Using an adenosine triphosphate bioluminescent assay to determine effective antibiotic combinations against carbapenem-resistant gram negative bacteria within 24 hours

Abstract

Background Current in vitro combination testing methods involve enumeration by bacterial plating, which is labor-intensive and time-consuming. Measurement of bioluminescence, released when bacterial adenosine triphosphate binds to firefly luciferin-luciferase, has been proposed as a surrogate for bacterial counts. We developed an ATP bioluminescent combination testing assay with a rapid turnaround time of 24h to determine effective antibiotic combinations. Methods 100 strains of carbapenem-resistant (CR) GNB [30 Acinetobacter baumannii (AB), 30 Pseudomonas aeruginosa (PA) and 40 Klebsiella pneumoniae (KP)] were used. Bacterial suspensions (105 CFU/ml) were added to 96-well plates containing clinically achievable concentrations of multiple single and two-antibiotic combinations. At 24h, the luminescence intensity of each well was measured. Receiver operator characteristic curves were plotted to determine optimal luminescence threshold (TRLU) to discriminate between inhibitory/noninhibitory combinations when compared to viable plating. The unweighted accuracy (UA) [(sensitivity + specificity)/2] of TRLU values was determined. External validation was further done using 50 additional CR-GNB. Predictive accuracies of TRLU were high for when all antibiotic combinations and species were collectively analyzed (TRLU = 0.81, UA = 89%). When individual thresholds for each species were determined, UA remained high. Predictive accuracy was highest for KP (TRLU = 0.81, UA = 91%), and lowest for AB (TRLU = 0.83, UA = 87%). Upon external validation, high overall accuracy (91%) was observed. The assay distinguished inhibitory/noninhibitory combinations with UA of 80%, 94% and 93% for AB, PA and KP respectively. Conclusion We developed an assay that is robust at identifying useful combinations with a rapid turnaround time of 24h, and may be employed to guide the timely selection of effective antibiotic combinations.