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: 9335957
• 负责人: Veronique Dartois
• 金额: $ 71.41万
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9335957
• 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 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; 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; killings; 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（由适用提供）：结核病（TB）是一种肺部疾病，是由结核分枝杆菌（MTB）感染引起的。结核病是可以治疗的，但需要多种抗生素> 6个月，并且抗药性MTB（MDR和XDR-TB）的出现使我们目前的有效TB药物的小武器库紧张。考虑到每年有900万例新的活跃结核病病例，情况迫切了。结核病的病理标志是颗粒，是保护宿主但也隔离病原体的免疫细胞的密集球形集合。肉芽瘤对结核病治疗提出了两倍的挑战：肉芽瘤具有抗生素渗透的物理障碍，细菌亚群体在肉芽肿内出现抗生素易感性降低。这些困难有助于制定新的和更有效的TB治疗策略的挑战：将正确的药物以正确的浓度送到正确的位置，以杀死适当的细菌亚群。参与这些动力学的过程跨越量表，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（例如抗生素的血液递送）到整个宿主到整个宿主。为了详细说明在这个复杂系统中推动动态的机制并回答这一至关重要的挑战，我们提出了一种多规模系统药理学方法。我们使用多尺度计算建模来跟踪颗粒中的药物分布和抗药性的发展。我们确定了宿主肺肉芽肿的尺度之间的新桥梁到疾病表现的整个宿主尺度上，我们使用新方法来预测更好的治疗选择。我们将其与最先进的实验方法合作，以成像人类肉芽肿的药物分布，非人类隐私。 （NHP）和兔子。我们进行虚拟临床试验，并测试我们对具有人类病理学的NHP模型中有效试验的特定方案的预测。为了解决这一挑战提案，我们建议：（1）确定肉芽肿内结核病抗生素的空间和临时分布，并预测抗性的发展； （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，剂量，时间表）； （3）在人群水平上执行虚拟临床试验，以测试我们识别的治疗方案，并针对NHP系统中的最佳方案测试针对标准方案的最佳方案。我们出色的跨学科团队和独特的方法将允许快速评估新策略，并最终减少全球结核病死亡人数。