skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: In silico evaluation and exploration of antibiotic tuberculosis treatment regimens

Abstract

Improvement in tuberculosis treatment regimens requires selection of antibiotics and dosing schedules from a large design space of possibilities. Incomplete knowledge of antibiotic and host immune dynamics in tuberculosis granulomas impacts clinical trial design and success, and variations among clinical trials hamper side-by-side comparison of regimens. Our objective is to systematically evaluate the efficacy of isoniazid and rifampin regimens, and identify modifications to these antibiotics that improve treatment outcomes. We pair a spatio-temporal computational model of host immunity with pharmacokinetic and pharmacodynamic data on isoniazid and rifampin. The model is calibrated to plasma pharmacokinetic and granuloma bacterial load data from non-human primate models of tuberculosis and to tissue and granuloma measurements of isoniazid and rifampin in rabbit granulomas. We predict the efficacy of regimens containing different doses and frequencies of isoniazid and rifampin. We predict impacts of pharmacokinetic/pharmacodynamic modifications on antibiotic efficacy. We demonstrate that suboptimal antibiotic concentrations within granulomas lead to poor performance of intermittent regimens compared to daily regimens. Improvements from dose and frequency changes are limited by inherent antibiotic properties, and we propose that changes in intracellular accumulation ratios and antimicrobial activity would lead to the most significant improvements in treatment outcomes. Results suggest that an increasedmore » risk of drug resistance in fully intermittent as compared to daily regimens arises from higher bacterial population levels early during treatment. In conclusion, our systems pharmacology approach complements efforts to accelerate tuberculosis therapeutic development.« less

Authors:
 [1];  [2];  [3];  [4]
  1. Univ. of Michigan, Ann Arbor, MI (United States); Univ. of Michigan Medical School, Ann Arbor, MI (United States)
  2. The State Univ. of New Jersey, New Brunswick, NJ (United States)
  3. Univ. of Michigan, Ann Arbor, MI (United States)
  4. Univ. of Michigan Medical School, Ann Arbor, MI (United States)
Publication Date:
Research Org.:
Univ. of California (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1241146
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
BMC Systems Biology
Additional Journal Information:
Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 1752-0509
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; computational model; pharmacokinetic/pharmacodynamic; isoniazid; Rifampin; tissue distribution

Citation Formats

Pienaar, Elsje, Dartois, Véronique, Linderman, Jennifer J., and Kirschner, Denise E. In silico evaluation and exploration of antibiotic tuberculosis treatment regimens. United States: N. p., 2015. Web. doi:10.1186/s12918-015-0221-8.
Pienaar, Elsje, Dartois, Véronique, Linderman, Jennifer J., & Kirschner, Denise E. In silico evaluation and exploration of antibiotic tuberculosis treatment regimens. United States. doi:10.1186/s12918-015-0221-8.
Pienaar, Elsje, Dartois, Véronique, Linderman, Jennifer J., and Kirschner, Denise E. Sat . "In silico evaluation and exploration of antibiotic tuberculosis treatment regimens". United States. doi:10.1186/s12918-015-0221-8. https://www.osti.gov/servlets/purl/1241146.
@article{osti_1241146,
title = {In silico evaluation and exploration of antibiotic tuberculosis treatment regimens},
author = {Pienaar, Elsje and Dartois, Véronique and Linderman, Jennifer J. and Kirschner, Denise E.},
abstractNote = {Improvement in tuberculosis treatment regimens requires selection of antibiotics and dosing schedules from a large design space of possibilities. Incomplete knowledge of antibiotic and host immune dynamics in tuberculosis granulomas impacts clinical trial design and success, and variations among clinical trials hamper side-by-side comparison of regimens. Our objective is to systematically evaluate the efficacy of isoniazid and rifampin regimens, and identify modifications to these antibiotics that improve treatment outcomes. We pair a spatio-temporal computational model of host immunity with pharmacokinetic and pharmacodynamic data on isoniazid and rifampin. The model is calibrated to plasma pharmacokinetic and granuloma bacterial load data from non-human primate models of tuberculosis and to tissue and granuloma measurements of isoniazid and rifampin in rabbit granulomas. We predict the efficacy of regimens containing different doses and frequencies of isoniazid and rifampin. We predict impacts of pharmacokinetic/pharmacodynamic modifications on antibiotic efficacy. We demonstrate that suboptimal antibiotic concentrations within granulomas lead to poor performance of intermittent regimens compared to daily regimens. Improvements from dose and frequency changes are limited by inherent antibiotic properties, and we propose that changes in intracellular accumulation ratios and antimicrobial activity would lead to the most significant improvements in treatment outcomes. Results suggest that an increased risk of drug resistance in fully intermittent as compared to daily regimens arises from higher bacterial population levels early during treatment. In conclusion, our systems pharmacology approach complements efforts to accelerate tuberculosis therapeutic development.},
doi = {10.1186/s12918-015-0221-8},
journal = {BMC Systems Biology},
number = 1,
volume = 9,
place = {United States},
year = {2015},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 13 works
Citation information provided by
Web of Science

