Routine susceptibility panels predict reserve-antibiotic resistance across ESKAPE pathogens: an ontology-anchored machine learning analysis with cross-species transfer

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ID: 337479
2026
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Abstract
Abstract Resistance to reserve antibiotics such as carbapenems, glycopeptides, and oxazolidinones is the most consequential and least measured part of antimicrobial resistance. We asked whether resistance to these last-line agents can be predicted from the routine susceptibility panel that laboratories already produce, whether the predictive signal reflects shared resistance mechanisms, and whether it generalises across species. Using per-isolate susceptibility data for seven high-priority bacterial pathogens from a public bacterial bioinformatics resource (about 24,000 isolates), we built gradient-boosted models that predict resistance to each antibiotic from the rest of the panel, with every agent annotated for drug class and mechanism from a curated resistance ontology. Of 220 candidate pathogen-antibiotic pairs, 73 met a pre-specified eligibility rule and were evaluated by five-fold cross-validation. Resistance was broadly predictable (mean area under the curve 0.80 across the 73 models; 0.82 for reserve agents), reaching 0.90 for carbapenem resistance in Klebsiella pneumoniae and 0.97 for glycopeptide and oxazolidinone resistance in Enterococcus faecium. Performance was largely retained after same-class predictors were removed, indicating genuine cross-class co-resistance, and carbapenem resistance transferred across Gram-negative species. Routine panels therefore carry a strong, mechanism-aware signal for reserve-drug resistance.
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Authors Oluwaseun Emmanuel Agboola, Samuel Sunday Agboola, Zainab Adenike Ayinla, Temitope Cosmas Aribigbola, Anuoluwapo Bukola Shaleye, Abiola Oluwatosin Obisesan, Adedeji O Selimot, Theophilus A Adegbuyi, Babatunji Emmanuel Oyinloye
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag322
URL
https://doi.org/10.1093/bioadv/vbag322
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