Many bacterial pathogens are becoming drug resistant faster than we can develop new antimicrobials. To address this threat in public health, a metamodel antimicrobial cocktail optimization (MACO) scheme is demonstrated for rapid screening of potent antibiotic cocktails using uropathogenic clinical isolates as model systems. With the MACO scheme, only 18 parallel trials were required to determine a potent antimicrobial cocktail out of hundreds of possible combinations. In particular, trimethoprim and gentamicin were identified to work synergistically for inhibiting the bacterial growth. Sensitivity analysis indicated gentamicin functions as a synergist for trimethoprim, and reduces its minimum inhibitory concentration for 40-fold. Validation study also confirmed that the trimethoprim-gentamicin synergistic cocktail effectively inhibited the growths of multiple strains of uropathogenic clinical isolates. With its effectiveness and simplicity, the MACO scheme possesses the potential to serve as a generic platform for identifying synergistic antimicrobial cocktails toward management of bacterial infection in the future.
Statistical approaches to describing the behaviour, including the complex relationships between input parameters and model outputs, of nonlinear dynamic models (referred to as metamodelling) are gaining more and more acceptance as a means for sensitivity analysis and to reduce computational demand. Understanding such input-output maps is necessary for efficient model construction and validation. Multi-way metamodelling provides the opportunity to retain the block-wise structure of the temporal data typically generated by dynamic models throughout the analysis. Furthermore, a cluster-based approach to regional metamodelling allows description of highly nonlinear input-output relationships, revealing additional patterns of covariation.
By presenting the N-way Hierarchical Cluster-based Partial Least Squares Regression (N-way HC-PLSR) method, we here combine multi-way analysis with regional cluster-based metamodelling, together making a powerful methodology for extensive exploration of the input-output maps of complex dynamic models. We illustrate the potential of the N-way HC-PLSR by applying it both to predict model outputs as functions of the input parameters, and in the inverse direction (predicting input parameters from the model outputs), to analyse the behaviour of a dynamic model of the mammalian circadian clock. Our results display a more complete cartography of how variation in input parameters is reflected in the temporal behaviour of multiple model outputs than has been previously reported.
Our results indicated that the N-way HC-PLSR metamodelling provides a gain in insight into which parameters that are related to a specific model output behaviour, as well as variations in the model sensitivity to certain input parameters across the model output space. Moreover, the N-way approach allows a more transparent and detailed exploration of the temporal dimension of complex dynamic models, compared to alternative 2-way methods.
Parameter-phenotype map; Dynamic models; Metamodelling; N-way Partial Least Squares Regression; Hierarchical analysis; HC-PLSR; Cluster-analysis; Input-output relationships; Circadian clock
Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological – physical – human systems. We describe a “metamodel” approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples – one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics – to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation.
Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function.
Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops.
HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.
Knowledge acquisition is a key step in the development of knowledge-based systems and methods have been proposed to help elicitating a domain-specific task model from a generic task model. We explored how an existing validated knowledge base (KB) represented by a decision tree could be automatically processed to infer a higher level domain-specific task model. On-codoc is a guideline-based decision support system applied to breast cancer therapy. Assuming task identity and ontological proximity between breast and lung cancer domains, the generalization of the breast can-cer KB should allow to build a metamodel to serve as a guide for the elaboration of a new specific KB on lung cancer. Two types of parametrized generalization methods based on tree structure simplification and ontological abstraction were used. We defined a similarity distance and a generalization coefficient to select the best metamodel identified as the closest to the original decision tree of the most generalized metamodels.
Engineering design often involves the determination of design variable settings to optimize competing performance requirements. In the early design stages we propose narrowing down the domain of design solutions using metamodels of principal components of the multiple performance levels that have been scaled by a multivariate quadratic loss function. The multivariate quadratic loss function is often used as the objective function in reaching optimal solutions because it utilizes the correlation structure of the design’s performance metrics and penalizes off-target performance in a symmetrical manner. We also compare the computational performance of these loss-scaled principal components when used to solve for the design with minimal expected multivariate quadratic loss under three modeling approaches: response surface methodology, multivariate adaptive regression splines, and spatial point modeling. We demonstrate the technique on the design of the mechanical frame of an electric vehicle with six desired performance levels determined simultaneously by the dimensions of eight mechanical design elements. The method is the focus in this work.
