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submitted on 24.07.2020 and posted on 28.07.2020by Robert Richter, Mohamed Ashraf M. Kamal, Mariel A. García-Rivera, Jerome Kaspar, Maximilian Junk, Walid A. M. Elgaher, Sanjay Kumar Srikakulam, Alexander Gress, Anja Beckmann, Alexander Grißmer, Carola Meier, Michael Vielhaber, Olga Kalinina, Anna K.H. Hirsch, Rolf W. Hartmann, Mark Brönstrup, Nicole Schneider-Daum, Claus-Michael Lehr
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 E. 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. Machine learning by random forest analysis of in vitro data revealed minimum projection area, molecular mass, 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 data set of 27 antibiotics from different structural classes to reported MIC values of seven clinically relevant pathogens allowed to distinguish active from non-active compounds based on their low in vitro permeability and in particular to identify poorly permeable antimicrobial candidates before testing them on living bacteria.