Crossing the Chasm: How to Approach Translational Pharmacokinetic-Pharmacodynamic Modeling of Phage Dosing.

IF 6.3 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Gauri G Rao, Quentin Vallé, Ramya Mahadevan, Rajnikant Sharma, Jeremy J Barr, Daria Van Tyne
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引用次数: 0

Abstract

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.

跨越鸿沟:如何进行噬菌体剂量的转化药代动力学-药效学建模。
由于药物开发渠道有限,而且缺乏能有效对付这些高度耐药病原体的新型制剂,因此有效治疗耐多药细菌感染仍具有挑战性。噬菌体(噬菌体)是抗菌治疗药物库中的潜在新成员。虽然噬菌体目前正在抗生素耐药性感染的临床试验中进行测试,但人们对噬菌体在人体中的最佳剂量缺乏基本了解。通过使用体外和体内感染模型进行合理设计的临床前研究,我们可以评估与临床相关的噬菌体+/-抗生素暴露(药代动力学)、治疗对感染病原体的影响(药效学)和宿主免疫反应(免疫动力学)。机理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来开发预测模型。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(爆发大小、吸附率)、噬菌体药代动力学、噬菌体-抗生素组合和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和用于指导这些机理模型开发的数据。我们还回顾了从基于模型的模拟中获得的启示,因为这些启示有助于确定噬菌体的关键参数,从而确定噬菌体的有效剂量。这些努力有助于缩小噬菌体疗法研究与临床转化之间的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.70
自引率
7.50%
发文量
290
审稿时长
2 months
期刊介绍: Clinical Pharmacology & Therapeutics (CPT) is the authoritative cross-disciplinary journal in experimental and clinical medicine devoted to publishing advances in the nature, action, efficacy, and evaluation of therapeutics. CPT welcomes original Articles in the emerging areas of translational, predictive and personalized medicine; new therapeutic modalities including gene and cell therapies; pharmacogenomics, proteomics and metabolomics; bioinformation and applied systems biology complementing areas of pharmacokinetics and pharmacodynamics, human investigation and clinical trials, pharmacovigilence, pharmacoepidemiology, pharmacometrics, and population pharmacology.
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