Synergy of Vancomycin with Penicillins and Cephalosporins Against Pseudomonas,Klebsiella, and Serratia

A model of antibiotic synergy based on a molecular mechanism of action which blocked sequential steps in a single metabolic pathway was tested. Twenty-five strains each of Pseudomonas, Klebsiella, and Serratia were tested in vitro against three different two drug combinations of vancomycin, carbenicillin, or cephalothin. Synergy was observed when vancomycin was combined with either carbenicillin or cephalothin against isolates of Pseudomonas or Serratia, whereas the combination of carbenicillin and cephalothin did not result in significant synergy against these isolates. The presence of synergy was not related to the sensitivity or resistance of the isolates to the drugs in the combination. Synergy was also observed with all three antibiotic combinations against Klebsiella isolates which may be related to enzyme inactivation by one of the drugs in the combination. These observations support the hypothetical model of antibiotic synergy based on sequential blocking of one biochemical pathway.     

Interaction of neomycin with other antibiotics on selected bacterial strains

Antimicrobial combinations are used most frequently to provide broad-spectrum empirical coverage in the treatment of bacterial infections. However, combination of two antibiotics may not influence their activity, may lead to synergy or antagonism in the activity. Neomycin may be combined with one of the following antibiotics: ampicillin, procaine penicillin, gramicidin, bacitracin, polymyxin B, lincomycin, oxytetracycline, and erythromycin in some human and veterinary multiantibiotic drugs distributed in Poland. The checkerboard method has been one of the traditional assays for the measurement of antibiotic interactions. The aim of this study was to analyse the activity interaction of neomycin with second antibiotic in multiantibiotic drugs distributed in Poland on standards and clinical bacterial strains. Checkerboard results for all strains demonstrated synergism for 2.5% of combinations, only for standards strains. In one case Salmonella Enteritidis, in combination of neomycin with bacitracin, inhibition effect was observed. Additive effects were predominant--49%. In 18% neutral effects were shown, but in 26% of combinations FIC indexes were not possible to calculate, because of the resistance of clinical strains to the highest concentration of at least one antibiotic. In combination of aminoglycoside (neomycin) with beta-lactams antibiotics (ampicillin, procaine penicillin) in vitro, no synergy was observed for all examined strains. The best results were achieved for combinations of neomycin with peptide antibiotics (polymyxin, gramicidin and bacitracin)--5 for all 6 synergy effect observed.     

In silico drug combination discovery for personalized cancer therapy

Background

Drug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space.

Results

We propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the 31 cell lines. For 28 out of the 31 cell lines, the model predicts the most synergistic combination in the top 10 of approximately 100 candidate drug combinations. Finally, we analyze the results, generate synergistic rules using the features, and validate the rules through the literature survey.

Conclusion

Using various types of genomic information of cancer cell lines, targets of drugs, and pharmacological information, a drug combination synergy prediction pipeline is proposed. The pipeline regresses the synergy level between two drugs and a cell line as well as classifies if there exists synergy or antagonism between them. Discovering new drug combinations by our pipeline may improve personalized cancer therapy.

     

In vitro activity of antibiotic combinations against multidrug-resistant strains of Acinetobacter baumannii and the effects of their antibiotic resistance determinants

Various combinations of antibiotics are reported to show synergy in treating nosocomial infections with multidrug-resistant (MDR) Acinetobacter baumannii (A. baumannii). Here, we studied hospital-acquired outbreak strains of MDR A. baumannii to evaluate optimal combinations of antibiotics. One hundred and twenty-one strains were grouped into one major and one minor clonal group based on repetitive PCR amplification. Twenty representative strains were tested for antibiotic synergy using Etest(?). Five strains were further analyzed by analytical isoelectric focusing and PCR to identify β-lactamase genes or other antibiotic resistance determinants. Our investigation showed that the outbreak strains of MDR A. baumannii belonged to two dominant clones. A combination of colistin and doxycycline showed the best result, being additive or synergistic against 70% of tested strains. Antibiotic additivity was observed more frequently than synergy. Strains possessing the same clonality did not necessarily demonstrate the same response to antibiotic combinations in vitro. We conclude that the effect of antibiotic combinations on our outbreak strains of MDR A. baumannii seemed strain-specific. The bacterial response to antibiotic combinations is probably a result of complex interactions between multiple concomitant antibiotic resistance determinants in each strain.     

