Towards the Translation of Synergistic Phage-Polymyxin Combination Therapy against Pandrug-resistant Klebsiella pneumoniae: A Systems Approach

针对泛耐药肺炎克雷伯菌的协同噬菌体-多粘菌素联合疗法的转化：系统方法

• 批准号: 10470088
• 负责人: Jian Li
• 金额: $ 13.72万
• 依托单位: MONASH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-16 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10470088
• 关键词: Address; Affect; Amino Acids; Animals; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Antimicrobial Resistance; Attention; Bacteria; Bacterial Infections; Bacteriophages; Biology; Cells; Citrates; Clinic; Clinical; Clinical Trials; Combined Antibiotics; Combined Modality Therapy; Complex; Critical Illness; Cytolysis; Dangerousness; Diagnostic; Dose; Drug Kinetics; Future; Health; Human; Immune; Immune response; Immunocompromised Host; In Vitro; Infection; Innovative Therapy; Klebsiella pneumoniae; Knowledge; Life; Literature; Lytic; Mediating; Medical; Membrane Lipids; Metabolism; Modeling; Multi-Drug Resistance; Multiomic Data; Mus; National Institute of Allergy and Infectious Disease; Nucleotides; Outcome; Patients; Pentosephosphate Pathway; Pharmacodynamics; Pharmacology; Plasmids; Play; Polymyxin B; Polymyxin Resistance; Polymyxins; Regimen; Reporting; Research; Resistance; Resistance development; Resort; Role; Safety; Security; Site; Specificity; Superbug; System; Therapeutic; Time; Toxic effect; Translations; Virus; World Health Organization; bacterial metabolism; bacterial resistance; carbapenem resistance; combat; dosage; global health; in vivo; innovation; metabolomics; multidisciplinary; multiple omics; novel; novel therapeutics; pathogen; priority pathogen; programs; rational design; resistance mechanism; resistant Klebsiella pneumoniae; synergism; virtual

Antimicrobial resistance (AMR) has become one of the greatest global threats to human health. Pandrug- resistant (PDR) Klebsiella pneumoniae has been identified by the World Health Organization as one of the 3 top- priority pathogens urgently requiring new treatments. Polymyxins are often used as the last option; however, plasmid-mediated polymyxin resistance highlights the urgency to develop novel therapeutics to treat PDR K. pneumoniae. Bacteriophage (i.e. phage) has recently attracted substantial attention as a promising option to treat PDR bacterial infections; unfortunately, resistance to phage monotherapy in K. pneumoniae can rapidly develop. Optimal phage-antibiotic combinations provide a superior approach; however, there is a significant lack of knowledge on the pharmacokinetics/pharmacodynamics/toxicodynamics (PK/PD/TD) of phage therapy. This situation has severely hindered the optimization of phage therapy against bacterial ‘superbugs’ and limited their clinical utility. Traditional PK/PD/TD plays a critical role in optimizing antibiotic dosage regimens, but lacks systems and mechanistic information. Furthermore, antibiotic PK/PD/TD cannot be easily extrapolated to phage therapy, mainly due to their unique PK, host specificity and self-amplification. As phage-antibiotic synergy also depends on the dynamics of infection and host responses, innovative strategies incorporating systems pharmacology and host-pathogen-phage-antibiotic interactions have the significant potential to optimize their clinical use. Excitingly, we have isolated a number of phages with superior activity against PDR K. pneumoniae, and identified several novel phage-antibiotic combinations (e.g. with polymyxins) that synergistically kill PDR K. pneumoniae in vitro and in animals without any regrowth. Considering the urgent need to optimize phage therapy and minimize resistance to the last-line polymyxins, it is essential to develop superior phage-polymyxin combinations using a systems approach by integrating PK/PD/TD and multi-omics. Therefore, the Specific Aims of this application are: (1) To identify superior synergistic combinations of phage and polymyxin B, and evaluate their PK/PD/TD against PDR K. pneumoniae using in vitro and animal infection models; (2) To elucidate the mechanisms of synergistic bacterial killing by the superior phage-polymyxin combinations and the host- pathogen-phage-polymyxin interactions using correlative multi-omics; and (3) To develop novel QSP models integrating PK/PD/TD and multi-omics data for the superior phage-polymyxin combinations targeting PDR K. pneumoniae, and propose optimal dosage regimens for future clinical trials. Our innovative multi-disciplinary project will generate urgently needed information for rational optimization of novel phage-polymyxin combinations. Importantly, this proposal aligns perfectly with the present NIAID RFA for exploiting phages to kill ‘superbugs’ and responds in a timely manner to the recent 2019 NIAID Antibiotic Resistance Framework to protect global health security.

