Version 1
: Received: 30 August 2024 / Approved: 30 August 2024 / Online: 30 August 2024 (23:41:06 CEST)
How to cite:
Salgado, J.; Rayner, J.; Ojkic, N. Addressing Antibiotic Resistance with Bacterial Cytological Profiling: A High-Throughput Method for Antibiotic Discovery. Preprints2024, 2024082249. https://doi.org/10.20944/preprints202408.2249.v1
Salgado, J.; Rayner, J.; Ojkic, N. Addressing Antibiotic Resistance with Bacterial Cytological Profiling: A High-Throughput Method for Antibiotic Discovery. Preprints 2024, 2024082249. https://doi.org/10.20944/preprints202408.2249.v1
Salgado, J.; Rayner, J.; Ojkic, N. Addressing Antibiotic Resistance with Bacterial Cytological Profiling: A High-Throughput Method for Antibiotic Discovery. Preprints2024, 2024082249. https://doi.org/10.20944/preprints202408.2249.v1
APA Style
Salgado, J., Rayner, J., & Ojkic, N. (2024). Addressing Antibiotic Resistance with Bacterial Cytological Profiling: A High-Throughput Method for Antibiotic Discovery. Preprints. https://doi.org/10.20944/preprints202408.2249.v1
Chicago/Turabian Style
Salgado, J., James Rayner and Nikola Ojkic. 2024 "Addressing Antibiotic Resistance with Bacterial Cytological Profiling: A High-Throughput Method for Antibiotic Discovery" Preprints. https://doi.org/10.20944/preprints202408.2249.v1
Abstract
Developing new antibiotics poses a significant challenge in the fight against antimicrobial resistance (AMR), a critical global health threat responsible for approximately 5 million deaths annually. Finding new classes of antibiotics that are safe, have acceptable pharmacokinetic properties, and are appropriately active against pathogens is a lengthy and expensive process. Therefore, high-throughput platforms are needed to screen large libraries of synthetic and natural compounds. In this review, we present bacterial cytological profiling (BCP) as a rapid, scalable, and cost-effective method for identifying the mechanisms of action of antibiotics, offering a promising tool for combating AMR and drug discovery. We present the application of BCP for different bacterial organisms and different classes of antibiotics and discuss BCP's advantages, limitations, and potential improvements. Furthermore, we highlight the studies that have utilized BCP to investigate pathogens listed in the Bacterial Priority Pathogens List 2024 and we identify the pathogens whose cytological profiles are missing. Lastly, we explore the most recent artificial intelligence and deep learning techniques that could enhance the analysis of data generated by BCP, potentially advancing our understanding of antibiotic resistance mechanisms and the discovery of novel druggable pathways.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
