Rao Gauri G, Vallé Quentin, Mahadevan Ramya, Sharma Rajnikant, Barr Jeremy J, Van Tyne Daria
USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, California, USA.
School of Biological Sciences, Monash University, Clayton, Victoria, Australia.
Clin Pharmacol Ther. 2025 Jan;117(1):94-105. doi: 10.1002/cpt.3426. Epub 2024 Sep 23.
Effectively treating multidrug-resistant bacterial infections remains challenging due to the limited drug development pipeline and a scarcity of novel agents effective against these highly resistant pathogens. Bacteriophages (phages) are a potential addition to the antimicrobial treatment arsenal. Though, phages are currently being tested in clinical trials for antibiotic-resistant infections, phages lack a fundamental understanding of optimal dosing in humans. Rationally designed preclinical studies using in vitro and in vivo infection models, allow us to assess clinically relevant phage +/- antibiotic exposure (pharmacokinetics), the resulting treatment impact on the infecting pathogen (pharmacodynamics) and host immune response (immunodynamics). A mechanistic modeling framework allows us to integrate this knowledge gained from preclinical studies to develop predictive models. We reviewed recently published mathematical models based on in vitro and/or in vivo data that evaluate the effects of varying bacterial or phage densities, phage characteristics (burst size, adsorption rate), phage pharmacokinetics, phage-antibiotic combinations and host immune responses. In our review, we analyzed study designs and the data used to inform the development of these mechanistic models. Insights gained from model-based simulations were reviewed as they help identify crucial phage parameters for determining effective phage dosing. These efforts contribute to bridging the gap between phage therapy research and its clinical translation.
由于药物研发渠道有限且缺乏有效对抗这些高耐药病原体的新型药物,有效治疗多重耐药细菌感染仍然具有挑战性。噬菌体是抗菌治疗武器库中一种潜在的补充手段。尽管目前噬菌体正在针对抗生素耐药感染进行临床试验,但对于其在人体中的最佳剂量仍缺乏基本认识。使用体外和体内感染模型进行合理设计的临床前研究,使我们能够评估临床相关的噬菌体+/-抗生素暴露(药代动力学)、对感染病原体产生的治疗影响(药效动力学)以及宿主免疫反应(免疫动力学)。一个机制建模框架使我们能够整合从临床前研究中获得的这些知识,以开发预测模型。我们回顾了最近发表的基于体外和/或体内数据的数学模型,这些模型评估了不同细菌或噬菌体密度、噬菌体特性(裂解量、吸附率)、噬菌体药代动力学、噬菌体-抗生素组合以及宿主免疫反应的影响。在我们的综述中,我们分析了研究设计以及用于为这些机制模型的开发提供信息的数据。对基于模型的模拟所获得的见解进行了回顾,因为它们有助于确定有效噬菌体剂量的关键噬菌体参数。这些努力有助于弥合噬菌体治疗研究与其临床转化之间的差距。