Optimization of bacteriophage therapy for difficult-to-treat musculoskeletal infections: a bench-to-bedside perspective

Front Cell Infect Microbiol. 2024 Sep 3:14:1434397. doi: 10.3389/fcimb.2024.1434397. eCollection 2024.

Abstract

Given the increasing threat of antimicrobial resistance, scientists are urgently seeking adjunct antimicrobial strategies, such as phage therapy (PT). However, despite promising results for the treatment of musculoskeletal infections in our center, crucial knowledge gaps remain. Therefore, a prospective observational study (PHAGEFORCE) and a multidisciplinary approach was set up to achieve and optimize standardized treatment guidelines. At our center, PT is strictly controlled and monitored by a multidisciplinary taskforce. Each phage treatment follows the same pathway to ensure standardization and data quality. Within the PHAGEFORCE framework, we established a testing platform to gain insight in the safety and efficacy of PT, biodistribution, phage kinetics and the molecular interaction between phages and bacteria. The draining fluid is collected to determine the phage titer and bacterial load. In addition, all bacterial isolates are fully characterized by genome sequencing to monitor the emergence of phage resistance. We hereby present a standardized bench-to-bedside protocol to gain more insight in the kinetics and dynamics of PT for musculoskeletal infections.

Keywords: bacteriophage therapy; bacteriophages; bench-to-bedside; musculoskeletal infections; treatment optimization.

Publication types

  • Observational Study

MeSH terms

  • Bacteria / virology
  • Bacterial Infections / therapy
  • Bacteriophages* / physiology
  • Humans
  • Musculoskeletal Diseases / microbiology
  • Musculoskeletal Diseases / therapy
  • Phage Therapy* / methods
  • Prospective Studies

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. LB, JO, SU, W-JM, IS, RL, YD, and LVG are supported by an ID-N(20/24) grant from KU Leuven. LVG is supported by a research grant of The Research Foundation Flanders (FWO), 18B2222N.