Antibiotic resistance, and, in a broader perspective, antimicrobial resistance (AMR), continues to evolve and spread beyond all boundaries. As a result, infectious diseases have become more challenging or even impossible to treat, leading to an increase in morbidity and mortality. Despite the failure of conventional, traditional antimicrobial therapy, in the past two decades, no novel class of antibiotics has been introduced. Consequently, several novel alternative strategies to combat these (multi-) drug-resistant infectious microorganisms have been identified. The purpose of this review is to gather and consider the strategies that are being applied or proposed as potential alternatives to traditional antibiotics. These strategies include combination therapy, techniques that target the enzymes or proteins responsible for antimicrobial resistance, resistant bacteria, drug delivery systems, physicochemical methods, and unconventional techniques, including the CRISPR-Cas system. These alternative strategies may have the potential to change the treatment of multi-drug-resistant pathogens in human clinical settings.

Abstract: Antimicrobial resistance (AMR) is a global health concern necessitating a holistic approach, integrating data across human, animal, and environmental health domains, known as the One-Health perspective. The advent of high-throughput genomics technologies has revolutionized the study of AMR, generating vast amounts of data that would provide the highest practicable level of structural detail on the individuating traits of an organism. Researchers can enhance data interoperability, facilitate meaningful comparisons, and derive comprehensive insights into AMR dynamics across various sectors by employing consistent metadata standards. Such a unified approach should emphasize the necessity of a standardized metadata framework for effectively utilizing genomic data, advancing our understanding of AMR within the broader One-Health perspective.

The advanced, affordable genome sequencing has led to a rise in genomic data for various pathogens worldwide; however, these datasets were not leveraged to enable meaningful comparisons and insights into AMR dynamics. In this study, we used a total of 1,20,700 publicly available genomes comprising 21,649 (E. faecium), 27,718 (S. aureus), 39,157 (K. pneumoniae), 15,046 (A. baumannii), 12,448 (P. aeruginosa), and 4,682 (Enterobacter spp) and their associated metadata were collected from different segments of One-Health and were analyzed using various bioinformatics tools to identify AMR genetic traits, and their trends were visualized using an interactive dashboard (iDB) to understand the global and region-wise spatiotemporal trends in antimicrobial resistance. The developed dashboard would become a global platform for the effective utilization of genomic data for effective intervention strategies, advancing our understanding of AMR within the broader One-Health perspective.