A hydrogel-based in vitro assay for the fast prediction of antibiotic accumulation in Gram-negative bacteria

Abstract

The pipeline of antibiotics has been for decades on an alarmingly low level. Considering the steadily emerging antibiotic resistance, novel tools are needed for early and easy identification of effective anti-infective compounds. In Gram-negative bacteria, the uptake of anti-infectives is especially limited. We here present a surprisingly simple in vitro model of the Gram-negative bacterial envelope, based on 20% (w/v) potato starch gel, printed on polycarbonate 96-well filter membranes. Rapid permeability measurements across this polysaccharide hydrogel allowed to correctly predict either high or low accumulation for all 16 tested anti-infectives in living Escherichia coli. Freeze-fracture TEM supports that the macromolecular network structure of the starch hydrogel may represent a useful surrogate of the Gram-negative bacterial envelope. A random forest analysis of in vitro data revealed molecular mass, minimum projection area, and rigidity as the most critical physicochemical parameters for hydrogel permeability, in agreement with reported structural features needed for uptake into Gram-negative bacteria. Correlating our dataset of 27 antibiotics from different structural classes to reported MIC values of nine clinically relevant pathogens allowed to distinguish active from nonactive compounds based on their low in vitro permeability specifically for Gram-negatives. The model may help to identify poorly permeable antimicrobial candidates before testing them on living bacteria.

Keywords

• Antibiotic screening
• Machine learning
• 3D-printing
• Starch hydrogel
• Structure–permeability relationships