The language of antimicrobial and antibiotic resistance is blocking global collective action.

Sustainable access to effective antibiotics is a foundational need for functioning health care that is increasingly threatened by antibiotic resistance. Although resistance has been known as long as antibiotics have been in clinical use, there are still multiple gaps in the global and local responses. One often cited cause for this complacency is the language that is used to describe the problem and its consequences. In this paper, we survey some examples of the current discussions around antibiotic resistance and seek to offer a path towards unified and understandable messaging that is relevant both to the public and policymakers by using narratives that highlight the individual and societal consequences of antibiotic resistance. Major shortcomings in the current language that hamper both the understanding of antibiotic resistance and needed behaviour change have been identified in scientific papers and special reports. These shortcomings range from terminology that is difficult to understand, through a lack of personal relevance, to a fragmented response in the policy field. We propose that scientists, including behaviour change experts, and other key stakeholders that are engaged in the issue take lead to agreement on the core scientific facts and to formulate a vision that can be a foundation for creation of consistent global narratives. These narratives must in turn be adapted to local contexts. Development of such narratives should be viewed as an essential component in national action plans on AMR to raise awareness, empower citizens and incentivise societal behaviour change, policy development and implementation of governance structures.

Colistin Is Extensively Lost during Standard In Vitro Experimental Conditions.

Colistin adheres to a range of materials, including plastics in labware. The loss caused by adhesion influences an array of methods detrimentally, including MIC assays and in vitro time-kill experiments. The aim of this study was to characterize the extent and time course of colistin loss in different types of laboratory materials during a simulated time-kill experiment without bacteria or plasma proteins present. Three types of commonly used large test tubes, i.e., soda-lime glass, polypropylene, and polystyrene, were studied, as well as two different polystyrene microplates and low-protein-binding microtubes. The tested concentration range was 0.125 to 8 mg/liter colistin base. Exponential one-phase and two-phase functions were fitted to the data, and the adsorption of colistin to the materials was modeled with the Langmuir adsorption model. In the large test tubes, the measured start concentrations ranged between 44 and 102% of the expected values, and after 24 h, the concentrations ranged between 8 and 90%. The half-lives of colistin loss were 0.9 to 12 h. The maximum binding capacities of the three materials ranged between 0.4 and 1.1 µg/cm2, and the equilibrium constants ranged between 0.10 and 0.54 ml/µg. The low-protein-binding microtubes showed start concentrations between 63 and 99% and concentrations at 24 h of between 59 and 90%. In one of the microplates, the start concentrations were below the lower limit of quantification at worst. In conclusion, to minimize the effect of colistin loss due to adsorption, our study indicates that low-protein-binding polypropylene should be used when possible for measuring colistin concentrations in experimental settings, and the results discourage the use of polystyrene. Furthermore, when diluting colistin in protein-free media, the number of dilution steps should be minimized.

Assessment of early combination effects of colistin and meropenem against Pseudomonas aeruginosa and Acinetobacter baumannii in dynamic time-kill experiments.

BACKGROUND: In view of the paucity of clinical evidence, in vitro studies are needed to find antibiotic combinations effective against multidrug-resistant Gram-negative bacteria. Interpretation of in vitro effects is usually based on bacterial growth after 24 h in time-kill and checkerboard experiments. However, the clinical relevance of the effects observed in vitro is not established. In this study we explored alternative output parameters to assess the activities of colistin and meropenem against Pseudomonas aeruginosa and Acinetobacter baumannii. METHODS: Four strains each of P. aeruginosa and A. baumannii were exposed to colistin and meropenem, alone and in combination, in 8 h dynamic time-kill experiments. Initial (1 h), maximum and 8 h bacterial reductions and the area under the bacterial time-kill curve were evaluated. Checkerboards, interpreted based on fractional inhibitory concentration indices after 24 h, were performed for comparison. RESULTS: In the time-kill experiments, the combination resulted in enhanced 1 h, maximum and 8 h bacterial reductions against 2, 3 and 5 of 8 strains, respectively, as compared to the single drugs. A statistically significant reduction in the area under the time-kill curve was observed for three strains. In contrast, the checkerboards did not identify synergy for any of the strains. CONCLUSIONS: Combination effects were frequently found with colistin and meropenem against P. aeruginosa and A. baumannii in time-kill experiments but were not detected with the checkerboard method. We propose that the early dynamics of bacterial killing and growth, which may be of great clinical importance, should be considered in future in vitro combination studies.

Dynamic interaction of colistin and meropenem on a WT and a resistant strain of Pseudomonas aeruginosa as quantified in a PK/PD model.

