Cefiderocol remains one of the last-resort treatment options against multidrug-resistant (MDR) Gram-negative bacteria. As resistance to older agents like carbapenems and colistin rises, the clinical value of cefiderocol is increasingly vital. Yet, early indicators of declining cefiderocol susceptibility are poorly understood, leaving clinicians and policymakers with little guidance on when and where this drug may begin to lose effectiveness.
This project aims to identify phenotypic early warning signs of emerging cefiderocol resistance by analyzing trends in susceptibility to cefiderocol and other last-line antibiotics. Using the SIDERO-WT dataset, which includes MIC values for cefiderocol and comparators across North America and Europe, we will examine whether resistance to certain antibiotics reliably precedes reduced susceptibility to cefiderocol.
To improve generalizability, we will request access to the Pfizer ATLAS dataset, which includes global MIC data for comparator antibiotics but not cefiderocol. While ATLAS cannot directly inform cefiderocol trends, it can help test whether phenotypic signatures found in SIDERO-WT also appear in other regions, thereby flagging settings where cefiderocol resistance could plausibly emerge.
Our analytical approach will be exploratory and flexible; combining descriptive analyses, resistance clustering, and machine learning where appropriate to uncover consistent phenotypic patterns across datasets.
The ultimate goal is to support antimicrobial stewardship by pinpointing geographic or pathogen-specific “blind spots” where cefiderocol may be at risk, even before widespread resistance is observed. This work could also inform treatment prioritization frameworks, guiding the responsible deployment of cefiderocol in regions with constrained options.
Our approach is scalable and adaptable, and the insights gained could shape how future antimicrobials are monitored and preserved across diverse global contexts.