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Combination of in vivo phage therapy data with in silico model highlights key parameters for treatment efficacy
bioRxiv - Microbiology Pub Date : 2021-03-04 , DOI: 10.1101/2021.03.04.433924
Raphaëlle Delattre , Jérémy Seurat , Feyrouz Haddad , Thu-Thuy Nguyen , Baptiste Gaborieau , Rokhaya Kane , Nicolas Dufour , Jean-Damien Ricard , Jérémie Guedj , et Laurent Debarbieux

The clinical (re)development of phage therapy to treat antibiotic resistant infections requires grasping specific biological properties of bacteriophages (phages) as antibacterial. However, identification of optimal dosing regimens is hampered by the poor understanding of phage-bacteria interactions in vivo. Here we developed a general strategy coupling in vitro and in vivo experiments with a mathematical model to characterize the interplay between phage and bacterial dynamics during pneumonia induced by a pathogenic strain of Escherichia coli. The model estimates some key parameters for phage therapeutic efficacy, in particular the impact of dose and route of administration on phage dynamics and the synergism of phage and the innate immune response on the bacterial clearance rate. Simulations predict a low impact of the intrinsic phage characteristics in agreement with the current semi-empirical choices of phages for compassionate treatments. Model-based approaches will foster the deployment of future phage therapy clinical trials.

中文翻译:

体内噬菌体治疗数据与计算机模拟模型的结合突出显示了治疗功效的关键参数

噬菌体疗法的临床(重新)开发用于治疗抗生素耐药性感染,需要掌握噬菌体(噬菌体)作为抗菌剂的特定生物学特性。然而,由于对体内噬菌体-细菌相互作用的了解不足,妨碍了最佳给药方案的确定。在这里,我们开发了一种在体外和体内实验与数学模型相结合的通用策略,以表征由致病性大肠杆菌引起的肺炎期间噬菌体与细菌动力学之间的相互作用。该模型估计了噬菌体治疗功效的一些关键参数,特别是剂量和给药途径对噬菌体动力学的影响以及噬菌体的协同作用和先天免疫应答对细菌清除率的影响。模拟预测内在噬菌体特性的低影响,与当前对同情治疗的噬菌体的半经验选择相一致。基于模型的方法将促进未来噬菌体疗法临床试验的部署。
更新日期:2021-03-05
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