OBJECTIVES: Combination therapy can be a strategy to ensure effective bacterial killing when treating Pseudomonas aeruginosa, a Gram-negative bacterium with high potential for developing resistance. The aim of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model that describes the in vitro bacterial time-kill curves of colistin and meropenem alone and in combination for one WT and one meropenem-resistant strain of P. aeruginosa. METHODS: In vitro time-kill curve experiments were conducted with a P. aeruginosa WT (ATCC 27853) (MICs: meropenem 1 mg/L; colistin 1 mg/L) and a meropenem-resistant type (ARU552) (MICs: meropenem 16 mg/L; colistin 1.5 mg/L). PK/PD models characterizing resistance were fitted to the observed bacterial counts in NONMEM. The final model was applied to predict the bacterial killing of ARU552 for different combination dosages of colistin and meropenem. RESULTS: A model with compartments for growing and resting bacteria, where the bacterial killing by colistin reduced with continued exposure and a small fraction (0.15%) of the start inoculum was resistant to meropenem, characterized the bactericidal effect and resistance development of the two antibiotics. For a typical patient, a loading dose of colistin combined with a high dose of meropenem (2000 mg q8h) was predicted to result in a pronounced kill of the meropenem-resistant strain over 24 h. CONCLUSIONS: The developed PK/PD model successfully described the time course of bacterial counts following exposures to colistin and meropenem, alone and in combination, for both strains, and identified a dynamic drug interaction. The study illustrates the application of a PK/PD model and supports high-dose combination therapy of colistin and meropenem to overcome meropenem resistance.

Colistin methanesulfonate and colistin pharmacokinetics in critically ill patients receiving continuous venovenous hemodiafiltration.

This report describes the pharmacokinetics of colistin methanesulfonate (CMS) and colistin in five intensive care unit patients receiving continuous venovenous hemodiafiltration. For CMS, the mean maximum concentration of drug in plasma (C(max)) after the fourth dose was 6.92 mg/liter and total clearance (CL) 8.23 liters/h. For colistin, the mean concentration was 0.92 mg/liter and CL/metabolized fraction (f(m)) 18.91 liters/h. Colistin concentrations were below the current MIC breakpoints, and the area under the concentration-time curve for the free, unbound fraction of the drug over 24 h in the steady state divided by the MIC (fAUC/MIC) was lower than recommended, suggesting that a dosage regimen of 160 mg CMS every 8 h (q8h) is inadequate.

Application of a loading dose of colistin methanesulfonate in critically ill patients: population pharmacokinetics, protein binding, and prediction of bacterial kill.

A previous pharmacokinetic study on dosing of colistin methanesulfonate (CMS) at 240 mg (3 million units [MU]) every 8 h indicated that colistin has a long half-life, resulting in insufficient concentrations for the first 12 to 48 h after initiation of treatment. A loading dose would therefore be beneficial. The aim of this study was to evaluate CMS and colistin pharmacokinetics following a 480-mg (6-MU) loading dose in critically ill patients and to explore the bacterial kill following the use of different dosing regimens obtained by predictions from a pharmacokinetic-pharmacodynamic model developed from an in vitro study on Pseudomonas aeruginosa. The unbound fractions of colistin A and colistin B were determined using equilibrium dialysis and considered in the predictions. Ten critically ill patients (6 males; mean age, 54 years; mean creatinine clearance, 82 ml/min) with infections caused by multidrug-resistant Gram-negative bacteria were enrolled in the study. The pharmacokinetic data collected after the first and eighth doses were analyzed simultaneously with the data from the previous study (total, 28 patients) in the NONMEM program. For CMS, a two-compartment model best described the pharmacokinetics, and the half-lives of the two phases were estimated to be 0.026 and 2.2 h, respectively. For colistin, a one-compartment model was sufficient and the estimated half-life was 18.5 h. The unbound fractions of colistin in the patients were 26 to 41% at clinical concentrations. Colistin A, but not colistin B, had a concentration-dependent binding. The predictions suggested that the time to 3-log-unit bacterial kill for a 480-mg loading dose was reduced to half of that for the dose of 240 mg.

Serum and cerebrospinal fluid levels of colistin in pediatric patients.

Using a liquid chromatography-tandem mass spectrometry method, the serum and cerebrospinal fluid (CSF) concentrations of colistin were determined in patients aged 1 months to 14 years receiving intravenous colistimethate sodium (60,000 to 225,000 IU/kg of body weight/day). Only in one of five courses studied (a 14-year-old receiving 225,000 IU/kg/day) did serum concentrations exceed the 2 microg/ml CLSI/EUCAST breakpoint defining susceptibility to colistin for Pseudomonas and Acinetobacter. CSF colistin concentrations were <0.2 microg/ml but increased in the presence of meningitis (approximately 0.5 microg/ml or 34 to 67% of serum levels).

Quantitative analysis of colistin A and colistin B in plasma and culture medium using a simple precipitation step followed by LC/MS/MS.

An analytical method for quantitation of colistin A and colistin B in plasma and culture medium is described. After protein precipitation with acetonitrile (ACN) containing 0.1% trifluoroacetic acid (TFA), the supernatants were diluted with 0.03% TFA. The compounds were separated on an Ultrasphere C18 column, 4.6 mm x 250 mm, 5 microm particle size with a mobile phase consisting of 25% ACN in 0.03% TFA and detected with tandem mass spectrometry. The instrument was operating in ESI negative ion mode and the precursor-product ion pairs were m/z 1167.7-->1079.6 for colistin A and m/z 1153.7-->1065.6 for colistin B. The lower limit of quantification (LLOQ) for 100 microL plasma was 19.4 and 10.5 ng/mL for colistin A and B, respectively, with CV <6.2% and accuracy <+/-12.6%. For culture medium (50 microL+50 microL plasma), LLOQ was 24.2 and 13.2 ng/mL for colistin A and B, respectively, with CV <11.4% and accuracy <+/-8.1%. The quick sample work-up method allows for determination of colistin A and B in clinical samples without causing hydrolysis of the prodrug colistin methanesulfonate (CMS).