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.

项目概要 脓肿分枝杆菌复合体（MABSC）是一组密切相关的非结核分枝杆菌， 主要导致患有潜在肺部疾病、免疫力受损或其他风险的人感染 因素。 MABSC 生物体对许多抗生素具有内在耐药性，治疗需要数月时间 多种抗生素的组合，通常具有显着的毒性和较低的治愈率。优化的难点之一 MABSC 感染治疗的关键在于缺乏强大的管道来确定多药治疗方案 可能比目前的护理标准更有效。优先考虑多种药物治疗方案的策略是 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 可供选择的药物有 220 种潜在的 3 种药物组合。 该项目建议开发 INDIGO（使用化学基因组学和直系学推断药物相互作用）， 机器学习建模方法，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。该项目的具体目标是： 1）构建 并通过收集药物处理的 MABSC 的转录组数据来完善 INDIGO-MABSC 模型 培养物，随后通过模型验证抗生素协同作用和拮抗作用的实验 通过迭代模型构建和测试进行细化； 2）评估最高预测的效果 MABSC 感染斑马鱼模型中的协同多药方案。 INDIGO 建模是模块化的，因此 新的代理可用，它们可能会被纳入模型中，以预测这些代理如何 最好将多种药物组合成多药治疗方案。该项目的总体目标是提供一个 简化的管道，用于确定可以推入高级临床前和临床试验的治疗方案 治疗 MABSC 感染。