| Title: |
Use of Artificial Intelligence in Current Fight Against Antimicrobial Resistance |
| Authors: |
Codde, Cyrielle; Faucher, Jean-François; Woillard, Jean-Baptiste |
| Contributors: |
Pharmacologie et Transplantation (P&T); CHU Limoges-Institut National de la Santé et de la Recherche Médicale (INSERM)-OmégaHealth (ΩHealth); Université de Limoges (UNILIM)-Université de Limoges (UNILIM); Service de Pharmacologie, toxicologie et pharmacovigilance CHU Limoges; CHU Limoges |
| Source: |
ISSN: 1076-6294 ; Microbial Drug Resistance ; https://hal.science/hal-05327344 ; Microbial Drug Resistance, 2025, ⟨10.1089/mdr.2024.0241⟩. |
| Publisher Information: |
CCSD; Mary Ann Liebert |
| Publication Year: |
2025 |
| Collection: |
Université de Limoges: HAL |
| Subject Terms: |
antimicrobial resistance; daptomycin; vancomycin; gangiclovir 1; machine learning; artificial intelligence; artificial intelligence machine learning antimicrobial resistance daptomycin vancomycin gangiclovir 1; [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology |
| Description: |
International audience ; Antimicrobial resistance (AMR) poses a significant global health threat, with projections indicating it could surpass cancer in mortality rates by 2050 if left unaddressed. Optimizing antimicrobial dosing is critical to mitigate resistance and improve clinical outcomes. Traditional approaches, including population pharmacokinetics (PK) models and Bayesian estimation, are limited by mechanistic hypothesis requirements and complexity. Artificial intelligence (AI) and machine learning (ML) offer transformative solutions by leveraging large datasets to predict drug exposure accurately, refine sampling strategies, and enable real-time dose adjustments through therapeutic drug monitoring. This review highlights the role of ML models, in managing PK and pharmacodynamic variability across diverse patient populations. AI models often equal or outperform traditional methods in achieving therapeutic targets while minimizing toxicity, as demonstrated in some case studies involving ganciclovir, vancomycin, and daptomycin. Despite challenges such as data quality, interpretability, and integration with clinical workflows, AI's dynamic adaptability and precision underscore its potential. Future directions emphasize integrating multi-omics data, developing bedside decision-support tools, and expanding AI applications to broader drug categories and populations. Continued research and clinical validation are essential to harness AI's full potential in advancing precision medicine and combating AMR effectively. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1089/mdr.2024.0241 |
| Availability: |
https://hal.science/hal-05327344; https://hal.science/hal-05327344v1/document; https://hal.science/hal-05327344v1/file/codde-et-al-2025-use-of-artificial-intelligence-in-current-fight-against-antimicrobial-resistance.pdf; https://doi.org/10.1089/mdr.2024.0241 |
| Rights: |
https://about.hal.science/hal-authorisation-v1/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.47C081C7 |
| Database: |
BASE |