Antimicrobial Resistance Co-occurrence Networks Exhibit Robust-Yet-Fragile Topology: A Network-Theoretic Basis for Hub-Targeted Combination Therapy

Antibiotic cycling — the sequential rotation of antibiotic classes — is structurally equivalent to random node removal in a scale-free network. AMR gene co-occurrence networks exhibit robust-yet-fragile (RYF) topology: random removal (cycling) preserves network connectivity while targeted hub removal causes rapid collapse. This provides a network-theoretic rationale for hub-targeted combination therapy over cycling strategies. We construct a 42-node, 73-edge AMR co-occurrence network calibrated to WHO GLASS 2022 and CARD co-occurrence priors and show that at 48% node removal, targeted hub removal reduces the largest connected component to 14% versus 67% under random removal (4.8-fold difference). blaKPC-3 (betweenness centrality 0.42) is identified as the highest-value structural target. Four exploitable collateral sensitivity pairs are curated from published clinical data. Code and data: https://github.com/AegisMindApp/amr-ryf-networks

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-28
DOI
https://doi.org/10.5281/zenodo.21636424
Primary Topic
Computational Drug Discovery Methods
Type
preprint
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preprint

Antimicrobial Resistance Co-occurrence Networks Exhibit Robust-Yet-Fragile Topology: A Network-Theoretic Basis for Hub-Targeted Combination Therapy

John Goodman
Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
preprint

Antimicrobial Resistance Co-occurrence Networks Exhibit Robust-Yet-Fragile Topology: A Network-Theoretic Basis for Hub-Targeted Combination Therapy

John Goodman
preprint en

Abstract

Antibiotic cycling — the sequential rotation of antibiotic classes — is structurally equivalent to random node removal in a scale-free network. AMR gene co-occurrence networks exhibit robust-yet-fragile (RYF) topology: random removal (cycling) preserves network connectivity while targeted hub removal causes rapid collapse. This provides a network-theoretic rationale for hub-targeted combination therapy over cycling strategies. We construct a 42-node, 73-edge AMR co-occurrence network calibrated to WHO GLASS 2022 and CARD co-occurrence priors and show that at 48% node removal, targeted hub removal reduces the largest connected component to 14% versus 67% under random removal (4.8-fold difference). blaKPC-3 (betweenness centrality 0.42) is identified as the highest-value structural target. Four exploitable collateral sensitivity pairs are curated from published clinical data. Code and data: https://github.com/AegisMindApp/amr-ryf-networks

Zenodo (CERN European Organization for Nuclear Research)
Institute of Oceanology (BG)
Computational Drug Discovery Methods
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