Genome-scale metabolic modelling in antimicrobial pharmacology: Present and future

The persistent surge in antimicrobial resistance (AMR) has propelled the search for innovative strategies in antimicrobial use. Genome-scale metabolic modelling (GSMM) has emerged as a transformative tool in this quest, offering a comprehensive understanding of host and microbial metabolism and their interactions with antimicrobial agents. This review emphasises current advancements in the application of GSMM to antimicrobial pharmacology, highlighting its role in deciphering complex microbial and host responses to drug exposure, identifying novel therapeutic targets and optimising therapeutic options. We discuss how GSMM has elucidated mechanisms of drug action, resistance pathways, and off-target effects, providing a systems-level perspective that challenges the traditional "one drug, one target" approach. The integration of GSMM with high-throughput omics technologies and machine learning showcases its potential to refine predictions of drug efficacy, optimise dosing strategies, and minimise toxicity. We also address the challenges and future directions of GSMM, including its expansion to host-pathogen-drug interactions and personalised medicine. Ultimately, GSMM stands as a critical approach in modern antimicrobial research, with the potential to revolutionise the development of effective treatments against MDR pathogens.