Representing antibiotic relationships using measurements of efficacy against clinical isolates

Introduction: Antimicrobial resistance (AMR) is a worrying and confusing problem for both patients and medical professionals. It is often difficult for non-specialists to understand how different antibiotics are related to one another. Here, I use experimental data from hundreds of thousands of clinical isolates to infer relationships between antibiotics and represent them with simple diagrams. Methods: . The minimum inhibitory concentration (MIC) of a bacterial isolate for a given antibiotic is defined as the lowest concentration that prevents visible growth. Measuring MICs for multiple antibiotics using the same isolate implicitly records the relationships of the antibiotics for a given species. The basic principle is that antibiotics with similar mechanisms of action should give rise to similar mechanisms of resistance, so should have correlated MICs across large numbers of isolates. This information can then be used to calculate distances between antibiotics based on pairwise correlations of their rank-ordered MICs. I apply this approach to a large historical AMR surveillance dataset (the Pfizer ATLAS surveillance dataset, 2004-2017). Results: . I demonstrate that clustering antibiotics in this way allows a simple visual comparison of how similar antibiotics are to each other based on their efficacy within a species. The resulting visualizations broadly recapitulate antibiotic classes. They also clearly show the dramatic effects of combining beta-lactam antibiotics with beta-lactamase inhibitors, as well as highlighting antibiotics which have unexpected correlations in MICs that are not predicted from their chemical similarities alone. Conclusion: Large AMR surveillance datasets can be used in a hypothesis-free manner to show relationships between antibiotics based on their real-world efficacy.