Figures / Tables:

Fig. 1 Fig. 1: a Computational model. Granuloma formation and function, plasma pharmacokinetics (PK), tissue PK and pharmacodynamics (PD) are integrated into a single computational framework. Cell recruitment, movement, states (e.g. activated), actions (e.g. tumor necrosis factor secretion), interactions (e.g. macrophage activation) and death of macrophages and T-cells are followed over time,more » with granuloma formation and function as emergent behavior. Bacteria are represented as three subpopulations: intracellular, extracellular replicating and extracellular non-replicating (i.e. residing in caseous areas). Plasma PK equations determine the concentration of antibiotic at vascular source sites on the simulation grid. Antibiotics permeate the vascular wall, diffuse within the granuloma, penetrate host cells and kill bacteria based on local intracellular and extracellular concentrations. Further model details are available in [20]. Artwork in (a) was constructed by combining and modifying artwork elements from Servier Medical Art (http://www.servier.com/Powerpoint-image-bank) provided under the Creative Commons Unported License 3.0. b Simulated antibiotic dosing regimens. Simulated infections are initiated at day 0 and granulomas evolve for the first 100 days (red bars). Regimens 1a, 1b, 2a and 3a, recommended by the CDC/WHO [22], are composed of different doses and frequencies. Regimens 1b and 2a switch from daily to 2 doses per week after 60 and 14 days of treatment, respectively. Each regimen is implemented with INH, RIF and INH + RIF« less

Save / Share:

Works referenced in this record:

Dosage Regimens of Antibacterials: Implications of a Pharmacokinetic/Pharmacodynamic Model
journal, January 1996


Resistant mutants of Mycobacterium tuberculosis selected in vitro do not reflect the in vivo mechanism of isoniazid resistance
journal, July 2009

  • Bergval, Indra L.; Schuitema, Anja R. J.; Klatser, Paul R.
  • Journal of Antimicrobial Chemotherapy, Vol. 64, Issue 3
  • DOI: 10.1093/jac/dkp237

A Four-Month Gatifloxacin-Containing Regimen for Treating Tuberculosis
journal, October 2014

  • Merle, Corinne S.; Fielding, Katherine; Sow, Omou Bah
  • New England Journal of Medicine, Vol. 371, Issue 17
  • DOI: 10.1056/NEJMoa1315817

The path of anti-tuberculosis drugs: from blood to lesions to mycobacterial cells
journal, February 2014


Multiscale Computational Modeling Reveals a Critical Role for TNF-α Receptor 1 Dynamics in Tuberculosis Granuloma Formation
journal, February 2011

  • Fallahi-Sichani, Mohammad; El-Kebir, Mohammed; Marino, Simeone
  • The Journal of Immunology, Vol. 186, Issue 6
  • DOI: 10.4049/jimmunol.1003299

