A Multi-scale systems pharmacology approach to TB therapy

结核病治疗的多尺度系统药理学方法

• 批准号: 9762970
• 负责人: Veronique Dartois
• 金额: $ 71.73万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762970
• 关键词: Animal Model; Antibiotic Therapy; Antibiotic susceptibility; Antibiotics; Automobile Driving; Bacteria; Biological; Blood; Cells; Cessation of life; Collaborations; Collection; Complex; Computer Simulation; Data; Differential Equation; Diffusion; Disease; Dose; Drug Combinations; Drug Exposure; Drug Kinetics; Drug resistance; Drug resistance in tuberculosis; Engineering; Extreme drug resistant tuberculosis; Frequencies; Genetic Programming; Geographic Information Systems; Grant; Granuloma; Human; Image; Immune; Immune response; Immunology; Infection; Length; Location; Lung diseases; Macrophage Activation; Mathematics; Microbiology; Modeling; Molecular; Multidrug-Resistant Tuberculosis; Mycobacterium tuberculosis; Organ; Oryctolagus cuniculus; Pathologic; Pathology; Penetration; Pharmaceutical Preparations; Pharmacodynamics; Pharmacology; Population; Positron-Emission Tomography; Primates; Process; Recommendation; Regimen; Research; Resistance development; Role; Schedule; Shelter facility; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Study models; System; Testing; Time; Tissues; Treatment Protocols; Tuberculosis; Variant; Work; base; biosafety level 3 facility; combinatorial; computer science; design; drug development; drug distribution; effective therapy; efficacy trial; human data; imaging modality; improved; in vivo; mathematical model; multi-scale modeling; next generation; nonhuman primate; novel; novel strategies; pathogen; performance tests; public health relevance; pulmonary granuloma; research clinical testing; serial imaging; statistics; treatment strategy; tuberculosis drugs; tuberculosis granuloma; tuberculosis treatment; virtual clinical trial

 DESCRIPTION (provided by applicant): Tuberculosis (TB) is a pulmonary disease resulting from infection with Mycobacterium tuberculosis (Mtb). TB is treatable but requires multiple antibiotics taken for >6 months, and the emergence of drug resistant Mtb (MDR and XDR-TB) has strained our current small arsenal of effective TB drugs. The situation is desperate considering there are 9 million new cases of active TB every year. The pathological hallmarks of TB are granulomas, dense spherical collections of immune cells that serve to protect the host but also isolate and shelter the pathogen. Granulomas pose a two- fold challenge to TB treatment: granulomas present a physical barrier for antibiotic penetration, and bacterial subpopulations with diminished antibiotic susceptibility emerge within granulomas. These difficulties contribute to the challenge of devising new and more effective treatment strategies for TB: getting the right drugs at the right concentration to the right location to kill the appropiate bacterial subpopulation. Processes that participate in these dynamics act across scales ranging from molecular (e.g. drug diffusion), cellular (e.g. macrophage activation), tissue (e.g. granuloma formation), organs (e.g. blood delivery of antibiotics) up to the entire host. To elaborate mechanisms driving dynamics in this complex system and to answer this vital challenge, we propose a multi-scale systems pharmacology approach. We use multi-scale computational modeling to track drug distributions in granulomas and development of resistance. We identify a novel bridge between the scale of host lung granulomas to the entire host scale where the disease manifests, and we use new approaches to predict better treatment options. We partner this with state-of-the-art experimental methods for imaging drug distribution within granulomas from humans, non-human primates (NHP) and rabbits. We perform Virtual Clinical Trials and test our prediction of a specific regimen for an efficacy trial in NHP models o TB with human-like pathology. To tackle this challenging proposition, we propose to: (1) Determine the spatial and temporal distributions of TB antibiotics within granulomas, and predict the development of resistance; (2) Identify optimal antibiotic treatment regimens for TB using genetic algorithms to narrow the combinatorial design space of antibiotics (e.g. drug classes, dosing, schedule); (3) Perform virtual clinical trials at a population level to test treatment regimens we identify, and test the optimal regimen in the NHP system against a standard regimen. Our outstanding interdisciplinary team and unique approach will allow for rapid assessment of new strategies and ultimately reduce the number of TB deaths world-wide.

 描述（由申请人提供）：结核病（TB）是一种由结核分枝杆菌（Mtb）感染引起的肺部疾病，结核病是可以治疗的，但需要服用多种抗生素超过 6 个月，并且会出现耐药 Mtb（MDR 和 XDR-）。考虑到每年有 900 万新的活动性结核病病例，目前的有效结核病药物库已经紧张。结核病的标志是肉芽肿，这是免疫细胞的密集球形集合，可以保护宿主，但也可以隔离和庇护病原体。肉芽肿对结核病治疗提出了双重挑战：肉芽肿为抗生素渗透提供了物理屏障，而细菌亚群则具有抗菌作用。肉芽肿内出现的抗生素敏感性降低给设计新的、更有效的结核病治疗策略带来了挑战：将正确的药物以正确的浓度输送到正确的位置来杀死病原体。参与这些动态的过程的作用范围从分子（例如药物扩散）、细胞（例如巨噬细胞激活）、组织（例如肉芽肿形成）、器官（例如抗生素的血液输送）到整个宿主。为了详细阐述驱动这种复杂颗粒系统动力学的机制并应对这一重大挑战，我们提出了一种多尺度系统药理学方法，我们使用多尺度计算模型来跟踪 omas 中的药物分布。我们确定了宿主肺肉芽肿的范围与疾病表现的整个宿主范围之间的新桥梁，并且我们使用新的方法来预测更好的治疗方案。我们进行了虚拟临床试验，并在具有类人病理学的 NHP 模型中测试了我们对特定方案的疗效试验的预测。这个具有挑战性的提议，我们提议：（1）确定肉芽肿内结核病抗生素的空间和时间分布，并预测耐药性的发展；（2）使用遗传算法确定结核病的最佳抗生素治疗方案，以缩小抗生素的组合设计空间（例如药物类别） (3) 在人群水平上进行虚拟临床试验，以测试我们确定的治疗方案，并根据标准方案测试 NHP 系统中的最佳方案。允许快速评估新策略并最终减少全世界结核病死亡人数。