multiple response; multivariate quadratic loss function; optimal solutions; robust engineering design
The gram-negative soil bacillus Burkholderia pseudomallei is the causative agent of melioidosis, a severe and potentially fatal septicemic disease that is endemic to Southeast Asia and northern Australia. Its intrinsic resistance to many antibiotics is attributed mainly to the presence of several drug efflux pumps, and therefore, inhibitors of such pumps are expected to restore the activities of many clinically important antimicrobial agents that are the substrates of these pumps. The phenothiazine antipsychotic and antihistaminic drugs prochlorperazine, chlorpromazine, and promazine have a synergistic interaction with a wide spectrum of antimicrobial agents, thereby enhancing their antimicrobial potency against B. pseudomallei. Antimicrobial agents that interacted synergistically with the phenothiazines include streptomycin, erythromycin, oleandomycin, spectinomycin, levofloxacin, azithromycin, and amoxicillin-clavulanic acid. The MICs of these antibiotics were reduced as much as 8,000-fold in the presence of the phenothiazines. Antimicrobial agents which did not interact synergistically with the phenothiazines include gentamicin, amoxicillin, and ampicillin. Omeprazole, a proton pump inhibitor, provided an augmentation of antimicrobial activities similar to that of the phenothiazines, suggesting that the phenothiazines might have interfered with the proton gradient at the inner membrane. B. pseudomallei cells accumulated more erythromycin in the presence of the phenothiazines, an effect similar to that of carbonyl cyanide m-chlorophenylhydrazone, a proton gradient uncoupler. In the presence of the phenothiazines, a much reduced concentration of erythromycin (0.06× MIC) also protected human lung epithelial cells and macrophage cells from B. pseudomallei infection and attenuated its cytotoxicity.
There have been renewed interests in natural products as drug discovery sources. In particular, natural product combinations have been extensively studied, clinically tested, and widely used in traditional, folk and alternative medicines. But opinions about their therapeutic efficacies vary from placebo to synergistic effects. The important questions are whether synergistic effects can sufficiently elevate therapeutic potencies to drug levels, and by what mechanisms and at what odds such combinations can be assembled. We studied these questions by analyzing literature-reported cell-based potencies of 190 approved anticancer and antimicrobial drugs, 1378 anticancer and antimicrobial natural products, 99 natural product extracts, 124 synergistic natural product combinations, and 122 molecular interaction profiles of the 19 natural product combinations with collective potency enhanced to drug level or by >10-fold. Most of the evaluated natural products and combinations are sub-potent to drugs. Sub-potent natural products can be assembled into combinations of drug level potency at low probabilities by distinguished multi-target modes modulating primary targets, their regulators and effectors, and intracellular bioavailability of the active natural products.
Therapeutic options for tuberculosis (TB) are limited and notoriously ineffective despite the wide variety of potent antibiotics available for treating other bacterial infections. We investigated an approach that enables an expansion of TB therapeutic strategies by using synergistic combinations of drugs. To achieve this, we devised a high-throughput synergy screen (HTSS) of chemical libraries having known pharmaceutical properties, including thousands that are clinically approved. Spectinomycin was used to test the concept that clinically available antibiotics with limited efficacy against Mycobacterium tuberculosis might be used for TB treatment when coadministered with a synergistic partner compound used as a sensitizer. Screens using Mycobacterium smegmatis revealed many compounds in our libraries that acted synergistically with spectinomycin. Among them, several families of antimicrobial compounds, including macrolides and azoles, were also synergistic against M. tuberculosis in vitro and in a macrophage model of M. tuberculosis infection. Strikingly, each sensitizer identified for synergy with spectinomycin uniquely enhanced the activities of other clinically used antibiotics, revealing a remarkable number of unexplored synergistic drug combinations. HTSS also revealed a novel activity for bromperidol, a butyrophenone used as an antipsychotic drug, which was discovered to be bactericidal and greatly enhanced the activities of several antibiotics and drug combinations against M. tuberculosis. Our results suggest that many compounds in the currently available pharmacopoeia could be readily mobilized for TB treatment, including disease caused by multi- and extensively drug-resistant strains for which there are no effective therapies.
