A pipeline for prioritizing and evaluating multidrug regimens for Mycobacterium abscessus

用于优先考虑和评估脓肿分枝杆菌多药治疗方案的管道

• 批准号: 9898274
• 负责人: Rafael E Hernandez
• 金额: $ 21.29万
• 依托单位: SEATTLE CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-21 至 2021-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9898274
• 关键词: Adjuvant; American; Amikacin; Animal Model; Antibiotic Therapy; Antibiotics; Bacteria; Biological Assay; Biological Models; Bronchiectasis; Caring; Chest; Clinical Treatment; Clinical Trials; Collection; Combined Antibiotics; Complex; Computer Models; Cystic Fibrosis; Data; Drug Antagonism; Drug Combinations; Drug Interactions; Drug Synergism; Drug resistance; Environment; Epidemiology; Escherichia coli; Exhibits; Exposure to; Fishes; Foundations; Future; Gene Expression Profiling; Genetic Transcription; Genomics; Goals; Guidelines; High Prevalence; Human; Imipenem; Immunity; In Vitro; Individual; Infection; Injections; Label; Larva; Lung; Lung diseases; Machine Learning; Macrolides; Medical; Modeling; Molecular; Mycobacterium Infections; Mycobacterium abscessus; Mycobacterium avium Complex; Mycobacterium tuberculosis; New Agents; Organism; Outcome; Patients; Persons; Pharmaceutical Preparations; Pharmacotherapy; Population; Preclinical Testing; Predisposition; Prevalence; Regimen; Resistance; Risk Factors; Societies; Testing; Toxic effect; Training; Treatment-related toxicity; Triage; Triplet Multiple Birth; Tuberculosis; Veins; Vertebral column; Work; Zebrafish; comorbidity; cystic fibrosis patients; data registry; design; drug candidate; drug testing; high risk; improved outcome; in silico; in vitro activity; in vivo; in vivo Model; model building; non-tuberculosis mycobacteria; novel; pathogen; patient screening; pre-clinical; preclinical trial; predictive modeling; resistant strain; response; standard of care; success; synergism; tool; transcriptomics; transmission process; treatment optimization

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.

项目摘要 腹肌分枝杆菌（MABSC）是一组密切相关的无结枝分枝杆菌， 这主要引起潜在肺部疾病，免疫力损害或其他风险的人感染 因素。 MABSC生物本质上对许多抗生素具有抗药性，并且需要数月 多种抗生素的毒性，通常具有明显的毒性和低的治疗速率。优化的困难之一 MABSC感染的治疗是缺乏可识别多药方案的强大管道 可能比目前的护理标准更有效。优先考虑多药方案的策略是 必要的，因为要测试的组合数量可能是压倒性的。例如，甚至只有12个 从中选择的药物有220个潜在的3种药物组合。 该项目建议开发靛蓝（使用化学基因组学和矫正学推断药物相互作用）， 一种机器学习建模方法，以识别具有强大潜在协同活性的药物组合 针对MABSC并在动物模型中测试这些方案。该项目的具体目的是：1）建造 并通过从药物治疗的MABSC收集转录组数据来完善Indigo-MABSC模型 培养物，随后对抗生素协同和拮抗作用的实验验证，然后是模型 迭代型建筑和测试的迭代回合； 2）评估最高预测的功效 MABSC感染的斑马鱼模型中的协同多药方案。靛蓝建模是模块化的，所以 新代理人可用 代理可能最好合并为多药方案。该项目的总体目标是提供 简化的管道识别可以推入高级临床和临床试验的方案的管道 MABSC感染的治疗。