A pipeline for prioritizing and evaluating multidrug regimens for Mycobacterium abscessus

项目总结 脓肿分枝杆菌复合体(MABSC)是一组亲缘关系密切的非结核分枝杆菌。 主要导致潜在肺部疾病、免疫力受损或有其他风险的人感染 各种因素。MABSC生物对许多抗生素具有内在抗药性，治疗需要数月时间。 使用多种抗生素，通常毒性大，治愈率低。优化的困难之一是 MABSC感染的治疗缺乏一个强大的管道来确定多药方案， 可能比目前的护理标准更有效。对多种药物方案进行优先排序的一个策略是 这是必要的，因为要测试的组合数量可能会非常多。例如，即使只有12个 有220种潜在的3种药物组合可供选择。 该项目建议开发Indigo(使用化学基因组学和整形学推断药物相互作用)， 一种机器学习建模方法，用于识别具有较强潜在协同活性的药物组合 对抗MABSC，并在动物模型中测试这些方案。这个项目的具体目标是：1)建造 并通过收集药物治疗的MABSC的转录数据来提炼Indigo-MABSC模型 培养，随后进行抗生素协同和拮抗的实验验证，然后建立模型 通过反复几轮的模型构建和测试进行改进；2)评估TOP预测的效果 斑马鱼MABSC感染模型中的多药协同治疗方案。Indigo建模是模块化的，因此 新的代理变得可用，他们可能会被合并到模型中，以预测这些 药物可能最好结合成多种药物方案。该项目的总体目标是提供一个 简化了确定可推向高级临床前和临床试验的方案的流程 MABSC感染的治疗。

PROJECT SUMMARY Mycobacterium abscessus complex (MABSC) is a group of closely related nontuberculous mycobacteria, which primarily cause infections in persons with underlying lung disease, compromised immunity or other risk factors. MABSC organisms are intrinsically resistant to many antibiotics and treatment requires many months of multiple antibiotics, often with significant toxicity and low cure rates. One of the difficulties in optimizing the treatment of MABSC infections is the lack of a robust pipeline for the identification of multidrug regimens that may be more efficacious than the current standard of care. A strategy for prioritizing multidrug regimens is necessary, because the number of combinations to test can be overwhelming. For example, with even just 12 drugs to select from there are 220 potential 3 drug combinations. This project proposes to develop INDIGO (INferring Drug Interactions using chemo-Genomics and Orthology), a machine learning modeling approach, to identify drug combinations with strong potential synergistic activity against MABSC and to test these regimens in an animal model. The specific aims of this project are: 1) Build and refine an INDIGO-MABSC model through the collection of transcriptomic data from drug treated MABSC cultures, subsequent experimental verification of antibiotic synergy and antagonism followed by model refinement with iterative rounds of model building and testing; 2) Assess the efficacy of top predicted synergistic multidrug regimens in a zebrafish model for MABSC infection. INDIGO modeling is modular, so as new agents become available they may be incorporated into the model to make predictions for how these agents may be best combined into multidrug regimens. The overall goal of this project is to provide a streamlined pipeline for identifying regimens that can be pushed into advanced pre-clinical and clinical trials for treatment of MABSC infections.