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.

Public Health Relevance

Tuberculosis causes 8 million deaths per year with 2 billion people infected with the bacteria. Treatment is available, but it is long (6-9 months) and requires multiple antibiotics. Rapid development of drug resistance is becoming an increasing threat. We propose a systems pharmacology approach that integrates state-of-the-art computational modeling and experimental data from humans, primates and rabbits to identify optimal antibiotics and regimens to improve treatment.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01HL131072-03
Application #
9540928
Study Section
Special Emphasis Panel (ZEB1)
Program Officer
Caler, Elisabet V
Project Start
2016-09-01
Project End
2020-08-31
Budget Start
2018-09-01
Budget End
2019-08-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Microbiology/Immun/Virology
Type
Schools of Medicine
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Wong, Eileen A; Joslyn, Louis; Grant, Nicole L et al. (2018) Low Levels of T Cell Exhaustion in Tuberculous Lung Granulomas. Infect Immun 86:
Cicchese, Joseph M; Evans, Stephanie; Hult, Caitlin et al. (2018) Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology. Immunol Rev 285:147-167
Joslyn, Louis R; Pienaar, Elsje; DiFazio, Robert M et al. (2018) Integrating Non-human Primate, Human, and Mathematical Studies to Determine the Influence of BCG Timing on H56 Vaccine Outcomes. Front Microbiol 9:1734
Pienaar, Elsje; Linderman, Jennifer J; Kirschner, Denise E (2018) Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. PLoS One 13:e0196322
Cicchese, Joseph M; Pienaar, Elsje; Kirschner, Denise E et al. (2017) Applying optimization algorithms to tuberculosis antibiotic treatment regimens. Cell Mol Bioeng 10:523-535
Kirschner, Denise; Pienaar, Elsje; Marino, Simeone et al. (2017) A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment. Curr Opin Syst Biol 3:170-185
Pienaar, Elsje; Sarathy, Jansy; Prideaux, Brendan et al. (2017) Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach. PLoS Comput Biol 13:e1005650
Warsinske, Hayley C; Pienaar, Elsje; Linderman, Jennifer J et al. (2017) Deletion of TGF-?1 Increases Bacterial Clearance by Cytotoxic T Cells in a Tuberculosis Granuloma Model. Front Immunol 8:1843
Warsinske, Hayley C; DiFazio, Robert M; Linderman, Jennifer J et al. (2017) Identifying mechanisms driving formation of granuloma-associated fibrosis during Mycobacterium tuberculosis infection. J Theor Biol 429:1-17
Warsinske, Hayley C; Wheaton, Amanda K; Kim, Kevin K et al. (2016) Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis. Front Pharmacol 7:183

Showing the most recent 10 out of 11 publications