When the Most Potent Combination of Antibiotics Selects for the Greatest Bacterial Load: The Smile-Frown Transition

Conventional wisdom holds that the best way to treat infection with antibiotics is to ‘hit early and hit hard’. A favoured strategy is to deploy two antibiotics that produce a stronger effect in combination than if either drug were used alone. But are such synergistic combinations necessarily optimal? We combine mathematical modelling, evolution experiments, whole genome sequencing and genetic manipulation of a resistance mechanism to demonstrate that deploying synergistic antibiotics can, in practice, be the worst strategy if bacterial clearance is not achieved after the first treatment phase. As treatment proceeds, it is only to be expected that the strength of antibiotic synergy will diminish as the frequency of drug-resistant bacteria increases. Indeed, antibiotic efficacy decays exponentially in our five-day evolution experiments. However, as the theory of competitive release predicts, drug-resistant bacteria replicate fastest when their drug-susceptible competitors are eliminated by overly-aggressive treatment. Here, synergy exerts such strong selection for resistance that an antagonism consistently emerges by day 1 and the initially most aggressive treatment produces the greatest bacterial load, a fortiori greater than if just one drug were given. Whole genome sequencing reveals that such rapid evolution is the result of the amplification of a genomic region containing four drug-resistance mechanisms, including the acrAB efflux operon. When this operon is deleted in genetically manipulated mutants and the evolution experiment repeated, antagonism fails to emerge in five days and antibiotic synergy is maintained for longer. We therefore conclude that unless super-inhibitory doses are achieved and maintained until the pathogen is successfully cleared, synergistic antibiotics can have the opposite effect to that intended by helping to increase pathogen load where, and when, the drugs are found at sub-inhibitory concentrations.     

Chemogenomics and orthology‐based design of antibiotic combination therapies

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms.     

In vitro activity of cefamandole, cefoxitin, cefuroxime, and carbenicillin, alone and in combination with aminoglycosides against Serratia marcescens.

Synergistic antibiotic studies were undertaken to compare the effectiveness of two new beta-lactamase resistant cephalosporins, cefamandole, and carbenicillin, with four aminoglycosides against clinical strains of Serratia marcescens. The strains demonstrated various combinations of resistance and/or susceptibility to the antibiotics tested. Tobramycin was the most effective aminoglycoside when used in combination with beta-lactam antibiotics. Carbenicillin and cefamandole demonstrated similar activity with aminoglycosides in synergy experiments. Tobramycin-carbenicillin was found to be the superior pairs as indicated by the total number of strains inhibited. This combination was the only one effective against certain high drug resistant strains and the strain resistant to all four aminoglycosides. Carbenicillin or cefamandole with tobramycin exhibited comparable activity against multiple drug resistant organisms. However, mutants significantly more resistant to cefamandole developed during susceptibility testing. The findings of this study have clinical relevance for treating infections by this formidable pathogen.     

Analysis of Compound Synergy in High-Throughput Cellular Screens by Population-Based Lifetime Modeling

Despite the successful introduction of potent anti-cancer therapeutics, most of these drugs lead to only modest tumor-shrinkage or transient responses, followed by re-growth of tumors. Combining different compounds has resulted in enhanced tumor control and prolonged survival. However, methods querying the efficacy of such combinations have been hampered by limited scalability, analytical resolution, statistical feasibility, or a combination thereof. We have developed a theoretical framework modeling cellular viability as a stochastic lifetime process to determine synergistic compound combinations from high-throughput cellular screens. We apply our method to data derived from chemical perturbations of 65 cancer cell lines with two inhibitors. Our analysis revealed synergy for the combination of both compounds in subsets of cell lines. By contrast, in cell lines in which inhibition of one of both targets was sufficient to induce cell death, no synergy was detected, compatible with the topology of the oncogenically activated signaling network. In summary, we provide a tool for the measurement of synergy strength for combination perturbation experiments that might help define pathway topologies and direct clinical trials.     

