抗生物質耐性と闘う阻害剤の最適化(Optimizing Inhibitors That Fight Antibiotic Resistance)

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2024-10-10 デューク大学(Duke)

デューク大学の研究者によると、抗生物質抵抗性を持つ細菌の「自己中心性」が治療効果に影響を与えることが判明しました。抗生物質抵抗性菌の中には、周囲の細菌を守る働きを持つものもあれば、自己防衛に特化したものもあります。研究は、自己中心的な菌株が治療後に生き残りやすい一方、共有的な菌株は治療後に減少する傾向があることを示しました。これにより、医師が特定の菌株に合わせて治療を調整し、抵抗性の進化を最小限に抑えるための指針が得られるとしています。

<関連情報>

β-ラクタマーゼの自己中心性が抗生物質併用療法の選択ダイナミクスを決定する Private benefit of β-lactamase dictates selection dynamics of combination antibiotic treatment

Helena R. Ma,Helen Z. Xu,Kyeri Kim,Deverick J. Anderson & Lingchong You
Nature Communications  Published:27 September 2024
DOI:https://doi.org/10.1038/s41467-024-52711-w

抗生物質耐性と闘う阻害剤の最適化(Optimizing Inhibitors That Fight Antibiotic Resistance)

Abstract

β-lactam antibiotics have been prescribed for most bacterial infections since their discovery. However, resistance to β-lactams, mediated by β-lactamase (Bla) enzymes such as extended spectrum β-lactamases (ESBLs), has become widespread. Bla inhibitors can restore the efficacy of β-lactams against resistant bacteria, an approach which preserves existing antibiotics despite declining industry investment. However, the effects of combination treatment on selection for β-lactam resistance are not well understood. Bla production confers both private benefits for resistant cells and public benefits which faster-growing sensitive cells can also exploit. These benefits may be differentially impacted by Bla inhibitors, leading to non-intuitive selection dynamics. In this study, we demonstrate strain-to-strain variation in effective combination doses, with complex growth dynamics in mixed populations. Using modeling, we derive a criterion for the selection outcome of combination treatment, dependent on the burden and effective private benefit of Bla production. We then use engineered strains and natural isolates to show that strong private benefits of Bla are associated with increased selection for resistance. Finally, we demonstrate that this parameter can be coarsely estimated using high-throughput phenotyping of clonal populations. Our analysis shows that quantifying the phenotypic responses of bacteria to combination treatment can facilitate resistance-minimizing optimization of treatment.

有機化学・薬学
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