Dosing Schedules of 6-Month Regimens and Relapse for Pulmonary Tuberculosis
journal, November 2006

  • Chang, Kwok C.; Leung, Chi C.; Yew, Wing W.
  • American Journal of Respiratory and Critical Care Medicine, Vol. 174, Issue 10
  • DOI: 10.1164/rccm.200605-637OC

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment
journal, February 2015

  • Pienaar, Elsje; Cilfone, Nicholas A.; Lin, Philana Ling
  • Journal of Theoretical Biology, Vol. 367
  • DOI: 10.1016/j.jtbi.2014.11.021

Pharmacokinetic Evaluation of the Penetration of Antituberculosis Agents in Rabbit Pulmonary Lesions
journal, October 2011

  • Kjellsson, Maria C.; Via, Laura E.; Goh, Anne
  • Antimicrobial Agents and Chemotherapy, Vol. 56, Issue 1
  • DOI: 10.1128/AAC.05208-11

Radiologic Responses in Cynomolgus Macaques for Assessing Tuberculosis Chemotherapy Regimens
journal, June 2013

  • Lin, Philana Ling; Coleman, Teresa; Carney, Jonathan P. J.
  • Antimicrobial Agents and Chemotherapy, Vol. 57, Issue 9
  • DOI: 10.1128/AAC.00277-13

Multidrug-Resistant Tuberculosis Not Due to Noncompliance but to Between-Patient Pharmacokinetic Variability
journal, October 2011

  • Srivastava, Shashikant; Pasipanodya, Jotam G.; Meek, Claudia
  • The Journal of Infectious Diseases, Vol. 204, Issue 12
  • DOI: 10.1093/infdis/jir658

Acceptability of community and health facility-based directly observed treatment of tuberculosis in Tanzanian urban setting
journal, October 2006


A medicinal chemists’ guide to the unique difficulties of lead optimization for tuberculosis
journal, September 2013

  • Dartois, Véronique; Barry, Clifton E.
  • Bioorganic & Medicinal Chemistry Letters, Vol. 23, Issue 17
  • DOI: 10.1016/j.bmcl.2013.07.006

A Dose-Ranging Trial to Optimize the Dose of Rifampin in the Treatment of Tuberculosis
journal, May 2015

  • Boeree, Martin J.; Diacon, Andreas H.; Dawson, Rodney
  • American Journal of Respiratory and Critical Care Medicine, Vol. 191, Issue 9
  • DOI: 10.1164/rccm.201407-1264OC

Treatment of tuberculosis and optimal dosing schedules
journal, December 2010


Synergy between Individual TNF-Dependent Functions Determines Granuloma Performance for Controlling Mycobacterium tuberculosis Infection
journal, March 2009

  • Ray, J. Christian J.; Flynn, JoAnne L.; Kirschner, Denise E.
  • The Journal of Immunology, Vol. 182, Issue 6
  • DOI: 10.4049/jimmunol.0802297

Shortening Treatment for Tuberculosis — Back to Basics
journal, October 2014

  • Warner, Digby F.; Mizrahi, Valerie
  • New England Journal of Medicine, Vol. 371, Issue 17
  • DOI: 10.1056/NEJMe1410977

Differential Risk of Tuberculosis Reactivation among Anti-TNF Therapies Is Due to Drug Binding Kinetics and Permeability
journal, February 2012

  • Fallahi-Sichani, Mohammad; Flynn, JoAnne L.; Linderman, Jennifer J.
  • The Journal of Immunology, Vol. 188, Issue 7
  • DOI: 10.4049/jimmunol.1103298

A Nested Case–Control Study on Treatment-related Risk Factors for Early Relapse of Tuberculosis
journal, November 2004

  • Chang, Kwok C.; Leung, Chi C.; Yew, Wing W.
  • American Journal of Respiratory and Critical Care Medicine, Vol. 170, Issue 10
  • DOI: 10.1164/rccm.200407-905OC