Ninety strains of Salmonella and 50 strains of Shigella were tested for susceptibility to fosfomycin, chloramphenicol, and ampicillin by the agar dilution method. Drug interaction between fosfomycin-ampicillin and fosfomycin-chloramphenicol was studied by the agar dilution method. The fractional inhibitory concentration was calculated. The combination of fosfomycin-ampicillin was synergistic against Salmonella in 74 cases, additive in 7, indifferent in 7, antagonistic in none, and nonevaluable in 2; against Shigella it was synergistic in 27 cases, additive in 9, indifferent in 14, and antagonistic in none. The combination of fosfomycin-chloramphenicol was synergistic against Salmonella in 56 cases, additive in 9, indifferent in 13, nonevaluable in 12, and antagonistic in none; against Shigella it was synergistic in 29 cases, additive in 10, indifferent in 9, nonevaluable in 2, and antagonistic in none. Killing curves with combinations of each antimicrobial agent showed that the cultures that had proven to be indifferent by the agar dilution method showed a bactericidal effect until h 4, with posterior regrowth of the culture after this time period. For the strains in which synergism was demonstrated, total bactericidal activity was reached at 24 h.
Interactions between quinupristin-dalfopristin and six other antimicrobials were examined by checkerboard arrays against 50 clinical isolates of vancomycin-resistant Enterococcus faecium selected to represent a range of susceptibilities to individual agents. Unequivocal synergistic or antagonistic interactions at clinically relevant concentrations were infrequently encountered when the streptogramin was combined with chloramphenicol, ampicillin, imipenem, vancomycin, or teicoplanin. Combinations with doxycycline resulted in synergistic inhibition in 36% of checkerboards. Against 10 strains of Enterococcus faecalis, synergistic interactions were found when quinupristin-dalfopristin was combined with doxycycline (four strains), either glycopeptide (three strains), or ampicillin (two strains). Combination with quinupristin-dalfopristin increased the ampicillin MIC from 1 to 4 μg/ml for one strain. For 10 strains of E. faecium, interactions were also assessed by time-kill methods using concentrations of the agents attainable in human serum. Most of these antimicrobials augmented killing by quinupristin-dalfopristin to a minor degree. Against 2 of the 12 strains in this collection that were not highly resistant to gentamicin, the combination of quinupristin-dalfopristin (2 μg/ml) plus gentamicin (5 μg/ml) resulted in killing approaching 3 log10 CFU/ml. With the exception of doxycycline, inhibitory interactions between quinupristin-dalfopristin and other agents tested against vancomycin-resistant strains of E. faecium were uncommon at clinically relevant concentrations.
The bacterial ribosome is a primary target of several classes of antibiotics. Investigation of the structure of the ribosomal subunits in complex with different antibiotics can reveal the mode of inhibition of ribosomal protein synthesis. Analysis of the interactions between antibiotics and the ribosome permits investigation of the specific effect of modifications leading to antimicrobial resistances.
Streptogramins are unique among the ribosome-targeting antibiotics because they consist of two components, streptogramins A and B, which act synergistically. Each compound alone exhibits a weak bacteriostatic activity, whereas the combination can act bactericidal. The streptogramins A display a prolonged activity that even persists after removal of the drug. However, the mode of activity of the streptogramins has not yet been fully elucidated, despite a plethora of biochemical and structural data.
The investigation of the crystal structure of the 50S ribosomal subunit from Deinococcus radiodurans in complex with the clinically relevant streptogramins quinupristin and dalfopristin reveals their unique inhibitory mechanism. Quinupristin, a streptogramin B compound, binds in the ribosomal exit tunnel in a similar manner and position as the macrolides, suggesting a similar inhibitory mechanism, namely blockage of the ribosomal tunnel. Dalfopristin, the corresponding streptogramin A compound, binds close to quinupristin directly within the peptidyl transferase centre affecting both A- and P-site occupation by tRNA molecules.
The crystal structure indicates that the synergistic effect derives from direct interaction between both compounds and shared contacts with a single nucleotide, A2062. Upon binding of the streptogramins, the peptidyl transferase centre undergoes a significant conformational transition, which leads to a stable, non-productive orientation of the universally conserved U2585. Mutations of this rRNA base are known to yield dominant lethal phenotypes. It seems, therefore, plausible to conclude that the conformational change within the peptidyl transferase centre is mainly responsible for the bactericidal activity of the streptogramins and the post-antibiotic inhibition of protein synthesis.
