Abel Zur Wiesch Pia, Abel Sören, Gkotzis Spyridon, Ocampo Paolo, Engelstädter Jan, Hinkley Trevor, Magnus Carsten, Waldor Matthew K, Udekwu Klas, Cohen Ted
Division of Global Health Equity, Brigham and Women's Hospital and Harvard Medical School, 641 Huntington Avenue, Boston, MA 02115, USA. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT 06510, USA.
Division of Infectious Diseases, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA. Department of Pharmacy, UiT, The Arctic University of Norway, 9037 Tromsø, Norway.
Sci Transl Med. 2015 May 13;7(287):287ra73. doi: 10.1126/scitranslmed.aaa8760.
Finding optimal dosing strategies for treating bacterial infections is extremely difficult, and improving therapy requires costly and time-intensive experiments. To date, an incomplete mechanistic understanding of drug effects has limited our ability to make accurate quantitative predictions of drug-mediated bacterial killing and impeded the rational design of antibiotic treatment strategies. Three poorly understood phenomena complicate predictions of antibiotic activity: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. We show that chemical binding kinetics alone are sufficient to explain these three phenomena, using single-cell data and time-kill curves of Escherichia coli and Vibrio cholerae exposed to a variety of antibiotics in combination with a theoretical model that links chemical reaction kinetics to bacterial population biology. Our model reproduces existing observations, has a high predictive power across different experimental setups (R(2) = 0.86), and makes several testable predictions, which we verified in new experiments and by analyzing published data from a clinical trial on tuberculosis therapy. Although a variety of biological mechanisms have previously been invoked to explain post-antibiotic growth suppression, density-dependent antibiotic effects, and especially persister cell formation, our findings reveal that a simple model that considers only binding kinetics provides a parsimonious and unifying explanation for these three complex, phenotypically distinct behaviours. Current antibiotic and other chemotherapeutic regimens are often based on trial and error or expert opinion. Our "chemical reaction kinetics"-based approach may inform new strategies, which are based on rational design.
寻找治疗细菌感染的最佳给药策略极其困难，而改进治疗方法需要进行成本高昂且耗时的实验。迄今为止，对药物作用的机制理解不完整限制了我们对药物介导的细菌杀灭进行准确定量预测的能力，并阻碍了抗生素治疗策略的合理设计。有三种尚未被充分理解的现象使抗生素活性的预测变得复杂：抗生素后生长抑制、密度依赖性抗生素效应和持留菌形成。我们表明，仅化学结合动力学就足以解释这三种现象，我们使用单细胞数据和大肠杆菌及霍乱弧菌在接触多种抗生素后的时间杀灭曲线，并结合一个将化学反应动力学与细菌群体生物学联系起来的理论模型。我们的模型重现了现有的观察结果，在不同的实验设置中具有很高的预测能力（R² = 0.86），并做出了几个可检验的预测，我们在新的实验中以及通过分析一项关于结核病治疗的临床试验的已发表数据对这些预测进行了验证。尽管此前人们援引了多种生物学机制来解释抗生素后生长抑制、密度依赖性抗生素效应，尤其是持留菌形成，但我们的研究结果表明，一个仅考虑结合动力学的简单模型为这三种复杂的、表型不同的行为提供了一个简洁统一的解释。当前的抗生素和其他化疗方案通常基于试错法或专家意见。我们基于“化学反应动力学”的方法可能为基于合理设计的新策略提供依据。
