A multifactorial pipeline to dissect combinatorial drug efficacy in Tuberculosis

摘要 多药耐药性的迅速蔓延，对新的联合用药产生了巨大的需求 治疗各种疾病的疗法，包括传染病和癌症。在一个 一个紧迫的例子是，耐多药结核病（TB）每年影响约50万人 迫切需要新的药物治疗方案。然而，确定新的治疗方案一直是 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，我们已经产生了一个实验接地，机器 学习算法INDIGO-MTB，预测结核药物的协同作用或拮抗作用 组合精度高。在这里，我们建议将INDIGO-MTB调整为多因素 这是一个分析组合药物疗效和推动结核病临床前治疗方案开发的管道。 我们将建立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 然后将结合联合收割机协同作用和疗效测量，以创建新的方案排名， 我们将在体外和结核病感染的小鼠模型中进行验证。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框架 其他需要新的多药疗法的疾病。

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.