Value Addition in the Efficacy of Conventional Antibiotics by Nisin against Salmonella

Frequent and indiscriminate use of existing battery of antibiotics has led to the development of multi drug resistant (MDR) strains of pathogens. As decreasing the concentration of the antibiotic required to treat Salmonellosis might help in combating the development of resistant strains, the present study was designed to assess the synergistic effects, if any, of nisin, in combination with conventional anti-Salmonella antibiotics against Salmonella enterica serovar Typhimurium. Minimum inhibitory concentrations (MICs) of the selected antimicrobial agents were determined by micro and macro broth dilution assays. In-vitro synergy between the agents was evaluated by radial diffusion assay, fractional inhibitory concentration (FIC) index (checkerboard test) and time-kill assay. Scanning electron microscopy (SEM) was also performed to substantiate the effect of the combinations. In-vivo synergistic efficacy of the combinations selected on the basis of in-vitro results was also evaluated in the murine model, in terms of reduction in the number of Salmonellae in liver, spleen and intestine. Nisin-ampicillin and nisin-EDTA combinations were observed to have additive effects, whereas the combinations of nisin-ceftriaxone and nisin-cefotaxime were found to be highly synergistic against serovar Typhimurium as evident by checkerboard test and time-kill assay. SEM results revealed marked changes on the outer membrane of the bacterial cells treated with various combinations. In-vivo synergy was evident from the larger log unit decreases in all the target organs of mice treated with the combinations than in those treated with drugs alone. This study thus highlights that nisin has the potential to act in conjunction with conventional antibiotics at much lower MICs. These observations seem to be significant, as reducing the therapeutic concentrations of antibiotics may be a valuable strategy for avoiding/reducing the development of emerging antibiotic resistance. Value added potential of nisin in the efficacy of conventional antibiotics may thus be exploited not only against Salmonella but against other Gram-negative infections as well.     

In Vitro Chemotherapeutic Combinations Against Isoniazid-Resistant Mycobacterium tuberculosis and Mycobacterium fortuitum

It is an acceptable medical practice to use second-line antimycobacterial drugs in combination with isoniazid in treatment of isoniazid-resistant tuberculosis. Recent investigations have demonstrated the importance of determining chemotherapeutic interaction in instances of multiple antibiotic use. We studied the inhibitory effect of combinations of isoniazid with ethambutol, rifampin, ethionamide, cycloserine, viomycin, and kanamycin against three isoniazid-resistant strains of Mycobacterium tuberculosis and three strains of M. fortuitum. The isobologram technique with drug concentrations of 0.4 to 100 mug/ml was used. With the exception of single instances in which kanamycin plus isoniazid (M. tuberculosis strain 9999) and ethionamide plus isoniazid (M. fortuitum strain 2080) seemed to have a synergistic effect, neither synergy nor antagonism was noted for any of the combinations. These studies show that the combined use of isoniazid and a second line antimycobacterial agent results in vitro in indifferent inhibitory activity.     

An in vivo platform for rapid high-throughput antitubercular drug discovery

Treatment of tuberculosis, like other infectious diseases, is increasingly hindered by the emergence of drug resistance. Drug discovery efforts would be facilitated by facile screening tools that incorporate the complexities of human disease. Mycobacterium marinum-infected zebrafish larvae recapitulate key aspects of tuberculosis pathogenesis and drug treatment. Here, we develop a model for rapid in vivo drug screening using fluorescence-based methods for serial quantitative assessment of drug efficacy and toxicity. We provide proof-of-concept that both traditional bacterial-targeting antitubercular drugs and newly identified host-targeting drugs would be discovered through the use of this model. We demonstrate the model's utility for the identification of synergistic combinations of antibacterial drugs and demonstrate synergy between bacterial- and host-targeting compounds. Thus, the platform can be used to identify new antibacterial agents and entirely new classes of drugs that thwart infection by targeting host pathways. The methods developed here should be widely applicable to small-molecule screens for other infectious and noninfectious diseases.     