抗菌素耐药性(AMR)已成为全球对人类健康的最大威胁之一。潘杜鲁- 耐药(PDR)肺炎克雷伯菌已被世界卫生组织确定为全球三大肺炎克雷伯菌之一。 优先病原体迫切需要新的治疗方法。多粘菌素通常用作最后一种选择；然而， 质粒介导的多粘菌素耐药凸显了开发治疗PDR K的新疗法的紧迫性。 肺炎。噬菌体(即噬菌体)作为一种有希望的选择，最近引起了人们的极大关注 治疗PDR细菌感染；不幸的是，肺炎克雷伯菌对噬菌体单一疗法的抗药性可以迅速 发展。优化的噬菌体-抗生素组合提供了一种更好的方法；然而，有一个显著的不足 了解噬菌体疗法的pharmacokinetics/pharmacodynamics/toxicodynamics(PK/PD/TD)。这 这种情况严重阻碍了噬菌体疗法对细菌超级细菌的优化治疗，并限制了它们的 临床应用。传统的PK/PD/TD在优化抗生素给药方案中起着至关重要的作用，但缺乏 系统和机械信息。此外，抗生素PK/PD/TD不能容易地外推到噬菌体 治疗，主要是由于其独特的PK、宿主特异性和自我放大作用。作为噬菌体-抗生素的增效作用 取决于感染的动态和宿主的反应，结合系统的创新策略 药理学和宿主-病原体-噬菌体-抗生素的相互作用具有巨大的潜力来优化它们的 临床应用。令人兴奋的是，我们已经分离出了一些对肺炎克雷伯菌具有优异活性的噬菌体， 并确定了几种新的噬菌体-抗生素组合(例如与多粘菌素)，它们可以协同杀死PDR K。 肺炎在体外和在没有任何再生的动物中。考虑到优化噬菌体疗法的迫切需要 为了减少对最后一株多粘菌素的抗性，培育优势噬菌体-多粘菌素是必不可少的 通过整合PK/PD/TD和多组学的系统方法进行组合。因此，具体目标是 本应用的目的是：(1)鉴定噬菌体和多粘菌素B的优良协同组合，并对其进行评价 用体外和动物感染模型研究其抗肺炎克雷伯菌的PK/PD/TD；(2)阐明 噬菌体-多粘菌素优势组合与寄主的协同杀菌机理 病原菌-噬菌体-多粘菌素相互作用的相关多组学研究；(3)发展新的QSP模型 整合PK/PD/TD和多组学数据，获得针对PDR K的优势噬菌体-多粘菌素组合。 肺炎，并为未来的临床试验提出最佳剂量方案。我们创新的多学科 该项目将产生合理优化新型噬菌体多粘菌素所急需的信息 组合。重要的是，这个提议与目前的NIAID RFA完全一致，因为它利用噬菌体来杀死 并及时对最近的2019年NIAID抗生素耐药性框架做出反应 保护全球卫生安全。

项目成果

Comparative metabolomics revealed key pathways associated with the synergistic killing of multidrug-resistant Klebsiella pneumoniae by a bacteriophage-polymyxin combination.

比较代谢组学揭示了与通过噬菌体 - polymyxin组合通过协同杀死多药耐药性肺炎肺炎的关键途径。

• DOI: 10.1016/j.csbj.2021.12.039
• 发表时间: 2022
• 期刊: Computational and structural biotechnology journal
• 影响因子: 6
• 作者: Han ML;Nang SC;Lin YW;Zhu Y;Yu HH;Wickremasinghe H;Barlow CK;Creek DJ;Crawford S;Rao G;Dai C;Barr JJ;Chan K;Turner Schooley R;Velkov T;Li J
• 通讯作者: Li J