Anticipating Resistance Risks to Cefiderocol in MDR Pathogens Data Challenge

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.

头孢地尔（Cefiderocol）仍是对抗多重耐药（MDR）革兰氏阴性菌的最后一线治疗选择之一。随着碳青霉烯类（carbapenems）、黏菌素（colistin）等传统抗菌药物的耐药率持续攀升，头孢地尔的临床价值愈发凸显。然而，目前学界对头孢地尔敏感性下降的早期预警信号尚缺乏深入认知，这使得临床医师与政策制定者难以预判该药物何时、在何地会出现疗效减退。

本研究旨在通过分析头孢地尔及其他最后一线抗菌药物的药敏趋势，识别头孢地尔耐药出现的表型早期预警信号。我们将使用涵盖北美与欧洲地区头孢地尔及对照抗菌药物最低抑菌浓度（MIC，Minimum Inhibitory Concentration）数据的SIDERO-WT数据集，探究针对特定抗菌药物的耐药是否会可靠地提前预示头孢地尔敏感性的降低。

为提升研究的可推广性，我们将申请获取辉瑞ATLAS数据集（Pfizer ATLAS）的使用权限。该数据集包含全球范围内对照抗菌药物的MIC数据，但未涵盖头孢地尔相关数据。尽管ATLAS数据集无法直接反映头孢地尔的药敏趋势，却可用于验证在SIDERO-WT数据集中发现的表型特征是否同样存在于其他地区，进而标记出头孢地尔耐药有可能出现的潜在场景。

本研究的分析方法兼具探索性与灵活性，将在适宜场景下结合描述性分析、耐药聚类分析与机器学习技术，以挖掘不同数据集间一致的表型模式。

本研究的最终目标是通过定位地理或病原体特异性的“盲区”，为抗菌药物管理提供支持——即使在广泛耐药尚未出现时，即可识别头孢地尔可能面临风险的场景。此外，本研究还可为治疗优先级框架提供参考，指导在治疗选择有限的地区合理部署头孢地尔。

本研究方案具备可扩展性与适配性，所获研究成果或可重塑未来抗菌药物在全球不同场景下的监测与保护策略。