Nicita Mehta, Andrew T Nguyen, Edward K Rodriguez, Jason Young
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Smart Phages: Leveraging Artificial Intelligence to Tackle Prosthetic Joint Infections.
Traditional antibiotic therapy has encountered significant challenges for clinical treatment of infections for multiple reasons, including antimicrobial resistance (AMR) and poor efficacy against biofilms, demanding research into alternative therapeutic agents. Because of their unique antimicrobial mechanisms as well as their target specificity, diversity, exponential self-amplification, and anti-biofilm activity, combined with recent advances in genomics and synthetic biology, bacteriophages have attracted increased interest as potential alternatives or therapeutic adjuncts to antibiotics. However, obstacles such as phage-host specificity, bacterial resistance, and the selection of optimal phages, amongst other factors, impede clinical adoption of phage therapy. Here, machine learning (ML) and artificial intelligence (AI) tools have the opportunity to revolutionize phage therapy by enhancing scalability, efficiency and precision of these therapies. This article highlights potential key applications of ML/AI in the study, development and deployment of phage therapy.
Antibiotics-BaselPharmacology, Toxicology and Pharmaceutics-General Pharmacology, Toxicology and Pharmaceutics
CiteScore
7.30
自引率
14.60%
发文量
1547
审稿时长
11 weeks
期刊介绍:
Antibiotics (ISSN 2079-6382) is an open access, peer reviewed journal on all aspects of antibiotics. Antibiotics is a multi-disciplinary journal encompassing the general fields of biochemistry, chemistry, genetics, microbiology and pharmacology. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, there is no restriction on the length of papers.