Metronidazole prevents reactivation of latent Mycobacterium tuberculosis infection in macaques
journal, July 2012

  • Lin, P. L.; Dartois, V.; Johnston, P. J.
  • Proceedings of the National Academy of Sciences, Vol. 109, Issue 35
  • DOI: 10.1073/pnas.1121497109

Intensified regimen containing rifampicin and moxifloxacin for tuberculous meningitis: an open-label, randomised controlled phase 2 trial
journal, January 2013


DFT Based QSAR/QSPR Models in the Development of Novel Anti-tuberculosis Drugs Targeting Mycobacterium tuberculosis
journal, November 2013


Advances in Immunotherapy for Tuberculosis Treatment
journal, December 2009

  • Churchyard, Gavin J.; Kaplan, Gilla; Fallows, Dorothy
  • Clinics in Chest Medicine, Vol. 30, Issue 4
  • DOI: 10.1016/j.ccm.2009.08.009

Anti-vascular endothelial growth factor treatment normalizes tuberculosis granuloma vasculature and improves small molecule delivery
journal, January 2015

  • Datta, Meenal; Via, Laura E.; Kamoun, Walid S.
  • Proceedings of the National Academy of Sciences, Vol. 112, Issue 6
  • DOI: 10.1073/pnas.1424563112

Association of slow N-acetyltransferase 2 profile and anti-TB drug-induced hepatotoxicity in patients from Southern Brazil
journal, April 2008

  • Possuelo, L. G.; Castelan, J. A.; de Brito, T. C.
  • European Journal of Clinical Pharmacology, Vol. 64, Issue 7
  • DOI: 10.1007/s00228-008-0484-8

Drug Forgiveness and Interpatient Pharmacokinetic Variability in Tuberculosis
journal, October 2011


New antituberculosis drugs, regimens, and adjunct therapies: needs, advances, and future prospects
journal, April 2014

  • Zumla, Alimuddin I.; Gillespie, Stephen H.; Hoelscher, Michael
  • The Lancet Infectious Diseases, Vol. 14, Issue 4
  • DOI: 10.1016/S1473-3099(13)70328-1

Direct observation of treatment for tuberculosis: a randomized controlled trial of community health workers versus family members
journal, May 2004


Isoniazid Pharmacokinetics-Pharmacodynamics in an Aerosol Infection Model of Tuberculosis
journal, July 2004


Shortening treatment of tuberculosis: lessons from fluoroquinolone trials
journal, February 2015


Controlled-release approaches towards the chemotherapy of tuberculosis
journal, October 2012

  • Saifullah, Bullo; Hussein, Mohd Zobir; Hussein Al Ali, Samer
  • International Journal of Nanomedicine
  • DOI: 10.2147/IJN.S34996

Bactericidal Activity of Streptomycin, Isoniazid, Rifampin, Ethambutol, and Pyrazinamide Alone and In Combination Against Mycobacterium Tuberculosis 1
journal, October 1977

  • Dickinson, Jean M.; Aber, V. R.; Mitchison, D. A.
  • American Review of Respiratory Disease, Vol. 116, Issue 4
  • DOI: 10.1164/arrd.1977.116.4.627

Population Modeling and Monte Carlo Simulation Study of the Pharmacokinetics and Antituberculosis Pharmacodynamics of Rifampin in Lungs
journal, April 2009

  • Goutelle, S.; Bourguignon, L.; Maire, P. H.
  • Antimicrobial Agents and Chemotherapy, Vol. 53, Issue 7
  • DOI: 10.1128/AAC.01520-08

Update—Pathogens of concern
journal, December 2013


Immunology studies in non-human primate models of tuberculosis
journal, February 2015

  • Flynn, JoAnne L.; Gideon, Hannah P.; Mattila, Joshua T.
  • Immunological Reviews, Vol. 264, Issue 1
  • DOI: 10.1111/imr.12258