The aim of this study was to evaluate the antimicrobial activity of aqueous extract of Psidium guineense Swartz (Araçá-do-campo) and five antimicrobials (ampicillin, amoxicillin/clavulanic acid, cefoxitin, ciprofloxacin, and meropenem) against twelve strains of Staphylococcus aureus with a resistant phenotype previously determined by the disk diffusion method. Four S. aureus strains showed resistance to all antimicrobial agents tested and were selected for the study of the interaction between aqueous extract of P. guineense and antimicrobial agents, by the checkerboard method. The criteria used to evaluate the synergistic activity were defined by the fractional inhibitory concentration index (FICI). All S. aureus strains were susceptible to P. guineense as determined by the microdilution method. The combination of the P. guineense extract with the antimicrobial agents resulted in an eight-fold reduction in the MIC of these agents, which showed a FICI ranging from 0.125 to 0.5, suggesting a synergistic interaction against methicillin-resistant Staphylococcus aureus (MRSA) strains. The combination of the aqueous extract of P. guineense with cefoxitin showed the lowest FICI values. This study demonstrated that the aqueous extract of P. guineense combined with beta lactamics antimicrobials, fluoroquinolones, and carbapenems, acts synergistically by inhibiting MRSA strains.
Combinations of antimicrobial agents were tested against 35 strains of zygomycetes. The interaction between amphotericin B and rifampin was synergistic or additive. Flucytosine alone was inactive and, upon combination with amphotericin B, synergy was not achieved. The combination of amphotericin B with terbinafine was synergistic for 20% of strains, and the interaction between terbinafine and voriconazole was synergistic for 44% of strains. Antagonism was not observed.
The inducible expression of antimicrobial peptide genes in Drosophila melanogaster is regulated by the conserved Toll and peptidoglycan recognition protein LC/immune deficiency (PGRP-LC/IMD) signaling pathways. It has been proposed that the two pathways have independent functions and mediate the specificity of innate immune responses towards different microorganisms. Scattered evidence also suggests that some antimicrobial target genes can be activated by both Toll and IMD, albeit to different extents. This dual activation can be mediated by independent stimulation or by cross-regulation of the two pathways. We show in this report that the Toll and IMD pathways can interact synergistically, demonstrating that cross-regulation occurs. The presence of Spätzle (the Toll ligand) and gram-negative peptidoglycan (the PGRP-LC ligand) together caused synergistic activation of representative target genes of the two pathways, including Drosomycin, Diptericin, and AttacinA. Constitutive activation of Toll and PGRP-LC/IMD could mimic the synergistic stimulation. RNA interference assays and promoter analyses demonstrate that cooperation of different NF-κB-related transcription factors mediates the synergy. These results illustrate how specific ligand binding by separate upstream pattern recognition receptors can be translated into a broad-spectrum host response, a hallmark of innate immunity.
Ribosomally synthesized (natural) peptides demonstrate antimicrobial potency and may represent a novel therapeutic approach for the treatment of infections. The aim of the present study was to investigate the interaction between polycationic peptides and clinically used antimicrobial agents in the treatment of clinical isolates of Gram-positive and Gram-negative aerobic bacteria in vitro, using the microbroth dilution method. The combination studies demonstrated synergies between ranalexin and polymyxin E, doxycycline and clarithromycin. Similarly, magainin II was demonstrated to be synergistic with ceftriaxone, amoxicillin clavulanate, ceftazidime, meropenem, piperacillin and β-lactam antibiotics. Buforin II, cecropin P1 and indolicidin were not observed to be synergistic with the clinically used antibiotics, but demonstrated additive effects with them. Notably, no antagonistic effects were identified in all the combinations examined.
