A Multi-scale systems pharmacology approach to TB therapy

 简介(申请人提供)：结核病(TB)是一种因感染结核分枝杆菌(Mtb)而引起的肺部疾病。结核病是可以治疗的，但需要服用多种抗生素6个月，耐药结核杆菌(耐多药和广泛耐药结核病)的出现使我们目前有效的结核病药物库变得紧张。考虑到每年有900万新的活动性结核病病例，情况令人绝望。结核病的病理特征是肉芽肿，这是一种致密的球形免疫细胞集合，用于保护宿主，但也隔离和庇护病原体。肉芽肿对结核病的治疗构成了双重挑战：肉芽肿是抗生素渗透的物理障碍，肉芽肿内出现了抗生素敏感性降低的细菌亚群。这些困难加剧了设计新的和更有效的结核病治疗策略的挑战：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。参与这些动态过程的范围从分子(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。为了阐述在这一复杂系统中驱动动力学的机制并回答这一关键挑战，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肉芽肿中的药物分布和耐药性的发展。我们确定了宿主肺肉芽肿的规模与疾病表现的整个宿主规模之间的新桥梁，并使用新的方法来预测更好的治疗方案。我们与最先进的实验方法合作，对人类、非人类灵长类动物(NHP)和兔子的肉芽肿内的药物分布进行成像。我们执行虚拟临床试验，并在具有类似人类病理的NHP模型中测试我们对特定方案的有效性试验的预测。为了应对这一具有挑战性的命题，我们建议：(1)确定结核抗生素在肉芽肿中的时空分布，并预测耐药性的发展；(2)使用遗传算法来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、剂量、时间表)；(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，并在NHP系统中与标准方案进行比较。我们优秀的跨学科团队和独特的方法将使我们能够快速评估新的战略，并最终减少世界各地的结核病死亡人数。

 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.