Lesion-centric optimization of multidrug therapies for tuberculosis

修改项目摘要/摘要部分 结核病（TB）需要同时给予多种抗生素以根除异质性细菌种群。治疗持续时间从药物敏感结核病的6个月到广泛耐药结核病的24个月或更长。随着一些最近的药物批准和有前途的临床开发候选人，有希望缩短急需的治疗时间。然而，我们需要高通量的方法来对大量可能的药物组合进行排名，并将其减少到可行的数量，以便在临床试验中进行测试。目前，药物方案的优先顺序是基于小鼠模型的功效，尽管该模型易于使用，但仅适用于所有可能组合的一小部分。此外，在人类肺部病变中发现的差异药物敏感性细菌亚群在小鼠肺部中没有很好地重现。结核病的一个标志是病变的形成和结核分枝杆菌在药物治疗期间在各种病变类型中持续存在的同时的显著能力。这些难以治疗的细菌亚群导致疾病复发。因此，优先考虑新方案的关键是系统的，高质量的多药方案效力的体外测量和将体外测量与不同类型的类人病变的疗效联系起来的框架。要做到这一点，我们需要开发体外模型，捕捉关键的病变特异性应激源，并利用联合治疗的潜力，以确定协同作用的药物。我们建议通过开发一个数据驱动的管道来弥合这一差距，通过将药物作用的体外和体内测量与数学建模相结合来快速优先考虑药物方案。(1)我们将利用在各种生长条件下的药物组合的有效测量来直接比较组合药物在病变中的作用。(2)我们将利用非人灵长类动物的类人病理学特性来查询不同病变区室中的药物疗效。(3)我们将应用多尺度（分子，细胞，肉芽肿和器官尺度）数学建模的力量，以确定最能预测体内疗效的应激源。为了建立管道，我们将利用一种新的药物方案，该方案在临床试验中表现令人惊讶，但其组分在体外标准效价测定中具有拮抗作用：NiX-TB方案，包括贝达喹啉-pretomanid-利奈唑胺。一旦针对NiX-TB与标准治疗进行了验证，该管道将用于使用数据驱动的计算模拟来合理优化和重新发明NiX方案。

Modified Project Summary/Abstract Section Tuberculosis (TB) requires the simultaneous administration of multiple antibiotics to eradicate heterogeneous bacterial populations. Treatment duration ranges from 6 months for drug susceptible TB to 24 months and longer for extensively resistant TB. With a number of recent drug approvals and promising clinical development candidates, there is hope for much needed treatment shortening. However, we need high-throughput methods to rank the very large number of possible drug combinations and reduce them to a feasible number for testing in clinical trials. Currently, drug regimens are prioritized based on efficacy in the mouse model, which despite its ease of use, is available for only a small subset of all possible combinations. In addition, differentially drug-susceptible bacterial subpopulations that are found in human pulmonary lesions are not well recapitulated in murine lungs. A hallmark of TB is the formation of lesions and the coincident remarkable ability of Mycobacterium tuberculosis to persist in a variety of lesion types during drug treatment. These hard-to-treat bacterial subpopulations cause disease relapse. Therefore, key to prioritizing new regimens is systematic, high-quality in vitro measurement of multidrug regimen potencies and a framework that links in vitro measurements to efficacy in different types of human-like lesions. To do so requires that we develop in vitro models that capture key lesion-specific stressors and harness the potential of combination therapies to identify drugs that act synergistically. We propose to bridge this gap by developing a data-driven pipeline to rapidly prioritize drug regimens by combining in vitro and in vivo measurements of drug action with mathematical modeling. (1) We will utilize efficient measurement of drug combinations in a variety of growth conditions for direct comparison of combination drug effects in lesions. (2) We will leverage the human-like properties of pathology in non-human primates to query drug efficacy in distinct lesion compartments. (3) We will apply the power of multiscale (molecular, cellular, granuloma and organ scales) mathematical modeling to identify the stressors that are most predictive of in vivo efficacy. To build the pipeline, we will leverage a new drug regimen that has performed surprisingly well in clinical trials but the components of which antagonize in standard potency assays in vitro: the NiX-TB regimen comprising bedaquiline-pretomanid-linezolid. Once validated for NiX-TB versus standard of care, the pipeline will be used to rationally optimize and re-invent the NiX regimen using data-driven computational simulation.