics, pharmacodynamics and disturbance variables. To date, there is no model that fully answers to this issue for a living organism and it is questionable whether this would be possible for any case of infection.

A model-guided analysis and perspective on the evolution and epidemiology of antibiotic resistance and its future

A simple epidemiological model is used as a framework to explore the potential efficacy of measures to control antibiotic resistance in community-based self-limiting human infections. The analysis of the properties of this model predict that resistance can be maintained at manageable levels if: first, the rates at which specific antibiotics are used declines with the frequency of resistance to these drugs; second, resistance rarely emerges during therapy; and third, external sources rarely contribute to the entry of resistant bacteria into the community. We discuss the feasibility and limitations of these measures to control the rates of antibiotic resistance and the potential of advances in diagnostic procedures to facilitate this endeavor.

Antagonism between bacteriostatic and bactericidal antibiotics is prevalent

Combination therapy is rarely used to counter the evolution of resistance in bacterial infections. Expansion of the use of combination therapy requires knowledge of how drugs interact at inhibitory concentrations. More than 50 years ago, it was noted that, if bactericidal drugs are most potent with actively dividing cells, then the inhibition of growth induced by a bacteriostatic drug should result in an overall reduction of efficacy when the drug is used in combination with a bactericidal drug. Our goal here was to investigate this hypothesis systematically. We first constructed time-kill curves using five different antibiotics at clinically relevant concentrations, and we observed antagonism between bactericidal and bacteriostatic drugs. We extended our investigation by performing a screen of pairwise combinations of 21 different antibiotics at subinhibitory concentrations, and we found that strong antagonistic interactions were enriched significantly among combinations of bacteriostatic and bactericidal drugs. Finally, since our hypothesis relies on phenotypic effects produced by different drug classes, we recreated these experiments in a microfluidic device and performed time-lapse microscopy to directly observe and quantify the growth and division of individual cells with controlled antibiotic concentrations. While our single-cell observations supported the antagonism between bacteriostatic and bactericidal drugs, they revealed an unexpected variety of cellular responses to antagonistic drug combinations, suggesting that multiple mechanisms underlie the interactions.

Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections

The successful treatment of bacterial infections is the product of a collaboration between antibiotics and the host's immune defenses. Nevertheless, in the design of antibiotic treatment regimens, few studies have explored the combined action of antibiotics and the immune response to clearing infections. Here, we use mathematical models to examine the collective contribution of antibiotics and the immune response to the treatment of acute, self-limiting bacterial infections. Our models incorporate the pharmacokinetics and pharmacodynamics of the antibiotics, the innate and adaptive immune responses, and the population and evolutionary dynamics of the target bacteria. We consider two extremes for the antibiotic-immune relationship: one in which the efficacy of the immune response in clearing infections is directly proportional to the density of the pathogen; the other in which its action is largely independent of this density. We explore the effect of antibiotic dose, dosing frequency, and term of treatment on the time before clearance of the infection and the likelihood of antibiotic-resistant bacteria emerging and ascending. Our results suggest that, under most conditions, high dose, full-term therapy is more effective than more moderate dosing in promoting the clearance of the infection and decreasing the likelihood of emergence of antibiotic resistance. Our results also indicate that the clinical and evolutionary benefits of increasing antibiotic dose are not indefinite. We discuss the current status of data in support of and in opposition to the predictions of this study, consider those elements that require additional testing, and suggest how they can be tested.

Fitness landscapes emerging from pharmacodynamic functions in the evolution of multidrug resistance

Adaptive evolution often involves beneficial mutations at more than one locus. In this case, the trajectory and rate of adaptation is determined by the underlying fitness landscape, that is, the fitness values and mutational connectivity of all genotypes under consideration. Drug resistance, especially resistance to multiple drugs simultaneously, is also often conferred by mutations at several loci so that the concept of fitness landscapes becomes important. However, fitness landscapes underlying drug resistance are not static but dependent on drug concentrations, which means they are influenced by the pharmacodynamics of the drugs administered. Here, I present a mathematical framework for fitness landscapes of multidrug resistance based on Hill functions describing how drug concentrations affect fitness. I demonstrate that these 'pharmacodynamic fitness landscapes' are characterized by pervasive epistasis that arises through (i) fitness costs of resistance (even when these costs are additive), (ii) nonspecificity of resistance mutations to drugs, in particular cross-resistance, and (iii) drug interactions (both synergistic and antagonistic). In the latter case, reciprocal drug suppression may even lead to reciprocal sign epistasis, so that the doubly resistant genotype occupies a local fitness peak that may be difficult to access by evolution. Simulations exploring the evolutionary dynamics on some pharmacodynamic fitness landscapes with both constant and changing drug concentrations confirm the crucial role of epistasis in determining the rate of multidrug resistance evolution.