Combination therapy is rarely used to counter the evolution of resistance in bacterial infections.
antibiotics: bactericidal or bacteriostatic?
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.
The problem of antibiotic resistance requires a solution that relies on more than just the development of new drugs.
What is Bactericidal?
Pathogens have been unrelenting in evolving mechanisms by which to survive in the face of every drug put on the market. Combination therapy, i. In the treatment of important infectious diseases such as HIV infection, tuberculosis, and malaria, combination therapy has become the standard approach precisely to delay the evolution of drug resistance 1 , — 4. In contrast, for common acute bacterial infections, combinations of drugs are prescribed in only a very limited number of cases and with a different rationale 5.
In those specific instances, two drugs are prescribed for their synergistic effects, that is, for the fact that their combined effects exceed the sum of their individual effects.
Bactericidal vs Bacteriostatic
Drug synergy has been demonstrated to result in more-efficient clearance of infections and to achieve clearance at lower drug concentrations 6. Examples of such cases include fusidic acid and rifampin for the treatment of methicillin-resistant Staphylococcus aureus infections and trimethoprim and sulfamethoxazole for the treatment of otitis media 7 , 8. Furthermore, recent theoretical work indicates that synergistic drugs can prevent treatment failure even when bacteria resistant to one of the drugs are present at the beginning of therapy 9.
Just as synergy can be exploited to improve treatment, it is necessary to avoid combinations of drugs that inhibit each other and may prolong infections.
Antagonism, when a drug hinders the effect of another drug, was reported early in the history of antibiotics and continues to function as a warning against indeterminate treatment Despite these findings, an increasing number of laboratory studies indicate that antagonistic drug combinations merit more investigation as clinical options Recent work in this area suggests that the different types of interactions have significant effects on the selection and maintenance of drug resistance mutations.
Using a direct competition experiment, Chait and colleagues demonstrated how a hyperantagonistic drug combination was able to select against a bacterial population resistant to one of the drugs and instead favored the completely sensitive wild type Furthermore, the rate of adaptation of laboratory bacteria to multiple drugs has been shown to correlate with the degree of synergism between individual antibiotics Although antagonistic drug combinations are currently eschewed in clinical settings, these studies suggest that antagonism between antibiotics may aid in devising treatment strategies specifically aimed at delaying the emergence of resistance.
In response to the slow development of new antimicrobials, there is renewed interest in old drugs that have fallen out of use due to toxicity or drawbacks in efficacy One approach that could be implemented to return these drugs to the clinic is to use an old drug in combination with a current drug The advantages of synergism and the diverse nontrivial effects of antagonism will play a central role in determining how best to implement combination therapy in clinical settings.
In order to exploit the potential benefits of combination therapy, we need a better understanding of the circumstances under which synergism versus antagonism is expected. Determining how a broader spectrum of drugs interact at inhibitory concentrations and delineating the mechanisms responsible for these effects could allow for a more-prudent application of antibiotics that maintains clinical capability and does not sacrifice the future utility of these drugs. In this study, we asked whether basic pharmacodynamic properties of all antibiotics can help predict which pairs would result in antagonism.
A widely recognized characteristic of antibiotics is that they are either bacteriostatic and inhibit growth without killing the cells or are bactericidal and result in cell killing Thus, if bactericidal drugs are most potent with actively growing cells, as hypothesized more than 50 years ago 19 , 20 , then the inhibition of growth induced by a bacteriostatic drug should result in a reduction of drug efficacy.
To test this hypothesis, we determined the types of interaction between five different drugs at inhibitory concentrations by estimating death rates using time-kill curves. We then extended our observations and employed screening methods to identify effects across pairs of 21 different drugs at subinhibitory concentrations. Since our hypothesis relies on the decreased antibiotic susceptibility of slowly growing cells and the ability of some drugs to influence this state, we repeated our experiments at the level of individual cells using time-lapse microscopy, in microfluidic devices, to investigate the cellular dynamics underlying combined effects of antibiotics.
We selected 21 antibiotics with a wide range of mechanisms of action, including drugs that target cell wall, nucleic acid, protein, and folic acid biosynthesis Table 1. Fresh antibiotic solutions were prepared from powder stocks on a weekly basis and were filter sterilized before use. All experiments were conducted in Escherichia coli K BW in minimal medium supplemented with 0. Combination screens were performed in well plates, using a liquid-handling robotic system Hamilton Star workstation to improve reproducibility.
Each plate contained two different 6-by-6 dose-matrix blocks one antibiotic in combination with two other antibiotics , with 4 replicates each. In addition to the dose-matrix blocks, each plate included 18 wells containing medium without antibiotics control wells.
Bacterial growth was monitored by measuring the optical density OD of the liquid cultures at a single time point. Preliminary experiments showed that a single reading of optical density after 18 h of incubation showed strong linear correlation with the area under the growth curve a descriptor of overall inhibitory effect that covers the entire growth period Briefly, this was determined using parallel cultures of E.
For a number of different ending time points e. Plates were prepared as multiple up to 6 biological replicates, and those with quality control problems e.
Bacteriostatic vs. Bactericidal Antibiotics
Second, our experimental setting was similar to that of a prior study 22 , allowing direct comparison of the results of the two studies. To overcome any measurement bias caused by within-plate inhomogeneity, we processed the raw optical density data as follows.
We included 18 control wells on each plate, containing medium without antibiotics and inoculated with E. We used these wells both to set a baseline for zero inhibition and to estimate and to eliminate within-plate systematic biases.
