基于生态的方法来预测无效抗生素度,以尽量减少环境选择的耐药性
David Kneis1, Magali de la Cruz Barron1, Diala Konyali1
1Institute of Hydrobiology, Technische Universität Dresden, Zellescher Weg 40, 01217 Dresden, Saxony, Germany.
The ISME journal
|August 12, 2025
概括
这项研究引入了一种新方法,用于预测环境抗生素度的选择性耐药性. 这些发现表明,目前的监管门可能过高,可能需要大幅降低,以防止抗生素耐药性传播.
科学领域:
- 环境微生物学环境微生物学
- 抗微生物耐药性 抗微生物耐药性
- 生态毒理学 生态毒理学
背景情况:
- 抗生素耐药性选择发生在低环境度下.
- 目前设定环境抗生素限值 (MSC) 的方法不考虑微生物社区的复杂性和健康成本.
- 现有的用于预测阻力选择无效度 (PNECres) 的计算模型缺乏生态基础.
研究的目的:
- 开发一个生物学上一致的框架来导出PNECres值.
- 将最小抑制度 (MIC) 数据与与耐力相关的健身成本的概率估计相结合.
- 为环境风险评估提供一种比传统评估因素更可靠的替代方案.
主要方法:
- 制定了一个框架,将最小选择性度 (MSC) 与阻力成本联系起来.
- 使用数学和经验证明来验证MSC/MIC比率近似值.
- 探索了健身成本的分布,以创建一个取代固定评估因素的概率模型.
主要成果:
- MSC/MIC比率接近于高级电阻的电阻成本.
- 实验验证显示,在66%的案例中,计算的MSC与经验值的偏差不超过2倍.
- 拟议的框架建议将监管环境度值降低至少一个数量级.
结论:
- 新的框架提供了一种可行和生物透明的方法来导出PNECres值.
- 目前对抗生素耐药性的环境风险评估可能需要大幅修订.
- 实施这种基于成本的方法可以更好地防止环境中抗生素耐药性的选择和传播.
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