Determination of in vitro synergy when three antimicrobial agents are combined against Mycobacterium tuberculosis
journal, October 2005


Pharmacokinetics-Pharmacodynamics of Rifampin in an Aerosol Infection Model of Tuberculosis
journal, July 2003


Schedule or Dosage?: The Need to Perfect Intermittent Regimens for Tuberculosis
journal, November 2006


Concentration-Dependent Mycobacterium tuberculosis Killing and Prevention of Resistance by Rifampin
journal, August 2007

  • Gumbo, T.; Louie, A.; Deziel, M. R.
  • Antimicrobial Agents and Chemotherapy, Vol. 51, Issue 11
  • DOI: 10.1128/AAC.01533-06

QSAR Based Design of New Antitubercular Compounds: Improved Isoniazid Derivatives Against Multidrug-Resistant TB
journal, November 2013


High-Dose Rifapentine with Moxifloxacin for Pulmonary Tuberculosis
journal, October 2014

  • Jindani, Amina; Harrison, Thomas S.; Nunn, Andrew J.
  • New England Journal of Medicine, Vol. 371, Issue 17
  • DOI: 10.1056/NEJMoa1314210

Four-Month Moxifloxacin-Based Regimens for Drug-Sensitive Tuberculosis
journal, October 2014

  • Gillespie, Stephen H.; Crook, Angela M.; McHugh, Timothy D.
  • New England Journal of Medicine, Vol. 371, Issue 17
  • DOI: 10.1056/NEJMoa1407426

Time-kill kinetics of anti-tuberculosis drugs, and emergence of resistance, in relation to metabolic activity of Mycobacterium tuberculosis
journal, October 2010

  • de Steenwinkel, J. E. M.; de Knegt, G. J.; ten Kate, M. T.
  • Journal of Antimicrobial Chemotherapy, Vol. 65, Issue 12
  • DOI: 10.1093/jac/dkq374

Effect of Duration and Intermittency of Rifampin on Tuberculosis Treatment Outcomes: A Systematic Review and Meta-Analysis
journal, September 2009


Design and synthesis of antituberculars: preparation and evaluation against Mycobacterium tuberculosis of an isoniazid Schiff base
journal, January 2004

  • Hearn, M. J.
  • Journal of Antimicrobial Chemotherapy, Vol. 53, Issue 2
  • DOI: 10.1093/jac/dkh041

Quantitative Comparison of Active and Latent Tuberculosis in the Cynomolgus Macaque Model
journal, July 2009

  • Lin, P. L.; Rodgers, M.; Smith, L.
  • Infection and Immunity, Vol. 77, Issue 10
  • DOI: 10.1128/IAI.00592-09

Advances in the development of new tuberculosis drugs and treatment regimens
journal, April 2013

  • Zumla, Alimuddin; Nahid, Payam; Cole, Stewart T.
  • Nature Reviews Drug Discovery, Vol. 12, Issue 5
  • DOI: 10.1038/nrd4001

Strategies for Efficient Numerical Implementation of Hybrid Multi-scale Agent-Based Models to Describe Biological Systems
journal, November 2014

  • Cilfone, Nicholas A.; Kirschner, Denise E.; Linderman, Jennifer J.
  • Cellular and Molecular Bioengineering, Vol. 8, Issue 1
  • DOI: 10.1007/s12195-014-0363-6

High-dose rifampicin: how do we proceed? [Correspondence]
journal, August 2011

  • Boeree, M. J.; Plemper van Balen, G.; Aarnoutse, R. A.
  • The International Journal of Tuberculosis and Lung Disease, Vol. 15, Issue 8
  • DOI: 10.5588/ijtld.11.0198

Intermittent versus daily therapy for treating tuberculosis in children
journal, January 2014


    Works referencing / citing this record:

    Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems
    journal, January 2018


      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.