Improving the ability of antimicrobial susceptibility tests to predict clinical outcome accurately: Adding metabolic evasion to the equation

•Many susceptible bacteria can survive antibiotics and cause recurrent infections.•They can enter in a slow or non-growing metabolic state in which they are tolerant of antimicrobials.•Current antimicrobial susceptibility tests (ASTs) are unable to detect this ‘metabolic evasion’ behaviour.•To impro...

Full description

Saved in:
Bibliographic Details
Published in:Drug discovery today 2021-09, Vol.26 (9), p.2182-2189
Main Authors: Tasse, Jason, Dieppois, Guennaëlle, Peyrane, Frédéric, Tesse, Nicolas
Format: Article
Language:English
Subjects:
Anti-Bacterial Agents - pharmacology
Anti-Bacterial Agents - therapeutic use
Antimicrobial resistance
Antimicrobial susceptibility test
Bacteria - drug effects
Bacteria - metabolism
Bacterial Infections - drug therapy
Bacterial physiology
Biofilm
Decision Trees
Dormancy
Drug Resistance, Bacterial
Humans
Intracellular growth
Microbial Sensitivity Tests
Minimal inhibitory concentration (MIC) breakpoints
Persisters
Slow growing bacteria
Tolerance
Treatment Outcome
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Many susceptible bacteria can survive antibiotics and cause recurrent infections.•They can enter in a slow or non-growing metabolic state in which they are tolerant of antimicrobials.•Current antimicrobial susceptibility tests (ASTs) are unable to detect this ‘metabolic evasion’ behaviour.•To improve the prediction of treatment outcomes, this phenomenon should be taken into account during diagnosis.•Activity on non-growing bacteria must be considered during the development of new drugs. Antimicrobial susceptibility tests (AST) are based on the minimal inhibitory concentration (MIC), the method used worldwide to guide antimicrobial therapy. Despite its relevance in correctly predicting clinical outcome for most acute infections, this approach is misleading for multiple clinical cases in which pathogens do not grow rapidly, uniformly or with physical protection. This behaviour, named ‘metabolic evasion’ (ME), enables bacteria to survive antimicrobials. ME can result from different, and sometimes combined, bacterial mechanisms such as biofilms, intracellular growth, persisters or dormancy. We discuss how ME can influence the MIC-based probability of target attainment. We identify clinical cases in which this approach is undermined by ME and propose a new approach that takes ME into account in order to improve patient management and the evaluation of innovative drugs.
ISSN:1359-6446
1878-5832
DOI:10.1016/j.drudis.2021.05.018