A hydrogel-based assay for the fast prediction of antibiotic accumulation in Gram-negative bacteria.
Name:
Publisher version
View Source
Access full-text PDFOpen Access
View Source
Check access options
Check access options
Average rating
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Star rating
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Authors
Richter, RobertKamal, Mohamed A M
García-Rivera, Mariel A
Kaspar, Jerome
Junk, Maximilian
Elgaher, Walid A M
Srikakulam, Sanjay Kumar
Gress, Alexander
Beckmann, Anja
Grißmer, Alexander
Meier, Carola
Vielhaber, Michael
Kalinina, Olga
Hirsch, Anna K H
Hartmann, Rolf W
Brönstrup, Mark
Schneider-Daum, Nicole
Lehr, Claus-Michael
Issue Date
2020-11-02
Metadata
Show full item recordAbstract
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.Citation
Mater Today Bio. 2020 Nov 2;8:100084. doi: 10.1016/j.mtbio.2020.100084.Affiliation
HIPS, Helmholtz-Institut für Pharmazeutische Forschung Saarland, Universitätscampus E8.1 66123 Saarbrücken, Germany.Publisher
ElsevierJournal
Materials today. BioPubMed ID
33313504Type
ArticleLanguage
enEISSN
2590-0064ae974a485f413a2113503eed53cd6c53
10.1016/j.mtbio.2020.100084
Scopus Count
The following license files are associated with this item:
- Creative Commons