A Multi-scale systems pharmacology approach to TB therapy

结核病治疗的多尺度系统药理学方法

• 批准号: 9032152
• 负责人: Veronique Dartois
• 金额: $ 80.05万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9032152
• 关键词: Animal Model; Antibiotic Therapy; Antibiotic susceptibility; Antibiotics; Automobile Driving; Bacteria; Biological; Blood; Cells; Cessation of life; Clinical Trials; Collaborations; Collection; Complex; Computer Simulation; Data; Development; Differential Equation; Diffusion; Disease; Dose; Drug Combinations; Drug Exposure; Drug Kinetics; Drug resistance; Drug resistance in tuberculosis; Engineering; Extreme drug resistant tuberculosis; Frequencies; Genetic Programming; Grant; Granuloma; Human; Image; Immune; Immune response; Immunology; Infection; Information Systems; Length; Location; Lung diseases; Macrophage Activation; Mathematics; Microbiology; Modeling; Molecular; Mycobacterium tuberculosis; Organ; Oryctolagus cuniculus; Pathology; Penetration; Pharmaceutical Preparations; Pharmacodynamics; Pharmacology; Population; Positron-Emission Tomography; Primates; Process; Recommendation; Regimen; Research; Resistance development; Role; Schedule; Shelter facility; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Study models; System; Testing; Time; Tissues; Treatment Protocols; Tuberculosis; Variant; Work; base; biosafety level 3 facility; combinatorial; computer science; design; drug distribution; effective therapy; efficacy trial; human data; imaging modality; improved; in vivo; killings; mathematical model; multi-scale modeling; next generation; nonhuman primate; novel; novel strategies; pathogen; performance tests; pharmacodynamic model; public health relevance; pulmonary granuloma; research clinical testing; statistics; treatment strategy; tuberculosis drugs; tuberculosis granuloma; tuberculosis treatment; virtual

 DESCRIPTION (provided by applicant): Tuberculosis (TB) is a pulmonary disease resulting from infection with Mycobacterium tuberculosis (Mtb). TB is treatable but requires multiple antibiotics taken for >6 months, and the emergence of drug resistant Mtb (MDR and XDR-TB) has strained our current small arsenal of effective TB drugs. The situation is desperate considering there are 9 million new cases of active TB every year. The pathological hallmarks of TB are granulomas, dense spherical collections of immune cells that serve to protect the host but also isolate and shelter the pathogen. Granulomas pose a two- fold challenge to TB treatment: granulomas present a physical barrier for antibiotic penetration, and bacterial subpopulations with diminished antibiotic susceptibility emerge within granulomas. These difficulties contribute to the challenge of devising new and more effective treatment strategies for TB: getting the right drugs at the right concentration to the right location to kill the appropiate bacterial subpopulation. Processes that participate in these dynamics act across scales ranging from molecular (e.g. drug diffusion), cellular (e.g. macrophage activation), tissue (e.g. granuloma formation), organs (e.g. blood delivery of antibiotics) up to the entire host. To elaborate mechanisms driving dynamics in this complex system and to answer this vital challenge, we propose a multi-scale systems pharmacology approach. We use multi-scale computational modeling to track drug distributions in granulomas and development of resistance. We identify a novel bridge between the scale of host lung granulomas to the entire host scale where the disease manifests, and we use new approaches to predict better treatment options. We partner this with state-of-the-art experimental methods for imaging drug distribution within granulomas from humans, non-human primates (NHP) and rabbits. We perform Virtual Clinical Trials and test our prediction of a specific regimen for an efficacy trial in NHP models o TB with human-like pathology. To tackle this challenging proposition, we propose to: (1) Determine the spatial and temporal distributions of TB antibiotics within granulomas, and predict the development of resistance; (2) Identify optimal antibiotic treatment regimens for TB using genetic algorithms to narrow the combinatorial design space of antibiotics (e.g. drug classes, dosing, schedule); (3) Perform virtual clinical trials at a population level to test treatment regimens we identify, and test the optimal regimen in the NHP system against a standard regimen. Our outstanding interdisciplinary team and unique approach will allow for rapid assessment of new strategies and ultimately reduce the number of TB deaths world-wide.