Gram-positive aerobic bacteria; Gram-negative aerobic bacteria; synergy; peptide antibiotics
Efflux-related multidrug resistance (MDR) is a significant means by which bacteria can evade the effects of selected antimicrobial agents. Genome sequencing data suggest that Staphylococcus aureus may possess numerous chromosomally encoded MDR efflux pumps, most of which have not been characterized. Inhibition of these pumps, which may restore clinically relevant activity of antimicrobial agents that are substrates for them, may be an effective alternative to the search for new antimicrobial agents that are not substrates. The inhibitory effects of selected phenothiazines and two geometric stereoisomers of the thioxanthene flupentixol were studied using strains of S. aureus possessing unique efflux-related MDR phenotypes. These compounds had some intrinsic antimicrobial activity and, when combined with common MDR efflux pump substrates, resulted in additive or synergistic interactions. For S. aureus SA-1199B, which overexpresses the NorA MDR efflux pump, and for two additional strains of S. aureus having non-NorA-mediated MDR phenotypes, the 50% inhibitory concentration (IC50) for ethidium efflux for all tested compounds was between 4 and 15% of their respective MICs. Transport of other substrates was less susceptible to inhibition; the prochlorperazine IC50 for acriflavine and pyronin Y efflux by SA-1199B was more than 60% of its MIC. Prochlorperazine and trans(E)-flupentixol were found to reduce the proton motive force (PMF) of S. aureus by way of a reduction in the transmembrane potential. We conclude that the mechanism by which phenothiazines and thioxanthenes inhibit efflux by PMF-dependent pumps is multifactorial and, because of the unbalanced effect of these compounds on the MICs and the efflux of different substrates, may involve an interaction with the pump itself and, to a lesser extent, a reduction in the transmembrane potential.
A contingency of observed antimicrobial activities measured for several compounds vs. a series of bacteria was analyzed. A factor analysis revealed the existence of a certain probability distribution function of the antimicrobial activity. A quantitative structure-activity relationship analysis for the overall antimicrobial ability was conducted using the population statistics associated with identified probability distribution function. The antimicrobial activity proved to follow the Poisson distribution if just one factor varies (such as chemical compound or bacteria). The Poisson parameter estimating antimicrobial effect, giving both mean and variance of the antimicrobial activity, was used to develop structure-activity models describing the effect of compounds on bacteria and fungi species. Two approaches were employed to obtain the models, and for every approach, a model was selected, further investigated and found to be statistically significant. The best predictive model for antimicrobial effect on bacteria and fungi species was identified using graphical representation of observed vs. calculated values as well as several predictive power parameters.
oils compounds; antimicrobial effect; bacteria and fungi species; probability distribution function; quantitative structure-activity relationship (QSAR); multiple linear regression (MLR)
When antibiotic combinations are used to provide a broader spectrum of antimicrobial activity or in an attempt to prevent the emergence of resistant organisms, it is rarely necessary or practical to perform tests of drug interactions in vitro. In vitro testing of combinations may be useful when combinations are used in an attempt to attain synergistic interactions. In some cases, screening methods can be used as substitutes for formal synergy testing. This paper examines the mechanisms of antibiotic interaction leading to synergism or antagonism, surveys attempts to correlate in vitro observations with efficacy in animal models, and reviews clinical data providing evidence for or against a useful role of synergistic antibiotic interactions in the treatment of human infections.
Pseudomonas aeruginosa is an opportunistic pathogen in environmental waters with a high prevalence of multidrug resistance. In this study the synergistic efficacy of synergy antibiotic combinations in multidrug-resistant P. aeruginosa strains isolated from an abattoir effluent was investigated. Water samples were processed using membrane filtration; Pseudomonas was isolated with Pseudomonas Isolation Agar and confirmed using polymerase chain reaction with specie-specific primer. Susceptibility studies and in vitro synergy interaction testing were carried out, employing agar dilution and Etest procedure, respectively. Resistance was noted for clinically relevant antipseudomonal agents tested. Finding from antibiotic synergy interaction studies revealed that cefepime, imipenem, and meropenem combined with amikacin resulted in statistically significant (P < 0.0001) in vitro antibiotics synergy interaction, indicating the possible use of this regimen in treatment of pseudomonal infections.
