A multifactorial pipeline to dissect combinatorial drug efficacy in Tuberculosis

摘要 多药耐药的快速蔓延催生了新的联合用药需求 治疗各种疾病的疗法，包括传染病和癌症。合而为一 紧迫的例子是，耐多药结核病(TB)每年影响约50万人 而且迫切需要新的药物治疗方案。然而，确定新的养生法一直是 令人望而生畏的部分原因是无法在大量可能的药物中确定优先顺序 组合。为了满足这一需求，我们开发了一台试验性接地的机器 预测结核病药物的协同或拮抗作用的学习算法Indigo-MTB 具有高精度的组合。在这里，我们建议将Indigo-MTB改造为多因素 分析组合药物疗效和推动结核病临床前方案开发的管道。 我们将内置并验证在压力环境下预测药物相互作用的能力 模拟结核病感染的条件，并提取药物相互作用的分子机制。我们 然后将结合协同效应和疗效测量来创建新的方案排名，这 我们将在体外和小鼠的结核病感染模型中进行验证。总之，我们的工作将 建立快速评估结核病药物组合的工具和应用该工具的框架 在需要新的多种药物疗法的其他情况下的方法。

ABSTRACT The rapid spread of multi-drug resistance has created a great need for new combination therapies to treat a variety of conditions, including infectious diseases and cancer. In one pressing example, multidrug resistant tuberculosis (TB) affects about 500,000 people each year and novel drug regimens are sorely needed. However, identifying new regimens has been daunting in part due to the inability to prioritize among a very large number of possible drug combinations. To address this need, we have generated an experimentally grounded, machine learning algorithm, INDIGO-MTB, which predicts the synergy or antagonism of TB drug combinations with high accuracy. Here we propose to adapt INDIGO-MTB into a multifactorial pipeline to dissect combinatorial drug efficacy and drive preclinical regimen development for TB. We will build in and validate the ability to predict drug interactions under stressful environmental conditions that mimic TB infection, and extract molecular mechanisms of drug interactions. We will then combine synergy and efficacy measurements to create new regimen rankings, which we will validate both in vitro and in a mouse model of TB infection. Altogether, our work will establish a tool for rapid assessment of TB drug combinations and a framework for applying this approach to other conditions where new multidrug therapies are needed.