A formal model for analyzing drug combination effects and its application in TNF-α-induced NFκB pathway

Background

Drug combination therapy is commonly used in clinical practice. Many methods including Bliss independence method have been proposed for drug combination design based on simulations models or experiments. Although Bliss independence method can help to solve the drug combination design problem when there are only a small number of combinations, as the number of combinations increases, it may not be scalable. Exploration of system structure becomes important to reduce the complexity of the design problem.

Results

In this paper, we deduced a mathematical model which can simplify the serial structure and parallel structure of biological pathway for synergy evaluation of drug combinations. We demonstrated in steady state the sign of the synergism assessment factor derivative of the original system can be predicted by the sign of its simplified system. In addition, we analyzed the influence of feedback structure on survival ratio of the serial structure. We provided a sufficient condition under which the combination effect could be maintained. Furthermore, we applied our method to find three synergistic drug combinations on tumor necrosis factor α-induced NFκB pathway and subsequently verified by the cell experiment.

Conclusions

We identified several structural properties underlying the Bliss independence criterion, and developed a systematic simplification framework for drug combiation desgin by combining simulation and system reaction network topology analysis. We hope that this work can provide insights to tackle the challenging problem of assessment of combinational drug therapy effect in a large scale signaling pathway. And hopefully in the future our method could be expanded to more general criteria.     

Prediction of drug combinations by integrating molecular and pharmacological data

Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.     

Correlations and the encoding of information in the nervous system

Is the information transmitted by an ensemble of neurons determined solely by the number of spikes fired by each cell, or do correlations in the emission of action potentials also play a significant role? We derive a simple formula which enables this question to be answered rigorously for short time-scales. The formula quantifies the corrections to the instantaneous information rate which result from correlations in spike emission between pairs of neurons. The mutual information that the ensemble of neurons conveys about external stimuli can thus be broken down into firing rate and correlation components. This analysis provides fundamental constraints upon the nature of information coding, showing that over short time-scales correlations cannot dominate information representation, that stimulus-independent correlations may lead to synergy (where the neurons together convey more information than they would if they were considered independently), but that only certain combinations of the different sources of correlation result in significant synergy rather than in redundancy or in negligible effects. This analysis leads to a new quantification procedure which is directly applicable to simultaneous multiple neuron recordings.     

Improving quantitative trait loci mapping resolution in experimental crosses by the use of genotypically selected samples

One of the key factors contributing to the success of a quantitative trait locus (QTL) mapping experiment is the precision with which QTL positions can be estimated. We show, using simulations, that QTL mapping precision for an experimental cross can be increased by the use of a genotypically selected sample of individuals rather than an unselected sample of the same size. Selection is performed using a previously described method that optimizes the complementarity of the crossover sites within the sample. Although the increase in precision is accompanied by a decrease in QTL detection power at markers distant from QTL, only a modest increase in marker density is needed to obtain equivalent power over the whole map. Selected samples also show a slight reduction in the number of false-positive QTL. We find that two features of selected samples independently contribute to these effects: an increase in the number of crossover sites and increased evenness in crossover spacing. We provide an empirical formula for crossover enrichment in selected samples that is useful in experimental design and data analysis. For QTL studies in which the phenotyping is more of a limiting factor than the generation of individuals and the scoring of genotypes, selective sampling is an attractive strategy for increasing genome-wide QTL map resolution.     

