基岩
莫西沙星
结核分枝杆菌
微生物学
肺结核
选择(遗传算法)
医学
突变体
病毒学
生物
抗生素
遗传学
计算机科学
病理
人工智能
基因
作者
Lindsay Sonnenkalb,Barbora Trubenová,Roland R. Regoes,Matthias Merker,Stefan Niemann
标识
DOI:10.1016/j.jinf.2025.106523
摘要
Global health is threatened by the rise of antibiotic resistance. Bacteria of the Mycobacterium tuberculosis complex (Mtbc) are a major contributor to this antibiotic crisis, with about 450,000 new multidrug-resistant tuberculosis (MDR-TB) cases per year. This study investigates resistance evolution by defining the resistance mutant selection window (MSW) for the important MDR-TB treatment drugs moxifloxacin and bedaquiline. We employed a combination of long-term in vitro experiments supplemented with mathematical modeling that combined pharmacodynamics with population genetics. We assessed resistance selection at concentrations below the minimum inhibitory concentration (MIC), the MSW and fitness cost of eight mutant clones with different resistance-associated variants. Both computational and experimental results show that mutant clone populations are selected far below the MIC, leading to a major growth advantage of resistant populations under weak selection pressure. An eighth of the MIC was enough to enrich mutant clone populations in the short term (five bacterial passages or 20 generations), even in mutant clones with a major competitive fitness loss. In fact, gyrA, gyrB and most Rv0678 mutations have virtually no effect on the bacteria's competitive fitness in vitro. This work highlights the risk that ineffective drug delivery and dosing can lead to the emergence of resistance.
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