title: A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment.
abstract: While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. Here we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. Finally, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.
authors.affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
authors.forename: Elsje
authors.initials: E
authors.lastname: Pienaar
authors.affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Nicholas A
authors.initials: NA
authors.lastname: Cilfone
authors.affiliation: Department of Pediatrics, Children׳s Hospital of Pittsburgh of the University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
authors.forename: Philana Ling
authors.initials: PL
authors.lastname: Lin
authors.affiliation: Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, USA.
authors.forename: Véronique
authors.initials: V
authors.lastname: Dartois
authors.affiliation: Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Joshua T
authors.initials: JT
authors.lastname: Mattila
authors.affiliation: Department of Health and Biomedical Sciences, Adventist University of Health Sciences, Orlando, FL, USA.
authors.forename: J Russell
authors.initials: JR
authors.lastname: Butler
authors.affiliation: Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: JoAnne L
authors.initials: JL
authors.lastname: Flynn
authors.affiliation: Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: kirschne@umich.edu.
authors.forename: Denise E
authors.initials: DE
authors.lastname: Kirschner
authors.affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA. Electronic address: linderma@umich.edu.
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Linderman
mesh_terms: D000818:Animals; D000900:Anti-Bacterial Agents / Q000493:pharmacokinetics / Q000494:pharmacology / Q000627:therapeutic use*; D000995:Antitubercular Agents / Q000493:pharmacokinetics / Q000494:pharmacology / Q000627:therapeutic use; D002138:Calibration; D003198:Computer Simulation; D004195:Disease Models, Animal; D004305:Dose-Response Relationship, Drug; D006099:Granuloma / Q000276:immunology / Q000473:pathology; D006801:Humans; D007109:Immunity / Q000187:drug effects*; D007538:Isoniazid / Q000493:pharmacokinetics / Q000627:therapeutic use; D051379:Mice; D008954:Models, Biological; D009169:Mycobacterium tuberculosis / Q000187:drug effects / Q000254:growth & development; D011323:Primates; D012293:Rifampin / Q000493:pharmacokinetics / Q000627:therapeutic use; D013997:Time Factors; D016896:Treatment Outcome; D014376:Tuberculosis / Q000188:drug therapy* / Q000276:immunology* / Q000382:microbiology / Q000473:pathology
chemical_list: D000900:Anti-Bacterial Agents; D000995:Antitubercular Agents; D007538:Isoniazid; D012293:Rifampin
keywords: Agent based model; Antibiotic gradients; Granuloma; Pharmacodynamics; Pharmacokinetics