Improving the ability of antimicrobial susceptibility tests to predict clinical outcome accurately: Adding metabolic evasion to the equation. Issue 9 (September 2021)

• Record Type: Journal Article
• Authors: Tasse, Jason; Dieppois, Guennaëlle; Peyrane, Frédéric; Tesse, Nicolas
• Is Part Of: Drug discovery today. Volume 26:Issue 9(2021)
• Page Start: 2182
• Page End: 2189
• Publication Date: 2021-09
• Subjects: Bacterial physiology -- Slow growing bacteria -- Antimicrobial resistance -- Minimal inhibitory concentration (MIC) breakpoints -- Tolerance -- Biofilm -- Dormancy -- Persisters -- Antimicrobial susceptibility test -- Intracellular growth; Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2021.05.018
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: 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. Abstract: 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.