Genome-scale metabolic modeling in antimicrobial pharmacology

• Published in: Engineering Microbiology Vol. 2; no. 2; p. 100021
• Main Authors: Zhu, Yan, Zhao, Jinxin, Li, Jian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-06-2022; Elsevier
• Subjects: Antimicrobial pharmacology; Antimicrobial resistance; Flux balance analysis; Genome-scale metabolic model; Pharmacokinetic/pharmacodynamic model; Toxicity; Flux balance analysis; Antimicrobial pharmacology; Antimicrobial resistance; Genome-scale metabolic model; Toxicity; Pharmacokinetic/pharmacodynamic model
• ISSN: 2667-3703; 2667-3703
• DOI: 10.1016/j.engmic.2022.100021

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades. This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant microbial pathogens. However, the detailed mechanisms of action, resistance, and toxicity of many antimicrobials remain uncertain, significantly hampering the development of novel antimicrobials. Genome-scale metabolic model (GSMM) has been increasingly employed to investigate microbial metabolism. In this review, we discuss the latest progress of GSMM in antimicrobial pharmacology, particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity, resistance, and toxicity. We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities (e.g., gene expression), identification of potential drug targets, and integration with machine learning and pharmacokinetic/pharmacodynamic modeling. Overall, GSMM has significant potential in elucidating the critical role of metabolic changes in antimicrobial pharmacology, providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.
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