Detection of a colonizing, aquatic, non-indigenous species

Detecting the presence of rare species has interested ecologists and conservation biologists for many years. A particularly daunting application of this problem pertains to the detection of non-indigenous species (NIS) as they colonize new ecosystems. Ethical issues prevent experimental additions of NIS to most natural systems to explore the relationship between sampling intensity and the detection probability of a colonizing NIS. Here we examine this question using a recently introduced water flea, Cercopagis pengoi , in Lake Ontario. The species has biphasic population development, with sexually-produced 'spring morphs' developing prior to parthenogenetically-produced 'typical' morphs. Thus, this biphasic morphology allows distinction between new colonists (spring morphs) from subsequent generations. We repeatedly sampled Hamilton Harbour, Lake Ontario for the presence of both spring and typical morphs. Probability of detection was positively related to both the number of samples taken and animal density in the lake; however, even highly intensive sampling (100 samples) failed to detect the species in early spring when densities were very low. Spatial variation was greatest when densities of Cercopagis were intermediate to low. Sub-sampling, which increased space between adjacent samples, significantly decreased the number of samples required to reach greater, calculated detection probabilities on these dates. Typical sampling protocols for zooplankton have a low probability (< 0.2) of detecting the species unless population density is high. Results of this study suggest that early detection of colonizing, aquatic NIS may be optimized through use of a risk-based sampling design, combined with high sampling intensity in areas deemed most vulnerable to invasion, rather than less intensive sampling at a wider array of sites.     

Sampling and Pooling Methods for Capturing Herd Level Antibiotic Resistance in Swine Feces using qPCR and CFU Approaches

The aim of this article was to define the sampling level and method combination that captures antibiotic resistance at pig herd level utilizing qPCR antibiotic resistance gene quantification and culture-based quantification of antibiotic resistant coliform indicator bacteria. Fourteen qPCR assays for commonly detected antibiotic resistance genes were developed, and used to quantify antibiotic resistance genes in total DNA from swine fecal samples that were obtained using different sampling and pooling methods. In parallel, the number of antibiotic resistant coliform indicator bacteria was determined in the same swine fecal samples. The results showed that the qPCR assays were capable of detecting differences in antibiotic resistance levels in individual animals that the coliform bacteria colony forming units (CFU) could not. Also, the qPCR assays more accurately quantified antibiotic resistance genes when comparing individual sampling and pooling methods. qPCR on pooled samples was found to be a good representative for the general resistance level in a pig herd compared to the coliform CFU counts. It had significantly reduced relative standard deviations compared to coliform CFU counts in the same samples, and therefore differences in antibiotic resistance levels between samples were more readily detected. To our knowledge, this is the first study to describe sampling and pooling methods for qPCR quantification of antibiotic resistance genes in total DNA extracted from swine feces.     

Assessing anostracan (Crustacea: Branchiopoda) cyst bank size: An attempt at a standardized method

Egg (cyst) banks play a fundamental role in the survival strategy of the hydrobionts of temporary pools. Therefore a quantitative estimate, as accurate as possible, of their size is the starting point for all the possible considerations concerning the life history of the inhabiting populations. We describe here, in some details, a sampling procedure to evaluate the number of cysts laid by the anostracan Chirocephalus ruffoi Cottarelli & Mura, 1984, in an experimental pool and we show that this procedure results in a good estimate of the cyst bank consistency. A pool 150 cm in diameter was divided into five concentrical rings, 13 cm apart from each other, and a central core (with a 10 cm radius). The number of samples needed to obtain an accurate estimate was calculated by taking into account that, to perform uniform sampling, the total volume of the samples must be proportional to the volume of the pool bed in each ring. The number of cysts in each of the rings was then estimated by considering the mean of the experimental results of a previous study (61 core samples along six transects across the pool itself), multiplied by the number of samples ideally performed in each sector. Evaluating the cyst bank size by the above method resulted in an estimate with an error of only 4% compared to the real cyst bank size. We also show that the number of samples needed can significantly be reduced, with an error on the total number of cysts that can be controlled. This result, that follows from statistical considerations on the confidence interval for the mean, allows us to obtain a general sampling procedure that can be applied to statistically comparable pools.