Clarelli F, Ankomah P O, Weiss H, Conway J M, Forsdahl G, Abel Zur Wiesch P
Department of Pharmacy, UiT - the Arctic University of Norway, Tromsø, Norway; Department of Biology, Pennsylvania State University, University Park, PA, USA.
Massachusetts General Hospital, Boston, MA, USA.
Biosystems. 2025 Feb;248:105385. doi: 10.1016/j.biosystems.2024.105385. Epub 2024 Dec 24.
Antimicrobial resistance is one of the most significant healthcare challenges of our times. Multidrug or combination therapies are sometimes required to treat severe infections; for example, the current protocols to treat pulmonary tuberculosis combine several antibiotics. However, combination therapy is usually based on lengthy empirical trials, and it is difficult to predict its efficacy. We propose a new tool to identify antibiotic synergy or antagonism and optimize combination therapies. Our model explicitly incorporates the mechanisms of individual drug action and estimates their combined effect using a mechanistic approach. By quantifying the impact on growth and death of a bacterial population, we can identify optimal combinations of multiple drugs. Our approach also allows for the investigation of the drugs' actions and the testing of theoretical hypotheses. We demonstrate the utility of this tool with in vitro Escherichia coli data using a combination of ampicillin and ciprofloxacin. In contrast to previous interpretations, our model finds a slight synergy between the antibiotics. Our mechanistic model allows investigating possible causes of the synergy.
抗菌耐药性是我们这个时代最重大的医疗挑战之一。治疗严重感染有时需要使用多种药物或联合疗法；例如，当前治疗肺结核的方案就联合使用了几种抗生素。然而，联合疗法通常基于漫长的经验性试验，而且很难预测其疗效。我们提出了一种新工具，用于识别抗生素的协同作用或拮抗作用，并优化联合疗法。我们的模型明确纳入了每种药物的作用机制，并使用一种机制方法来估计它们的联合效果。通过量化对细菌群体生长和死亡的影响，我们可以确定多种药物的最佳组合。我们的方法还允许研究药物的作用并检验理论假设。我们使用氨苄青霉素和环丙沙星的组合，通过体外大肠杆菌数据证明了该工具的实用性。与之前的解释不同，我们的模型发现这两种抗生素之间存在轻微的协同作用。我们的机制模型能够研究这种协同作用的可能原因。