At eukaryotic promoters, multi-faceted protein-protein and protein-DNA interactions can result in synergistic transcriptional activation. NFAT and AP-1 proteins induce interleukin-2 (IL-2) transcription in stimulated T cells, but the contributions of individual members of these activator families to synergistically activating IL-2 transcription is not known. To investigate the combinatorial regulation of IL-2 transcription we tested the ability of different combinations of NFATc2, NFATc1, cJun, and cFos to synergistically activate transcription from the IL-2 promoter. We found that NFATc2 and cJun are exclusive in their ability to synergistically activate human IL-2 transcription. Protein-protein interaction assays revealed that in the absence of DNA, NFATc2, but not NFATc1, bound directly to cJun/cJun dimers, but not to cFos/cJun heterodimers. A region of NFATc2 C-terminal of the DNA binding domain was necessary and sufficient for interaction with cJun in the absence of DNA, and this same region of NFATc2 was required for the synergistic activation of IL-2 transcription in T cells. Moreover, expression of this C-terminal region of NFATc2 specifically repressed the synergistic activation of IL-2 transcription. These studies show that a previously unidentified interaction between human NFATc2 and cJun is necessary for synergistic activation of IL-2 transcription in T cells.
The emergence of bacterial drug resistance encourages the re-evaluation of the potential of existing antimicrobials. Lantibiotics are post-translationally modified, ribosomally synthesised antimicrobial peptides with a broad spectrum antimicrobial activity. Here, we focussed on expanding the potential of lacticin 3147, one of the most studied lantibiotics and one which possesses potent activity against a wide range of Gram positive species including many nosocomial pathogens. More specifically, our aim was to investigate if lacticin 3147 activity could be enhanced when combined with a range of different clinical antibiotics.
Initial screening revealed that polymyxin B and polymyxin E (colistin) exhibited synergistic activity with lacticin 3147. Checkerboard assays were performed against a number of strains, including both Gram positive and Gram negative species. The resultant fractional inhibitory concentration (FIC) index values established that, while partial synergy was detected against Gram positive targets, synergy was obvious against Gram negative species, including Cronobacter and E. coli.
Combining lacticin 3147 with low levels of a polymyxin could provide a means of broadening target specificity of the lantibiotic, while also reducing polymyxin use due to the lower concentrations required as a result of synergy.
Antimicrobial; Synergy; Lantibiotic; Bacteriocin; Lacticin 3147; Polymyxin
Antimicrobial peptides (AMPs) are integral components of innate immunity and are typically found in combinations in which they can synergize for broader-spectrum or more potent activity. Previously, we reported peptoid mimics of AMPs with potent and selective antimicrobial activity. Using checkerboard assays, we demonstrate that peptoids and AMPs can interact synergistically, with fractional inhibitory concentration indices as low as 0.16. These results strongly suggest that antimicrobial peptoids and peptides are functionally and mechanistically analogous.
Lactocin 160 is a vaginal probiotic-derived bacteriocin shown to selectively inhibit the growth of Gardenerella vaginalis and some other pathogens commonly associated with bacterial vaginosis. The natural origin of this peptide, its safety, and selective antimicrobial properties make it a promising candidate for successful treatment and prophylaxis of bacterial vaginosis (BV). This study evaluated interactions between lactocin 160 and four other natural antimicrobials in the ability to inhibit G. vaginalis. We report that zinc lactate and soapnut extract act synergistically with lactocin 160 against this pathogen and therefore have a potential to be successfully used as the components of the multiple-hurdle antimicrobial formulation for the treatment of BV.
Bacteriocin; Natural antimicrobial; Antimicrobial synergy
Medical and pharmacological communities have long searched for antimicrobial drugs that increase their effect when used in combination, an interaction known as synergism. These drug combinations, however, impose selective pressures in favour of multi-drug resistance and as a result, the benefit of synergy may be lost after only a few bacterial generations. Furthermore, there is experimental evidence that antibiotic treatment can disrupt colonization resistance by shifting the balance between enteropathogenic and commensal bacteria in favour of the pathogens, with the potential to increase the risk of infections. So, we ask, what is the best way of using synergistic drugs? We pose an evolutionary model of commensal and pathogenic bacteria competing in a continuous culture device for a single limiting carbon source under the effect of two bacteriostatic and synergistic antibiotics. This model allows us to evaluate the efficacy of different drug deployment strategies and, using ideas from optimal control theory, to understand whether there are circumstances in which other types of therapy might be favoured over those based on fixed-dose multi-drug combinations. Our main result can be stated thus: the optimal deployment of synergistic antibiotics to remove a pathogen in the presence of commensal bacteria in our model system occurs not in combination, but by deploying them sequentially.
antimicrobial resistance; drug interactions; control theory