Then we calculated relative inhibition values based on the initial OD maximum inhibition and the average OD of antibiotic-free control wells maximum growth. To estimate and to eliminate within-plate spatial effects, first we fitted a linear trend to the control wells to eliminate spatial gradients.
Next, for the residuals, we employed Gaussian process regression 24 to eliminate the remaining systematic spatial biases using the control wells. Synergistic effects between combinations of chemicals, resulting in increased benefits or increased toxicity, have important implications across the fields of biomedicine 25 , Correspondingly, many approaches have been devised to quantify drug interactions To assess antagonism and synergy between pairs of antibiotics, we used the Loewe additivity model 28 , which assumes that a drug does not interact with itself.
Geometrically, Loewe additivity can be represented as lines of equally effective dosages isoboles in the two-dimensional linear concentration space for the two drugs. Deviation of the shape of the isoboles from linearity indicates either synergy concave isoboles or antagonism convex isoboles.
To identify interactions for each pair of antibiotics, first we merged data from replicate dose-matrix blocks located on the same well plates. Next we fitted sigmoidal dose-response curves Hill equation to the single-agent responses using a maximum likelihood fitting procedure. Based on the single-agent response curves for the two antibiotics, we calculated the dose-response relationship for the antibiotic combination expected with the Loewe additivity model.
The transformation relies on a single parameter to describe the concavity of the observed isoboles, which we used as a measure of antibiotic interaction.
This score is zero in the absence of interaction, negative for antagonistic pairs, and positive for synergistic pairs. Finally, interaction scores B for each antibiotic pair were calculated by taking the median scores obtained from biological replicates i.
Measurement errors of interaction screens were estimated by testing 5 antibiotics for interactions with themselves in multiple replicates Because under Loewe additivity a drug shows no interaction with itself, deviation of the interaction score from zero provides an estimate of the experimental error of interaction measurements.
Thus, we considered two antibiotics as significantly interacting when the score was significantly different from the mean score for self-self antibiotic combinations. For calculation of EC 50 values, we followed established protocols EC 50 refers to the concentration of drug that induces growth inhibition halfway between the baseline and maximum values after the specified exposure time. Determination of the clinical utility of exploiting different interactions between pairs of antibiotics will require extensive testing across organisms, sites of infection, and the drugs used for particular infections.
In contrast, we consider our work a proof-of-principle study, and future work should confirm potential clinical implications. Although some of the antibiotics we used have advanced derivatives in the clinic, the choice of the drugs we used was based on two criteria, i.
Similarly, the antibiotic concentrations used do not reflect clinical recommendations. For example, although the concentration of erythromycin used in our time-kill and time-lapse microscopy experiments is higher than that achievable in blood, this drug was included to illustrate the effect of antagonism between a drug at an inhibitory concentration and a second drug at a subinhibitory concentration.
Although the MIC for erythromycin in E.
Bacteriostatic vs. Bactericidal
Determination of the MICs for the antibiotics used in the time-kill and time-lapse microscopy studies was carried out using a broth dilution protocol similar to that recommended by the Clinical and Laboratory Standards Institute LB medium was used instead of Mueller-Hinton broth Overnight cultures of E.
A further dilution was performed before introduction into flasks containing either a single antibiotic or a pair of bactericidal and bacteriostatic antibiotics. Samples were taken at 1-h intervals for up to 5 h.
Bacteriostatic and bactericidal antibiotics pdf download
Cell densities for each sample were estimated from colony counts by dilution in phosphate-buffered saline and plating on LB agar. Each time-kill experiment was performed twice. Specific details of the microfluidic system used in this study, the mother machine, have been described previously In brief, this device consists of 4, growth channels arranged at right angles against a large trench, through which growth medium is passed.
Nutrients then diffuse into the channels and flush out growing cells as they emerge from these channels. An automated microscope stage allows for the monitoring of multiple fields of view, spanning the entire device.
This method results in the continuous observation of the growth and division of a large number of individual cells as they experience different antibiotic-containing environments, as well as their survival or death after the drug has been removed.
Time-lapse microscopy experiments were conducted as follows.
What is Bacteriostatic?
BSA and salmon sperm DNA are blocking agents that are used to bind to the surface of the microfluidic device to prevent the formation of air bubbles and excessive adhesion of the cells to the channels. After at least 4 h of growth in LB broth, the medium was switched to LB broth containing BSA, salmon sperm, and either one or two antibiotics.
Cells were exposed to this medium for at least 20 h before being switched back to fresh LB broth supplemented with BSA and salmon sperm DNA for up to 10 h.
Each experiment involved fields scanned continuously for at least 30 h. The resulting time-lapse images were then analyzed with a custom-designed plug-in for ImageJ, to provide information on cell size and division rates during the three different phases of the experiment. The first step of the analysis consists of defining the length of the cell abutting the end of the channel. The increasing length of the growing cell over succeeding frames is tracked and recorded; division events are also registered based on cell length.
Manual verification and annotation were performed after every experiment. In this way, we were able to extract quantitative information on an individual cell's elongation and division rates.
We also tracked the proportion of cells that survived treatment exposure and were able to divide again upon the return to an antibiotic-free environment.
The occurrence of filamentation during exposure to the antibiotics erythromycin and nalidixic acid led to elongated cells being pulled out of their growth channels during flow.
For this reason, only channels containing cells that could be followed for the entirety of the experiment were considered in the analysis.